How do you measure the effectiveness of your loyalty management system? The Forrester Wave Loyalty Technology Solutions, Q1 2023 report is here with an answer. Gathering the industry’s most significant loyalty technology solution vendors, this report shows us which providers matter most. What are the main criteria that Forrester evaluated its participants on? And what can this report teach us about the characteristics of the top loyalty software providers? Let’s dive into it.

What is a Forrester Wave Loyalty Technology Solutions, Q1 2023 report?

The Forrester Wave Loyalty Technology Solutions, Q1 2023 is a thorough, 28-criterion evaluation of the most significant loyalty technology solution providers on the market. It provides insight into the most successful initiatives and critical features that enable their capability, evaluating the 12 most significant players and dividing them into leaders, strong performers, contenders, and challengers. It’s a guide for B2C marketing decision-makers looking for loyalty program software tailored to their brand’s needs.

Top loyalty management software providers – Key features

Based on the criteria in Forrester’s assessment, here’s a rundown of Comarch’s loyalty program features that we believe are shown to turn a loyalty solution into a success:

Zero-party data

Data obtained directly from users is the most valuable type of information about customers (due to the trustworthiness of its source). And without knowing your members, creating an effective loyalty program has a low chance of working. That’s why the best customer loyalty software focuses on gathering this information via member attributes, pop-up quizzes, questionnaires, contests, and online polls.

Also important is having a plethora of methods to collect zero-party data and the ability to adapt the customer data model and data sources at any point in time by business users – no IT knowledge  required. These tools enable assembling any data point to target members with more personalized promotions.

Rewards/benefits management

The best loyalty program software uses personalized incentives to boost customer engagement – but the rewards themselves are not enough to stand out from the competition. A high-quality solution is also about the complete customer experience and the ability to recognize their special status or engage them through non-financial benefits such as gamification. Supporting every possible rewarding mechanism, from cashback through points and rewards programs with a large variety of rewards and burn functionalities for members (cashback, vouchers, lotteries, auctions, charity donations, or gifts), gamified engagement to tiers and statuses.

Emotional loyalty measurement

Behavioral loyalty metrics are not enough to fully understand customers’ behavior. Loyalty program platforms need to combine them with the measurement of emotional loyalty to understand the “why” behind the numbers. – as Comarch Loyalty Management does. CLM uses multiple metrics for stipulating the levels of customer satisfaction.

Referral program management

Key loyalty software features include strong customer referral program management that capitalizes on existing members’ positive experiences. This functionality lets you create a customer referral program that stands out – allowing segmentation of referrals, incentivization of both new and existing users, as well as fake account prevention. It’s characteristics like those that make Comarch’s solution the 4th best on the market, according to Forrester.

Community engagement

The strength of a vendor’s customer community translates to the strength of their solution. Comarch focuses on fully digitalizing our growing client base by implementing a Learning Management System backbone structured around three main pillars:

1. Loyalty Marketing Academy: An e-learning course for marketing practitioners focused on gaining knowledge as well as designing and running loyalty marketing programs.

2. Customer community: An online space exclusive for Comarch customers that features 50+ product and strategy-related channels (discussion groups), recognition for most active contributors, and industry-oriented sessions moderated by Comarch and industry experts.

3. Partner community: A closed community and portal dedicated to different types of official partners.

“As Head of R&D of our loyalty products, I’m thrilled with Comarch’s recognition in Forrester Wave. Loyalty is becoming mainstream and growing fast. We need new business models and simplified delivery/integration in martech ecosystems. In these difficult times of financial instability, it’s essential to focus on customer loyalty through great experience, personalization, and providing significant value to loyal consumers. Our AI-based marketing platform and wide range of business services makes it much easier for marketers. We’re here to support you at every step of the journey, from building strategy to day-to-day operations.”

– says Łukasz Słoniewski, Head of R&D – Loyalty & Marketing Solutions at Comarch.

The Future of loyalty marketing

The Forrester Wave: Loyalty Technology Solutions Q1 2023 report gives us insight into the direction the loyalty landscape is headed. Considering the challenges and needs faced by B2C marketing decision-makers, this evaluation shows the most critical pain points of customers looking for the best loyalty management software for their business. Forrester sets the bar high – its conditions for being included among its top loyalty vendors list are extensive and often, in our opinion, difficult to meet, but that’s what makes this profile so worthwhile. With the features, functionalities, and offerings mentioned above, Comarch is constantly pushing to not only fulfill but also exceed those expectations.

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Creating a loyalty marketing strategy is critical to understanding that every customer is unique. Every member has different needs, preferences, communication channels, behavior and emotions. Approaching all these unique members as one via mass communication is a big mistake when trying to scale your business. Segmenting your customers is critical to your success. But what is customer segmentation, why is it important and which segmentation models should be considered?

1. What is customer segmentation?

Customer segmentation is the practice in which marketers divide their customer base into specific groups in order to deliver more effective communication and a personalized experience. These segments can be based on one or several characteristics that clients have in common, such as demographics, psychographics, preferences, or even behavior.

For example, customers can be split based on demographics such as their age, gender or location – then these characteristics can be combined with preferences such as how they want to be contacted –  in addition to account history, say the number of transactions made in the last few months or their lifetime value. Segments can be as simple or as detailed and granular as you prefer.

Defining a customer segmentation strategy requires your organization to understand who your audience is, as well as their needs and behavioral tendencies. This will allow the right allocation of budget and resources to provide more personalized communication and make your business grow faster.

2. Why is customer segmentation important?

Defining a customer segmentation strategy will bring many valuable benefits to any business. Here are some examples of why customer segmentation should be a top priority:

• More effective communication
  Brands that understand the importance of customer segmentation have the greatest chance of hitting their target. The messaging used for a 20-year-old female shouldn’t be the same as for a 60-year-old male. A deep analysis of your customers will lead to a more personalized experience considering the segment group and the channel they are being communicated through. Ultimately, this will translate to higher response rates and engagement.
  
  It is also important to consider the number of times per day and days per week a customer should be contacted. Optimizing the communication time will prevent overcommunication and its potential consequence of losing the customers’ trust.

• Improve marketing ROI
  Effective customer segmentation also helps in allocating internal human and capital resources. Companies can determine which groups will be more and less profitable and decide which ones deserve more or less attention. The final result – your revenue will grow while simultaneously stabilizing your customer base.

• Increase customer lifetime value
  Improving the customer experience naturally leads to increased engagement. The key is to then maintain this engagement over extended periods. Some techniques to achieve this include custom offers tailored to the preferences of specific groups. This, together with recognizing their loyalty to your brand with incentives (coupons, rewards or promotions), all lead to gaining a larger share of your members’ wallets and spend.
  
  While most brands tend to target their top-spending customers to reward them and maintain their engagement, it is imperative not to forget about your underperforming members. Those who gave the company a high benefit in the past and whose purchasing trend has dwindled over time can receive special attention to bring them back to their old spending habits
  
  Personalizing these communications depending on loyalty segments and/or trends also improves customer service and the way the business assists in customer loyalty and customer retention.

• Product improvements
  By understanding what motivates customers to buy your brand’s products, you can tailor offers to suit client needs better. This will maximize customer satisfaction and in turn, create brand ambassadors. What is better than a happy customer recommending your brand to their friends?

• Separate your brand from the competition
  All the customer segmentation benefits detailed above enhance clear distinctions from your competitors and prepare your brand to adapt to any and all market changes. Clients can be impulsive, and their opinions, behavior and needs can vary often. Executing customer segmentation means being ahead of the curve in terms of upcoming trends, understanding the clients’ new priorities and adapting to them.

3. Actionable strategies to implement customer segmentation

When embarking on your customer segmentation journey, you must first ask yourself, “What is the goal?” What are your brand’s unique selling points? How many members of the marketing team will be involved? Once this is established, the focus shifts to your customer base. Some tasks could include determining the audience size, the number of potential segments needed, identifying which customer will spend more and which will spend less, etc.

Then, decide what data needs to be collected and how it will be collected. Remember that this information is crucial when creating your segments and will be the foundation of the marketing campaigns and initiatives. To execute any of these segmentation exercises, you must ensure you have access to the most important piece of it all… the data!

Data can be collected in different stages throughout the loyalty lifecycle, whether mandatory upon enrollment or optional after the fact, in the form of a survey. Now, all information received will come directly and voluntarily from clients. This is what is known as zero-party data.

What’s more, these data points can be compiled into segments. It is recommended to start with a broad focus and continuously narrow them down over time. AI and ML can do wonders in unearthing and analyzing behavior and trends, as well as shaving time and resources off the shoulders of your workforce. While defining these customer segmentation models, set up the main strategy to retain and gain customers loyalty, and after that focus on acquiring new ones. 

5. Defining customer segmentation models

Customer segmentation models are the different ways in which a company decides to divide its customers. In loyalty, there is a wide spectrum of customer data points to consider, but the most relevant across the industry are:

1. Demographic
   Demographic segmentation is when customers are divided by their social characteristics as part of the population. Some examples include age, location, gender and birthday.
   
2. Psychographic
   Psychographic segmentation considers an individual’s personality, attitudes, aspirations, interests, and values. For example, in customer segmentation for fashion, the audience can be divided depending on their size, favorite store, product, and even their concern for recycled clothes. For a Veterinary clinic, it will be more interesting to know what type of animals their clients have at home. Each industry will have very distinct data points that are more important to its unique business.
   
3. Customer behavior
   Customer behavior is one of the most important attributes when building customer segments. This division will allow a deeper knowledge of customer trends, habits, and product usage. Some examples are the total amount spent on transactions, the total number of transactions, the date of the last purchase, the number of points spent in the last month, the number of redemptions completed, etc. Here is where customer engagement with the loyalty program can be measured.
   
   Now, member activity can be tracked, and trends can be identified automatically. Thanks to artificial intelligence and machine learning, segments can be created according to customer lifetime value (CLV) trends. Just to give an in-practice example, retail customer segmentation models should focus on targeting their best performers in stores whose lifetime value is greatest, but also send out communications and promotions to those members who have had a high spend in the past, but their trend is now decreasing.
   
4. Campaign responses
   Campaign response segmentation can be set up according to, for example, the way your members wish to receive communication -email, SMS, push notifications, etc.-, the channel used to enroll in the loyalty program – mobile app or web portal – or the number of referrals made to a friend. The response to the program awarding customers with different recognition tiers can be another factor to consider for segmentation.
   
5. Multiple conditions
   Customer segmentation can apply to multiple different characteristics simultaneously. For instance, companies can target members by a condition as simple as membership type or by their membership in combination with their location and a purchase of a specific product. The most effective segments are as narrow and specific as possible to small groups to personalize the campaign at its lowest level.

Understanding your members needs, preferences and behavior is the beginning of your customer segmentation journey. Defining goals, resources, unique selling points and crucial data points will prepare your business for all segmentation possibilities. Customer segmentation helps target the right clients, who present more profitable opportunities at the right time and in the most efficient way. With it, their retention and satisfaction will be ensured, as well as the maximization of sales and revenue.

To learn more about customer segmentation models and how to build the most effective loyalty marketing strategy, request a demo with our Loyalty Experts! They will match you with an experienced Loyalty Consultant with experience in your unique market space and industry.

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We all have our habits and routines. Some enjoy jogging first thing in the morning, while others would rather stay up late watching their favorite show.

As discouraging as it may seem, our daily lives are full of repetitions. Most people take the same route every day when they commute, drink the same coffee on their way to work, and then greet the same friendly or not-so-friendly face at the reception desk.

Similarly, when it comes to shopping habits, some have their dedicated shopping day and a list of recurring items they buy every time. Others go with the flow, look for the best deals whenever they find some time to shop, not even looking at the brands or what they chose in the past.

So why not train a machine learning model to predict the next purchase a given customer will make and estimate when this transaction will take place?

Machine learning strikes again

There are two approaches to the challenge of creating a next-best offer recommendation based on the customer’s purchase history:

1. Searching for general (macro) shopping preferences of customers and essentially returning global best-selling items as recommendations
2. Analyzing sequential customer behaviors (micro) – perhaps they have recently started to buy some specific products — and avoid any global bestsellers at all?

The great thing about AI/ML is that it is possible to combine multiple approaches and make the solution dynamically choose the best recommendation based on the model results and some configurable parameters. In this case, a popular AI solution is the way to go — deep neural networks. 

In a nutshell, these are the algorithms that attempt to recreate the way the information is processed and remembered by the human brain. By feeding a large volume of historical baskets as an input, it can detect patterns in how particular customers tend to buy. If developed and tuned well enough, it is possible to make a high-accuracy prediction of what each customer is most likely to buy in their next transaction.

Skipping the technical details, a model like that can produce a personalized ranking of historical products for each customer. The ranking is based on the “score” of the products. The model training is based on the observation that a given customer prefers a product that they purchased over some other product they didn’t. The training process is based on processing several thousands of such pairs. This way of recommendation generation can be found in the literature as a Bayesian recommendation system, which is one of the most popular recommendation systems as of now.

But how to use it?

If you can predict the most likely contents of the next transaction and when is it going to happen, this creates a vast spectrum of marketing or loyalty opportunities.

Marketers may use this data in a number of possible ways:

• Creating an offer just for members who are likely to buy in the next three days.
• Targeting a cross/up-selling campaign for promoted products at customers who have other products from the same category or brand predicted as a likely purchase in the next transaction.
• Extra bonus/reward for members who purchase before their predicted next transaction date.
• Differentiating campaign communication based on the member’s next basket predictions. 
• “Come-back” offer for members who have not purchased anything in 10 days after their next predicted purchase date.
• Adding gamification elements to members who tend to “follow the predicted pattern” of purchases – perhaps adding additional loyalty points type, progress bars, achievements, badges…?

These are just a few examples of how the transaction time and content prediction can be used to create a genuinely engaging loyalty program. With the combination of other information on the customer – segment assignments, predicted lifetime value, demographic information… the possibilities are nearly endless.

To learn more about AI and machine learning, please visit the resources on the Comarch site.

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Having incorporated mobile applications into our daily lives, it’s extremely difficult to imagine a world without them. This is also why statistics such as those reporting that the average smartphone owner uses about 30 apps per month come as no surprise to anyone. At the same time, knowing that an ordinary retail customer is active in six or seven loyalty programs, we can reasonably assume that those 30 applications must include at least one loyalty app – though we should definitely expect more. Why is that information relevant? Because it shows that using loyalty apps has become a common element of our day-to-day lives.

In fact, some retailers consider their loyalty applications to be the best channel of communication for driving business. The prospect of benefits in the form of discounts and rewards helps companies convince their clients to download the apps and share their personal data – information which can later be used for creating personalized customer journeys and product offers that are more likely to hit the spot.   

Yet, considering that we are living in a time where attention spans are becoming alarmingly shorter, companies cannot expect their clients to be highly engaged with their loyalty applications at all times. That is, unless they keep making them even more attractive and, in some ways, more addictive.

The important question is — how?

UI design

Nobody wants to use an app if the interface is poorly designed. An app should be user-friendly from the moment it’s launched. Of course, this does not change the fact that its visuals need to be attention-grabbing and in line with current trends; every entrepreneur knows that we all buy with our eyes. Nonetheless, companies still need to focus on keeping things practical.

To help retailers find that the right balance, today’s technology providers are now incorporating the latest artificial intelligence/machine learning algorithms into their loyalty management systems. Thanks to those technological innovations, companies can find patterns in their customers’ behavior, analyze them, and use their findings to design a UI that meets their clients’ needs or allows users to adjust its layout without any IT expertise.

In-store shopping experience

Apart from being practical in terms of design, a modern loyalty app needs to be of use in the real world, meaning it has to serve a particular purpose (or several) in the shopping environment, such as providing customers with personalized, location-dependent product offers.

To do that, you can turn your app into a virtual guide that directs clients to designated store areas (indoor navigation), or one that presents them with new product offers using push messages whenever a customer goes by a given stand or any product-related object (proximity marketing).

Neither should we forget about mobile payments, which allow customers to buy products using discount coupons, loyalty points, or their stored credit cards. Today’s clients can also save a lot of time by placing their orders in advance, paying for products ahead of their arrival in the store. This functionality was perfected by Starbucks, whose application was recently found to be the most popular loyalty rewards app (48%) on its market. The app allows users to buy a cup of coffee while on their way to a selected Starbucks Café and pick up their order as soon as they get there – helping them to avoid waiting in line.

Out-of-store engagement

Don’t be under the impression that indoor experience is the only area in which an app can drive customer loyalty effectively. To make apps more popular among their target clients, companies have to find a way to engage them outside of retail stores. Here’s where gamification comes in. Badges, leaderboards, quizzes, mini-games – all of these can help form an emotional connection between a client and the app. Gamification elements also happen to be essential for introducing special offers that are exclusive to the most dedicated mobile app users. The list of possibilities goes on, and they’re all worth a try.

Customer service

Last but not least, it is vital to provide the right customer support at any given moment. Whenever a customer is looking for a solution to a problem, the app should offer some valid options for self-service (FAQ or knowledge base, for example), and a means of interacting with a company representative either through a contact form or via live chat. Customers should also have the opportunity to rate the app and its services, so that a company can fix and improve it over time.

Loyalty apps are still evolving, becoming ever more technologically advanced solutions. Therefore, in order to become successful at driving better loyalty results, each company needs to begin by working with a well-known and trusted technology provider that can cover all of the abovementioned ground. Although choosing a provider may be easier said than done, the important thing is to find the one that meets your company’s needs and requirements.

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Loyalty fraud is on the rise again and, while account takeovers seem to be the most common type of fraud reported, this type of theft is only the tip of the iceberg; program rules violation, unauthorized redemptions, privilege escalations, flawed integrations and data breaches are also on the rise.

To complicate matters, the increasing complexity of loyalty programs makes it even harder for companies to defend themselves against fraudsters.

A typical project for a loyalty platform implementation involves dozens of integrations with other systems, partners, point transfers, reversals, conversions and so on. The more complex the implementation, the higher the probability that there will be loopholes that can be exploited.

Under attack

Believe it or not, the chances are good that there is a teenager out there who, at this very moment, is doing something to try to take advantage of open vulnerabilities that they found in your loyalty program. Statistically speaking, no matter what your reporting solution is, what kind of fraud rules you have in place, what type of security policy you have implemented — your program will become a target for fraudsters.

How to fight back? The only thing that can potentially give you the upper hand is an automated security system that doesn’t require explicit configuration; a system that automatically adapts to the data processed by your loyalty program — that can dig through enormous amounts of data to detect a few subtle patterns and correlations between billions of data points and parameters that are changing constantly. This system should also get better at detecting anomalies over time.

Machine learning strikes again!

Despite the enthusiastic buzz surrounding this subject, machine learning is by no means a magic wand that can make all threats disappear. It will never fully replace traditional fraud prevention and detection methods. Well-designed reporting and fraud rules, strong end-point security, policies, and procedures are and will always remain a must. But machine learning will take your company from being reactive to having a proactive fraud prevention process in place that detects anomalies before they can cause damage on both the program-wide and individual member levels.

Here’s a quick example. A gas station chain defined a fraud rule which would block a member’s account if more than five sale transactions were recorded in a day. The rule was meant to prevent cashiers from swiping their own loyalty cards whenever a paying customer was not enrolled in the program, and from accumulating points in violation of loyalty program rules. However, cashiers realized that car wash services were being processed by separate Point-of-Sale software and were treated as a different type of transaction. Soon enough, cashiers focused on the car wash clients, as those transactions were not covered by the configured limits and allowed for quick and easy points gains.

Human error

Another example. A security team configured an alert that activates whenever new member enrollments reach a specified level. The marketing team created a new sign-up promo that successfully brought in a significant influx of new members. Those two teams rarely interact with each other and did not think to discuss the promotion and its potential consequences on the system. Therefore, when the security team started receiving the unusual number of alerts, they assumed a mass enrollment fraud attempt and decided to shut down the entire platform. It took them an hour or two to verify that all the new member accounts were legitimate.

These are two are real-life examples of loyalty programs that have millions of active members. What makes them similar is that although the traditional fraud prevention measures in place were based on the right assumptions, there are always some scenarios in which those assumptions won’t be enough to meet all of the program’s needs.

Benefits for loyalty programs

The benefit of machine learning modeling is that it requires just one simple assumption — that the vast majority of staff and members mean no harm to the program; members obey the rules and enjoy the program as it was initially designed. Using their data, machine learning models can “learn” the typical behaviors and extract patterns and relationships between millions of data points, whether they are transactions, points, values, or activity patterns. These may, of course, change over time, and machine learning will adjust to changes in configuration.

What is also impressive about this approach (which some AI-nerds call “unsupervised machine learning”) is that it doesn’t need any explicit definitions of what is a normal type of behavior and what is not. It will adapt itself to the volumes of data it receives as the input and return any anomalies as soon as it “decides” that they are worth triggering a warning. This way, it is possible to proactively prevent fraudulent activities that have not been seen in the past and, finally, to be one step ahead of the fraudsters.

The missing piece

Although machine learning is not a universal solution to all loyalty program issues and challenges, it can be THAT missing piece of the puzzle when it comes to the security of loyalty program configuration. Together with traditional fraud countermeasures, it enables a genuinely proactive loyalty fraud prevention method that is ready to meet the challenges of the ever-changing landscape of modern information systems.

ther success stories from Comarch about the management of loyalty programs can be found here.

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Loyalty programs of the 1980s and early 1990s offered quite limited options when it came to using your points (the brand’s loyalty currency ). The only meaningful way to spend these points was to exchange them for a reward from a catalog. Or maybe you could exchange them for a discount, as in the case of stamp-based programs. This was a natural approach at the time, as each brand treated its loyalty program as its own marketing tool. When deployed alongside other tools, the loyalty program should strengthen relations between the customer and the brand – and only that brand.

Increasing liquidity

But this was about to change. As the market became saturated with various loyalty schemes, from retail to telco, and programs grew in size and reach, the attractiveness of the basic loyalty proposition started deteriorating, to the point that customers began to view it as almost a commodity.

Brands began searching for ways to differentiate themselves, especially those that owned programs with strong, well-regarded loyalty currencies. One way to do this was to allow program members to earn and spend points outside the “home business” ecosystem, for example by using them for products or services offered by a partner company. Another was to let them exchange “home program” currency for another and vice versa (albeit at highly unfavorable rates).

Take for example Miles & More, which established itself as a well-recognized brand in the travel loyalty world. Launched in 1993 as Lufthansa’s frequent flyer program, it has steadily evolved into the largest FFP in Europe, helped by the company’s expanding association with Star Alliance. This program currently allows members to collect and spend miles across more than 40 airline partners and close to 300 non-airline partners. Moreover, the Miles & More program lets members convert its loyalty currency (in this case, miles), into other programs’ currencies and back.

Following in the footsteps of Lufthansa and other large loyalty program operators, the entire landscape has changed. Now, cross-program currency flows are part of a push to improve member experience and enhance engagement.

Technological advances

This evolution wouldn’t have been possible without some of the technological changes of recent years. One of the most important changes – the emergence of web services and API-based connectivity – created an interlinked ecosystem in which loyalty currencies can be exchanged and spent in a user-friendly way, and in nearly real-time. Another major outcome of these technological advances is the emergence of dedicated platforms that specialize in loyalty point exchange between programs (so-called points brokers), and even conversion to cash.

Points.com is one of the best-known loyalty point exchange platforms, letting members exchange points between different programs (although the majority of the revenue generated by Points.com comes from members buying points through the company’s third-party services).  Another example is PointsPay, which allows online shoppers to convert unused loyalty points from the banking and travel sectors into cash for purchases across a wide portfolio of online retailers.

Technological advance certainly had a major impact on the growing liquidity of currencies in the loyalty programs market. Now, though, another change is beginning; and this one might send shivers down the spine of some loyalty program operators.

Towards full liquidity

In June 2019, the tech world and millions of users around the world were electrified by the announcement of Facebook’s plan to introduce its own cryptocurrency – Libra. Although the company has yet to disclose details of how Libra can be acquired and used, it is understood that the blockchain-based virtual currency will be available for purchase via a digital wallet.

One may reasonably speculate that it will also be awarded to users for certain activities and behavior (which is similar to the way in which loyalty currencies already work). Users will be able to send currency to other members, convert it into other cryptocurrencies, and use it to pay partner companies that accept it. While U.S. regulators have temporarily halted Facebook’s plans and raised a question mark over Libra, the social media giant’s intentions in this regard can be seen as part of a broader shift.

Large ecommerce companies and digital platforms (such as Rakuten and Line), as well as some digital startups, are experimenting with developing token-based ecosystems in which customers can freely transfer their digital currencies and loyalty points between different programs and wallets in real-time, and trade or pay for virtually anything.

Consider for example the startup called GOZO. This company aims to establish a blockchain-based decentralized clearing house mechanism for loyalty points. Another example is GAT (Global Awards Token), a startup that has developed a platform through which members of different loyalty programs can trade, deal and make offers via GAT’s own, listed cryptocurrency. Big players with long-standing loyalty programs across different sectors are monitoring this situation closely. Earlier this year, my team worked with a leading Asian company on a loyalty consulting engagement which, among other topics, included moving from a standard loyalty program to cryptocurrency-based architecture.

It must be noted that these cryptocurrency models are yet to be proven. Nevertheless, if they and similar initiatives are successful, there is no doubt that they will bring loyalty programs of all kinds closer to full liquidity scenario. In such a landscape, boundaries between loyalty programs will no longer exist, and currency earned in one program will be easy to use outside.

Opportunity or threat?

Increasing liquidity of loyalty points (and loyalty currencies in general)  definitely presents a tremendous opportunity to improve loyalty program member experience. It would also lead to fewer points being unused (lower breakage rate), by improving redemption rates, hence increasing customer satisfaction, while lowering the financial reserves that need to be maintained to cover unused points in customer accounts. But at the same time, this evolution raises serious questions related to the loss of in-house control over loyalty currency outflows/inflows, and the financial implications of this.

Some companies may find they are at risk of becoming only currency issuers, while others will bear the costs and benefits of higher redemption rates. Thus, the biggest players must monitor developments closely. It may also benefit those with sufficient resources to start small-scale experimentation with loyalty cryptocurrencies, in order to test concepts early and be ready to scale up for mass roll-out before the market changes irreversibly and they find themselves left behind.

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