case study on customer loyalty

10 best customer loyalty programs and their case studies

6X more expensive to win a new customer than retain as existing one ( Parature )
5% increase in customer retention can lead to a 25-100% increase in profit for your company. ( Harvard Business Review )
68% of millennials wou l dn’t be loyal to a brand that doesn’t have a good loyalty program. ( Loyalty Report )

Rob Petersen

21 examples of successful loyalty programs — and what makes them work

Done right, loyalty programs are a uniquely effective way to improve customer retention. Research from Accenture found that loyalty program members drive between 12 and 18% revenue growth each year.

There are countless examples of brands that have seen big results. Although many of them make it look easy, their success is down to a keen understanding of what makes their customer tick, and, by extension, what kind of loyalty program will resonate.

It’s not a one-size-fits-all approach, and the options for structuring loyalty offerings are ever more varied. The humble loyalty card is still often effective, but online loyalty programs present innovative and intricate ways to encourage and track loyal customer behavior.

So let’s dig beneath the shiny numbers of some of the world’s most successful loyalty programs and see what makes them work.

Examples we’ve used:

1. Sephora’s “Beauty Insider Program” 2. “LEGO Insiders” 3. Barnes & Noble 4. TheCHIVE 5. Astrid & Miyu’s “Astrid & You” 6. Amazon Prime 7. Lively 8. Pulse Boutique’s “Pulse Perks” 9. Annmarie’s “Wild and Beautiful Collective” 10. Pacifica Beauty 11. Mirenesse 12. REI’s “Co-op membership” 13. The North Face’s “XPLR Pass” 14. Lucy and Yak 15. Waterdrop 16. Nicce Clothing 17. Eat Bobo’s 18. MoxieLash Insider 19. 100% Pure 20. Spoiled Mama 21. Dr. Axe

The four main types of loyalty program (and examples of each)

There are four main kinds of loyalty program:

– Points-based – Value-based – Tiered – Subscription-based

Each has been used to great effect. Here’s why the best have succeeded (and why others might fail).

Points-based (“earn and burn”)

Points-based loyalty programs reward customers with points for purchases, which can be redeemed for discounts, exclusive products or other rewards.

One of the most lauded points-based loyalty programs is Sephora’s Beauty Insider Program .

Using their points, members have the chance to test a range of “trial-sized” products and unique experiences – such as full-face makeovers.

This works particularly well in the beauty industry , where creativity and experimentation are so fundamental.

Sephora also capitalizes on the community-driven nature of the beauty industry — Reddit’s r/makeupaddiction community alone has over 4 million members — with its “ Beauty Insider Community ”, a space that lets beauty lovers find their inspiration and seek advice from other members.

The French brand stays ahead of the curve by getting one vital thing right: it continues to transform and tweak the loyalty program to best meet the ever-changing needs of its customers. And that’s a great way to stop them from losing interest.

Another classic example of a points-based loyalty program is the loyalty card. You buy your items in a store, scan your card, and receive points based on the total value of your purchase. It’s a simple, clear reward that plays especially well in those industries where purchases are frequent and increasing average order value is a priority.

Value-based loyalty programs

Value-based loyalty programs are a bit like points-based ones, but points can be earned in other ways than just making purchases.

The LEGO Insiders program , for example, rewards customers not just for purchases, but also for registering LEGO sets and getting involved in the customer community.

It works because LEGO enjoys such a strong emotional connection with its customers. People love their LEGO, and the company maintains this sense of reciprocity through initiatives like LEGO Ideas, through which customers’ original designs can be turned into official products via a voting process.

Building this kind of loyalty is incredibly valuable for a brand that sells high-ticket items, and for which increasing purchase frequency is essential.

The MoxieLash Insider program is another excellent example of a value-based loyalty program. Like Sephora, they understand the value of building a community of advocates, especially on social media , where a highly visual product like theirs plays particularly well.

That’s why they offer points not just for purchases, but for social media engagement and app downloads. They know that, in their case, the downside of giving product discounts for non-transactional activities is more than offset by the benefits of social media engagement.

Tiered loyalty programs

Tiered loyalty programs involve levels of membership that offer increasing rewards and benefits based on customer spending or engagement.

They’re usually more complex to manage, but they can be more effective at creating customer loyalty.

Tiered loyalty programs are particularly well-suited to high-end, luxurious products, where the feeling of exclusivity can heighten the sense of luxury.

Astrid & Miyu’s loyalty program Astrid & You is a great example. At the silver and gold tiers, customers get early access to sales and promotions. At gold, they get £20 off on their birthday.

Hospitality companies tend to use tiered loyalty programs, too. Hilton Honors is a notable example.

Tiered loyalty programs are particularly effective in industries with fluctuating demand. Where purchases are frequent and consistent, and average order values low, exclusive tiered benefits can be less of an incentive to make repeat purchases.

What makes these tiered loyalty programs so effective is that they understand their status as “luxury” purchases in the eyes of potential customers , and that therefore they might have to work harder to incentivize repeat custom. At the same time, they recognise that it’s exactly this luxury status that makes exclusivity an enticing benefit.

Subscription-based loyalty programs

With subscription-based loyalty programs, customers pay an ongoing fee to get access to perks, products or exclusive events.

Because customers have to pay up-front — rather than passively accruing points as they shop — it’s more difficult to enroll customers onto your loyalty program . The benefits have to be significant.

Subscription-based loyalty programs are best suited to frequent, less-considered purchases — such as books. Barnes & Noble Membership involves an annual fee for exclusive discounts, free shipping and special offers.

Similarly, Amazon Prime gives customers unlimited to extremely fast delivery for a set fee.

In commoditized markets like bookselling, where it’s difficult to compete without a race to the bottom on price, these bonuses tend to have a “lock-in” effect on customers. Once they’ve paid their membership fee, it’s a sunk cost, and they can choose between your next-day delivery or your competitor’s comparatively slow delivery.

Compare these industries to some of the luxury brands adopting tier-based loyalty programs. You’d have a hard time convincing someone to pay $10 a month for a subscription for a brand they purchase from a few times per year at most.

In Amazon’s case especially, subscription-based loyalty programs can also smooth the move into new markets. When Amazon launched Prime Video, they had a ready-made audience of Prime customers ready to adopt it, and they could strengthen the loyalty program by adding it as an additional feature.

The results can be huge. A 2015 report by the Consumer Intelligence Research Partners (CIPR) found that Amazon Prime members were spending an average of $1,500 per year on Amazon.com , compared to $625 per year spent by non-Prime Amazon customers.

Gamification in loyalty programs

It’s not exactly a type of loyalty program, but lots of brands use gamification to boost engagement with their programs. One notable example is TheCHIVE .

Walker Information’s 2020 customer study predicted back in 2013 that “customer sentiment, or feelings that customers have about their interactions, will increase in value as companies look to differentiate based on the experience they deliver.”

To offer this superior experience, customer loyalty programs provide a great platform for brands. And photo entertainment website TheCHIVE is doing exactly this. By rewarding their customers for photo uploads and social sharing, community is being put at the heart of their gamified blog.

As a result, the brand has gained an active community of highly engaged customers. They now attribute 6% of their annual revenue to their loyalty strategy – generating 6,800 referred visits per month.

By giving customers a reason to return to the site, they are more likely to shop again and again.

TheCHIVE leaderboard

Examples by industry

Fashion and apparel ecommerce: crafting style-centric loyalty in online fashion.

The fashion and apparel ecommerce sector thrives on the allure of exclusivity and the joy of personal expression. Loyalty programs in this space are uniquely positioned to tap into these consumer sentiments. Here, loyalty isn’t just about transactions: it’s about becoming part of a fashion-forward community and enjoying the perks that come with it.

Fashion brand Lively is an example of a loyalty program that makes its most loyal customers feel special. It rewards members with points on their birthday, when they refer a friend and if they follow Lively on social media.

It’s easy for members to see the perks because benefits are clearly explained across their site.

Not only is it an effective loyalty strategy for a fashion brand, but customers also see the value of joining straight away. They’ll get 100 “Lively points” just for signing up. And that translates into $10 off their next purchase.

So, it’s a customer loyalty program that offers instant gratification. It also creates a strong and lasting emotional bond with the customer early on.

The results are impressive: the loyalty led to an increase in average customer lifetime value of 39%, and average spend increased by 36%.

Women’s fashion store Pulse Boutique offers customers a better chance to engage with their brand by inviting them to be part of a trusted fashion community.

This customer loyalty program is called Pulse Perks . It aims to encourage customer engagement on social media. At the same time though, it also helps increase user-generated content (UGC), customer reviews and photo submissions.

UGC and reviews are vital to brands because research has found that at least 9 2% of people trust word-of-mouth recommendations from friends or family. Meanwhile, people are four times more likely to buy a product if a friend or family member recommends it.

Since its launch, this example of a loyalty program generated a 39% uplift in returning shoppers. It also drove a 19% growth in average order value (AOV).

These programs work because fashion is a highly visual and shareable industry. Encouraging customers to share their fashion hauls, style tips, or outfit of the day (#OOTD) posts with brand-centric hashtags can amplify a brand’s presence on social media. Brands like Zara and H&M have effectively used user-generated content to engage their community, creating a ripple effect of brand visibility and customer engagement.

The key for fashion ecommerce brands is to tailor loyalty programs that not only reward purchases but also encourage brand interaction and social sharing. This approach resonates well with fashion consumers who value both the product and the experience surrounding it.

Beauty and cosmetics brands

In the beauty and cosmetics ecommerce industry, loyalty programs play a pivotal role in enhancing customer experience and brand loyalty. This sector, characterized by its emphasis on personal care, self-expression, and frequent product launches, finds unique value in loyalty programs that offer personalized rewards and exclusive experiences.

The aim of Annmarie Skin Care was to develop a loyalty program where members felt part of a community that shared similar beliefs and values. So, they created the “ Wild and Beautiful Collective ” to bring their members together.

It’s so important to create a sense of community – especially in the beauty industry. Beauty shoppers are more vocal on social media, for example. They’ll share tips, recommendations and look to learn and thrive from each other.

The “Wild and Beautiful Collective” is a membership program example that nurtures this sense of inclusion through its name and mission. And Annmarie Skin Care’s approach speaks volumes. Members spend (on average) 140% more than non-members. The brand generated more than 1000 reviews too.

Meanwhile, they enjoy high levels of engagement with members, with 40% of points being redeemed within three months of being awarded.

Pacifica Beauty ’s customer loyalty offering seeks to engage customers through the whole buyer journey . It has a branded, easy-to-use loyalty page . And there’s a pop-up display that will show you how many points you can redeem in the cart. At each stage of the process, the brand aims to make it easy for the customer.

As you near the end of your buyer journey, Pacifica Beauty shows a checkout slider. This lets you claim the rewards you’ve earned towards your purchase.

Now, thanks to this example of a customer loyalty strategy, Pacifica Beauty’s members spend 130% more on average than non-members.

Beauty brand Mirenesse elevated their loyalty program by introducing a special paid member tier. This is known as the “Gold” tier.

In exchange for a $10 monthly membership, customers of this VIP tier get extra perks on top of the basic rewards. This includes two points for every dollar spent, access to secret sales and the chance to trial new products as part of a tester panel.

Sports and outdoor ecommerce: elevating adventures with rewarding experiences

Outdoor brands have long done more than most to connect with their customers based on shared values. Patagonia set the standard with their “Don’t buy this jacket” PR campaign . The ad, which encouraged potential customers to buy a used Patagonia jacket rather than a new one, detailed the environmental impact in the creation of each new jacket.

A number of other big outdoor brands have incorporated this apparently-genuine, values-based customer connection into their loyalty programs.

The North Face’s XPLR Pass membership program, which offers rewards ranging from discounts to competitions and early product access, gives loyal customers 20% off their next order for completing a course on racial diversity.

REI’s Co-op Membership program , along with the more standard benefits of discounts and free delivery , builds on the connection between customer and brand by giving cashback on purchases based on business profits. They’re also invited to elect candidates to the board.

So why does it work? Because the products customers buy from these brands are used towards a hobby they care about. They’re not necessary purchases, they’re purchases that reflect these customers’ values . And brands share those values in an authentic way — through their transparency, ethical business structures and focus on sustainability.

It’s not something you can fake. And it’s harder — but not impossible — to build this kind of connection when you sell an everyday commodity.

Loyalty program branding: 4 brands that get it right

Loyalty programs succeed and fail by the attractiveness of their rewards — but how you design and brand your loyalty program can make a big difference too.

Lucy and Yak , a sustainable dungarees brand, is notable for its visually appealing integrated loyalty page. Using UGC photos of customers wearing their products and lifestyle imagery to showcase how customers can earn points, it is fun and on-brand.

Lucy and Yak describes its customer loyalty platform in their own tone of voice . You don’t just earn points, you earn “Yak points”. You don’t get “tier 1”, “tier 2” and “tier 3”, you get “Comfort Lover” “Yak Enthusiast” and “Dungaree Devotee”.

Lucy and Yak makes the most of its strong brand personality to boost its loyalty program. And that, in turn, strengthens the brand further.

Waterdrop – a “micro drinks” company – are paying careful attention to the look and feel of their loyalty program page. It means the Waterdrop Club is synonymous with the overall branding .

It uses fresh colors, light imagery and inclusive language.

On the page itself, everything is signposted clearly – from actions that customers must take to earn points, to information on the free gifts they can acquire.

Waterdrop’s loyalty program helpedg the brand to generate more than €58,000 from referrals . It’s one of the most effective health and wellness loyalty programs out there .

But if you’re looking for good examples of loyalty programs, sometimes the clue is in the name.

Nicce Clothing paid special attention to the name of their program and how they named their points system. “N Coin” is a simple, yet memorable term. It’s a customer loyalty program that clearly has its own brand. But Nicce Clothing also ensures it works with their overall contemporary and minimalist identity.

A family-run oat bar company, Eat Bobo’s got creative in naming their loyalty program example. Bobo’s Love Mitts is the name, while the points are called “Love Mitts”. Like Lucy and Yak, Eat Bobo’s loyalty program leverages and strengthens their down-to-earth, crafted-by-hand image.

6 examples of effective loyalty program marketing

If you want people to sign up to your loyalty program, you need to tell them about it. Here are a few brands that are particularly good at marketing their loyalty program.

Email marketing

MoxieLash get their loyalty emails right. They’re visually appealing, to-the-point, and the value proposition is crystal clear.

Loyalty pages

Encourage your customers to enroll in your loyalty program by building it a standalone page.

100% PURE built a beautifully designed loyalty page to explain the benefits of the loyalty program and to encourage customers to enroll.

Time-limited promotions

“Short-time only” promotions encourage loyalty program sign-ups by creating a sense of scarcity. You could try “double points week”, or offering money off a product to loyalty program members for a month.

Dr. Axe has run promotions like triple point weeks, seasonal offers and higher rewards for referrals.

They also incorporate the loyalty program into all their email marketing and across their website.

Refer-a-friend

Word-of-mouth referrals are often the best way to acquire high-value customers at low cost. Building specific rewards for referrals into your loyalty program is a good way to increase its value.

Banners and popups

Like Pulse Boutique , you can use website banners to announce the launch of your loyalty program. It’s a solid, concise way to explain the benefits and include a strong call-to-action.

Contests and leaderboards

An often-effective way to scale up your loyalty program is to introduce a competitive element — for example, by creating a kind of “leaderboard” of referrals, and rewarding those customers who are most prolific.

And by having an effective loyalty program marketing plan for those newly referred customers, this can turn into a virtuous cycle.

Ready to use loyalty marketing to your advantage?

These examples show us just how powerful loyalty programs can be, how great brands are making the most out of theirs, and how you can use your understanding of your own audience to design your own successful loyalty program.

But for many smaller ecommerce businesses, the biggest challenge is implementation. How do you set up, optimize and analyze your loyalty program without significant investment of both time and money?

With this, LoyaltyLion can help. When you’re ready to get started, book a demo .

About the author

Mollie woolnough-rai.

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Ecommerce tips, strategies, and news – all without ever having, [case study] build customer loyalty like starbucks + costco.

Building customer loyalty is a primary way to increase conversions and multiply profits. Below we explore two case studies in how to build customer loyalty, featuring Costco and Starbucks. But first, we cover customer loyalty program basics, including how to define customer loyalty, why it is important, and ultimately, the types of loyalty you can build. If you would like to skip the theory and head straight to the case studies , click here .

What is customer loyalty program marketing?

A customer loyalty program, often called a rewards program, is a customer retention strategy that focuses on repeat purchases. Customer loyalty programs are designed to incentivize customer actions. Programs can offer many types of benefits such as rewards, points, cash back, gifts, free shipping and more.

Next we will see the benefits of customer loyalty, including improved add to cart rates, conversions, and AOV.

Why is customer loyalty important in eCommerce?

Returning visitors dramatically increase profitability. In our study on New vs Returning customers , we found that retained visitors

Added items to carts 65.16% more than first time visitors
Converted 73.72% more than first time visitors
Spent 16.15% more per transaction

Further studies confirmed these benefits of customer loyalty. One study by Harvard Business School found that "Loyalty leaders - companies at the top of their industries in NEt Promoter Scores or satisfaction rankings for three or more years - grow revenues roughly 2.5 times as fast as their industry peers and deliver two to five times the shareholder returns over the next 10 years." And another study by ZenDesk found that 75% of customers are willing to spend more from companies that give them a good customer experience, while 50% will switch to a competitor after one bad experience.

50% will switch to a competitor after one bad experience

Image Credit: Zendesk's 2021 Customer Experience Trends Report

How to build customer loyalty?

There are two major types of customer loyalty. The first is transactional loyalty, and the second is emotional loyalty.

How to build transactional customer loyalty

The first is transactional loyalty. This type of loyalty is rational in nature. Customers perceive it is in their best interest to stay with this company based on repeated past experiences. For example, they may believe that the brand is the least expensive, most convenient, or least risky option. Famous brands who have built their business through transactional loyalty include Walgreens and CVS. In fact, certain industries compete primarily on transactional loyalty, including pharmacies, gas stations, and grocery stores. There are many ways to generate transactional loyalty. Below are few principles.

Loss aversion: Customers value losing more than gaining. By incorporating rewards, points, and bonuses you can influence customers to continue shopping with you else they lose the points they have earned
Recency Bias: Customers overweigh what has happened most recently. Brands can take advantage of this by introducing daily deals or other tactics to boost
Reciprocity: Finally reciprocity is a well documented social norm where people respond to a positive action with another positive action. (link to Wiki). Brand can create transactional loyalty by leading with value, such as a gift or credit, which can spur customers to want to respond.

How to build emotional customer loyalty

The second type of loyalty is emotional loyalty. As the name implies, this type of loyalty is not based on a "rational" calculation that being loyal is better for the customer, but instead is based on an emotional connection. Typically, this connection takes the form of status, the brand becoming part of the customer's identity, or the brand being used to express their identity. Because of this, they are much less likely to be influenced by competing offers and brands may enjoy higher price elasticity when they set their pricing strategy . Again, there are a few principles at work that eCommerce stores may take advantage of to maximize customer emotional loyalty. There are many famous examples of brands that have created emotional loyalty with customers, including Apple, Disney, Starbucks, Coke, and Pepsi.

We've covered how Starbucks creates loyalty through their omnichannel-retail strategy here .

Customer loyalty examples: tactics and strategies from top companies

Below we break down examples of customer loyalty programs, and call out specific tactics these companies make in a running list.

How Costco creates customer loyalty

Costco is a multinational company that operates membership big-box retail stores. The company has grown to 152.7 billion dollars annually, and makes for a fantastic case study on customer loyalty. As their management states in their latest 10-Q filing , "We believe the most important driver of our profitability is sales growth, particularly comparable warehouse sales growth.... Comparable sales growth is achieved through increasing shopping frequency from new and existing members and the amount they spend on each visit (average ticket)."

“We believe the most important driver of our profitability is sales growth, particularly comparable warehouse sales growth.... Comparable sales growth is achieved through increasing shopping frequency from new and existing members and the amount they spend on each visit (average ticket) ” - Costco 10-Q, 2021

1. Cross subsidizing with a membership model

Costco's loyalty program begins with their membership business model. Members pay an upfront cost to be able to enter the store and purchase goods. In return, Costco provides low, competitive prices, private label items, and other exclusive perks.

“Our membership format is an integral part of our business model and has a significant effect on our profitability. This format is designed to reinforce member loyalty and provide continuing fee revenue. ” - Costco 10-Q, 2021

2. Using loss leaders to promote transactional loyalty

Part of Costco's loyalty strategy is to "provide members with quality goods and services at the most competitive prices." Instead of maximizing profits per transaction, they focus on maximizing each customer's lifetime value to the company. This is exemplified most clearly in their gasoline service. "We believe that our gasoline business draws members, but it generally has a significantly lower gross margin percentage relative to our non-gasoline business." Costco takes measures to ensure members will always save on their gas bill. They are able to do this because of their membership business model, which doesn't rely on profits in gasoline to fuel the business.

3. Using unique, private label products to increase loyalty

Private label products increase customer loyalty in a number of ways. First, it distinguishes Costco. Instead of competing in purely commoditized products, Costco can offer exclusive options for their members. Further, as customers become habitualized to these products, it improves their membership retention rate . If customers want to enjoy these products, they must continue shopping at Costco.

Because of the exclusivity and collapsing the supply chain, these product are often more profitable on a unit basis as well.

Image Credit

4. Maxime loss leaders with creative financing options

Finally, Costco compliments their product offering with a branded credit card. They have tailored the rewards card to maximize customer loyalty, highlighting some of the biggest reasons to be a member.

4% cash back on Costco gas
2% cash back on Costco purchases
Costco membership is bundled with the price of the card

How Starbucks improves customer loyalty

Starbucks is another premier case study of how to improve customer loyalty.

5. Cross channel marketing

Starbucks does an incredible job driving customers into their loyalty program. We've covered before in our case study of Starbucks' omnichannel strategy how they tie in different channels, all leading to subscribing to their renowned loyalty program. Below we see a clear example. Once subscribed to their email list, customers receive member exclusive offers. If they would like to partake, they must enroll in the loyalty program.

Starbucks extends their Rewards program to lower margin grocery items as well. The rewards themselves help drive traffic to their in-store locations, increasing frequency, lifetime value, and accessing a whole new group of customers.

6. New product development

Starbucks also drives repeat purchases through new product development. With a steady stream of new options, customers are nudged to come back and try their latest creations. These new products often embody other aspects of the business, in this case Starbuck's new relationship with OATLY.

“We recognize that we are a beverage-forward concept. Beverage is our key point of differentiation.” - Pat Grismer

7. Create urgency with limited time products

Additionally, seasonal holiday beverages create excitement in their customer base and provides a sense of urgency to come into the store. In fact, there are now many seasonal beverages that customers pine for, including Pumpkin Spice Latte, Peppermint Mocha, and Irish Cream Cold Brews.

Further, Starbucks constantly rotates in limited run premium products. Combined, holiday and limited time products give a reason to return.

8. Unique experiences for Rewards Members

Finally, Starbucks routinely creates rewards member engagement campaigns. These often take the form of exclusive games and rewards Starbucks Rewards Members can participate in. Below is just one example. In Starbucks For Life, Rewards Members collect game pieces that can earn them rewards ranging from a $500 gift card, Bose Earbuds, and of course, the namesake grand price, free Starbucks for Life.

Next Steps...

Starbucks and Costco are great case studies in how to build customer loyalty.

At it's core, customer loyalty is about providing a better experience. While there are many ways to improve customer's experience in an eCommerce context, the primary ways revolve around creating personalized, relevant experiences and offers across all channels. Below are a few resources we have put together to help.

Advanced eCommerce personalization examples - In this guide, we showcase numerous personalization examples, including digital first brands such as Thrive Market and Third Love.
Triggered email examples and best practices - Email remains the best channel for driving profit. In this guide, we showcase how top brands like Nike, Amazon, and Google use triggered emails to increase AOV.
Database marketing examples - Finally, leveraging first party data on your customers is the best way to create better offers. We've found RFM analysis to be especially useful here.

Lastly, we help hundreds of premier eCommerce stores generate customer loyalty and repeat purchases. If you would like to see if we can help your store, schedule a personal demo here .

Your business depends on repeat purchases. To get customers to buy from you again and again, you must lower custom

RFM analysis increases eCommerce sales. Today, we explore how we can use recency, frequency, and monetary data to

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Starbucks Loyalty Program Case Study

Starbucks, renowned as one of the foremost coffee brands globally, owes its success not just to its exceptional range of products. A significant contributor to its widespread acclaim is its highly popular loyalty program , which has been an inspiration for many U.S. restaurant chains and retail stores. Launched on December 26th, 2009, the Starbucks rewards program has amassed over 30 million active members, contributing to nearly 60% of the brand's total revenue. The program effectiveness was strikingly evident in 2019, with Starbucks reporting a substantial 7% increase in sales primarily attributed to the efficacy of its rewards program.

Program type

Points system.

Points for rewards
Coalition Loyalty

Starbucks Rewards leverages a points-based system ingeniously designed to enhance customer engagement and loyalty. Members accumulate 'Stars' with each purchase made via their registered Starbucks Card or through the Starbucks mobile app. The program is structured to encourage varied forms of engagement, enhancing the overall customer experience and brand loyalty.

Earn 1 Star per $1 spent when you pay in cash
Earn 2 Stars per $1 spent when you preload your digital Starbucks Card
Earn additional bonus Stars through special promotions
Earn double Stars on Delta Airlines travel days
Earn double Stars on promotional days
Points expire after 6 months

Starbucks Rewards allows its members to redeem their accumulated Stars at various levels, each unlocking distinct rewards. This structure caters to diverse preferences, allowing customers to either redeem their Stars for immediate benefits or save them for more substantial rewards.

25 Stars: Redeem for a free drink customization, such as extra flavor, espresso shot, or alternative milk.
50 Stars: Get a complimentary hot coffee, tea, or bakery item like a bagel or croissant.
150 Stars: Choose a free handcrafted drink, hot breakfast, or a parfait.
200 Stars: Earn a complimentary lunch item, like a salad, sandwich, or protein box.
400 Stars: Exchange for packaged coffee beans or select Starbucks merchandise.

Coalition loyalty

Starbucks and Delta Airlines have embarked on a partnership, merging the world of coffee with the skies. Linking your Delta SkyMiles® and Starbucks® Rewards accounts opens up a new realm of earning opportunities and exclusive benefits.

Travel-day perks: Double Stars on Delta travel days
Everyday earn: 1 mile per $1* spent at Starbucks
Exclusive offers: More opportunities to earn Stars and miles

Interesting takeaways

Multi-channel communication.

Starbucks leverages multiple channels to communicate about its rewards program, ensuring widespread awareness and engagement. The use of a dedicated landing page, mobile app, points balance reminder emails , company blogs, and social media platforms creates an ecosystem where information about the program is readily accessible and consistently updated. The clarity and frequency of updates, including press releases for significant changes, demonstrate Starbucks’ commitment to transparency. This approach not only keeps existing members informed but also attracts new participants by clearly outlining the program’s benefits. Good communication is likely a significant factor in the program's high membership numbers.

Program simplicity and incentive structure

The simplicity of the points system (earn points and redeem for rewards) is user-friendly and easy for customers to grasp. This clarity enhances the appeal of the program and encourages participation. The structured nature of rewards incentivizes customers to accumulate more points to access greater rewards. This gamification element adds an extra layer of engagement, as customers are motivated to reach the next level. As customers get closer to a significant reward, their motivation to attain it increases. This psychological aspect of goal setting and achievement is a powerful tool in enhancing customer loyalty and encouraging more frequent purchases.

Incentivizing preloaded payments

Starbucks ingeniously incentivizes customers to preload their Starbucks account by offering double points for purchases made using a preloaded Starbucks Card. By preloading their accounts, customers commit themselves financially to future purchases at Starbucks, which effectively guarantees a degree of customer loyalty and repeat business. This strategy turns occasional visitors into regular customers, as the preloaded funds will likely be used exclusively at Starbucks locations. From a financial perspective, this strategy is beneficial for Starbucks. When customers preload their accounts, the brand receives cash in advance, improving its cash flow. This tactic also promotes increased engagement with the Starbucks mobile app, where preloading and tracking of rewards can be easily managed.

Coalition program with Delta Airlines

The partnership with Delta SkyMiles is a strategic move that broadens the program’s appeal. By collaborating with an airline, Starbucks extends its reach beyond the typical coffee shop customer. This alliance allows both companies to tap into each other’s customer bases, potentially attracting new customers who are loyal to one brand to become interested in the other. It’s a creative way to expand market reach without direct competition. For customers who already patronize both brands, this coalition loyalty program reinforces their loyalty and increases their engagement with both Starbucks and Delta. It creates a more integrated customer experience and can deepen the sense of brand loyalty.

Areas of improvement

The removal of the tiers system.

The previous iteration of the program had a 'Gold' tier, which was a smart way to acknowledge and reward the most loyal customers. By offering them the ability to earn points faster and providing exclusive benefits, Starbucks recognized and cherished its most valuable customers. This tier system not only incentivized increased spending but also gave a sense of exclusivity and status. While simplifying the program might make it more accessible for new customers, it risks diminishing the perceived value for power shoppers. The elimination of the tier system could lead to a decrease of engagement from these highly valuable customers.

Lack of personalized engagement or rewards

In the current market, personalization is key to customer engagement. Starbucks’ generic approach to rewards and communication, without considering individual customer preferences and behaviors, is a missed opportunity. Personalizing rewards and communications could significantly enhance customer loyalty and encourage repeat business. Leveraging customer data to offer personalized rewards and product recommendations could not only improve customer satisfaction but also drive product discovery and sales.

Limited post-purchase engagement

Post-purchase engagement is crucial in maintaining a connection with the customer even after they leave the store. This ongoing engagement is essential for converting first-time buyers into regular customers. While Starbucks has started initiatives like Starbucks Odyssey , which offers additional ways to earn rewards through digital journeys, its integration with the main rewards program appears to be lacking. A more seamless integration could enhance the overall experience and effectiveness of the loyalty program. Recognizing that some of these initiatives are in their early stages, there is potential for growth and improvement.

Learn from our library of loyalty program case studies

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Sephora Beauty Insider Loyalty Program Review

While Sephora’s Beauty Insider has maintained its position of one of the most successful loyalty programs, it has gone through several phases to ensure that its customer base is glued to the benefits and experiences.

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Preparing for loyalty’s next frontier: Ecosystems

Today’s consumers are the ultimate surfers, hopping between channels, devices, and sites as they shop. Loyalty programs are static by comparison, relying on increasingly outdated rewards and the redemption strategies of the past.

To create loyalty programs as dynamic as their consumers, companies need to develop an underlying consumer-loyalty strategy built around a fluid partnership of products, services, and experiences. We expect some brands to go even further, pursuing partnerships as a new way to compete. That means multiple companies from different sectors will develop a joint loyalty program around a unifying customer value proposition.

We see early signs of this happening already. Of the Financial Times’ top 25 brands of 2019, more than 40 percent have partnered with at least one other consumer brand to improve the customer experience and drive increased engagement. Indeed, as industry boundaries continue to blur, consumer and loyalty strategy will require a broader frame of reference.

Benefits of this approach include a deeper understanding of customer behavior through richer data. Ecosystem-centric loyalty programs are still in their nascent stages, but they will become increasingly prominent as consumer brands realize the need to compete at scale. And the value at stake is significant. Top-performing loyalty programs show a significantly higher success rate at changing customer behavior to drive value, and they have brands that are more likely to be preferred by customers (Exhibit 1).

Despite the opportunities, many loyalty programs have plenty of room to grow. We looked at a basket of 50 loyalty programs from top brands and found that just 18 percent have experience-led programs, 6 percent have connected offerings, and 2 percent have ecosystem offerings (Exhibit 2).

We see three important phases as companies evolve to next-generation loyalty programs:

Phase 1: From rewards and redemption to experience and content

“Earn and burn,” or pure discount-based programs, are no longer enough. Consumers are looking for something more. When it comes to loyalty rewards, it’s no longer just about the transaction at point of purchase; it’s about additional experiences and services that create new touchpoints. Companies are pursuing content and experiences to drive up engagement and get consumers more connected with the brand on a daily or weekly basis. Programs that balance monetary rewards with experiential offerings (exclusive events, early access, unique discoveries or adventures) can provide value beyond the transaction by appealing to the consumer’s head and heart (Exhibit 3).

Phase 2: From siloed programs to connected offerings

Given the shift toward experience and content, brands are tapping into their complementary product and service offerings to create their own brand ecosystem. These brands are looking across all they can offer to their customers to tie together both commerce and noncommerce moments.

As consumer brands increase their ability to leverage scale, data, and technology to create this connected offering, brands will continue to migrate toward smarter programs where personalized, “underwater” tactics are at the center of value creation, in which case published loyalty benefits will represent only a fraction of the total consumer value. The consumer engagement model will look to increase touch frequency and move into other areas of the ecosystem, such as credit cards or subscriptions.

There is at least one model of a connected loyalty offering on the market today: Amazon Prime. Prime serves as a loyalty ecosystem, serving the currency of on-demand convenience across sub-brands that include Amazon’s dot-com business, Prime Video, Prime Now, Whole Foods, and others.

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Phase 3: from company focused to ecosystem enabled.

Even as companies move toward connected offerings, it will be hard for any one player to replicate the size and scale of an ecosystem like Prime’s in any one sector, let alone across sectors. As a result, companies will need to think creatively and strategically about partnerships across categories, including with would-be competitors.

Brands should look to strategic partnerships to provide consumers with more holistic experiences tailored to individual consumer lifestyles and ecosystems. Loyalty is a fitting platform for these sorts of partnerships, enabling a disparate set of brands to craft a coherent set of benefits that feel personally relevant to the consumer. Likewise, it offers a host of benefits for member brands: broader access to consumer data, cross-marketing opportunities for customer acquisition and engagement, and economies of scale stemming from shared IT infrastructure.

Coalition loyalty programs are challenging

At the same time, brands must be open to the trade-offs inherent in an ecosystem-based loyalty partnership. Ecosystem-centric loyalty may require companies to partner with previous indirect competitors, giving rise to “frenemy” relationships across regions and channels. In contrast to loyalty coalitions, which were largely operated by third-party organizations, ecosystems are more likely to arise organically as companies forge strategic partnerships, placing the onus on the brands to govern their partnership’s strategy and architecture. (See sidebar, “Coalition loyalty programs are challenging.”) Nevertheless, if executed properly, brands stand to gain more from ecosystem-centric loyalty than they lose.

No customer left behind: How to drive growth by putting personalization at the center of your marketing

What this means.

As brands begin to partner around ecosystems, what changes will these partnerships herald for the brands’ pre-established, stand-alone loyalty programs? In the near term, we expect standalone programs will continue to operate beside or within the ecosystem, much as individual airline loyalty programs can function independently while also yielding benefits across an alliance with other airlines. However, as ecosystems grow more conventional and their value becomes more apparent to brands, we envision that stand-alone programs may begin to consolidate under single-flagship ecosystem loyalty programs, or that stand-alone programs may evolve into ecosystems as they incorporate partnerships as part of the core program value proposition.

As we shift into the era of ecosystem-driven loyalty, brands must learn from the failures of the coalitions of old. To succeed, a loyalty ecosystem should be centered around the following seven design principles:

Shared consumer in mind: The program must be highly personalized to the individual consumer, leveraging the full stack of consumer data across brands to offer personalized messages, offers, and experiences. Furthermore, brands should mutually understand the value of the consumer.
Brand synergy: The program must be structured to elevate brand equity. Every brand must have an important role in the given ecosystem and provide value to that ecosystem through its products and/or services.
Diversity of products and services: The program must provide consumers with value through tangible goods, services, or experiences in order to reinforce the program’s underlying appeal. Brands should develop common features and benefits together.
Seamless consumer experience: Sign-ups, use, and redemption must be intuitive and responsive, keeping the program simple for consumers.
All-in brand commitment: Companies must be willing to commit to the partnership, allocating adequate marketing spend and having clear agreements about sharing consumer data in a way that adheres to all relevant regulations.
Alignment on governance process: Brands should enter the partnership with aligned strategies and goals, defined roles and responsibilities, and a process for managing risk effectively.
Data and tech focused on connectivity: Brands will need to ensure, within the confines of the shifting regulatory landscape, that their data can be exported or used as needed—for example, through the use of application programming interfaces (APIs). Brands within the ecosystem need to commit to standards and processes for tracking key performance indicators, adhering to regulations, and establishing protocols for connecting technologies and sharing data.

As we shift into the era of ecosystem-driven loyalty, brands must learn from the failures of the coalitions of old.

As ecosystem-centric loyalty flourishes, all parties stand to gain. Consumers will receive heightened experiential benefits in addition to faster loyalty rewards growth, more-flexible redemptions, and an unmatched simplicity and daily relevance. Brands will see a rise in reach and frequency of usage. In addition, they will gain access to richer, more privileged consumer data, shared infrastructure, and cross-marketing opportunities. Finally, ecosystem-centric loyalty will provide hitherto unseen competition to today’s retail goliaths, a benefit that will trickle down to the consumer brands and, ultimately, back to the consumer.

Ecosystems are here to stay. It’s time for all consumer brands to start thinking about how they can participate in one. Ecosystem-centric loyalty is a start.

Julien Boudet is a partner in McKinsey’s Southern California office, Jess Huang is a partner in the Silicon Valley office, Phyllis Rothschild is a partner in the Boston office, and Ryter von Difloe is a consultant in the Seattle office. The authors wish to thank Michael Tappel for his contributions to this article.

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Customer Loyalty: The Ultimate Guide

Learn about why customer loyalty is critical to your business's long-term success and how you can turn your satisfied customers into delighted brand advocates.

HOW BEST IN CLASS COMPANIES BUILD CUSTOMER LOYALTY

An actionable guide on building customer loyalty and retention.

Updated: 02/02/23

Published: 02/02/23

Having a strong customer loyalty strategy can greatly aid brand success. Customers are 50% more likely to try a new product of yours as well as spend 31% more than new customers. Conversely, it costs six to seven times more to acquire new customers than it does to retain current customers.

Whether or not you currently have a customer loyalty program that encourages your customers to return and conduct more business with you, the above statistics clearly show the importance and impact of a successful customer loyalty strategy.

In this guide, you'll discover everything you need to know about customer loyalty and how to create a successful customer loyalty program.

What Is Customer Loyalty?

How to Keep Customers Loyal

Types of customer loyalty programs, best customer loyalty programs, how to measure customer loyalty.

Let's kick things off by defining customer loyalty.

What is customer loyalty?

Customer loyalty is a customer's willingness to repeatedly return to a company to conduct business. This is typically due to the delightful and remarkable experiences they have with that brand.

One of the main reasons to promote customer loyalty is because those customers can help you grow your business faster than your sales and marketing teams. There are several other reasons why customer loyalty is critical to your success.

Why is customer loyalty important?

Customer loyalty is something all companies should aspire to simply by virtue of their existence: The point of starting a for-profit company is to attract and keep happy customers who buy your products to drive revenue.

Cultivating customer loyalty is similar to creating fans. These are customers who are already engaged and can help you gain more business. Just like you know when your favorite band is going on tour or releasing a new project, loyal customers are in tune with what their preferred brands are doing.

They may follow you on social media or subscribe to your newsletter to stay in the loop about new products or updates. This is why customer loyalty marketing is crucial to your brand’s strategy. You already have a built-in fanbase. You’ll need to continue to delight those repeat customers to reap the benefits.

Customer Loyalty in Marketing

Building customer loyalty should be top of mind when creating your brand’s marketing strategy, as loyal customers are happy customers. Customer loyalty will also aid in customer retention . Here are a few additional benefits customer loyalty marketing provides.

Increased Share-of-Wallet

Share-of-wallet refers to the amount of money a customer spends on a certain brand compared to how much they spend on the brand’s competitors. Customers convert and spend more time and money with the brands to which they're loyal. That’ll mean more share-of-wallet for you.

Better Word-of-Mouth Referrals

Customers convert and spend more time and money with the brands to which they're loyal. These customers also tell their friends and colleagues about those brands, too which drives referral traffic and word-of-mouth marketing .

Increased Trust

Customer loyalty also fosters a strong sense of trust between your brand and customers — when customers choose to frequently return to your company, the value they're getting out of the relationship outweighs the potential benefits they'd get from one of your competitors.

Since we know it costs more to acquire a new customer than to retain an existing customer , the prospect of mobilizing and activating your loyal customers to recruit new ones — simply by evangelizing a brand — should excite marketers, salespeople, and customer success managers alike.

But how do you do it? How do you turn happy, satisfied customers into loyal brand evangelists? How do you use positive Yelp reviews, glowing tweets, and Instagram mentions to propel your brand's growth?

Well, we have a few ideas.

Be as generous as your customers.
Show your gratitude.
Provide benefits to your customers with every purchase.
Scratch the program completely.
Build a useful community for your customers.
Communicate effectively with your customers.
Improve upon your customer loyalty program.
Continuously evolve your business over time.

1. Be as generous as your customers.

From the outside looking in, customer loyalty programs can appear to be nothing more than a scheme to get customers to spend even more money. (Let's face it; we can all be cynics sometimes.)

That's why truly generous loyalty programs stand out among the rest.

If your loyalty program requires customers to spend a lot of money only to be rewarded with meager discounts and samples, you're doing it wrong. Instead, walk the walk and show customers how much you value them by offering perks that are so good, it would be foolish not to become a member.

2. Show your gratitude.

You might think that, by offering a loyalty program, you're expressing your gratitude for their business and loyalty. Think again. Your customers are routinely bombarded by businesses — your competitors included. Your competitors likely offer a loyalty program, too.

What sets you apart in a way that keeps customers loyal? Expressing your gratitude through handwritten notes or direct, one-to-one messages. Include thank you notes in your product deliveries or purchase confirmation emails, or send special cards around the holidays .

3. Provide benefits to your customers with every purchase.

Build loyalty by rewarding them for each purchase, or offering an incentive to purchase more. This could be a point system where a certain amount of purchases earns a discount or something else of value.

For example, Fabletics offers members of their VIP program points for every purchase. Those points can then be redeemed anytime for different merch tiers. Customers can choose to redeem points as soon as they qualify for the lowest tier or continue saving points to earn something from the higher tier, like an outfit.

4. Scratch the program completely.

Considering how many businesses offer loyalty programs, one innovative idea to make yourself stand out is to nix the idea of employing a " program" altogether. Instead, build loyalty by providing customers with awesome benefits related to your business and product or service with every purchase.

This minimalist approach works best for companies that sell unique products or services. That doesn't necessarily mean that you offer the lowest price, or the best quality, or the most convenience; instead, I'm talking about redefining a category.

If your company is pioneering a new product or service, a loyalty program may not be necessary. Customers will be loyal because there are few other options as spectacular as you, and you've communicated that value from your first interaction.

5. Build a useful community for your customers.

Customers will always trust their peers more than they trust your business. Between social media, customer review sites, forums, and more, the slightest slip can be recorded and uploaded for the world to see. But, you can turn this into a positive by managing a community that encourages customer-to-customer interactions.

One way to do this is with self-service support resources. If you have a knowledge base , you can add a community forum . A community forum encourages customers to communicate with one another on various topics, like troubleshooting the product or retelling service experiences. Even if they leave negative feedback, at least it's left on your domain where you can respond to it and deal with it accordingly.

A community forum can benefit your business in other ways, too — for example on the HubSpot Ideas Forum , customers can pitch ideas and upvote each other's posts. If the idea is good, the product team will consider it for an upcoming sprint. If the idea can already be done with the product, the support team will reach out with a solution. This lets our team provide both proactive and reactive customer service through one resource.

As online communities progress, you may formalize them to keep things organized. Having a consistent system in place ensures fairness and keeps customers satisfied over time.

This is where customer loyalty programs come in handy.

6. Communicate effectively with your customers.

Building and maintaining customers’ trust requires continuous communication. When a customer knows that your business is transparent and honest, they have trust that will have a positive experience in their interactions. This should still be the case especially when it comes to customer service and has a problem to be resolved.

Good communication means your customers should know what is going on with your business. If there is a new product, a major change, company update, shortage, change in hours, or anything that a customer should be aware of so they aren’t surprised, it should be clearly communicated. This is a part of good customer service that is essential for making customers want to continue doing business with you.

7. Improve upon your customer loyalty program.

While earlier we suggested forgoing the customer loyalty program, it’s still an essential cornerstone of any customer loyalty-building endeavor. It’s one of the best ways to build customer loyalty, especially if the brand keeps adding perks that make it impossible to walk away.

Companies provide customer loyalty programs to their most frequent customers to encourage loyalty and long-term business by offering free merchandise, rewards, coupons, or even advance-released products. Continue improving upon yours by offering more perks and rewards as time goes on.

8. Continuously evolve your business over time.

The market and the desires of your audience and customers are going to continuously change over time. Doing research and staying up-to-date in your industry reassures customers that you will always be innovative and offer the best options for meeting their needs. This doesn’t just apply to new technologies, though.

Evolving should include the branding, culture, marketing, and your product itself. Customers develop trust over knowing that companies won’t be stagnant. Being aware of how to continuously improve your business ensures you’re always getting better. Loyalty to your business and your products comes with customers knowing they’re always getting the best.

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What is a customer loyalty program?

So, how do you ensure your customer loyalty program is beneficial for your business and your customers? Check out the types of loyalty programs below.

How do loyalty programs work?

Gaining and retaining customers is the primary goal for businesses. A lot of marketing focuses on gaining customers but retaining them is often a whole different ball game. Loyalty programs work by giving customers an incentive to continue doing business with you. The incentives can be a variety of things, but they should offer a strong benefit to keep them coming back.

Point-based loyalty program
Tiered loyalty program
Paid loyalty program
Value-based loyalty program
Coalition loyalty program
Game-based loyalty program

1. Point-Based Loyalty Program

This is arguably the most common loyalty program methodology in existence. Frequent customers earn points that translate into rewards such as a discount code, freebie, or another type of special offer. Where many companies falter in this method, however, is making the relationship between points and tangible rewards complex and confusing.

Poor option:

"Fourteen points equals one dollar, and twenty dollars earn 50% off your next purchase in April!"

That's not rewarding. That's a headache.

Better option:

Earn a point for each dollar spent. Get a 20% discount after earning 50 points.

If you opt for a points-based loyalty program , keep the conversions simple and intuitive. Although a points system is perhaps the most common form of loyalty program, it isn't necessarily applicable to every type of business. It works best for businesses that encourage frequent, short-term purchases, like Dunkin' Donuts .

2. Tiered Loyalty Program

Finding a balance between attainable and desirable rewards is a challenge for most companies designing loyalty programs. One way to combat this is to implement a tiered system that rewards initial loyalty and encourages more purchases.

Present small rewards as a base offering for being a part of the program, and then encourage repeat customers by increasing the value of the rewards as they move up the loyalty ladder. This solves the potential issue of members forgetting about their points (and never redeeming them) because the time between purchase and gratification is too long.

The biggest difference between the points system and the tiered system is that customers extract short-term versus long-term value from the loyalty program. You may find tiered programs work better for high commitment, higher price-point businesses like airlines, hospitality businesses, or insurance companies.

3. Paid (VIP) Loyalty Program

Loyalty programs are meant to break down barriers between customers and your business ... so are we seriously telling you to charge them a fee? In some circumstances, a one-time (or annual) fee that lets customers bypass common purchase barriers is quite beneficial for both business and customer. If you identify factors that may cause your customers to leave, you can customize a fee-based loyalty program to address those specific obstacles.

For example, have you ever abandoned your online shopping cart after tax and shipping were calculated? This is a frequent issue for online businesses. To combat it, you might offer a loyalty program like Amazon Prime — by signing up and paying an upfront fee , customers automatically get free two-day shipping on orders (plus other awesome benefits like free books and movies).

4. Value-Based Loyalty Program

Truly understanding your customer requires you to identify the values and desires of your target audience — in doing so, you can encourage customer loyalty by targeting those characteristics. While any company can offer promotional coupons and discount codes, some businesses may find greater success in resonating with their target audience by offering value in ways unrelated to money — this can build a unique connection with customers, fostering trust and loyalty.

This could look like a company donating a percentage of the purchase to designated charities that align with a brand’s values.

5. Coalition (Partnership) Loyalty Program

Strategic partnerships for customer loyalty (also known as coalition programs) can be an effective way to retain customers and grow your company.

Which company would be a good fit for a partnership? The answer depends on your customers' everyday lives, needs, and purchase processes.

For example, if you're a dog food company, you might partner with a veterinary office or pet grooming facility to offer co-branded deals that are mutually beneficial for your company and your customer.

When you provide your customers with relevant value that goes beyond what your company alone can offer them, you're showing them that you understand and care about their challenges and goals (even those you can't solve alone). Plus, it helps you grow your network to reach your partners' customers, too.

6. Game-Based Loyalty Program

Who doesn't love a good game? Turn your loyalty program into a game to encourage repeat customers and — depending on the type of game you choose — solidify your brand's image. With any contest or sweepstakes, though, you run the risk of having customers feel like your company is jerking them around to win business. To mitigate this risk, ensure your customers don't feel like you're duping them out of their rewards.

The odds should be no lower than 25%, and the purchase requirements to play should be attainable. Also, make sure your company's legal department is fully informed and onboard before you make your contest public. When executed properly, this type of program could work for almost any type of company and makes the process of making a purchase engaging and exciting.

How to End a Loyalty Program

Loyalty programs are not meant to last forever, for you or your customers. The programs, marketing, and benefits should change over time. It’s important, however, to not end loyalty programs in a way that is off-putting for customers currently in the program. A major way to avoid this is to establish a time limit to the program at the start, and remind users of the ending as it approaches. You can also give a small gift or one-time bonus at the end to counteract the negative feeling coming from losing the benefit.

Now that you have some ideas for your new customer loyalty program, or how to enhance the program you already offer, you'll also need to ensure you have a reliable way to measure its effectiveness.

Customer Retention Rate
Negative Churn
Net Promoter Score®
Customer Effort Score
Purchase Habits
Referral Traffic
Social Media Mentions

As with any initiative you implement, you should have some way to measure success. Customer loyalty programs should increase customer delight, happiness, and retention — and there are ways to measure these things (aside from rainbows, sunshine, and smiles).

Different companies and programs call for unique analytics, but here are a few of the most common metrics companies watch when rolling out loyalty programs.

1. Customer Retention Rate

Customer retention is an indication of how long customers stay with you. With a successful loyalty program, this number should increase over time, as the number of loyalty program members grows. According to The Loyalty Effect , a 5% increase in customer retention can lead to a 25% to 100% increase in profit for your company.

Run an A/B test against program members and non-program customers to determine the overall effectiveness of your loyalty initiative.

Discover everything you need to start running effective split tests with this complete A/B testing kit.

2. Negative Churn

Customer churn is the rate at which customers leave your company. Negative churn, therefore, is a measurement of customers who do the opposite: either they upgrade or purchase additional services.

These help to offset the natural churn that goes on in most businesses. Depending on the nature of your business and loyalty program, especially if you opt for a tiered loyalty program, this is an important metric to track.

3. Net Promoter Score®

NPS® is a customer satisfaction metric that measures, on a scale of 1-10, the degree to which people would recommend your company to others.

NPS is calculated by subtracting the percentage of detractors (customers who would not recommend your product) from the percentage of promoters (customers who would recommend you).

The fewer detractors, the better. Improving your net promoter score is one way to establish benchmarks, measure customer loyalty over time, and calculate the effects of your loyalty program.

4. Customer Effort Score

Customer Effort Score (CES) asks customers, "How much effort did you personally have to put forth to solve a problem with the company?"

Some companies prefer this metric over NPS because it measures actual experience rather than the emotional delight of the customer.

In this way, customer service impacts both customer acquisition and customer retention. If your loyalty program addresses customer service issues, like expedited requests, personal contacts, or free shipping, this may be one way to measure success.

Now, here are some examples to offer inspiration while you build your own customer loyalty program .

5. Purchase Habits

How long do customers take to make another purchase from you? How many customers are returning products after purchasing them from you? These purchase habits let you know whether customers are loyal to your brand — or whether they’re leaving you for your competitors.

It’s important to understand what, if anything, is causing a return or delaying a repeat purchase. That’s why it’s important to consistently carry out customer satisfaction surveys at least once every quarter or whenever appropriate. You could send one to a customer after they tried out your product, for example, or right after they returned it.

6. Referral Traffic

Referral traffic refers to any traffic that comes to your website from places other than search engines (such as Google). Social media sites, news websites, and other online properties would all count as referral traffic. Traffic from display ads doesn't count. What matters is how many of your customers are linking to your site or talking about you on their social media profiles. You can measure referral traffic using a tool such as Google Analytics.

7. Social Media Mentions

What are people saying about you on social media? Is it positive? Or are they spreading bad word? While this isn’t so much of a quantitative measurement as others on this list, it’s still an excellent way to track customer loyalty. You can track social media mentions using a tool such as HubSpot .

Tracking mentions is an important part of a social listening strategy and one of the best ways to see whether customers are loyal. You can also find out what they’re saying about you in comparison to your competitors.

Sephora Beauty INSIDER
Virgin Atlantic Flying Club
Amazon Prime
TOMS Passport Rewards
Hyatt Loyalty Program
United Mileage Plus
Odacité Rewards
Starbucks Rewards
PetSmart Treats
Sweet Green Sweet Rewards
The North Face
The Body Shop

1. Sephora Beauty Insider

Image Source

Sephora offers a points-based loyalty program. Customers swipe their Beauty Insider cards at every purchase to track the amount of money they spend. Depending on a customer's average purchase threshold, they're grouped into one of three types of Beauty Insider — these tiers identify the top spenders among the already loyal group of customers.

Every dollar spent earns the member one Beauty Insider point — shoppers can redeem points for top-notch beauty products at checkout. Sephora speaks the language of its audience by measuring points in dollars and rewards in cosmetic items.

Why We Love It

One of the best parts of a Beauty Insider at any tier is the free samples! Being able to try before you buy is very beneficial for skin care and beauty products.

2. Virgin Atlantic Flying Club

The Virgin Atlantic Flying Club allows you to earn miles and tier points by flying as well as through your daily purchases — you can apply these miles to your future travels.

Within the Club, there are three tiers customers are grouped into — each of which offers different benefits. Each tier provides many perks for the customers — but, the more customers spend, the higher their tier, and the greater the benefits.

The Gold tier benefits are top-notch because they are extended not only to you but also to guests that are traveling with you.

3. Amazon Prime

For $139 a year , Amazon Prime users get free, two-day shipping on millions of products with no minimum purchase among other benefits. This also includes their streaming service, Prime Video. For example, because of Amazon's acquisition of Whole Foods, Amazon offers Prime users substantial savings on their groceries .

This deal on efficient, reliable shipping on almost any product imaginable offers enough value to frequent shoppers that the annual payment makes sense (think about how much you normally pay on standard shipping for your online purchases).

Saving on the thousands of products on Amazon itself plus saving on all your groceries at Whole Foods equals a ton of savings !

4. TOMS Rewards

TOMS Rewards has a free, point-based reward system that shows their customers what they value as an organization and how they give back to different communities.

Customers sign up for free, make their purchases to earn points, and then use those points to purchase more products, obtain offers, or donate the points (which have a one-to-one monetary value) to support a giving fund or foundation.

There are three tiers customers are placed in that determine their special offers and perks based on the amount they spend with the company.

TOMS has a variety of awesome grassroots partners that are incorporated into their rewards program. Being able to donate your points to the causes you support is an impactful benefit.

5. Hyatt Loyalty Program

Hyatt has a five-tier loyalty program to encourage customer loyalty — although their highest tier requires customers to spend dozens of nights in hotels every year and travel a great deal more than the average person might, they offer a membership that's completely free and has no required thresholds members need to meet — meaning, Hyatt's loyalty program is open to everyone.

Member perks include discounts at their participating hotels, access to special member-only offers, and the ability to earn points on nights spent at the hotel, dining, spa, Exhale classes, and more. Customers can also choose how they want to spend or apply the Hyatt points they earn (e.g. free nights at the hotel or flight miles).

Most rewards programs with different tiers have two or three levels. Hyatt has a total of five which means that there is always a benefit that fits how much the customer spends.

6. REI Co-op

REI's Co-op membership program harkens back to the outdoor gear company's roots as a co-op — a consumer organization that is truly owned by the consumers and managed to meet the needs of its members.

The program makes customers feel good about spending their money at REI because of the company's commitment to this cooperative vision of giving back to outdoor conservation and their prioritization of the members over the profits.

Co-op customers become lifetime members after paying a flat fee of $20. Then, they're rewarded with 10% of the total amount they spent at REI in the previous year, access to deeply-discounted "garage sales," discounted wilderness and outdoor adventure classes, and members-only special offers.

Being able to support the cooperative vision of REI and all the impactful work that they do is an extra perk on top of the regular rewards benefits.

7. United MileagePlus Program

United's MileagePlus Program is meant to streamline the process of earning points on daily purchases and applying those points to payments for global flights, hotels, rental cars, and more for customers.

For the most frequent United customers, they can choose to become a Premier user and receive a MileagePlus card (associated with their tier) to use on purchases so they can rack up even more points and reach greater travel-related perks (e.g. free, checked baggage, upgraded seating, priority boarding, and access to deals with partner hotels and car rental companies).

United has the most global destinations of any airline, so it gives you the most opportunities for where to earn and redeem points.

8. Odacité Rewards

Imsge Source

Odacité Rewards is a program that encourages customers to return to the brand for their regular skincare purchases. Customers earn one point for every dollar spent and are grouped into one of three tiers depending on the amount they spend.

Odacité's program offers rewards unrelated to purchases as well. Customers can earn points for sharing their Facebook page, inviting a friend, following them on Instagram, sharing their birthday, and creating an account.

Points can be exchanged for a monetary value and applied to a customer's purchase for a discounted price at checkout. These tasks are easy to complete and benefit both customers and the business.

Having ways to earn points that don’t require you to spend money is a big plus because it’s essentially getting free rewards!

9. Starbucks Rewards

Starbucks Rewards is a star-based loyalty program in which customers download the Starbucks app or sign up online, add any amount of money they'd like to their digital card, and scan it upon checkout, whether that's in-store or via mobile order.

Every purchase results in the earning of stars — two stars per $1 spent. Within the app, there are prizes and games such as double-star days (customers earn double the normal amount of stars they would), free beverage coupons on their birthday, and other ways to earn bonus stars.

Members can apply the stars they earn to their purchases for discounts and free beverages (and food).

The Starbucks Rewards program makes it fun and exciting to earn rewards with games, bonus challenges, and double star days.

10. PetSmart Treats

What pet owner doesn't love treating their best friend to delicious food, new toys, a haircut, and more? The PetSmart Treats loyalty program makes this easy for pet owners to do while also saving them money.

Pet owners earn points every time they spend (eight points per dollar, to be exact). They can redeem these points in-store or online. Members get free shipping and are notified about member-only sales and in-store discounts, and can use their earned points on grooming, PetsHotel, puppy training, or even donate their points to a PetSmart-affiliated animal charity.

(Not to mention, members earn a free surprise for their pets on their birthday.)

Pet care can be quite expensive, so being able to use points for things like grooming, puppy training, and pet hotels is a great way to save.

11. SweetGreen Rewards and Challenges

SweetGreen Rewards and Challenges program aims to help customers achieve healthier practices as well as boost app engagement. Challenges change weekly and may entice customers to purchase by offering a freebie for certain items. For example, customers may receive a free drink when they order a side.

The program offers a fun way for Sweetgreen to reward its most customers, who often order multiple times per week, to save on their purchases.

Delivery fees can add up if you order food frequently, so removing those provide a lot of great savings for rewards members.

Designer Shoe Warehouse has a long-standing VIP loyalty program that is points-based. Customers earn rewards on a tiered system that gives more as they spend more. A convenient part of the DSW loyalty program is that it doesn't require a physical card. The company’s online system can recognize program members with their name, phone number, or payment information.

VIPs earn 1 point for every $1 spent and Elite members earn 2 points per dollar. Plus, each purchase also earns 5% back in rewards, 10% for Elite members. The program also includes free shipping on every purchase, a 50-point reward for donating old shoes, and a birthday reward.

Shoe lovers are often recycling out their old shoes as they buy new ones, so being able to get rewards for donating them is a big plus.

13. The North Face

The popular outdoor apparel and gear retailer has a loyalty program named the XPLR Pass . It’s easy for customers to earn points through more than just spending. Attending The North Face events, bringing a reusable bag when shopping in-store, checking in at National Parks, and participating in surveys are some of the other ways to rack up rewards. Members earn 1 point for every $1 spent, and a $10 reward for every 100 points earned. However, members are limited to 100 points per calendar year.

Another great benefit of the XPLR Pass is the Renewed Trade-In Program. Members can trade in their gently used gear from the store for a gift card. As well, members can freely return any purchased gear that didn’t work for them within 60 days. The program also offers early access to limited edition collections and chances to wear test products before they are made available to the public.

The members-only field testing is a very useful benefit because there is a lot of gear that you can’t know will fit your needs until you try it out for yourself.

14. The Body Shop

As a business with a long-standing commitment to social responsibility, The Body Shop brings that sentiment to its rewards program. The Love Your Body club is points-based but focuses a lot on the community aspect. Members get 1 point for every $1 spent, and a $10 reward for every 100 points earned.

The Love Your Body club offers exclusive members-only parties and sneak peeks of new collections. Points can be redeemed for discounts but also donations to a variety of charities. Members also receive a $10 coupon for their birthday.

The community aspect of the Love Your Body club is a special benefit because it makes you a part of something special and gives you access to events where only others in the community can attend.

Begin (or Enhance) Your Customer Loyalty Program

Customer loyalty is directly tied to your business's bottom line, retention, and ability to grow better . So, get started today by determining which customer loyalty tactics you're going to tap into and use the examples we reviewed above for inspiration.

(Net Promoter, Net Promoter System, Net Promoter Score, NPS and the NPS-related emoticons are registered trademarks of Bain & Company, Inc., Fred Reichheld and Satmetrix Systems, Inc.)

Editor's note: This post was originally published in October 2019 and has been updated for comprehensiveness.

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Open access
Published: 23 April 2020

Predictive analytics using big data for increased customer loyalty: Syriatel Telecom Company case study

Wissam Nazeer Wassouf ORCID: orcid.org/0000-0001-8301-6320 1 ,
Ramez Alkhatib 2 ,
Kamal Salloum 1 &
Shadi Balloul 3

Journal of Big Data volume 7 , Article number: 29 ( 2020 ) Cite this article

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Given the growing importance of customer behavior in the business market nowadays, telecom operators focus not only on customer profitability to increase market share but also on highly loyal customers as well as customers who are churn. The emergence of big data concepts introduced a new wave of Customer Relationship Management (CRM) strategies. Big data analysis helps to describe customer’s behavior, understand their habits, develop appropriate marketing plans for organizations to identify sales transactions and build a long-term loyalty relationship. This paper provides a methodology for telecom companies to target different-value customers by appropriate offers and services. This methodology was implemented and tested using a dataset that contains about 127 million records for training and testing supplied by Syriatel corporation. Firstly, customers were segmented based on the new approach (Time-frequency- monetary) TFM (TFM where: Time (T): total of calls duration and Internet sessions in a certain period of time. Frequency (F): use services frequently within a certain period. Monetary (M): The money spent during a certain period.) and the level of loyalty was defined for each segment or group. Secondly, The loyalty level descriptors were taken as categories, choosing the best behavioral features for customers, their demographic information such as age, gender, and the services they share. Thirdly, Several classification algorithms were applied based on the descriptors and the chosen features to build different predictive models that were used to classify new users by loyalty. Finally, those models were evaluated based on several criteria and derive the rules of loyalty prediction. After that by analyzing these rules, the loyalty reasons at each level were discovered to target them the most appropriate offers and services.

Introduction

The telecom sector is witnessing a massive increase in data, and by analyzing this massive data, telecom operators can manage and retain customers. It is also important for companies to be able to predict the amount of income they may receive from their active customers. For this purpose, they need models able to determine customer loyalty. The cost associated with customer gain is usually higher than the cost associated with maintaining it [ 1 ]. Prediction can be directed at customer loyalty to identify both customers who have great loyalty to their preservation as well as customers with intentions to change to the competitors. This capability is necessary, especially for modern telecommunications operators. Nowadays companies face more complexity and competition in their business and need to develop innovative activities to capture and improve customer satisfaction and retention [ 2 ]. Growing profitability is the goal of most companies, to reach this goal, companies must provide an analysis of customer relationship management (CRM) and provide appropriate marketing strategies [ 3 ]. Some studies provided a new model of transactions based on both the services and customer satisfaction and showed that the price is not the only measure affecting customer buying decisions, but it is also important that both the customer and the company agree on product value and good customer services. Therefore, organizations should not seek to develop a product to satisfy their customers, but they must follow the customer purchasing behavior and offer distinct products for each segment. In other words, segmenting customers based on purchasing behavior is necessary to develop successful marketing strategies, which in turn cause the creation and maintenance of competitive advantage. Current methods of customer value analysis which are based on past customer behavior patterns or demographic variables are limited to predict future customer behavior. So, better patterns were exch

Research objectives

Our goals of this research

Customers value was Analyzed by segmenting them according to the new approach TFM and then determine, the level of loyalty for each segment in a big data environment in telecom.

A set of features was derived from the telecom data.

The best behavioral features for customers with their demographic information were Chosen, based on these features and the level of loyalty for each segment, the following classification algorithms were applied and the classification models were built: random forest classifier, Decision tree classifier, Gradient-boosted tree classifier, and Multiplexer perceptron (MLPC).

These models were evaluated based on several criteria that evaluated and selected the most accurate model.

The loyalty rules were derived from this model, these rules showed the characteristics of each level of loyalty and thus the loyalty reasons were identified in each segment to target them in a representative manner. The other advantage of classification algorithms application was building a model to classify new users by loyalty.

Related works

Various efforts have been made to build an effective prediction model for retaining customers using different techniques. To better understand how Many studies have built their own predictive models suggested by Oladapo et al. [ 4 ]. Logistic regression model design, a good model of customer data to predict customer retention in a telecommunications company with 95.5% accuracy. This model predicts customer retention based on billing, value-added services, and SMS service issues.

Aluri et al. [ 5 ] have focused on using machine learning to determine the value of customers in the hospitality sectors of customers, such as restaurants and hotels, by engaging dynamic customers with the loyalty program brand. Their results also show that automated learning processes excel in identifying customers with greater value in specific promotions. They have deepened the practical and theoretical understanding of automated learning in the value chain of customer loyalty, in a structure that uses a dynamic model for customer engagement.

Wiaya and Gersang [ 6 ] predict customer loyalty at the National Multimedia Company of Indonesia, using three data mining algorithms, to form a customer loyalty classification model, namely: C4.5, Naive Bayes and Nearest Neighbor. These algorithms were applied to the set of data contained 2269 records and 9 attributes to be used. By comparing the analysis models, the C4.5 algorithm with its own data set segment (80% for training data and 20% of test data) has the highest accuracy results of 81.02% compared to algorithms and other data segments. In the attribute analysis, the disconnection attributes (the attribute that is interpreted as the reason why customers have stopped) get the most influential attribute on the accuracy of the results in the data extraction process to predict customer loyalty. This article does not discuss the algorithms of features selection, methods of obtaining important features, and its impact on model accuracy.

Wong and Wei [ 7 ] presented a research to develop a tool to analyze customer behavior and predict their upcoming purchases from Air Travel Company. They provided an integration tool between data mining Pricing for competitors, customer segmentation and predictive analysis. Results In customer segmentation analysis, 110,840 clients are identified and segmented based on their purchasing behavior. Customers’ profiles are split using a weighted RFM model, and customer purchasing behavior is analyzed in response to competitor price changes. The following destinations are expected for high-value customers identified using pre-link rules and custom packages promoted to targeted customer segments.

Moedjionom et al. [ 8 ] have predicted customer loyalty in a multimedia services company, offering many services to win the market. This research contribution is to use data related to the segmentation and splitting of potential customers based on the RFM model, then applying the classification, Proportion of accuracy in customer loyalty rating research. Although the C4.5 algorithm with the k-mean segmentation give a better result, there are some important action that can be added to the search: using optimization algorithm to select the features or to adjust the value of the label to obtain a more accurate model.

Kaya et al. [ 9 ] have built a predictive model based on spatial, temporal and optional behavioral features using individual transaction logs. Our results show that proposed dynamic behavioral models can predict change decisions much better than demographics-based features and that this effect remains constant across multiple data sets and different definitions of customer leakage. They have examined the relative importance of different behavioral features in predicting leakage, and how predictive power differed across different population groups.

Cheng and Sun [ 10 ] have viewed other application of the RFM model (named TFM) to identify high-value customers in the communications industry. Use three main features to describe users who have accumulated a greater amount of service time (T), often purchase 3G services (F) and create large amounts of invoices per month (M).

This study proposes a comprehensive CRM strategy framework that includes customer segmentation and behavior analysis, using a dataset that contains about 500 million (full dataset in syriatel company). Al Janabi and Razaq [ 11 ] used intelligent big data analysis to design smart predictors for customer churn in the telecommunication industry. The goal of this research maintain customers and improve the level of revenue. The proposed system consists of three basic pashas: First Phase: an understanding of the company’s data. This phase focuses on the initial processing of data that is fragmented and unbalanced. They addressed the problem of imbalance by building the DSMOTE algorithm. Second Phase: construct a GBM-based predictor after it was developed, replace its decision-making part, which is (DT) with a (GA) algorithm. The impact of this is able to overcome DT problems and reduce time implementation. Third Stage: The accuracy of the predictor results was verified by using the matrix of the conflict matrix. A comparison was made between the traditional method of initial treatment, which is SMOTE, DSMOTE in terms of error rate and accuracy. GBM-GA method has higher Accuracy than GBM.

One of the biggest challenges of the current big data landscape is our inability to process vast amounts of information in a reasonable time. Reyes-Ortiz et al. [ 12 ] explored and compared two distributed computing frameworks implemented on commodity cluster architectures: MPI/OpenMP on Beowulf that is high-performance oriented and exploits multi-machine/multi- core infrastructures, and Apache Spark on Hadoop which targets iterative algorithms through in-memory computing. The Google Cloud Platform service was used to create virtual machine clusters, run the frameworks, and evaluate two supervised machine learning algorithms: KNN and Pegasos SVM. Results obtained from experiments with a particle physics data set show MPI/OpenMP outperforms Spark by more than one order of magnitude in terms of processing speed and provides more consistent performance. However, Spark shows better data management infrastructure and the possibility of dealing with other aspects such as node failure and data replication.

There are several studies in the field of communication that deal with predicting the age and gender of the customer in big data platform by analyzing their personal data, including the study presented by Zaubi [ 13 ]. Where he designed a model using a reliable data set of 18,000 users provided by SyriaTel Telecom Company, for training and testing. The model applied by using big data technology and achieved 85.6% accuracy in terms of user gender prediction and 65.5% of user age prediction. The main contribution of this work is the improvement in the accuracy in terms of user gender prediction and user age prediction based on mobile phone data and end-to-end solution that approaches customer data from multiple aspects in the telecom domain.

Other studies have also dealt with the prediction of customer churn in telecom using machine learning in big data platform, including the study presented by Ahmad [ 14 ]. The main contribution of his work is to develop a churn prediction model which assists telecom operators to predict customers who are most likely subject to churn. The model developed in this work uses machine learning techniques on big data platforms and builds a new way of features’ engineering and selection. In order to measure the performance of the model, the Area Under Curve (AUC) standard measure is adopted, and the AUC value obtained is 93.3%. Another main contribution is to use customer social networks in the prediction model by extracting Social Network Analysis (SNA) features. The use of SNA enhanced the performance of the model from 84 to 93.3% against the AUC standard.

With regard to how some studies approached customer value analysis, retention, and loyalty. A study in [ 4 ] did not apply to big data as it studied all customers according to some features and using a method of machine learning (a logistic regression model) to show the role of machine education in retaining and increasing customer loyalty. In the study [ 5 ]. Machine learning was implemented in a major hospitality location and compared to traditional methods to determine customer value in the loyalty program. In the study [ 6 ] predict customer loyalty at the National Multimedia Company of Indonesia, using three data mining algorithms, These algorithms were applied to the set of data obtained are 2269 records and contain 9 attributes to be used. By comparing the analysis models, the C4.5 algorithm with its own data set segment has the highest accuracy results of 81.02% compared to algorithms and other data segments. In my study, a model is built to increase customer loyalty predictions based on the new TFM methodology and machine learning. My experiences were demonstrated that TFM most appropriate for the telecom sector than RFM. The concept of the TFM is adjusted, where T is the sum of the duration of calls and the periods of internet sessions during a certain period. The set of data obtained is 127 million records and contains 220 features to be used. Binary and multi-classification are applied. After comparing the classifiers, the Gradient-boosted-tree classifier was found to be the best in binary and Random Forest Classifier is the best in multi-classification.

Research tools

Hortonworks data platform (hdp).

An open-source framework for distributed storage and processing of large and multi-source datasets [ 15 ]. HDP enables flexible application deployment, machine learning, deep learning workloads, real-time data storage, security, and governance. It is a key element in the modern data structure of data (Fig. 1 ).

Hortonworks data platform: HDP 3.1 [ 15 ]

The HDP framework was custom- installed to obtain only the tools and systems required to track all stages of this work. these tools and systems were: a distributed file system [ 16 ], Hadoop HDFS Footnote 1 for data storage, Spark Footnote 2 implementation engine for data processing [ 17 ], Yarn for resource management, Zeppelin Footnote 3 as a development user interface,Ambari for system monitoring, Ranger for system security and (Flume Footnote 4 System and Scoop Footnote 5 ) for data acquisition from Syriatel company data sources to HDFS in our dedicated framework.

Hive Footnote 6 is an ETL and data warehouse tool on top of the Hadoop ecosystem and used for processing structured and semi-structured data. Hive is a database present in the Hadoop ecosystem that performs DDL and DML operations, and it provides flexible query language such as HQL for better querying. Hive in Map reduce mode is used because data was distributed across multiple data, nodes to execute queries with better performance in a parallel way. The hardware resources were used included 12 nodes with 32 GB of RAM, 10 TB storage capacity, and 16-core processor per node. The Spark engine [ 17 ] was used in most phases of model building such as data processing, feature engineering, training and model testing because it is able to save its data in compute engine’s memory (RAM) and also perform data processing over this data stored in memory, thus eliminating the need for a continuous Input/Output (I/O) of writing/reading data from disk. In addition, there are many other advantages. One of these advantages is that this engine contains a variety of libraries to implement all stages of the machine learning life cycle.

Syriatel data sources

Call details record of cdrs.

Each time a call is made, a message is sent, the Internet is used, or an operation is performed on the network, the descriptive information is stored as a call details record (CDR). Table 1 illustrated some types of call logs, messages, and Internet details available in Syriatel that were used in this research to predict customer loyalty:

Rec: Call log, SMS message log, MMS Multimedia Message log MMS multimedia messaging log, DATA internet data usage log, Mon fee log,Vou recharge log, Mon monthly log information, web metadata information, EGGSK tab In roaming.

Detailed data stored in relational databases

The Call details record was linked to the customer detailed data stored in the relational databases using this GSM, which is detailed as follows: Customer Management Database, Customer Complaints Database, Towers Information Database, Towers Information Database, Mobile Phone Information Database.

Customer services

All services recorded by the client were collected and classified manually based on the type of service such as political related services News, sports news, horoscopes, etc $\ldots$ , these categories are treated as a customer Advantages. As a result, a customer service table is produced. Table 2 is a sample.

Customer contract information

Customer contract information was fetched from the CRM system, and contains basic customer information (gender, age, location $\ldots$ ) and customer subscription information, as a single customer. You may have more than one subscription (two or more GSM networks) with different types of subscriptions: pre-subscription, prepay, 3G, 4G $\ldots$ subscription.

Database of cells and sites

Telecommunications companies related to location data and their components were stored in the relational database. This data was used to extract spatial features. Table 3 is a sample.

Demographics data for customers

Building such a predictive system requires a data containing the real demographics such as gender and age for each GSM, whatever the demographics of the GSM owner sometimes the real user and the GSM owner, not much. Table 4 is a sample.

Extraction of features

The features were engineered and extracted based on our research and our experiment in the telecom domain. 223 features were extracted for each GSM. These features belong to 6 feature categories; each category provided with examples.

Segmentation Features T, F, M (3 features)

total of calls and Internet duration in a certain period of time (Fig 2 ).

Frequency (F): use services frequently within a certain period (Fig. 3 ).

Monetary (M): The money spent during a certain period (Fig. 4 ).

Classification Features (220 features)

Individual Behavioral Features

Individual behavior can be defined as how an individual behaves with services.

For example:

Calls duration per day: calls duration per day for each GSM.

Duration per day: calls and sessions duration per day for each GSM (Figs. 5 , 6 , 7 ).

Entropy of duration

High entropy means the data has high variance and thus contains a lot of information and/or noise.

Daily outgoing calls: for each GSM the daily outgoing calls.

Calls incoming daily night: for each GSM the daily outgoing calls at night (Fig. 8 ).

SMS received daily at work time, $\ldots$ About (200 features).

Social behavior features

Is behavior among two or more organisms within the same species, and encompasses any behavior in which one member affects the other. This is due to an interaction among those members.

Some examples about this features:

Number of contacts: for each customer number of contacts with other customers.

Transactions received per customer: number of calls, sms,Internet sessions received by each customer.

Transactions sent per contact, etc. (20 features).

spatial and navigation features

Features about the spatial navigation of customers

holiday navigation: Customer Movements on holiday.

Home zone: location of customer home.

Antenna location: location of antenna.

Daytime antenna: antenna which are used by customer transactions in daytime.

Workday antenna: antenna which are used by customer transactions in workday.

Vacation antenna: antenna which used by customer transactions in vacation, $\ldots$ , etc. Their number is about (21 features).

Timestamps for each working day (Sunday to Thursday), on holidays, or during the day (9 to 16) and at night: average number of SMS received per day (17 to 8) on holidays, etc. (165 features).

Types of services registered

technical news services, educational services, sports news services, political news services, entertainment services, etc., (13 features).

Contract information tariff type

GSM type, (2 features).

The total number of features listed is 421, but there are about 201 features belonging to more than one category, so the total number of features is 220. Mass distribution for Some features with loyalty.

Mass distribution for T in our Study

Mass distribution for F in our Study

Mass distribution for M in our Study

Mass distribution for “Avg dur per day daylight”

Mass distribution for “Avg dur perday night”

Mass distribution for “Avg dur per day worktime”

Avg dur per call

Features engineering-ways to choose features

Feature engineering is the process of using domain knowledge of data to create features that make machine learning algorithms work well. The most important reasons to use the selection of the features are:

It enables machine learning algorithm to train faster.

It reduces the complexity of the model and makes it easy to interpret.

It improves the accuracy of the model if the correct subset is selected.

It reduces overfitting.

Next, we’ll discuss several methodologies and techniques that you can use to set your feature space and help your models perform better and more efficiently.

Attribute Selection Algorithms (Features)

Feature selection algorithms discovered and reported in the literature.

The feature selection algorithms are categorized into three categories such as filter model, embedding model (or aggregator) and embedded model according to mathematical models.

Filter model

It depends on the general characteristics of the data and the evaluation of features without involving any learning algorithm [ 13 ]. Filter model Algorithms are Relive F, Information Gain. Entropy (H) was used to calculate the homogeneity of a sample.

Information gain is decrease in entropy after dividing the data set by an attribute. Gain index, Chi-Squared, Gain Ratio [ 18 ].

Wrapper model

A predefined learning algorithm is required and its performance is used as a benchmark for evaluation and feature identification.

Embedded model

It chooses a set of features for training and building a model, then test its feature importance depending on the goal of learning model. You can get the feature importance of each feature of your dataset by using the feature importance property of the model. Feature importance gives you a score for each feature of your data, the higher the score more important or relevant is the feature towards your output variable. Feature importance is an inbuilt class that comes with Tree Based Classifiers.

The big data analytics is used technological advances are based on memory usage, data processing and scrutiny of big data include handling high volume data with decreasing cost of storage and CPU power, the effective usage of storage management for flexible computation and storage and Distributed computing systems through flexible parallel processing [ 19 ] with the development of new frameworks such as Hadoop. And also big data frameworks such as the Hadoop ecosystem, No SQL databases efficiently handle complex queries, analytics and finally extract, transform and load (ETL) which is complex in conventional data warehouses. These technological changes have shaped a number of improvements from conventional analytics and big data analytics.

The feature selection aims to select a feature subset that has the lowest dimension and also retains classification accuracy so as to optimize the specific evaluation criteria. feature selection methods can be filter and wrapper. Filter methods make a selection on the basis of the characteristics of the dataset itself. The selection may be fast but it leads to poor performance. Wrapper methods takes a subsequent classification algorithm as the evaluation index, it achieves high classification accuracy but results in very low efficiency for a large amount of computation. Backward feature elimination and Forward feature construction have been used. Feature selection methods such as backward elimination, Forward selection [ 19 ].

Backward elimination: In contrast to the forward selection strategy, the backward elimination strategy starts with the complete attribute set as initial subset and iteratively (and also heuristically) removes attributes from that subset, until no performance gain can be achieved by removing another attribute.

Forward selection initially uses only attribute subsets which exactly one attributes. Then additional attributes are added heuristically until there is no more performance gain by adding an attribute. Both of the methods are handled carefully Hadoop ecosystem.

Implement methodology

The intended model was described to determine customer loyalty. This model relied on data mining and customer value analysis methods based on TFM to improve customer relationship management. Customers were divided using the Calculating TFM Score.

After the division of customers and determining the degree of loyalty in each department. The classification was performed based on descriptors expressing the level of loyalty in each segment and a set of behavioral and demographic features using several algorithms and evaluated their models to obtain the best model in terms of the highest accuracy. Steps required for the proposed model illustrated in Fig 9 .

Steps required for the proposed model

The loyalty features of these models were extracted to find out the causes of loyalty and the precise targeting of these segments.

Data preparation

The data was collected from Syriatel data sources to the Hadoop environment.

The Scala language was chosen to perform data preparation, attribute extraction, model training, and testing because it is the language in which the distributed implementation engine (spark) has developed. spark achieves a high-speed execution in addition to stability. In Spark, the library of automated learning provided by the Spark implementation engine, ML extension is used.

The original data was divided into two sub-parts: the training group and the test group by 70/30, respectively.

Address missing and text values

Filling in the missing values with values of either zero or average of several nearby values was an advantage so that it enabled us to use the information in most attributes for the exercise. In this research, the following were applied:

The attributes whose 70% at least include a missing value were deleted.

The missing numerical values were replaced with the mean attribute itself.

StringIndexer has been applied to String style attributes to convert them into numbers. This represents a common way to produce labels using StringIndexer, train a model with these pointers, and retrieve the original labels from the predicted pointers column using IndexToString. However, you are free to provide your own labels. Emphasis was placed on the development of the attribute preparation and attribute selection process.

Emphasis was placed on the development of the attribute preparation and attribute selection process.

Data processing and application of extraction and selection of attributes

The T, F, M features were calculated for each customer, and the behavioral features were chosen.

The most important attributes were chosen based on Chi-Squared function. this function was applied to groups of categorical characteristics and selected features to assess the probability of correlation or correlation between them using frequency distribution.

Compilation using calculating TFM

To calculate the TFM results, the three input parameters were divided into five subcategories. The Time, frequency and monetary scores were calculated and then combined, to obtain a comprehensive TFM analysis score.

Cumulative total duration (cumulative total duration) T

Time (T): total of calls and Internet sessions duration in a certain period of time.

The customers have been divided into different categories (Table 5 ). The cumulative total duration of service, for example, the total of calls and Internet sessions duration in 3 months (T).

Some research defines it as the average time you spend communicating or using application services in one month and in our study for three months. When the user’s connection time is greater than the previously calculated average, the value of T is 2; otherwise, it is 1. In our study, There were 5 levels which were calculated after the maximum cumulative value of the total duration of calls and usage per GSM and the smallest value was calculated. Calculations were made and 5 levels of values T resulted. Where T1 was the first category with the lowest value, T2 was the second category, T3 was the third category, T4 was the category Fourth was high value, T5 Class V was very high.

Frequency (F): use services frequently within a certain period

Customers were divided into different frequency categories (Table 6 ). Totalize the number of times he/she performed with the company (communication, message, internet access) during the past 3 months.

F1 represents clients who have performed less than or equal to 2 transactions in the last 3 months and F5 represents customers who have performed more than or equal to 11 transactions in the last 3 months.

In our study, there were 5 levels calculated after calculating the maximum cumulative value of the total number of calls and the number of uses per GSM and the smallest value was calculated. Calculations were made and 5 levels of values F resulted.

Monetary (M): The money spent during a certain period. Customers were divided into different cash categories (Table 7 ) according to the total amount he/she paid for transactions with the organization over the past 3 months. M1 as clients paid less than or equal to 100 transactions in the last 3 months M5 customers who have paid greater than or equal to 10,000 transactions in the last 3 months.

In our study, There were 5 levels calculated after calculating the maximum cumulative value of the total cash mass of calls and the number of times of use per GSM and the smallest value was calculated. Calculations were made and 5 levels of values M resulted.

Based on the TF results calculated above (Table 8 ), we calculate the TFM results (Table 9 ).

Segment and target customers

Customer categories.

Customer segmentation involves splitting the customer base into different subsets. A specific subsets with the same interest and spending habits [ 20 ]. Based on the TFM results as calculated above, customers can be divided into five parts:

Very high value customers (greater loyalty) These are the customers who make the highest profit for the operator. Without them the operator will lose its market share and competitive advantage. These customers are given appropriate care and attention from the operator.

High value customers (great loyalty) These are the customers who make the highest profit for the operator. Without them, the operator will lose its market share and competitive advantage. These customers are given appropriate care and attention from the operator.

Medium value customers (average loyalty) These are the customers who make medium profitability.

Low value customers (little loyalty) These are the clients who make very little profit.

Customer churn from the company (very little loyalty).

Customers who have the least loyalty are those who have left the company or are about to leave. Endeavors were taken to prevent them from leaving, and if they leave the company, the cost of customer service will be calculated. The total cost (1) associated with these potential customers if they stop their relationship Total cost (customer leakage) = lost revenue Marketing cost (1) Lost revenue is the revenue that these customers can make if they do not cease their relationship with the operator. The cost of marketing is the cost associated with replacing these customers with new customers.

Target customers

By calculating the TFM score, individually the status of the total time spent on calls, sms and the total Internet data, high-value customers were recognized as well as potential customers to leave the company. Today, however, most people have access to a range of telecommunications operator services that include both the price of the connection and the amount of Internet data. The sudden exceptions to this were people who used only the operator’s services for the Internet or calls and not both. Therefore, to target customers based on these things, considerations about both the TFM score for the total call time, SMS and the amount of Internet data were taken (Tables 10 , 11 ).

In both TFM score for total call time and number of messages.

If TFM is High, then Customers who use large Internet data and spend a lot of time on calls and send a large number of messages. Average customers who use Internet data, spend time on calls and average messages. If TFM is low, then Customers use less Internet data and spend less on calls and fewer messages. Large users can be targeted using loyalty points and personalized offers tailored to them, as they are the key to the competitive operator advantage in the marketplace, for medium users who can make offers based on a combination of both call rates and data bundle and for low user operators can deliver Offers tend to make these users use more of the services provided by operators. for example, free local calls to the same telephone numbers of the operator late at night, etc. For Potential Customers churn, TFM points were integrated with unstructured data such as social media data and call center feedback data to accurately predict them. Customers who were likely to churn must be taken seriously into consideration by telecom operators because of the cost of revenue and marketing associated with each. Therefore, the telecom operator must be made offers such as free talk time, free packet data for a specified period and an additional number of messages, for example, 200 MB of data for 3 days, to retain it.

Results and discussion

Apply classification algorithms.

Having segmented using grades and recognizing loyalty for each segment, at this stage, the causes of loyalty were needed, i.e. The behavioral features of customers in each segment. The behavioral 220 features were taken and the descriptions resulting from the segmentation process as an input to the classification algorithms to identify the causes of loyalty and to identify the influential features at each level of loyalty. The other benefit of applying classification algorithms was to build an accurate predictive model for classifying new users by loyalty. Multiple and binary classifiers were built and the results were compared using different criteria. It is the highest accuracy classifier that gave us the best correlation between behavioral features and loyalty categories and gave the best behavioral features that were described categories (classes) and thus assist in decision-making in building marketing presentations for each category thus increasing the company’s profit.

Performance measurement

The correlation matrix shown in Table 12 contains information on the actual and predicted classifications made by the binary classification system where (Loyal 1, Not Loyal 0). Each term corresponds to a specific situation as follows:

True Positive (TP) is expressed as an example when the prediction is yes (the customer has loyalty to the company), and the truth has loyalty to the company.

True negative (TN): When the prediction is no (no customer loyalty to the company), in fact the customer has no loyalty to the company.

False Positive (FP): The prediction is yes (the customer has loyalty to the company), but the customer left the company is also known as “Type 1 error”.

D False negative (FN): When the prediction is not (the customer has no loyalty to the company), but the customer has loyalty and did not actually leave the company. Also known as “Type 2 error”.

Some performance measures can be calculated directly from the confusion matrix [ 21 ].

TPR is also known as recall or allergy.

The accuracy standard does not rate the rate of correctly classified cases from both categories. It is expressed by the following equation:

Area under the curve (AUC): measures the effectiveness of the work can be calculated by [ 21 ]:

F1-measure: harmonic mean of the precision and recall. Can be calculated by:

Compare binary classifiers

Confusion matrix.

An example of the confusion matrix for the Multilayer Perceptron Classifier algorithm (Tables 13 , 14 ).

Loyalty categories (loyal 1, not loyal 0)

After comparing the classifiers, Gradient-boosted-tree classifier was found to be the best.

Comparison of multiple classes

Example of confusion matrix for binary classes.

Avrage recall = [R(A) + R(B) + R(C) + R(D)]/4 = 0.775: 4, number

Recall calculation from the correlation matrix of model TP: 100, FN:100 R(A) = 100/200 TP:9, FN: 1 R(B) = 9/10 TP:8, FN:2 R(C) = 8/10 TP:9, FN:1 R(D) = 9/10

Recall = TP/(TP + FN) Table 15 .

Multi-classification (1, 2, 3, 4, 5)

It reflects the loyalty levels wherethe 5 is very high loyalty, 4 is high loyalty, 3 is medium loyalty, 2 is low loyalty, 1 is very low loyalty.

Note 1: Multilayer Perceptron Classifier Input: 220 feature with 4 layers, 5 node in each layer Output: 5 classes

Note 2: Gradient-boosted tree classifier currently only supports binary classification (Table 16 ).

After comparing the Classification algorithms, it turns out that Random Forest Classifier is the best. An example of the distinctive features of each level of loyalty derived from the binary classification model (Table 17 ):

TFM segmentation and setting loyalty levels were been relied on. The classification algorithms were applied based on the loyalty levels as classification categories and the selected attributes, compared the results and selected the best classification model in terms of accuracy. Then the rules of loyalty prediction were derived from this model, which expressed the correlation of behavioral features with classification categories and thus known the causes of loyalty in each segment. target customers were optimized with appropriate offers and services. The other benefit of applying the classification algorithms was to build an accurate predictive model for classifying new users by loyalty.

Availability of data and materials

The data that support the findings of this study are available from SyriaTel Telecom Company but restrictions apply to the availability of this data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of SyriaTel Telecom Company.

https://hadoop.apache.org/ .

https://spark.apache.org/ .

https://zeppelin.apache.org/ .

https://flume.apache.org .

https://sqoop.apache.org/ .

https://hive.apache.org/ .

Abbreviations

Global system for mobile communications

Call detail record

Customer relationship management

Short message service

Quality of experience

Hadoop distributed file system

Linear discriminant analysis

Extreme gradient boosting

Gradient boosting machine

Bagging classification and regression trees

Area under the curve

Standard deviation

Received by the customer; send by the customer

True positive

True negative

False negative

True positive + false negative

True negative + false positive

True positive rate

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Acknowledgements

This research was sponsored by SyriaTel telecom Co. We thank our colleagues Mem. Mjida (SyriaTel CEO), Mr. Adham Troudi (Big Data manager) who provided insight and expertise that greatly assisted the research, although they may not agree with all of the interpretations/conclusions of this paper. Also, the authors thank Housam Wassouf, Hazem S Nassar, Majdi Msallam, Mahmoud Eissa, Nasser abo saleh, Mustafa Mustafa for great ideas, help with the data processing and their useful discussions. the authors thank Moral help Rama Saleh, Marita wassouf, Amal Abbas, Nazier wassouf, weaam wassouf, walaa wassouf, Rawad wassouf.

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WNW-W took on the main role so he performed the literature review, implemented the proposed model, conducted the experiments and wrote the manuscript. RA and KS took on a supervisory role and oversaw the completion of the work. All authors read and approved the final manuscript.

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Wassouf, W.N., Alkhatib, R., Salloum, K. et al. Predictive analytics using big data for increased customer loyalty: Syriatel Telecom Company case study. J Big Data 7 , 29 (2020). https://doi.org/10.1186/s40537-020-00290-0

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10 Customer Retention Case Studies from Top Companies

Investing in customer retention techniques is one of the best ways to increase your company’s ROI without spending money on acquiring new customers.

But what are customer retention techniques, and how do you use them to increase customer retention? Keep reading to learn about customer retention techniques and get examples from well-known companies you can use to inspire your business.

What are customer retention techniques?

Customer retention techniques are proactive approaches businesses use to nurture customer relationships. These approaches are meant to increase customer loyalty and maximize customer lifetime value. This, in turn, leads to sustainable profitability since it is 5x cheaper to retain customers than to acquire newer ones.

While you’re here, if you want to learn more about customer retention, what it’s about, and how to calculate them, you can check out this article – 5 ways to improve customer retention rates in SaaS. (hyperlink goes here when the article has been published)

10 customer retention techniques used by top companies

Not every customer retention technique works. But here are 10 proven-effective techniques (with case studies from popular brands you know) that can help you choose the best retention techniques for your business.

Reward loyalty

One of the best customer retention techniques is to reward your customers for their loyalty through a loyalty program.

You have complete control over your loyalty programs and the rewards you choose to offer. Some of the best rewards are discounts, sneak peeks, exclusive events, free trials, and more.

Yes, free trials. Many people think of free trials as incentives to convert potential leads, but they can also be used in loyalty programs. For instance, if you have a new product that has yet to be in the market, you can release a free trial for customers to try it for free. You can also partner with another company to offer a free trial for your customers – this is a bomb!

Case Study: Shazam

Shazam is a music discovery app that helps customers identify the music playing around them – Great product! But we have something to learn from them today.

Shazam offers up to 4 months of free Apple music subscription to users to say thank you and to keep them “Shazaming” music with the app and using Apple Music for free.

This action alone would nudge users to always “Shazam” music around because they can listen to the music for free with Apple Music – customer retention at its finest!

Ask for feedback

It’s easier to improve your business if you know how your customers feel about it. To know how your customers feel about your business, you need their feedback.

Andrew Gazdecki , CEO of MicroAcquire, tweeted this on customer feedback, and I totally agree:

There are a few ways to collect customer feedback. The most common way is with a survey like the NPS, CSAT, or CES, or you can ask customers to participate in user testing and focus groups.

Case study: Clearword

Clearword helps customers create smart meeting summaries that they can search and share in one central library. Today, they teach us a valuable lesson about customer feedback.

Instead of sending regular emails, Clearword’s team is not shy to ask for feedback from their customers to know what they are doing well and what isn’t working. This information is then used to make better decisions, contributing to customer retention.

Start a customer education program

A customer education program helps your customers stick to your brand because you’re not only offering a great product/service. You’re also establishing yourself as an expert in your niche by teaching them how to run their businesses better and possibly make more money through your product.

Case study: SEMRush Academy

I already love SEMRush for what they do for businesses in terms of SEO, but they take it up a notch by offering free content marketing and SEO training videos and certifications that anyone can use to learn and grow their skills. By watching training videos on SEMRush, users can learn more about how to use SEMRush tools in their everyday workflow.

Provide a personalized customer experience

Every customer is different, and no two customers have the same exact needs. So the last thing you want to do is provide a one-size-fits-all solution for all customers – they will churn.

One statistic in the Fifth edition of the State of the Connected Customers shows that 66% of customers expect companies to understand their needs and expectations.

But how do you personalize customer experience? By using data.

You can collect customer data through feedback surveys, reviews, purchase behavior, or by asking them during their onboarding with welcome screens surveys. Welcome screen surveys allow you to know more about your users and provide a more personalized in-app experience for them.

Case Study: Monday.com

Monday, a cloud-based project management tool, asks qualifying questions at the beginning of the onboarding process to segment users.

This ensures they don’t use the one-size-fits-all onboarding strategy for their customers. Instead, when users receive a personalized experience tailored to them, it increases the rate of satisfaction and retention.

Make your customers feel special and send a thank you message

Everyone likes to be appreciated, and your customers are no exception. Therefore, thanking your customers is a must-do for every business. Emotions are the most significant drivers of loyalty, and what better way to generate positive emotions than saying, “we really appreciate you, thank you!”

Case study: Canva

Canva is one of the best brands out there, and it’s no surprise they are doing so well. Apart from having a great product that solves a problem: access to easy-to-use design tools for non-designers, they are also great with something else – connecting with their customers.

Canva would send their customers awards for completing 1000 designs and thank you messages for being part of the company – it’s refreshing to see.

Engage on social media

A report by Spoutsocial revealed that nearly two-thirds of customers want brands to connect with them, and social media is the best platform.

Engaging with your customers through social media can help you create a sense of community around your brand, increasing brand loyalty and customer retention.

Case study: Hotjar

I love Hotjar, but I love their social media more. Big ups to Hotjar’s social media team. They are constantly engaging with followers, sharing customer-generated content, and being an overall relatable brand.

Listen to your customers’ complaints and empathize with them

Making your customers feel heard and letting them know that you’re always ready to serve them is a crucial part of any client retention technique. So, whenever you receive a complaint from a customer, try to listen, empathize with them and take bold steps to improve the issue.

Case Study: Piggyvest

Piggyvest is a Nigerian online savings platform that helps people achieve their financial goals by making it easy to save and invest.

I’ve been using Piggyvest for a while, and then I had an issue with their service. I wanted to withdraw some of my savings into my bank, but I couldn’t, so I reached out to them, and in less than 5 minutes, they replied with the email below:

Apart from the fact that their response was swift, they also listened and empathized with me. It’s one thing to say, “We are sorry about your experience, and we will get back to you.” it’s another to empathize with the customer, explain to them why they’re facing the issues, and proffer a solution – that’s what Piggyvest did.

Capitalize on social proof

The greatest form of advertising is done by your customers for your business. Why? Because customers trust other customers more than ads and branded content.

Soliciting reviews and testimonials from satisfied customers should be a part of every customer retention technique. Not only does social proof help you acquire new customers, but it can also help boost retention rates and reduce churn.

Case Study: Zoom

Using the power of testimonials and customer stories, Zoom uses social proof to show other business leaders like Hubspot values their product. And they didn’t just say it; it came directly from the horse’s mouth – the VP of Corporate Development & Business Development at Hubspot.

Give your customers post-purchase support

Once customers buy your product, don’t leave them hanging – they’ll churn. Instead, support them and help them recognize the value your product provides.

You can offer support by sending emails about topics your customers will be interested in, or you could provide tips on how to make the most of your product.

Case Study: SEMRush

SEMRush does a great job with post-purchase support. They send emails to users about topics that help them better with SEO and content marketing. These emails provide information that empowers customers to use SEMRush more effectively.

Track customer retention metrics

The most important part of any customer retention technique is tracking the right metrics and KPIs. These includes:

Customer Lifetime Value (CLV): How much revenue you earn from each customer (on average).
Customer Churn Rate: This is how many customers you lose in a period (monthly, quarterly, or annually).
Customer Acquisition Rate: How many customers you acquire in a given period.

Customer Retention Techniques Summary

To recap, here are the 10 best customer retention techniques used by top companies:

Listen to your customers’ complaints and empathize with them
GIve your customer post-purchase support

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Online Customer Experience Leads to Loyalty via Customer Engagement: Moderating Role of Value Co-creation

Farooq ahmad.

1 Faculty of Management Sciences, University of Okara, Okara, Pakistan

2 Fatima Jinnah Women University, Rawalpindi, Pakistan

Khurram Mustafa

Syed ali raza hamid.

3 Hamdard Institute of Management Sciences, Hamdard University, Islamabad, Pakistan

Kausar Fiaz Khawaja

4 Faculty of Management Sciences, International Islamic University, Islamabad, Pakistan

Shagufta Zada

5 Business School, Henan University, Kaifeng, China

6 Department of Business Administration, ILMA University, Karachi, Pakistan

Saqib Jamil

Muhammad nawaz qaisar.

7 Faculty of Management Sciences, National University of Modern Languages, Islamabad, Pakistan

8 National Accountability Bureau, Peshawar, Pakistan

Alejandro Vega-Muñoz

9 Public Policy Observatory, Universidad Autónoma de Chile, Santiago, Chile

Nicolás Contreras-Barraza

10 Facultad de Economía y Negocios, Universidad Andres Bello, Santiago, Chile

Naveed Anwer

11 Faculty of Management Sciences, Shaheed Zulfikar Ali Bhutto Institute of Science and Technology, Larkana, Pakistan

12 Lahore Business School, University of Lahore, Lahore, Pakistan

Associated Data

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

With the increasing growth of online shopping, businesses are intertwining to establish new shopping antecedents. Customer experience has steadily become the most important source of retailers’ long-term competitive advantage via difference. To preserve long-term and sustained consumer loyalty, retailers must continually improve the customer experiences. This study presents a framework for online retailing in a digital environment called the Online Customer Experience-Engagement Context model in the presence of value co-creation (VCC). Data was gathered from 189 people who purchased products online. For additional analysis, testing the hypothesis, and model construction, SPSS 26 and Smart-PLS were used. The data was then evaluated further using descriptive statistics, path analysis, measurement, and structural model. The findings show that the online customer experience (measured as shopping environment, shopping procedure, staff service experience, and product experience) substantially influenced customer engagement, which improved customer loyalty. The VCC strengthened the link between online customer experience and customer engagement. It suggests that including consumers in co-creating a delightful online customer experience from time to time may be a valuable strategy for online retailers to increase customer engagement and loyalty. To create an overarching outcome, information integration theory (IIT), multi-attribute utility theory (MAUT), and the attitude-behavior-context theory (ABC) theories are converged to explain the proposed model in the study.

Introduction

The presence of customers for online shopping is on the surge nowadays. The core factors for this trend are international dynamics, sophisticated infrastructure, the evolving lifestyle of consumers, and the mushroom growth of information communication and technology. Altogether these factors have enhanced consumers’ needs regarding updated trends and improved living compatible with socially accepted standards. The most beneficial aspect of online shopping is that it induces customer experience due to comparing products and services ( Sivanesan, 2017 ). The customers of various age groups, particularly the youth segment of society, have exhibited a profound presence on different online sources for shopping purposes. Thus, customers’ online presence for shopping purposes has increased their shopping options enormously and widened their choice ( Ellison et al., 2020 ).

The pervasive engagement of people in online shopping sites is attributed to ease, timeliness, and loyalty factors surfaced due to behavioral learning. While online customer experience evolves based on heterogeneous factors, it has become a kind of query for researchers and managers to examine the customer engagement process in online shopping ( Gao et al., 2021 ). Probing into the engagement process and assessing online customer activities related to shopping may further help reduce consumers’ security concerns regarding online shopping; augment customer loyalty, brand proliferation, and general customer satisfaction. Previous research affirms that customer experience, by and large, may be attributed to various factors; however, attitudinal and behavioral engagements have underlying connections with consumers’ utilitarian and hedonic values ( Barari et al., 2020 ). This study conjectures that while customers’ online shopping behavior is not predictable due to the technology evolution, disruption, and diffusion, what factors could moderate the relationship between customers’ engagement behavior and their online shopping experience. Furthermore, under the presently prevailing COVID phenomenon, an unequivocal surge in online economic activities has slightly strengthened consumers’ behavioral intention, decision-making for online transactions, and perception. However, this study will indeed provide a pathway to assess value co-creation (VCC) concerning online consumers’ platforms ( Watson and Popescu, 2021 ). This will enable the managers of online ventures to harness the experience of customers in terms of valued services that may be co-created (providers and customers) on the one hand; increase the likelihood of repetitive online shopping experiences, particularly regarding purchasing habits and behavioral choices contributing to the vitality of brand on the other ( Kumar and Anjaly, 2017 ; Rydell and Kucera, 2021 ).

Another essential concept of VCC is integrated as part and parcel of the theoretical framework. The concept evolved in recent times as an overarching phenomenon that refers to mutual and shared ownership of services with participatory and engagement approaches and has a predisposition for augmented customer experience in principle. VCC refers to a phenomenon that implies an active customer’s involvement psychologically and physically in crafting, utilizing, and evaluating the services. Particularly framing VCC as an integral part of online shopping activities reinforces the online shopping experience’s compass. As customers experience business context and propose value based on their perceived intentions, VCC has proved to be an interactive business concept that further outlines their attitudinal predisposition for online shopping.

The study proposes information integration theory (IIT) to examine the theoretical framework of the online shopping experience and customers’ behavior. According to the theoretical framework’s antecedents of customer experience, customer experience comprises the shopping environment, staff service environment, shopping procedure, and product experience. Customers process these antecedents as a source of information integration to encapsulate customer experience. The IIT represents customers’ attitudinal ability to assess information in the given context to have behavioral outcomes. In light of IIT, the study is most likely to see the continuity of customer attitudes developed concerning the antecedents of customer experience and their relationship with behavioral intention and engagement. The primary question in the study is how VCC helps customers devise their attitudinal predisposition to get engaged in online shopping behavior ( Meilhan, 2019 ). In this study, the VCC construct remains to be studied as an instrumental one in light of the proposed theory. Since VCC is an arrangement whereby interactive participation and dialogue further crystalize the information, thus helping participants or shoppers to involve meaningfully in the process on a hedonic basis. Moreover, the study seeks to determine the imperatives of online customer engagement behavior that lead to consumers’ loyalty factors ( Miricǎ, 2019 ). Hence, there is a gap existed in literature about the moderation of VCC over the relationship of online customer experience and customer engagement. Therefore, the following questions will be addressed in this study: (1) what is role of antecedents of online customer experience on the customer attitudinal and behavioral engagement?, (2) how customer attitudinal and behavioral engagement impacts the customer loyalty?, (3) what degree of effect of VCC on the relationship of antecedents of online customer experience and customer attitudinal and behavioral engagement?, and (4) how customer attitudinal and behavioral engagement mediates the relationship between antecedents of online customer experience and customer loyalty?

Conversely, the deficit on account of VCC operationalization in the given model may be endorsed by underpinning IIT. In light of the stated theories, hypothesis testing will be accomplished conveniently. Furthermore, every variable of the underlying model will have a clear underpinning relationship.

Theory and Hypotheses Development

The nature of the products plays an integral part in the evolution and enhancement of online shopping. As a result, merchants must understand the elements that influence customers’ attitudes and the consequences, such as purchase intentions and loyalty to online stores. This study used the multi-attribute utility theory (MAUT) and the attitude-behavior-context theory (ABC) theories to investigate this. MAUT aids the decision-making process. It is used in this study because it provides a systematic technique for examining and considering various options. It assists decision-makers in gaining access to and selecting from a variety of options ( Geoffrion et al., 1972 ). It strengthens the overall decision-making process by providing a framework for identifying improved qualities across all key measures ( Collins et al., 2006 ). The ABC theory predicts environmental and consumer behavior in a variety of situations. Guagnano et al. (1995) proposed the idea, and it describes how contextual elements might assist in predicting consumer attitudes toward displaying particular behaviors. According to the researchers, simply having a positive mindset is insufficient to legitimize consumer behavior ( Goh and Balaji, 2016 ) in online shopping. It is proposed that the relationship between attitude and behavior is greater when behavior is encouraged by structural conditions at a modest level rather than when it is extended to the point that even individuals with the least favorable attitudes would engage in it ( Olander and Thogersen, 2005 ). This study will evaluate the impact of online customer experience on customer engagement and loyalty, with the help of VCC.

According to researchers, online shopping is a low-involvement activity that does not necessitate extensive information gathering. As a result, it falls into the category of habitual purchasing behavior, in which the customer is less involved ( Hansen et al., 2004 ). It was also suggested that when customers shop online, they are likely to use cognitive resources to craft their beliefs about related attributes, leading to an overall attitude toward the behavior ( Zaichkowsky, 1985 ). As a result, the ABC theory has been used to investigate the role of online customer experience in predicting customer loyalty through their engagement in buying groceries online ( Guagnano et al., 1995 ). In addition, VCC is included in the model as a moderator to investigate the customer’s engagement further (see Figure 1 ). It will assist in determining whether and to what extent customers could be involved, which could help businesses grow and provide a win-win situation for both online retailers and customers.

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Proposed research model.

Online Customer Experience

Online customer experience is expressed as the impression a customer retains after interacting with products, services, and enterprises and forming a perception based on sensory data ( Anshu et al., 2022 ). It is a critical component of providing customer satisfaction, setting expectations, developing consumers’ faith and gaining confidence, retaining loyal customers, and forming effective ties ( Slack and Singh, 2020 ). Experience is a broad and universal phrase that can be applied to various fields and contexts of study. The topic has received much interest from academics and professionals alike; however, researchers claim that study in this sector is limited and scattered ( Bilgihan et al., 2016 ).

Customer experience, according to Hult et al. (2019) , is an internal and subjective reaction that occurs during the process of direct and indirect contact between a customer and an enterprise and includes a variety of aspects of service quality provided by the company, such as advertising, packaging, function, user-friendliness, and product and service reliability. As a result, the availability of a widely accepted study on consumer experiences in retail appears to be restricted ( Petermans et al., 2013 ). According to the literature, online customer experience may be grouped into four categories: product experience, experience with the service procedure, experience with the shopping environment, and experience with staff service ( Pei et al., 2020 ).

Online Customer Experience and Customer Loyalty

Given that consumer brand loyalty is based on long-term and close contact between a customer and a brand, past research ( Mascarenhas et al., 2006 ; Meyer and Schwager, 2007 ; Brakus et al., 2009 ) has found a correlation between customer experience and customer loyalty. According to certain studies, a good customer experience can significantly increase brand loyalty ( Biedenbach and Marell, 2010 ; Morgan-Thomas and Veloutsou, 2013 ). Lin and Kuo (2013) discovered that recent purchases influence consumers’ loyalty intentions, implying that a pleasant brand customer experience may be the key to strong customer loyalty. Online customer experience is a multidimensional construct, according to Brakus et al. (2009) , and all types of customer encounters can influence customer loyalty. Customer loyalty is a crucial determinant of customer experience ( Chahal and Dutta, 2015 ). According to Srivastava and Kaul (2016) , it can promote attitudinal and behavioral loyalty to the service provider, while prior research found a strong link between customer experience and loyalty ( Klaus and Maklan, 2013 ). They also believe that customer experience, rather than customer pleasure, predicts a link between service evaluations and consumer behavior and is a stronger predictor of loyalty. Similarly, Sirapracha and Tocquer (2012) claim that it improves customer loyalty, as measured by a lower proclivity to transfer brands.

Customers spend varying amounts of time and effort exchanging information online, offering comments, and participating in decision-making processes ( Auh et al., 2007 ). This research adopts a behavioral approach to capturing customers’ engagement in the service process ( Dabholkar, 1990 ), or, in other words, the extent to which they contribute effort and resources to the online service creation process insofar as they actively participate in consuming and providing value ( Nysveen and Pedersen, 2014 ). Customers who co-produce with a service provider share fresh ideas ( Chen et al., 2011 ), suggestions, and problems with the company, making them feel more fulfilled due to their commitment ( Cermak et al., 2011 ). Customers offer resources to assist the process, according to Ranjan and Read (2016) , so co-production is a shared act of satisfaction. It is because customers contribute resources to help with the online process. Their collaboration with a brand increases brand engagement, influencing their brand experience ( Nysveen and Pedersen, 2014 ). As a result, client satisfaction ( Flores and Vasquez-Parraga, 2015 ; Ranjan and Read, 2016 ) and loyalty ( Auh et al., 2007 ; Nysveen and Pedersen, 2014 ) are effectively predicted by co-production. Customer–employee online connections can help build customer loyalty ( Guenzi and Pelloni, 2004 ). Even if customers’ expectations are not satisfied, as Lemmink and Mattsson (1998) demonstrate, an employee’s perceived warmth can lead to favorable online views of productivity and loyalty. According to Sivadas and Baker-Prewitt (2000) , customers with good online relationships with employees are more likely to return and suggest the company to their friends. Delcourt et al. (2016) emphasized the importance of employee competency and customer happiness and loyalty in particular. Therefore:

H 1 : Online customer experience positively impacts customer loyalty.

Mediating Role of Customer Engagement

Involvement, commitment, passion, enthusiasm, absorption, focused effort, zeal, dedication, and energy are all common connotations of engagement ( Schaufeli, 2013 ). Similarly, the Merriam-Webster dictionary defines engagement as “emotional involvement or commitment” and “getting in gear.” Chebat and Michon (2003) discovered that shoppers’ perceptions of the shopping environment stimulate and alter their emotions in the setting of retail. Online shoppers who are in a better mood before going shopping will have a more favorable impression of the product they see and are more likely to purchase ( Puccinelli, 2006 ). Hence, antecedents of online customer experience have impact over customer engagement. According to Dennis et al. (2010) , enhancing the online shopping environment boosts sales and customer spending. There are three dimensions to it. The first is the existing legal framework, which protects customers in online transactions from any type of loss ( Li and Zhang, 2002 ). The second is the third-party recognition system, in which several third-party certification bodies seek to verify that online suppliers are trustworthy ( Borchers, 2001 ). Consumers’ trust in online retailers is favorably correlated with these two criteria. The third criterion is the number of competitors, defined as the number of websites that offer the same service and products ( Lee et al., 2000 ). The fewer the competing vendors, the more likely current merchants will engage in opportunistic behavior to maximize earnings. This raises the consumer’s transaction expenses, lowering their desire to return to a specific online store. This study will focus on environmental effects on customer’s attitudinal and behavioral engagements. The amount to which a website supports efficient and thriving shopping, purchasing, and delivery of items and services is defined as online staff service quality ( Zeithaml et al., 2002 ). Many people believe that the work happiness and engagement of the employees with whom they deal influence client attitudes ( Furnham and Milner, 2013 ). As expected, Tsai and Huang (2002) discovered that staff member friendliness was associated with clients’ pleasant attitudes after a service encounter. Hence, this study elaborate the impact of antecedents of online customer experience on customer engagement in online businesses.

Dabholkar (1996) did an early study on online service quality that looked at how users build expectations about technology-based self-service quality and proposed five primary quality attributes: speed of delivery, simplicity of use, reliability, enjoyment, and control ( Rita et al., 2019 ). Some online firms offer staff service that allows customers to inquire about the product they want to buy in greater detail. Organizations frequently employ digitally synchronous communications such as chat sessions, an online support desk, and social sites ( Turel and Connelly, 2013 ). The impact of online staff service on consumer attitude and behavior is not well documented in the literature. To fill the gap, this study determines the impact of online customer experience on customer attitudinal and behavioral engagement. This research will fill that prerequisite. In terms of capability and efficiency, the online shopping method must fulfill consumer satisfaction as much as feasible, and the efficiency of its operation defines a store’s competitiveness ( Artusi et al., 2020 ). The online shopping procedure’s design and management quality impact the service quality provided and the store’s performance and customer loyalty. Poor business procedure efficiency during online shopping results in a negative consumer experience and discontent ( Chen et al., 2020 ). In terms of the relationship between customer experience and customer satisfaction in the online retail industry, Oliver et al. (1997) stated that incentives and good emotions please customers and contribute to customer happiness. Product experience, according to Zarantonello and Schmitt (2010) , can improve consumer satisfaction, product attention, and brand reliability. Hence, these features lead to customer loyalty with the help of customer attitudinal and behavioral engagement in online customer experience. This study will check the mediating role of customer attitudinal and behavioral engagement on the relationship of online customer experience and customer loyalty. According to Šeriæ et al. (2020) , it is most likely the outcome of an emotional connection rather than the concept of an emotional link. In contrast, emotion is the result of internal stimulation. The study concludes that product experience is a higher-level analytical and convergent mental experience in nature rather than basic cognitive behavior. It will lead to customer brand appraisal as a critical factor in brand attitude and repeat online purchase behavior. Hence:

H 2 : Customer engagement mediates the positive relationship between online customer experience and customer loyalty.

Moderating Role of Value Co-creation

There is a dearth of empirical evidence of customer value through online customer interaction in a business-to-business scenario. In order to close this gap, the authors looked into and found evidence that consumer participation did result in VCC for customers. Chan et al. (2010) findings further support Mascarenhas et al. (2004) claim that customers’ active online participation in a customer value chain beyond the consumption of an end-product can bring value to customers. Generally, customers today seek engaging experiences that can ultimately take them beyond satisfaction and into the realm of delight. The added value is important because today’s consumers seek satisfaction beyond that provided by an end product or service; instead, they seek engaging experiences that can ultimately take them beyond satisfaction and into the realm of delight ( Flores and Vasquez-Parraga, 2015 ). Individual value is derived through feelings of accomplishment, prestige, personal growth, or enjoyment derived from VCC ( Flores and Vasquez-Parraga, 2015 ). According to previous research, the sensations above are inherently motivating elements for online consumers who are thinking about trying out VCC via self-service technology for the first time ( Meuter et al., 2005 ). However, because those sensations are experienced from online participation in the VCC process, they can be legitimately classified as a source of value co-created concerning a customer experience ( Dong et al., 2008 ). Customers in high-participation online environments go so far as to rate their happiness with their performance, according to Matzler et al. (2005) , implying that participation might elicit a thoughtful response from customers.

Consumers have recently evolved from passive purchasers of goods and services to active, self-motivated online participants capable of defining and generating value for themselves. As a result, they are no longer a willing and obedient end to a retailer’s marketing efforts but have evolved into a more creative contributor and provider ( Anshu et al., 2022 ). It creates value through the definition, manufacturing, delivery, and usage of products and services by producers and consumers working together. Customers interact online with businesses and exchange ideas during the design, creation, delivery, and final consumption of products, expanding the number of interfaces ( Choo and Petrick, 2014 ). Online businesses can use the concept of VCC as one of their retention tactics. According to studies, online co-creation amplifies the impact of experiences, improving perceived value, positive attitude ( Meng and Cui, 2020 ), purchasing intention, and behavior ( Pee, 2016 ). According to research, VCC has a moderating effect on the outcomes of experiences ( Meng and Cui, 2020 ). Customers return to retailers who appreciate their ideas, which has resulted in online customer traffic being directed to companies where their ideas are sought, appraised, and included, resulting in VCC ( Pathak et al., 2017 ). However, depending on the type of service, the level of VCC might range from minimal to high. Despite its acceptance, actual research on the results of co-creation is sparse ( Carbonell et al., 2009 ). This research offers to investigate the interactions supported by VCC (the operational state), in which it functions as a moderator to further investigate the customer’s engagement purpose. This will aid in determining if customers can participate in VCC in online stores and, if so, to what extent this may assist businesses in growing and providing a win-win situation for both ( Anshu et al., 2022 ). As a result, the researcher wanted to see if online stores might take advantage of this concept and use it to grow their consumer base and bring loyalty to the relationships.

H 3 : Value co-creation moderates the relationship between online customer experience and customer engagement, such that the relationship is stronger when value co-creation is higher.

Materials and Methods

Pilot study.

A pilot study was done who were using internet purchasing participants. A total of fifty young adults took part in the pilot trial. They were invited to share their online store and brand experiences. Customers stated that they shop online via online application platforms such as Facebook and Instagram. They also discussed the brands that they purchased from internet retailers. They bought outfits, footwear, jewelry, bracelets, cellphones, and other fashion products. The researchers used the pilot study results to determine what consumers want and how they buy from online retailers. As a result, it provided context for this study and mapped out the research process.

Sample and Technique

A cross-sectional survey acquired information through a structured online questionnaire from young adult customers purchasing things online in Pakistan. The reason for selecting youthful customers is that they are more likely to be loyal to online retailers in the long term and are more eager to try out new things ( Kumar, 2021 ). The online poll was distributed over several online platforms, and only those performing online shopping were eligible to participate. The information was gathered between January 15th and February 14th, 2022. There were 196 responses out of which 189 valid responses were used for further investigation. The demographic data are presented in Table 1 . The statistical programs SPSS and Smart-PLS were employed to analyze the data and draw inferences. Participants were chosen based on their age limit and online buying experience using the judgmental sampling (non-probability sampling) method. The age range was set at 16–35 years old, and they had to have done some internet shopping at least once. This strategy was also used as the first question in a questionnaire. Participants who did not match the criteria were not endorsed to continue the survey. The data was collected from the target sample in this manner. The demographic results are shown in Table 1 .

Participants’ demographic details.

This study used validated measures to collect data, utilizing a 7-point Likert scale ranging from 1 (strongly disagree) to 7 (strongly agree). Furthermore, the scaled items of variables were given hereunder.

This study used four dimensions of online customer experience named product experience, shopping environment, staff service experience, and shopping procedure. All four dimensions of online customer experience and their scale were adapted from Parasuraman et al. (1988) . Product experience (PE, α = 0.74) contains five items; such an item is “This online store has a variety of categories and colors.” The shopping environment (SE, α = 0.79) comprises three items; one of the items is “This online store shows neat and attractive web design.” Staff service experience (SSE, α = 0.81) has three items: “The staff of this online store shows frequent communication with the customer and good service attitude.” At last, the shopping procedure (SP, α = 0.77) has three items: “This online store makes sure the availability of pictures and reviews at all times.” All four measures of online customer experience indicated good reliability in this study (PE: CR = 0.90, α = 0.87; SE: CR = 0.89, α = 0.85; SSE: CR = 0.94, α = 0.92 and SP: CR = 0.89, α = 0.85).

Customer Loyalty

Brakus et al. (2009) adapted the customer loyalty scale, containing three items (α = 0.63). One of the items is “I intend to stay on as a customer of this online store for the next 5 years.” The scale (CR = 0.95, α = 0.93) showed good reliability in this study.

Customer Engagement

This scale contains two dimensions; attitudinal engagement and behavioral engagement. These two dimensions have been adapted from Vivek et al. (2014) , in which attitudinal engagement (AE, α = 0.74) has six items (“Anything related to this online store grabs my attention”) and behavioral engagement (BE, α = 0.74) has four items (“I provide this online store some useful suggestions to improve services”). Both measures of customer engagement indicated good reliability (AE: CR = 0.90, α = 0.87; BE: CR = 0.89, α = 0.85).

Value Co-creation

The scale of VCC is adapted from Prahalad and Ramaswamy (2004) in the form of six items (α = 0.63), for example, “I am actively involved when this online store develops new solutions for me.” The measure of VCC exhibited good reliability (CR = 0.95, α = 0.93). This study used four dimensions of online customer experience named product experience, shopping environment, staff service experience, and shopping procedure. All four dimensions of online customer experience and their scale were adapted from Parasuraman et al. (1988) . Product experience (PE, α = 0.74) contains five items: “This online store has a variety of categories and colors.” The shopping environment (SE, α = 0.79) comprises three items; one of the items is “This online store shows neat and attractive web design.” Staff service experience (SSE, α = 0.81) has three items: “The staff of this online store shows frequent communication with the customer and good service attitude.” At last, the shopping procedure (SP, α = 0.77) has three items: “This online store makes sure the availability of pictures and reviews at all times.” All four measures of online customer experience indicated good reliability in this study (PE: CR = 0.90, α = 0.87; SE: CR = 0.89, α = 0.85; SSE: CR = 0.94, α = 0.92 and SP: CR = 0.89, α = 0.85).

Measurement Model

The measurement model used in this study showed a high construct validity and reliability level. Except for one item for online customer experience (OCE = 0.566), almost all factor loadings surpassed the benchmark of 0.700 in Figure 2 . Cronbach’s Alpha (>0.700), composite reliability (>0.700), and average variance extracted (AVE) values over 0.500 fulfilled the minimal standards, as shown in Table 2 . All constructions’ composite reliabilities were likewise higher than their respective AVEs. The study also looked at discriminant validity, which requires that the square root of all AVEs be greater than the correlations between constructs ( Fornell and Larcker, 1981 ). Table 3 reveals that square roots of AVEs are larger than inter-construct correlations, indicating that discriminant validity has been established. As a result, the structural model’s hypothesis testing assumed that the measurement model was sufficiently trustworthy and valid.

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Measurement model.

Construct reliability and validity.

α, Cronbach’s Alpha; CR, Composite Reliability; AVE, Average Variance Extracted.

Correlations and discriminant validity.

n = 189, **p < 0.01, SD, Standard Deviation; Bold diagonal values are √AVE.

Descriptive and Correlational Analyses

The mean, standard deviations, and correlation coefficients for the research variables are shown in Table 3 . As expected, online customer experience indicated significant positive correlations with customer engagement (β = 0.599, p < 0.01) and loyalty (β = 0.667, p < 0.01). VCC reflected a significant positive correlation with customer engagement (β = 0.555, p < 0.01) and loyalty (β = 0.657, p < 0.01). In this investigation, these correlations revealed the possibility of forecasting theoretical connections without the danger of multicollinearity.

Structural Model and Hypothesis Testing

The path analysis in this study was done using a partial least square (PLS) method. The direct, indirect, and total effects were studied to evaluate the proposed hypotheses, as indicated in Table 4 . To begin, the study looked at the direct and cumulative effects of online customer experience (OCE) on customer loyalty (CL) and customer engagement (CE). Second, the direct and total effects of OCE on CE and the effects of CE on CL were investigated. Finally, the four-step approach ( Baron and Kenny, 1986 ) was used to observe the mediation impact of CE on the link between OCE and CL. Finally, the analysis examined the moderating effects of VCC on CE and CL, respectively. The complete moderated-mediation model and the estimated PLS path model ( Figure 3 ) show that OCE (β = 0.499), VCC (β = 0.330), and their interaction term (OCE × VCC; β = 0.101) explained a 48.6% variance in CE ( R 2 = 0.486). The overall model explained a 72.7% variance in customer loyalty ( R 2 = 0.727).

Path analysis.

**p < 0.01, *p < 0.05; CE, Customer Engagement; CL, Customer Loyalty; OCE, Online Customer Experience; VCC, Value Co-Creation.

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Estimated path model.

The OCE has a substantial positive relationship with CL. The total (β = 0.502, t = 46.834, p < 0.01) of OCE on CL was positive and significant, validating hypothesis 1 (see Table 4 ). According to Hypothesis 2, CE mediates the link between OCE and CL. Given that hypothesis 2 satisfy the mediation requirements ( Baron and Kenny, 1986 ), the stated path analysis (see Table 4 ) indicated that OCE has an indirect influence on CL (β = 0.221, t = 4.281, p < 0.01) through CE. As a result, hypothesis 2 was found to be true. VCC positively moderates the associations between OCE and CE, according to hypothesis 3. The results of the path analysis revealed that VCC made a significant contribution to CE (β = 0.330, t = 4.362, p < 0.01) and its moderating effect (OCE × VCC) on CE (β = 0.101, t = 1.626, p < 0.05) was positive and significant, supporting and implying that the link between OCE and CE grows stronger at greater levels of VCC.

According to the findings, customer engagement, i.e., attitudinal and behavioral engagement, are significantly correlated with the shopping environment, shopping procedure, product experience, and staff service experience. According to the study, customer engagement has a significant positive relationship with customer loyalty. Hypotheses H 1 , H 2 , and H 3 were therefore accepted.

Hypothesis 1: By highlighting the relationship in an online setting in an e-commerce environment. Based on these findings, it can be concluded that an online customer experience, which is a one-time event, directly impacts a long-term phenomenon like customer loyalty. However, because a better online flow state leads to a better overall brand experience, it aids in the development of stronger client loyalty ( Ertemel et al., 2021 ). The findings show that investing in a flawless online experience that puts customers in the flow keeps them amused and improves brand loyalty over experience.

Hypothesis 2: In the long run, the online customer experience improves consumer involvement with the company ( Brodie et al., 2013 ; Wirtz et al., 2013 ). Innovative digital channels and customer touchpoints, such as social media and mobile devices, for example, allow brand marketers to engage customers in new ways ( Chan, 2012 ; Sashi, 2012 ), while customers have more opportunities to interact with their favorite brands and stay active in online brand communities ( Brodie et al., 2013 ). In the context of online gaming, Luo et al. (2010) empirically evaluated and found favorable results that improving online customer experience promoted customer loyalty, notably repeat purchases and WOM. Wirtz et al. (2013) summarized the existing research on online consumer engagement. They argued that online brand communities give new avenues for customers to connect with the brand while also increasing customer involvement. Attitudinal engagement improves positive attitudes about a company or speeds up the shift from attitudinal to behavioral loyalty ( So et al., 2016 ; Harrigan et al., 2017 ). According to studies, increased brand engagement in self-concept as an attitudinal component of consumer engagement leads to higher customer loyalty ( Sprott et al., 2009 ). Hence, the mediation of customer engagement is confirmed in this study regarding online customer experience and loyalty.

Hypothesis 3: The client is no longer merely the end recipient of products and services in the new reality of internet commerce. They have turned into players in their value generation. The product in VCC is a development in which the consumer becomes enthralled and participates in the process ( Auh et al., 2007 ). Improved levels of co-creation have been linked to increased customer engagement and the desire to co-create in the future ( Frasquet-deltoro and Lorenzo-romero, 2019 ), resulting in a rise in the number of interactions ( Choo and Petrick, 2014 ) and loyalty. Companies may use the notion of VCC as one of their retention tactics in the form of loyalty. Studies show that customer experience and the VCC process are inextricably linked; as a result, online co-creation amplifies the impact of experiences, enhancing perceived value and positive attitude ( Meng and Cui, 2020 ), and that positive attitude leads to customer loyalty ( Barari et al., 2020 ). The investigation also revealed that VCC has a strong moderating influence on all interactions. Except for the Delivery experience, this effect was stronger at lower levels of VCC and declined as the amount of co-creation grew.

Theoretical Implications

This study created and experimentally tested a complete OCE-ABC model to examine the impact of factors that precede OCE on consumer behaviors and loyalty in online buying. The study established an ABC and MAUT conceptual model based on current literature, framework, and theories. Although numerous researches have been undertaken in internet enterprises, very few studies are accessible in online purchasing. Also offered is an all-inclusive moderation model that considers the convergence of e-commerce, customer experience, customer engagement, and new elements supplied by the internet, such as networking effects and VCC. This has not been mentioned in the current internet shopping literature.

Second, past research has revealed that the shopping process, product experience, shopping environment, and staff services are all essential factors in determining customer loyalty; however, none of these factors positively or substantially impacted the consumer’s attitude in this study. This was addressed in the study’s discussion section. These aspects should be considered, and any obstacles should be removed to motivate people to buy items online. It was also shown that recovery has emerged as a critical component in consumer behavior and online retail loyalty.

The inclusion of VCC as a moderator in the model then helped to understand better consumers’ intent and expectations in an online shopping scenario. Except for the delivery experience, the numerous interactions revealed that, in the current scenario, co-creation has a minor impact in shaping the various relationships between the antecedents and the consumers’ attitudes and behavior toward online shopping. This revealed that customers who buy things online are more engaged in co-creating value with businesses to improve their shopping experience.

Overall, the findings are exciting and contribute significantly to the online customer experience literature in e-commerce. The study may benefit academics who want to learn more about the factors that influence OCE’s attitude and behavior and the impact of VCC with enabled and empowered consumers in the ever-changing and overwhelming world of e-commerce. In light of the ABC theory and MAUT in the current environment of online experiential, the study contributes to the conceptual model for OCE and customer loyalty in online shopping.

Existing research has looked at characteristics that impact customer loyalty in physical stores, but it has not looked at the link between customer experience and loyalty in online buying. Existing research also fails to recognize the uniqueness of the circumstance in which online store business mixes and develops. The current study used empirical research and included the component of VCC as a moderating variable to investigate changes in characteristics that impact consumer loyalty in online purchasing, overcoming the shortcomings of earlier studies to some extent. Second, the research looked at the link between customer experience and loyalty in the complex world of retail, which has seen the merger of online and brick-and-mortar stores. It systematically investigated the impact of the consuming process on customer loyalty in various online stores from the perspective of online customer experience, contributing to the theory of the experience mechanism of how customer loyalty takes shape. This provides for more in-depth, methodical, and complete customer loyalty research. As a result, the theoretical contribution combines and expands the link between online customer experience, loyalty, and long-term competitive advantage. The introduction of new technologies, such as internet technology, has increased the rivalry in the retail business. Particularly in technology-driven sharing economies, the need for joint and collaborative consumer activities is a mounting trend ( Graessley et al., 2019 ). This has led to a widespread effect on consumers’ online shopping trust and their decision-making regarding online business activities. As a result, the link between online customer experience, loyalty, and long-term competitive advantage has grown stronger.

The model’s validation showed numerous vital implications for the literature on customer engagement behavior, mainly organic and encouraging engagement routes. Through perceived online value, functional and experiential activities drive customer engagement directly and indirectly. Previous research on firm-initiated engagement ( Ryu and Feick, 2007 ; Tafesse, 2016 ; Wirtz et al., 2019 ) has only looked at the direct influence of firm-initiated activities on engagement. However, the current study shows that the impact of functional and experiential activities is not restricted to direct consequences. They are linked to the organic pathway via perceived online worth. Furthermore, experiential activities have a significantly more direct influence than operational initiatives ( Hilken et al., 2021 ).

The link between the attitudinal and behavioral components of customer involvement is also explored in this study. Although the literature supports the motivational driver of engagement behavior ( van Doorn et al., 2010 ; Lemon and Verhoef, 2016 ), the importance of behavioral engagement for firm performance encourages researchers to concentrate on this component ( Kumar and Pansari, 2015 ). While the results acknowledge the significance of behavioral engagement and believe that attitudinal engagement is as essential in creating engagement. The correlation between commitment and experimental efforts with attitudinal engagement is significantly more significant in both organic and promoted engagement than the link between commitment and active–behavioral engagement. Attitudinal components appear to be more important than behavioral components in establishing engagement ( Barari et al., 2020 ). Moreover, other important constructs like relationship quality and brand love should also be included in future research.

Furthermore, no study on the interaction between customer engagement components has been done. Attitudinal engagement, like staff engagement ( Saks, 2006 ; Shuck and Wollard, 2010 ), is a necessary precondition for behavioral engagement. These findings emphasize the importance of attitudinal involvement in establishing customer engagement.

The findings show that the connections between attitudinal and behavioral engagement and their consequences, such as customer loyalty and firm performance, are confined to attitudinal engagement–loyalty and behavioral engagement–firm performance. According to the cognitive-affective-behavioral hierarchy, attitudinal involvement was a rational predictor of customer loyalty ( Oliver, 1999 ). Similarly, the findings show that behavioral engagement can directly influence firm performance ( Kumar and Pansari, 2015 ; Beckers et al., 2017 ) and that attitudinal engagement has an indirect impact on performance through behavioral engagement. These findings help to clarify the connection between engagement and its outcomes. Speculative attitudes and behaviors, such as referring to a new customer or making a social media comment, are required for customer interaction.

Managerial Implications

This research also has some practical implications for online businesses that want to pay attention to the essential factors influencing the customer experience. Customer experience is a multifaceted component, and the impact each of its antecedents has on customers’ attitudes and online retailers must recognize loyalty. They must also understand the value of VCC in the changing environment and its influence on the total online customer experience and its consequences.

The findings revealed that the shopping procedure significantly impacted customers’ attitudes and behavioral intentions regarding online shopping. Customers are concerned about finding out where their things are and when they will be delivered. They save time and avoid the stress of standing in lengthy lines thanks to the product’s ease and doorstep delivery. They want to be a part of the co-creation process to create value through an easy purchasing process. This is because the customer has already committed time, energy, and, in some instances, money in purchasing the items and so attempts to become more active in the co-creation process to guarantee that delivery is efficient. As a result, it is proposed that businesses aim to include customers in developing efficient shopping procedures.

Another critical aspect of merchants’ loyalty was efficient staff service. Customer complaints may be turned into satisfaction and converted into loyal customers ( Miller et al., 2000 ) with the support of efficient and effective staff service. Weak recovery may disappoint consumers for the second time, resulting in lousy WOM and customers moving to rivals ( Lewis and Mccann, 2004 ). Another criterion that did not directly impact online shopping loyalty was the antecedent product experience. However, in the presence of co-creation as a moderator, these characteristics affected consumers’ desire to repurchase considerably, albeit at a lesser degree of involvement.

Customers’ expectations should be used by online shops to co-creating value for them and gain their loyalty. Circumstances directly influenced customers’ attitudes before OCE, influencing their online customer loyalty. However, at a lower degree of co-creation than at a greater level of co-creation, co-creation served as a moderator for most of the antecedents. This might be because customers regard purchasing things online as a low-involvement activity, and investing more time and energy in them is not seen as providing a value proposition. As a result, online shops must ensure that clients receive the most valuable experience possible. The study supports the ABC theory, which states that when structural factors enable behavior at a moderate level, the association between attitude and behavior is higher, but not to the point where even people with the least favorable attitudes would execute it ( Olander and Thogersen, 2005 ). As a result, regularly including consumers in co-creating an excellent customer experience may be a helpful approach for online merchants to improve online customers’ attitudes and repurchase intent in online buying. Customers may be influenced by the outcomes in the future, making them more inclined to purchase online.

Online website customers are increasingly expecting to be engaged in extraordinary experiences that allow them to experience a sense of time and space distortion without thinking about superfluous details. This is especially true in today’s attention economy-driven, highly fragmented, and hectic daily lives. In such an environment, customers are inundated with multiple impulses, making it impossible to devote their limited and essential time and attention to any of them. As a result, online shopping may be regarded as a break from the daily grind, and as a result, today’s customers place a higher value on it. The findings show that investing in a smooth online experience that puts clients in the flow keeps people amused and improves brand loyalty and customer satisfaction in the long run.

Gummerus et al. (2012) and Wirtz et al. (2013) found that the online brand community delivers various unique benefits that improve customers’ experiences with the firm’s products and services. The marketing manager can directly use functional and experiential activities to affect customer engagement in the promoted strategy directly. Experiential activities are particularly successful in generating attitudinal engagement, whereas functional initiatives have a relatively minimal impact on behavioral engagement. Suppose marketing managers assess their marketed strategy based on short-term impact. In that case, they should invest more in experiential initiatives in the form of a game or event, especially on social media, to indirectly affect consumer engagement behavior and attitudinal engagement.

Furthermore, perceived value influences consumer engagement through functional and experiential activities through organic pathways. As a result, marketing managers might mix organic and promoted engagement techniques to target current consumers with a long-standing connection. This strategy might improve customer engagement attitudes and behaviors by creating synergy between the two engagement tactics.

The following areas of a company’s processes should be improved. To boost consumer experience and loyalty, merchants must first implement online store retail. It may assist merchants in gaining a better and faster knowledge of the consumer experience and long-term competitive advantage goals. Retailers should: (1) diversify their promotional methods, attracting customers through multiple channels and building strong relationships with them through online marketing on mobile devices; (2) generate traffic through social media and promotional coupons for customers and followers; and (3) use affiliate marketing to penetrate target customer groups.

Second, shops must exercise sales management and recognize the importance of internet buying. They should establish a scientific and practical sales management system, consider the impact of different situations and genders on the relationship between OCE and customer loyalty, provide the best shopping experience for customers, build a good brand image, and launch customer-centric marketing campaigns in order to achieve a sustainable competitive differentiation. Finally, businesses should link their online and physical store channels to give customers the ideal buying experience: (1) They should employ digital technology to create two-way online store commerce interconnectivity; (2) they should link the coupling channel of online store commerce and create a comprehensive system network; (3) They should rely on system synergy and data analysis capabilities to help them optimize OCE and effectively maintain long-term customer loyalty.

Limitations and Future Research Directions

This work has certain limitations, and researchers in the future may revisit it. The population of online shoppers in various geographic locations and other nations may not be represented in the sample analyzed. As a result, the study needs to be duplicated on a bigger sample size in diverse and online consumers in other countries to generalize the findings. The study focused on cost and time restrictions and mainly was a cross-sectional, one-time survey study. On the other hand, long-term longitudinal research would make a more significant contribution to knowledge. This study focused on business-to-consumer (B2C) online commerce and may not apply to other online business categories such as B2B or others.

Further research on these aspects and other new elements in the field of OCE might be conducted to examine various behavioral intents and results. The previous study has mainly focused on customer engagement as a positive term, although engagement behavior contains both good and negative aspects. Future research may consider other dimensions of online customer experience as well. Such dimensions may be applied on this model, for example convenience, privacy and security, network effect and recovery as antecedents of online customer experience.

Furthermore, this model suffers from customer-related aspects since past research has concentrated on firm-related antecedents and consequences of engagement. Consumer relationship creation and customer reaction to advertised engagement activities, for example, will be influenced by psychological variances among customers. Similarly, because it lacks customer-related outcomes, the result simply reflects the firm’s advantages of customer involvement.

Due to the rising usage of online media, e-commerce adoption rates surged even more due to the COVID-19 epidemic. Providing flawless experiences and building long-lasting, lucrative connections with clients have become more vital than ever during these trying times for e-businesses. The findings of this study show that online purchasing, as regarded by consumer trust among young adults in Pakistan, has an indirect impact on their loyalty to e-commerce companies through customer experience. Online shopping aims to provide a completely immersive and engaging experience with a brand’s website. Naturally, this event is predicted to occur soon.

As a result, theoretically, it is unlikely to impact long-term phenomena such as consumer loyalty. Even though online shopping has been investigated from several perspectives, the number of studies evaluating the impact of online shopping on the entire brand experience and consumer loyalty is limited in the current literature. However, only the customer experience-loyalty links were examined in that study. Using Smart-PLS, this study attempted to contribute to the existing research by introducing the customer loyalty dimension and assessing the direct and indirect effects of all of those constructs’ phenomena combined.

The findings suggest that in an e-commerce scenario, encouraging a flow state should not be considered a nice-to-have feature. However, doing so aids e-commerce companies in achieving their long-term goals. When it comes to customer loyalty, the instant, a client accesses an online platform for a product, the length of time he or she spends there, and the pleasure obtained are essential factors. Customized and individualized products and influencing aesthetic designs on online platforms produce a more crucial customer experience than the purchase experience, enabling and making the shopping process joyful. In conclusion, websites that keep the client engaged will be favored.

This study attempts to supplement previous research on customer attitudes and behaviors in online buying and loyalty. The study used the Multi-Attribute-Utility theory and the ABC theory to investigate retailing on an online platform, focusing on VCC. Customers and retailers collaborate to develop products and services. The OCE as antecedents of customers’ engagement and loyalty towards online buying has been investigated in this study. This research also investigated and found a positive moderating effect of VCC on the relationship between OCE and consumer engagement with online shopping.

Data Availability Statement

Ethics statement.

Ethical review and approval was not required for the study on human participants in accordance with the local legislation and institutional requirements. The patients/participants provided their written informed consent to participate in this study.

Author Contributions

All authors listed have made a substantial, direct, and intellectual contribution to the work, and approved it for publication.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Open access publication was partially financed through the publication incentive fund 2022 by the Universidad Andrés Bello (Code: C.C. 21500) and the Universidad Autónoma de Chile (Code: C.C. 456001).

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Broadband TV News

Independent. Since 2003

Showtime to withdraw standalone app on April 30

April 12, 2024 12.14 Europe/London By Julian Clover

The 9 year-old streaming service is being rolled into Paramount+, which is currently running a week-long free trial to US consumers, after which the service will cost $11.99.

The entry level to Paramount+ is currently a monthly $5.99.

Showtime was officially bundled with Paramount+ in January when the two apps merged into a single service, heralded by parent Paramount Global as a “first of its kind” integration of streaming and linear content.

The strategy we are seeing here is similar to that of the Warner Bros. Discovery Max, which is integrating HBO into its wider offer.

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About Julian Clover

Julian Clover is a Media and Technology journalist based in Cambridge, UK. He works in online and printed media. Julian is also a voice on local radio. You can talk to Julian on Twitter @julianclover , on Facebook or by email at [email protected] .

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COMMENTS

10 best customer loyalty programs and their case studies
Here are the case studies of 10 best customer loyalty programs. AMAZON PRIME: Offers selection, convenience and immediate gratification. A list of best customer loyal programs has to Amazon Prime. For $99 a year, Amazon Prime offers not only free shipping, but also other benefits such as live streaming music, movies and TV shows, storage for ...
Customer loyalty: The new generation
Loyalty programs are a part of the ever-expanding movement to digital for consumers and businesses alike. McKinsey partner Jess Huang discusses the most important aspects and components of successful loyalty programs, from putting consumers first, to learning what companies want to gain from loyalty programs, to the data gathered from such ...
Next in loyalty: Eight levers to turn customers into fans
Our research has found that top-performing loyalty programs can boost revenue from customers who redeem points by 15 to 25 percent annually, by increasing either their purchase frequency or basket size or both. However, we have observed that around two-thirds of established loyalty programs fail to deliver value, with many actually eroding value.
Unlock the Power of Loyalty: 21 Case Studies for Success
30th November 2023. Done right, loyalty programs are a uniquely effective way to improve customer retention. Research from Accenture found that loyalty program members drive between 12 and 18% revenue growth each year. There are countless examples of brands that have seen big results. Although many of them make it look easy, their success is ...
[case study] Build customer loyalty like Starbucks + Costco
Further studies confirmed these benefits of customer loyalty. One study by Harvard Business School found that "Loyalty leaders - companies at the top of their industries in NEt Promoter Scores or satisfaction rankings for three or more years - grow revenues roughly 2.5 times as fast as their industry peers and deliver two to five times the shareholder returns over the next 10 years."
Starbucks Loyalty Program Case Study
Learn from our library of loyalty program case studies. By incentivizing preloaded payments, Starbucks receives cash in advance, improving its cash flow while maximizing brand loyalty at the same time. Customers commit themselves financially to future purchases at Starbucks, which effectively guarantees loyalty and repeat business.
Preparing for loyalty's next frontier: Ecosystems
Exhibit 1. [email protected]. Despite the opportunities, many loyalty programs have plenty of room to grow. We looked at a basket of 50 loyalty programs from top brands and found that just 18 percent have experience-led programs, 6 percent have connected offerings, and 2 percent have ecosystem offerings (Exhibit 2).
Customer Satisfaction Perpetuates Loyalty: A Case Study
Hence we can assess the customer satisfaction of e-commerce, and we run a case study in which the 5 ecommerce websites are assessed with 10 experts of e-commerce. This study aims to analyze the ...
PDF The Business Case for a Customer Loyalty Program
Moreover, a number of additional statistics make the case that loyalty programs can make a significant impact on customer engagement, loyalty, and revenue: • A loyalty increase of 7% can boost lifetime profits per customer by as much as 85%, and a loyalty increase of 3% can correlate to a 10% cost reduction.
Customer Loyalty: The Ultimate Guide
Having a strong customer loyalty strategy can greatly aid brand success. Customers are 50% more likely to try a new product of yours as well as spend 31% more than new customers. Conversely, it costs six to seven times more to acquire new customers than it does to retain current customers.. Whether or not you currently have a customer loyalty program that encourages your customers to return ...
Case Study: Starbucks Rewards
Sep 28, 2023. 114. In the realm of customer loyalty programs, Starbucks Rewards is a shining star. Renowned for its unparalleled success, this program has played a pivotal role in fostering ...
An Analysis On Brand Loyalty: A Case Study On Starbucks
brand loyalty because consumers affect a brand's success. Brand loyalty is the bottom. line for companies because repeat purchasing leads to higher sales volume that helps. the company grow (Giddens, 2010). When a product is deemed to have value, consumers are less price conscious. regarding the brand.
Full article: Customer satisfaction, loyalty, knowledge and
This demonstrates that customer loyalty in the case of products and services is influenced by different variables, and that the researchers conducting the relevant studies (including us), only agree on the fact that customer loyalty is directly influenced by their satisfaction (see Cronin et al., Citation 2000; Türkyilmaz & Özkan, Citation ...
(PDF) Customer Loyalty: An Empirical Study
Abstract. Purpose - The purpose of this paper is to focus on establishing individuals' levels of loyalty and what sustains and develops their customer loyalty. This paper recognises the ...
What Is Customer Loyalty? [Definition, Tips & Examples]
Customer loyalty definition and importance. Customer loyalty describes a customers' willingness to return to a company in order to purchase its services or products. It is manifested when a customer makes repeat purchases, choosing a specific company over its competitors.
Effectiveness of Loyalty Programs in Customer Retention: A Multiple
The impact of loyalty programs on customer retention was investigated in this study. Concomitantly, the mediating effects of brand association and customer satisfaction between loyalty programs and customer retention were also tested in three ways, viz., in parallel, distinct, and sequential through a series of structural models.
Predictive analytics using big data for increased customer loyalty
Kim S-Y, Jung T-S, Suh E-H, Hwang H-S. Customer segmentation and strategy development based on customer lifetime value: a case study. Expert Syst Appl. 2006;31 (1):101-7. Article Google Scholar Oladapo K, Omotosho O, Adeduro O. Predictive analytics for increased loyalty and customer retention in telecommunication industry.
10 Customer Retention Case Studies from Top Companies
To recap, here are the 10 best customer retention techniques used by top companies: Reward loyalty. Ask for feedback. Start a customer education program. Provide a personalized customer experience. Make your customers feel special and send a thank you message. Engage on social media.
The impact of corporate social responsibility on customer loyalty: The
In recent years, customer loyalty construct has become a key topic among researchers and practitioners (Nyadzayo and Khajehzadeh, 2016). Scholars have identified key determinants responsible for customer loyalty, including customer satisfaction & trust (Pivato et al., 2008), and service quality (Bell et al., 2005). Mainly, some studies have ...
5 Innovative Customer Retention Examples and Case Studies
How CustomerGauge Can Help with Customer Retention. Every business is unique, and B2B customer retention is very different to B2C retention — where there have been years of investment in building customer loyalty strategies. This makes it hard to find tailor-made and effective solutions specifically for B2B businesses.. Fortunately, at CustomerGauge, we live, eat, and sleep B2B.
Online Customer Experience Leads to Loyalty via Customer Engagement
Companies may use the notion of VCC as one of their retention tactics in the form of loyalty. Studies show that customer experience and the VCC process are inextricably linked; as a result, online co-creation amplifies the impact of experiences, enhancing perceived value and positive attitude (Meng and Cui, 2020), and that positive attitude ...
(PDF) Customer Loyalty: A Case Study Involving the Three Indian
Customer Loyalty: A Case Study Involving the Three Indian Airlines Indigo, SpiceJet, and AirIndia March 2022 Shanlax International Journal of Management 9(S1-Mar):65-73
Customer Loyalty: A Case Study of Spotify
ABSTRACT. The development of computer gadgets has changed the way we listen to music. This study examines the influence of electronic customer relationship management on customer loyalty. This research was conducted using a causal research framework that enabled the authors to understand a causal relationship between research variables.
Showtime to withdraw standalone app on April 30
Exclusive Case Study: Learn how Orange has bet on Dolby technologies to build customer loyalty with a premium offering. Picture and sound quality increase Orange user engagement, increasing their ...

10 best customer loyalty programs and their case studies

Rob Petersen

21 examples of successful loyalty programs — and what makes them work

Examples we’ve used:

The four main types of loyalty program (and examples of each)

Points-based (“earn and burn”)

Value-based loyalty programs

Tiered loyalty programs

Subscription-based loyalty programs

Gamification in loyalty programs

Examples by industry

Beauty and cosmetics brands

Sports and outdoor ecommerce: elevating adventures with rewarding experiences

Loyalty program branding: 4 brands that get it right

6 examples of effective loyalty program marketing

Email marketing

Loyalty pages

Time-limited promotions

Refer-a-friend

Banners and popups

Contests and leaderboards

Ready to use loyalty marketing to your advantage?

About the author

Related Articles

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The psychology of rewards and loyalty programs in ecommerce

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What is customer loyalty program marketing?

Why is customer loyalty important in eCommerce?

How to build customer loyalty?

How to build transactional customer loyalty

How to build emotional customer loyalty

Customer loyalty examples: tactics and strategies from top companies

How Costco creates customer loyalty

1. Cross subsidizing with a membership model

2. Using loss leaders to promote transactional loyalty

3. Using unique, private label products to increase loyalty

4. Maxime loss leaders with creative financing options

How Starbucks improves customer loyalty

5. Cross channel marketing

6. New product development

7. Create urgency with limited time products

8. Unique experiences for Rewards Members

Next Steps...

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1. Be as generous as your customers.

2. Show your gratitude.