Photo by Jess Bailey on Unsplash

The Covid-19 pandemic has disrupted people’s daily routines. Want to grab that favorite cup of coffee down the street? Sorry, the coffee shop is closed. Spaghetti and meatball night? Too bad, the local store ran out of ground beef. Client meeting at the office? Nope, Zoom meeting with surprise appearance of adorable kids or pets is the way to go now.

In these and many other areas of life, the well-rehearsed habits people had from the “old” days are suddenly thrust into the spotlight of their consciousness. As a result, old habits are breaking, and in their place new habits sprout. These transformations in habits have significant consequences for business. Some brands are being left behind from broken old habits, while others are discovering opportunities among the new habits. Overall, businesses that understand this habit transformation process about their customers will be better prepared to transition through the pandemic and beyond.

This article looks into psychology research to shed light on the habit transformation process and discusses how customer relationship management should respond to such disruptions.

When and Why Do Habits Break?

To understand why habits break, let’s first take a quick look at what habit is. In psychology, a habit evolves from an action that a person frequently repeats under stable circumstances. With enough repetition and stability, the action becomes an automatic habit that is triggered by contextual cues and can be carried out with minimal thinking.

Consider an illustrative example of someone with the habit of grabbing a cup of joe at the corner coffee shop on the way to work in the morning. We will call her Jennifer. The first time it happened, Jennifer happened to notice the coffee shop, and she desperately needed an energy boost for the long day ahead. The coffee tasted decent or maybe even great. So she decided to go again the next day, and again the day after. Over time, it became a part of Jennifer’s morning going-to-work routine. She does it every workday morning without really thinking about it any more. It has become a habit.

What makes these automated habits break? There are at least three common reasons.

1. Disappearing or Changing Triggers

A habit is usually associated with one or more of the following four types of contextual cues:

• Time cue: time of the day, day of the week, etc.;
• Location cue: where you are;
• Social cue: who you are with;
• Event cue: what happens immediately before or after.

For Jennifer’s morning coffee habit, it was tied to three cues: time cue (morning), location cue (around the corner), and event cue (work right after). Suppose because of Covid-19, Jennifer is now working remotely from home instead of going to the office everyday. The changing work situation took away the location cue. The time cue may change too if Jennifer starts to get up later in the morning because there is no need for a long commute. Since the location and time cues are strong triggers of Jennifer’s morning coffee habit, the habit is likely to break now because the triggers are no longer there or have changed.

2. Rude Awakening

“Oh no, the coffee shop is closed!” Sometimes a habit can also screech to a halt when it is simply not possible to engage in the action dictated by the habit any more. For example, the closure of coffee shops and restaurants around the country has disrupted many people’s caffeine consumption or dining out habits. The empty shelves at the grocery stores may also force consumers who had habitually bought brand A to now relearn the available options and go through a more deliberate choice process again.

3. Think Habit Away

Because habit uses an automatic triggering process, making someone consciously think about the habitual action can cause a habit to break down. For example, an article Jennifer read may make her question how much coffee she should drink in a day. As another example, a competing brand may be offering a substantial discount. It may give your customers pause and make them question why they chose your product instead of the competitor’s. That questioning brings the automatic process to a conscious level, which may stop the automatic habit triggering from happening.

A simple analogy of this is driving. Drivers usually don’t need to think about where the gas pedal is or what to do if a red light is ahead. But if you were to ask a driver to try to think about the steps it takes to drive a car, the previously effortless actions would become more difficult to do.

This tendency to “think habit away” has been found in cross-selling promotions. A research study showed that when a store tried to cross-sell to its customers with a one-shot marketing campaign, it broke the habit of previously habitual buyers.

What Happens After Habits Break?

When a habit breaks, people may deal with it in different ways.

One route people may take is to retake over the habitual behavior with intentional control. In other words, rather than being guided automatically by contextual triggers, people now make decisions and choices based on whatever their intention may be at the present. For example, instead of habitually buying the same brand of cereal, consumers may start to look at nutrition label again and choose the best health/taste balance. Or the more economically conscious consumers may follow a new rule of buying whatever is on sale or buying store brands whenever possible. Decision rules and preferences are prominent in consumer choice under this route.

A second possible route is to develop a new habit to replace the old one. Habits are efficient ways of dealing with a stressful world, since they require little effort and help simplify decision making. When an old habit breaks, people may gravitate towards a new one so that they can go on autopilot again. We have seen these new behavioral patterns emerge since the beginning of the pandemic. Weekly grocery shopping trips are replaced by Instacart orders and HelloFresh deliveries; dining out on the weekends has traded places with DoorDash and UberEats; and gym workouts are now done with at-home exercise gears. According to Adobe Analytics, the online sales of these gears jumped 535% from February to March of this year, compared with a 70% increase during the same period last year.

Is it also possible that people will go back to their old habit, especially after the pandemic shutdown is over? That is certainly possible. But it will require the right set of conditions to happen. First and foremost, to recover old habits, the same contextual cues need to be restored. This means ideally the consumers’ environment should be returned to a pre-pandemic normal state. But that expectation is unrealistic. So how much the “new normal” will look like the “old normal” and how quickly things can stabilize will affect how much we will see old habits return.

Another factor that will help bring back an old habit is a consistent goal before vs. after. For example, although people may not be able to work out at their regular gym during the shutdown, those keen on staying healthy will pick up working out at the gym again afterwards. With their goal of staying in shape, they will be driven to repeat the gym routine and become habitual gym goers again. The challenge here is that the pandemic has prompted many people to re-examine their life’s priorities. So the domains relatively unaffected by the wave of soul-searching will be more likely to see old habits return.

Strategies for Customer Relationship Management

What do all of these mean for a business? In this last section, we offer some advice for managing customer relationship around the current habit transformation.

Assess the Impact on Customer Habit

Four critical questions to ask here:

1. How much is the purchase and consumption of your product/service driven by habit? If your products or services are low-involvement and are used frequently, habit is likely to be a strong consideration.
2. What contextual cues are relevant to your customers’ habit? If you have captured previous customer purchase or interaction data, deep analytics may help uncover these cues.
3. Which of these contextual cues have changed because of Covid-19, and what will remain changed even after? The more your contextual cues have changed, the more likely your customers’ habits will also change.
4. How much has your customers’ habit strength actually changed? Pay close attention to the stability of your customers’ behaviors. Are they buying around the same time? Are they using the same devices? Change in stability is a telltale sign that your customers’ habit is shifting.

Focus on Top of Mind

If your business happens to be more severely affected by the current habit disruption, it is important to keep your name salient in consumers’ mind. Many of your previously habitual customers had developed habits around your product because they initially liked it. With the habits broken and choices subject to a more conscious decision-making process, it is high time to build brand equity and connection with the brand. Can’t open your restaurant? Send at-home cooking tips and recipes to your customers. Customers not traveling? Bring the world to them, like what Omni Hotels’ & Resorts’ Discovery Loyalty Program is doing with their destination guides. A number of museums have also turned to virtual tours and educational materials to keep people connected. So even if the door of your business is closed at the moment, keep communicating with your customers, and keep them engaged through social media, emails, and other online tools.

Habit-Friendly Marketing Design

If building new customer habit is your objective, keep in mind the two conditions for habit development: high-frequency repetition and stable contexts. Build repetition into your marketing tactics by encouraging multiple episodes of the same behavior (e.g., do this x times and you will get this). If you already have a loyalty program, it can serve as the perfect channel for encouraging repetition.

Be aware also that the Covid-19 situation is far from being clear at the moment, despite plans to reopen being worked out. Without a clear stable context, it will be challenging to build long-lasting habits. When designing the contextual cues for new customer habits, focus on cues that are likely to remain stable across different possible scenarios. For example, with the length of the current shutdown uncertain and the possibility of second-wave shutdowns, location cues are likely to be a moving target. Reduce the weight of location cues in your behavioral design. Alternatively, if you associate the new habit with people’s home, try to pair it with a time cue when people will likely be home, shutdown or not.

In conclusion, the Covid-19 epidemic has brought massive disruptions in people’s daily habits. It is important that you assess the impact of these habit disrupters on your business, remain engaged with your customers during this time, and consciously design habit-friendly processes into your marketing tactics.

Note: This article was previously published on Medium.com by Loyalty Science Lab.

In the last couple of months, I have been super busy starting a new exciting research initiative, the Loyalty Science Lab. Housed at my university, the Lab’s mission is to create and promote cutting-edge scientific research on brand and customer loyalty. Through collaborative efforts between marketing practitioners and academic researchers from multiple disciplines and multiple industries, the Loyalty Science Lab identifies high-priority loyalty-related issues, engages in deep, evidence-based scientific research on these issues, and disseminates the insight to benefit loyalty research and practice. I am happy to say that the Loyalty Science Lab is now up and running!

As its first major initiative, the Loyalty Science Lab spoke with a group of leading marketing practitioners and academics. The purpose of the conversations was to identify important loyalty-related issues that need deep, focused research. The questions generated through this process were further narrowed down by the experts on our advisory board into a list of top-tier and second-tier questions. 

In this article, I would like to share with you the major loyalty issues we’ve identified that need more research at this time. For a complete list of the research questions, you can download The Loyalty Puzzle 2020–2022 edition.

Top-Tier Loyalty Research Areas

Area 1: Dealing with and recovering from large-scale disruptions

The COVID-19 pandemic has caused widespread disruptions across businesses and industries. More unexpected large-scale disruptions are likely to happen in the future. What can businesses do to best prepare for and handle such large-scale disruptions to ensure continuity in customer experience and loyalty? How do these interruptions change the consumer psyche and the meaning of loyalty? How will loyalty affect the post-disruption market landscape?

Area 2: Changing nature of loyalty and loyalty evolution

With the changing beliefs and values across generations of consumers and across time for the same consumers, what does loyalty mean today? Where are brands and consumers on the loyalty curve? How can businesses move their customers across the loyalty curve from apathetic to enthusiastic and from simple repeat purchase to love? What is the role of social in customer loyalty?

Area 3: Loyalty metrics and ROI issues

Businesses need valid and reliable measures of loyalty both to benchmark themselves and to quantify the impact of loyalty marketing strategies. What are the right metrics for loyalty today? Can we effectively measure loyalty through social conversations? How can the social values of consumers (e.g., as content creators, influencers, etc.) be captured in a holistic customer lifetime value measure? How to demonstrate the ROI on loyalty investments?

Area 4: Customer Experience

Few will dispute the importance of customer experience in driving loyalty. But what constitutes an “elevated” experience for consumers? Is it the reduction of friction, pleasant surprises, or something else? How can businesses meet the rising expectations about customer experience and delight their customers while remaining profitable?

Second-Tier Research Areas

Area 1: The role of social influence on loyalty

By now, most businesses have embraced social media into their marketing strategy. How are social media impacting loyalty? What types of social connections are most relevant to building and transferring loyalty? In the social media space, what is the best balance between overt brand messaging vs. subtle nudging? How to identify positive and negative trends through social conversations? How should brands talk to other brands in social media?

Area 2: Data, analytics, and privacy issues

There are lots of discussions about consumer privacy and ethical use of customer data. But consumers’ privacy concerns are more nuanced than general anxiety and fear. When and why do consumers trust enough to share their data? What are the best ways to manage data, data usage and data rights? How can data be effectively and ethically used to enhance customer experience and loyalty? How can businesses combine data and behavioral economics principles to nudge consumers?

Area 3: Enhancing customer experience and loyalty with technology

Companies now have a lot of technology-enabled touch points with consumers. When does technology benefit or do harm to customer experience? What is the right amount and right approach for technology-assisted customer interactions? How do AI and voice-assistive devices such as Amazon Echo and Google Home affect consumers’ information search and decision-making processes?

Area 4: Loyalty program design and management

An average consumer belongs to a dozen or more loyalty programs. Have consumers developed point fatigue for traditional points-based loyalty programs? How can social and engagement be built into a loyalty program? What should loyalty programs of the future look like? What is the value of having a formalized loyalty program? How to measure the ROI of such a program? How to communicate more effectively with members of a loyalty program?

Area 5: Loyalty in non-profit organizations

Non-profit organizations need loyal donors and supporters as much as businesses need loyal customers. But loyalty in the non-profit space is surprisingly low, and many non-profit organizations are spending most of their energy on going after new donors. Given limited resources, what is the proper balance between maintaining existing loyal donors and going after new ones? How do lifecycle stages affect consumers’ pattern of support for non-profit organizations and causes? How to turn individuals sympathetic toward a cause into actively supporting the organization?

Now that we’ve got the important questions down, it is time to get to work! In the coming months, the Lab will be engaging in various research projects and will share research insights to answer these questions. Please follow the Loyalty Science Lab on LinkedIn if you’d like to receive updates. In the meantime, I welcome your thoughts and ideas about these research questions. If you are interested in working together to solve some of these puzzles, I’d love to chat with you too.

Are loyalty programs effective? With the amount of money invested in customer loyalty programs, this is a question that matters greatly to a lot of stakeholders. We may now finally have some scientifically sound answers, at least as it relates to grocery retail loyalty programs. In the largest scale academic study of 358 retail brands across 27 different countries, Professor Bombaij from Tilburg University and his coauthor found some good and some bad. Their research findings are reported in a paper to be published in International Journal of Research in Marketing. In this research focus feature post, I would like to discuss the key findings from the research and what they may mean to your loyalty program management.

Overview of the Research

The two researchers were interested in finding out if loyalty programs really work, what program designs are effective, and if program impact depends on the retail type and on country and cultural differences. They started with the top 15 grocery retail brands in 27 countries (17 in Western Europe and 10 in Eastern Europe). For each brand, they tried to gather information on its sales, loyalty program design (if there is one), and other business characteristics. Combined with country level information, these data allowed the researchers to systematically analyze the impact of different factors on each brand’s sales per square meter. Importantly, they used rigorous statistical methods to make sure that any differences found are real differences due to the loyalty program, not because of some other things mixed in there.

Do Grocery Retail Loyalty Programs Work?

68% of the brands in the study offered a loyalty program. Out of these, 115 or 47% had a positive impact on sales. 127 did not show any impact, and 3 showed surprising negative effect on sales. When the researchers extrapolated their findings to eight other countries not in their sample, they found that, assuming a moderately competitive setting, supermarkets and hypermarkets had a half-and-half chance of succeeding/failing with their loyalty programs. These insights are valuable because previous research has usually studied a single program in isolation. This large-scale analysis offered a big picture view of loyalty program impact. An important takeaway is that loyalty program performance varies widely. To say such programs simply work or not work would be oversimplifying matters.

What Loyalty Program Design Factors Help?

So what types of loyalty programs have a higher chance of being successful (as measured by sales)? The researchers looked at four design factors:

• Reward timing: does the program offer immediate rewards (e.g., cash discounts at the time of purchase) or delayed rewards through point accumulation?
• Reward type: are the rewards offered directly related to the business (e.g., a free ham for a grocery store) or not directly related to the business (e.g., a movie ticket for a grocery store)?
• Reward structure: does the program offer a constant reward structure where consumers are rewarded the same way no matter their spending (e.g., buy 10 coffees and get the 11th one for free)? Or does the program offer a progressive reward structure favoring high-spending, high-tier consumers?
• Single vs. multi-vendor program: is the program run by a single main retail brand, or is it a coalition program that includes multiple brands?

Somewhat surprisingly, neither reward timing nor reward type had any impact on loyalty program success in this sample. But the last two factors did. Specifically, a simple constant-reward structure was found to be more effective than a progressive-structure program. In addition, single-brand loyalty programs worked better than multi-vendor loyalty programs.

It is important to note a couple of caveats with these findings: (1) These are the top grocery retail brands in each country. Single-brand loyalty programs make more sense in that setting. For small retailers that do not have the ability to manage a standalone program, a multi-vendor program may still make sense. (2) Also because of these brands being well-known top brands, the brand-reinforcement effect from offering direct (brand-relevant) rewards found in previous research may not be noticeable here. (3) Grocery purchases are typically considered transactional and utilitarian. This will also affect how people respond to simple vs. complex programs and how they favor immediate satisfaction vs. delayed gratification.

Do Business Types Matter?

Across different retail formats, the researchers found that loyalty programs were equally effective for supermarkets (similar to Kroger) versus hypermarkets (similar to Wal-Mart). But such programs were less effective for discounters with lower price and more limited product assortment and service (similar to Aldi). How big the brand is or whether the brand follows a high-low pricing strategy or an everyday-low-price strategy did not make a difference on loyalty program success.Across different retail formats, the researchers found that loyalty programs were equally effective for supermarkets (similar to Kroger) versus hypermarkets (similar to Wal-Mart). But such programs were less effective for discounters with lower price and more limited product assortment and service (similar to Aldi). How big the brand is or whether the brand follows a high-low pricing strategy or an everyday-low-price strategy did not make a difference on loyalty program success.

Additional analyses within the same research also showed that business type does tend to drive the design of the program. For example, bigger retailers tended to favor delayed rewards. In comparison, discounters favored immediate rewards, indirect rewards, a constant-reward structure, and a single-brand program. Basically discounters seem to offer simpler programs with less moving components, an understandable choice.

One thing this research was not able to answer is whether a particular type of design would be more effective for a specific type of business. We know from the above that different types of businesses tend to choose different types of program design. Therefore, it is difficult to tease apart whether a particular design worked well for a particular type of business because the business type favored that design for some pre-existing reasons or because those program designs are particularly suitable for that business type. A much larger sample would be needed to answer that question, where each design variation needs to exist in at least a few businesses within each type.

Do Markets, Countries, and Cultures Matter?

Very little research has been done on whether loyalty programs may work differently under different cultures. This research fills that big gap. It found that loyalty programs tend to be more effective in individualistic cultures. As individualistic cultures value the uniqueness of each individual and believe in success through individual efforts, it’s not surprising that the individual reward based loyalty programs worked better in such cultures. How much a culture is long-term oriented mattered too. Since loyalty programs typically involve long-term commitments, they worked better in more long-term oriented cultures.

At a market level, how competitive a country’s retail market is did not make loyalty programs more or less successful. But if a lot of competing grocery stores all offered loyalty programs, the effectiveness of the programs tended to suffer. These results echoed my own analyses of airline loyalty programs a while back. However, there I also found that category flexibility (i.e., how much the industry can attract demand from other related categories) can make program saturation less of a problem.

Putting It All Together

One important lesson from this research is that loyalty program effectiveness is highly contingent on program design and operational settings. Often there is no single rule that will work across the board, although there are some emerging patterns. The researchers’ simulation analysis showed what would happen if the programs modified their design characteristics to the more optimal structure. That is, if the programs all switched to a constant reward structure offered by a single dominant brand. In that case, 160 or 65% of the programs would have become effective instead of the original 115 or 47%. Do keep in mind though that these findings were based on the grocery retail context. The same rules may not work for another industry, say, cosmetics, apparel, or travel.

From entertaining Super Bowl commercials to wildly viral video campaigns, marketing has always had a flair for the playful. It is no surprise then that the concept of gamification is readily integrated into many aspects of marketing. Well-known examples include Starbucks’ creative use of gamification in its reward program and McDonald’s adaptation of the Monopoly Game. In this article, I’d like to outline several key trends I observe in the application of gamification in marketing.

Gamification and Branding

Good gamification and good branding share one thing in common: a good story. In a world of social media and user-generated content, a brand has to shift from hard sell to telling good stories about the brand to attract consumers. The new generations of consumers appreciate a brand that is authentic and relatable. Gamification will play an important role in brand storytelling by integrating bits and pieces of brand stories into gamified experiences. It can bring the brand story alive and let the fun experiences translate into memorable brand moments for users. Coca-Cola’s huggable vending machine is a good example of such experiences.

Using Gamification to Increase Customer Engagement

Games are engaging, and customer engagement is a valuable asset in today’s marketing. One brand adept at leveraging board game concepts is Starbucks. The Starbucks Hopscotch game that ran this spring challenged users to make specific purchases (e.g., buying lunch, mobile ordering) in order to hopscotch their way to 400 bonus stars. Compared to an old boring promotion, gamification keeps users on their toes and offers them the gratification of winning challenges and rewards. The result? An audience who won’t easily get comfortable or bored with a brand and who will want to be part of the exciting experiences that the brand has to offer.

Integrating Gamification into Augmented Reality

Augmented reality has brought about fun games such as Pokemon Go and Ingress and has revolutionized the idea of games. It has made no less impact on marketing. Brands such as IKEA, Sephora, and Visa have all tried their hands at augmented reality. However, whether augmented reality truly helps marketing is still being debated. Academic research shows that consumers may be so engrossed in augmented reality technology that the most important brand information becomes forgotten. The future of combining gamification and marketing in augmented reality may offer an answer to this problem. Through gamification’s ability to engage users in the brand story, augmented reality can become more purposeful in delivering the right brand stories and messages and in engaging users along the way. The key will be to make augmented reality technology actually useful based on the product being marketed and integrate fun into that process. Google Map’s originally planned use of a virtual fox guiding users through the labyrinth of a city is a great example of this idea. Although Google eventually scrapped the fox due to safety concerns (who can resist the charm of a magical fox?), the idea of combining useful AR reality technology with engaging gamification concepts is likely to show up in other places in the near future.

Gamification for Social Good

A UN Foundation survey last year shows that many Gen Z consumers seriously consider a brand’s stance on social issues when making their purchase decisions. Many marketers have adopted corporate social responsibility (CSR) strategies to meet consumers’ increasing expectation of brands as important social citizens. But many CSR campaigns can come across as dull or contrived. Gamification can make the experience more fun and engaging. SAP, for example, developed the TwoGo app to help businesses encourage carpooling among their employees. It allows users to earn points, find the perfect ride match, and donate saved money to charities. We can expect to see more integration of gamification into CSR efforts to make these efforts more fun and interactive.

To conclude, gamification has diverse applications in marketing. It can help brands better tell their stories and create more interesting customer experiences. The challenges will be to find the best integration approaches and to effectively adapt the concept of gamification for the individual brand.

Note: A previous version of this article appeared in the CIO Review.

Image by MyTechLogy Where IT Talents Shine from Pixabay

Changes are coming to Starbucks Rewards. On April 16, Starbucks Rewards will be expanding its reward structure to five levels. A few other changes will be made to the program too. Will these changes make this leading loyalty program even better? What might be motivating these changes? How will the changes affect the program and its members? In this post, I attempt to dissect these changes and offer my assessment of the new Starbucks Rewards program design.

What Changes Exactly?

The biggest change to the program is the addition of more reward thresholds. Instead of a single reward threshold of 125 stars, it will feature five levels of rewards ranging from 25 to 400 stars. The new program will also erase the current difference between a Gold member and a base member, allowing both to enjoy the same set of benefits. The table below compares the current vs. the new program structure.

	Current	From April 16, 2019
Earning Stars	2 stars per $ spent	2 stars per $ spent (unchanged)
Reward Levels	A single level, at 125 stars	Five levels, at 25, 50, 150, 200, and 400 stars
Reward Items	A free food or drink item, excluding alcoholic beverages and multi-serve food and beverage items	25 stars: free drink customization (e.g., extra shot, flavor, etc.); 50 stars: a free hot brewed coffee or tea, or a free bakery item; 150 stars: a free handcrafted drink, hot breakfast, or parfait; 200 stars: a free lunch sandwich, protein box, or salad; 400 stars: a free select merchandise (e.g., gift) or at-home coffee.
Membership Tier Structure	Green and gold. Gold is qualified by earning 300 stars in a 12-month period.	Single tier
Base Tier Benefits	A birthday reward Free in-store refills of hot or iced brewed coffee and tea Early or extended access to some promotions and offers Personalized offers and coupons Can NOT redeem stars for free rewards until after reaching Gold level	Same as before, but now everyone has the same ability to redeem their earned stars for rewards. All members now also receive a monthly double-star day that used to be reserved for Gold members.
Gold Tier Benefits	All Green benefits The ability to redeem every 125 stars earned for a free reward A double-star day as selected by Starbucks A personalized gold card	Not applicable, as there will no longer be a premium tier. Gold members do not lose any benefits. They just have to share them with everyone else.
Star Expiration	6 months after the star is earned	6 months after the star is earned (unchanged)

More Complex Reward Thresholds

The new design significantly expands the reward structure of the program from one to five levels. What could be driving this change?

Increasing Program Relevance

An attractive loyalty program should be relevant to consumers. If it takes too long to earn a reward, consumers may lose interest in the program. Assuming an average price of $2.5 for a tall coffee, the current threshold of 125 stars requires buying roughly 25 cups of coffee before one will receive a reward. The new program design rewards consumers at as low as 25 stars. For some, this level can be reached easily in one or two trips if you buy the more expensive items on the menu or if you buy for a group. For consumers who do not visit Starbucks all that often, the new design can be much more appealing to them. Even if someone goes to Starbucks only once a month or only buys black coffee, the consumer can still be rewarded for the effort in a reasonable amount of time. This increased relevance should attract the lighter buyers.

Inducing Habit

One of my earlier posts discussed research showing smaller but more frequent rewards are more conducive to developing habit than larger but less frequent rewards. The new Starbucks Rewards design does exactly that. Consumers can now get a treat much faster than before and can repeat that cycle over and over again in shorter time intervals. This can help promote the habit of buying from Starbucks. A counter force to this will be the very frequent program communication Starbucks seems to send out to its members these days. Habit needs to be reinforced through repetition and without much thinking. With each message, it is quite possible to jolt people out of their routine and break their habit. Therefore, the frequency and design of these messages will need to be carefully considered to avoid interference with the development of habit for some consumers.

Redirecting Redemption Behavior and Cost

With the currently undifferentiated rewards, many Starbucks Rewards members would redeem their free reward for the most expensive thing possible. I have actually had baristas suggesting that I order Venti instead of Tall and add yummy flavors to my free reward drink because…it would all be free! This policy blindly increases the reward cost. The new thresholds create a big gap among the different rewards. Getting a free Caramel Macchiato or Cinnamon Shortbread Frappuccino will now cost three times the number of stars for getting a Venti black coffee. The result? The impatient and instant-gratification-seeking members will likely just redeem for a regular cup of coffee or a flavor addition, significantly reducing the liability from rewards. For consumers who just want a healthy good ol’ cup of joe, they will be happy to get their free fix at a much sweeter star deal. Assuming an average of $2.5 for a regular hot brewed coffee, the new 50-star threshold is equivalent to 1 free coffee after 10 regular coffee purchases, as opposed to the current 1 free coffee after 25 cups of coffee.

Increasing Point Pressure

Psychology research shows that as we move closer to a goal, we are likely to work harder. Translated into loyalty programs, we often see a point pressure effect. As consumers get closer to a reward, they are likely to spend more or make another transaction faster in order to “get there”. In the current Starbucks Rewards program, that goal is 125 stars. In the new program, it will be 25, 50, 150, 200, and 400. This is like adding more mile markers to a race to make runners work hard toward that next target…“if only I can get past that next marker”. The redesign will make consumers sprint more frequently toward their reward goals, creating more opportunities for point pressure.

A downside, however, is that dividing a goal up into more sub-goals sometimes can make people be content with the lower goals (e.g., 50-star reward) rather than aiming for the highest goal possible (i.e., 400-star reward). But assuming those who settle on the lower goals will still want to do it over and over again, there is minimal loss for Starbucks and can actually save reward cost as I mentioned earlier.

No More Membership Tiers

*Starbucks’ announcement about the new design did not explicitly state a change in the program’s tier structure. So I tweeted to Starbucks and inquired about the change. Here’s the reply I received:

So it looks like the program is doing away with the tiers. This is not the first time Starbucks Rewards has tinkered with its member tier structure over its ten-year existence. I compiled below a timeline of the tier changes in Starbucks Rewards history:

	November 2008	Introduced Starbucks Gold Card. It charged a $25 membership fee and offered members one free drink and 10% off purchases.
	December 2009	Starbucks Gold was folded into the combined My Starbucks Rewards program as the top membership tier, requiring 30 purchases to qualify and offering a free drink after every 15 purchases. Two other tiers existed below the Gold tier: a welcome tier that offered a free birthday beverage and two hours of Wi-Fi; and a green tier that required 5 purchases to quality and enjoyed the benefits of free refills, free flavors and soy, and a free drink with each 1lb whole bean coffee purchase.
	April 2016	Starbucks Rewards simplifies to only two tiers, removing the previous welcome tier. Qualification and star earning changes from frequency based to spending based. Gold tier now required 300 stars ($150 in spending) to qualify.
	April 2019	Moving to a single-tier structure, no longer offering differentiated benefits across members.

Going through this timeline, it appears that Starbucks Rewards had the full intention of creating a hierarchical program in its early days. However, over the years, it has gradually flattened the program structure from three tiers to two tiers, and now to the single-tier structure in this redesign. Two reasons may be behind this change:

One, it is not easy to create truly differentiated benefits among tiers in a coffee shop setting. In the current program, Gold members need to earn 300 stars (the equivalent of $150) to reach the tier. What do they get in return? Not a whole lot. Most of what they receive in addition to green-tier benefits are simply the right to spend more money and redeem those earned stars to redeem for rewards. One can say the return is quite meager compared with the effort, unlike the often coveted seat or room upgrades offered to premium members of travel loyalty programs. Without a way of making Gold members truly appreciated above and beyond an average member, the existence of a premium tier does not mean much in making people aspire for more.

Another reason for the flatter structure may be to appeal more to the general public. The new design removes the barrier to a higher tier and allows all consumers to earn rewards right from the beginning. It makes the cost of entry much lower, especially for consumers who currently do not spend that much at Starbucks. In combination with the low beginning reward threshold, the new program design will likely help the program expand in the lower spending segment and enroll more consumers in a would-be saturated market.

How will the flatter structure affect the cream of the crop consumers? There could be some negative impact there as these consumers will no longer be made special with their own tier. Although objectively speaking they are not losing any of their existing benefits, the psychological impact can still resonate. That is probably why Starbucks has been very indirect in talking about the new tier structure. The last thing they want to do is to alienate these best customers and tell them “you are no longer that important to us”. Regardless, some consumers will likely take offense. Only if Starbucks had been more creative in discovering the sweet spot of more distinctive benefits at a reasonable cost for the higher-tier members…

Overall Conclusion about the Starbucks Rewards Redesign

Taken together, I believe the new Starbucks Rewards design is intended to expand its member base and to more effectively target the lighter end of the customer spectrum. It is a growth-oriented strategy, which is likely a priority given Starbucks’ reported slower sales growth now that every street corner seems to have a Starbucks store. This goal of attracting lighter customers will likely be successful under the new design.

However, the long-term loyal customers of Starbucks may not feel particularly appreciated. Some of them may turn to the locally owned coffee shop across the street instead. The more complex program rewards together with frequent (in my opinion too frequent) bonus deals may also make the program more confusing to customers. Starbucks started as a higher-end coffee shop that served as a social and cultural gathering place for the community. I worry that the frequent bonuses Starbucks has been offering and will likely continue to offer will erode the perception of the brand in the long run. It may be trading long-term prosperity for short-term sales growth.

Ultimately, the balance between the good and the bad will determine whether Starbucks Rewards is headed for the better or worse with the upcoming changes.

Many loyalty programs do not stand alone. Frequent flyer programs and hotel loyalty programs take on partners such as other airlines, hotels, restaurants, banks, and retailers. Other programs, such as Fuel Rewards in the US and Air Miles in Canada, are by design a coalition of businesses that come together to share a common program currency and reward structure. For consumers, such coalition programs are great because they allow consumers to earn points faster through many different means. But what about the participating businesses? Is it worthwhile to be part of such a program? If you do, should you set a generous point earning and redemption policy? In today’s Research Focus feature, I find some answers in a recent Marketing Science Institute Working Paper (Report No. 19-101) by Professor Stourm and her colleagues.

What Did They Do?

The researchers looked at a credit card based coalition loyalty program in Europe. In the program, card holders earn points by shopping at various partner businesses. Each participating business decides its own reward ratios, from less than 1% up to 3%. When a preset reward threshold is reached, consumers are automatically issued a reward voucher that they can use toward future purchases. Participating businesses can choose whether they accept reward vouchers or not. Accepting reward vouchers comes at a short-term cost as the program operator reimburses only 90% of the values of the vouchers.

In their analyses, the researchers focused their attention on one particular European city. They studied 1,636 consumers that used their card at least once across 40 partner stores located in the city. The research team was interested in finding out whether offering rewards created positive or negative impact among these partner stores, what businesses benefited, and how point devaluation as a result of an overall program policy change affected the landscape.

What Did They Find?

As a baseline, offering a higher reward ratio at one store had a negative impact on other stores in the partnership. But not all stores were affected to the same extent. How much a store was influenced by other stores in the coalition depended on:

• Does the store allow reward voucher redemption? Stores accepting reward vouchers were less vulnerable to the negative spillover from partner stores.
• Product offering similarity. Stores that carried similar product categories were more likely to experience a synergy from each other. Slightly over half of the store-to-store effects between stores selling the exact same product categories were positive. That is, for these stores, at least half of the store-to-store effects represented synergy rather than competition. This synergistic effect was much less likely between dissimilar stores with no product category overlap. Only 36% of the cross-store effects were positive among unrelated stores.
• Geographic proximity. Stores that were close to each other were more likely to experience a synergy. However, stores that competed in the same product categories AND were located near each other did not enjoy synergy. They negatively affected each other instead.

During the study period, the coalition loyalty program put into place new policies that de-valued the program points. The research found that stores on average became less influential on each other. What’s more, the effects became more similar across the board. Accepting the reward voucher no longer insulated a store from negative spillovers.

What Does This Mean for Marketing Practice?

This paper confirms that there are indeed competition among businesses that belong to the same coalition loyalty program. But careful choice of partners may neutralize the negative effect and create a synergy instead. So before joining such a program, you will want to carefully study the makeup of existing businesses in the coalition. You will want the other businesses to either sell similar product categories as you or are located close to you to create scale, but NOT both simultaneously. If you are an early member of a coalition loyalty program, it may be advisable to maintain some level of exclusivity so that there are no other stores just like you located in your vicinity. Similar stores from farther away are OK though, as even similar stores located within the boundaries of the same city in the study still could positively affect each other. It just cannot be really close.

When selecting how you want to reward program members, it pays to be a little more generous by allowing consumers to redeem their rewards at your business. Although accepting reward redemption comes at a direct cost, it can help your business be less vulnerable to other coalition members’ negative influences. The cost of the redemption itself may be offset if it helps you attract new customers or if it motivates consumers to indulge and spend more at your store.

As far as setting your own reward ratio is concerned, setting a higher reward ratio will make your business put a damper on others in the coalition. But having more clout this way will not insulate you from being vulnerable to others. In the research, there were some stores with high clout but also high vulnerability, and some stores with high clout but low vulnerability. So do not rush to set a really high reward ratio just to stand out from competition. It makes participation in the program more expensive to your business and may also trigger others to raise their reward ratios, resulting in essentially a price war where everyone may lose.

Caution

The specific European city context of the research deserves some attention. Although we do not know exactly which city it was, my own travel in European cities suggests that they are much more densely populated than a typical US city. People are much more likely to be on foot than driving a car. I suspect what is considered “far” in that setting may be quite different from a sprawling suburb in the United States. So geographic proximity expectations will need to be adjusted. The likely defining distance is that the two stores should not be competing for the same customers within their geographic radius.

The researchers also warn us about the possibility that geographic closeness may not mean much in the age of online shopping. So if you are an online business, you may not want many other online businesses selling the same things as you do in the coalition loyalty program. Without a physical distance to separate you, these competitors would be quite literally next door to you, accessible by simply the tapping of a few keys.

Reference

Stourm, Valeria, Eric T. Bradlow, and Peter S. Fader (2019), “Market Positioning Using Cross-Reward Effects in a Coalition Loyalty Program,” Marketing Science Institute Working Paper Series 2019.

Behavioral segmentation is the practice of segmenting your customers based on what they do. I wrote previously about the basics of behavioral segmentation. During the last year, I’ve been putting it into practice through a customer segmentation project with a CASC business partner. Today I’d like to share with you some of the lessons I’ve learned through that experience.

NOT Just an Analytics Exercise

Behavioral segmentation has the advantage of revealing segments of similarly behaved consumers that you may not have thought of previously. It requires a lot of data crunching. There is no doubt about that. However, it is important to remember that behavioral segmentation is not entirely an exercise in numbers. If data crunching is all that you do, you risk creating segments that either are based on artificial or fake relationships or are not very actionable from a target marketing perspective.

Successful behavioral segmentation should be a collaborative exercise between your analytics team and those who have a good grasp of your business and your customers. The latter are most likely found in your marketing or sales department. The process of behavioral segmentation should be developed as an iterative process that goes back and forth between analytics and marketing. The analytics team should start by understanding from the marketing team the purpose of the segmentation exercise, the observed behaviors at hand, and the capability of marketing to implement behavioral segmentation insights. Based on this initial information, the analytics team can produce an initial set of behavioral segments based on customer data.

This initial segmentation scheme should be presented to the marketing team both to make sense of the results and to see if meaningful actions can be taken to target each segment. The input from the marketing team is then fed back to the next round of data crunching to adjust the segmentation focus and approach. This process is repeated until both sides are satisfied with a meaningful set of segments to be implemented in practice.

Data Quality is Really Important

The former CEO of Reddit, Ellen Pao, caused a stir in January when he stated that web analytics is fake. Although his statement may seem radical, the underlying message was actually quite reasonable. His point was that user metrics in web analytics are often inaccurate and therefore are of limited value. This trash-in trash-out mentality applies to all data analytic situations, including behavioral segmentation.

Before you dive into behavioral segmentation, first verify the quality and accuracy of your customer data. Are numbers free of errors? Do you see duplicate records? Are there significant omissions in a systematic pattern? Simple summary statistics are often insufficient in spotting these issues. You won’t see the problems until you really dig deep into the data. I have no doubt that there are a few businesses out there that may maintain squeaky clean data. But in my experience, data cleaning and quality verification are usually the most time-consuming part of an analytics project. So, although discovering new customer segments is exciting, curb your enthusiasm and check your data first.

Determine What to Include

In most situations, there are many different ways of describing customers’ behaviors. Describing customers in a retail setting may include, for example, purchase frequency, order size, basket composition, brand choices, coupon usage, time of purchase, to list just a few. Demographic and psychographic profiles may also be mixed in with pure behavioral data. The results of a segmentation analysis can be vastly different depending on which of these inputs you provide to your analysis.

Which ones should you include? Some might answer that we should just include all available information. But this is neither wise nor feasible in some situations. Depending on the size of the data (both the number of variables and the number of observations), the computational cost may be prohibitively high to justify the inclusion of all possible information. Including unimportant information can also clutter up your analysis with garbage.

Here are a few considerations I would like to offer:

• Select variables that show significant variations. If your customers all behave very similarly on something, say, purchase frequency, including that information is not going to be very informative in identifying different pockets of customers.
• Try different combinations of variables and observe their contribution to the discovered clusters. The ones that do not help much in setting the different segments apart from each other can be dropped. The resulting sizes of the segments can also offer a clue as to the desirability of the solution.
• Choose information whose quality you can trust. This is pretty self-explanatory from the earlier discussion.
• Pay attention to the scale of the variables. Some clustering algorithms such as k-means clustering are sensitive to the scaling of the variables. If you have a variable (e.g., income) that ranges from $10,000 to over $1 million, it will overshadow another smaller variable (e.g., order size) that ranges from a few dollars to a few hundred dollars. If you are using a scale-sensitive algorithm, be sure to transform your variables so that their scales are more comparable with each other.

Mutually Exclusive or Overlapping Segments?

Customer segments are often defined as mutually exclusive categories. That is, a consumer can belong to one segment only. This mutual exclusivity does not have to be the case. For your business, it may be perfectly reasonable or even helpful to allow one customer to belong to two or more identified segments. More complex to understand and manage, overlapping segmentation schemes may require a higher level of organizational sophistication, especially in terms of customer messaging. Having such sophistication will help avoid sending inconsistent messages to customers in overlapping regions of multiple segments.

Your choice of the desired segmentation scheme will affect your choice of the clustering algorithm. While k-means clustering and hierarchical clustering are great for creating mutually exclusive segments, methods such as expectation-maximization (EM) clustering are needed to allow overlapping segment memberships. More broadly, it is important to recognize that each clustering algorithm has its strengths and weaknesses. One size does not fit all in this case. Your choice of methods should be driven by your objectives and the nature of your data.

Data science is context-bound. Truly useful data analytics, including behavioral segmentation, require a combination of data skills and business acumen. The issues listed here are by no means an exhaustive list of all nuances in behavioral segmentation. But they should serve as a good starting point for making sure your segmentation exercise yields valid and useful results.

Find the information helpful? Please share it with others. Thank you for reading!

The ancient philosopher Heraclitus said that “Life is Flux”. That statement has important meaning to how a business should connect with its customers. Consumers’ needs change constantly depending on what is happening in their life at the moment. It is impossible to catch every ripple in life. But a business needs to anticipate and respond to the most significant life events in order to build long-term loyalty. The first step is to understand what it means to have a significant life event, what happens to consumers during that time, and how it manifests in consumers’ activities. I will offer some answers in this article based on my own and others’ research in this area.

Consumers’ Experience of Significant Life Events

Significant life events abound in everyone’s life. Some of these are positive events, such as getting married, having a baby, or getting promoted at one’s job. Some are not so happy, such as getting a divorce, losing a loved one, or getting laid off. There are also events with bittersweet feelings such as retirement and sending kids off to college. Whether good or bad, these events are considered “significant” because they change our identity. That is, who we are as a person. For example, getting married means the addition of a new role as a husband or wife. Losing one’s job means the loss of an existing role as a worker and bread earner.

Such role transitions and identity shifts often create a sense of instability and can be quite stressful to the person going through the transition. To cope with the demand of role transition, existing research shows that one can simply cope with the emotional stress itself, or one can take a more proactive approach to do one or both of two things: (1) clarifying what one’s role will be following the significant event; and (2) adjusting one’s resource mix to be prepared for the new role.

Take having a baby for the first time as an example. A soon-to-be parent may talk to others to gain a perspective on what it is like to be a parent (role clarification). The future parent may also try to line up financial resources (e.g., more money in the bank) and human resources (e.g., getting onto the waitlist for a daycare or soliciting family support) to make sure that he or she is ready for the baby (resource preparation).

A 4×3 View of Significant Life Events

It is clear that people tend to make adjustments as a result of role transition as described in the last section. Oftentimes we can sense these significant life events through observable actions by customers. My colleagues and I have been working on predicting significant life events through changes in observable account activities. Our research has created a framework for analyzing manifestations of significant life events through a 4×3 matrix. One dimension of the matrix represents specific needs the consumer’s manifested activities are trying to address. Most activities address one of three needs:

• Survival. Survival represents the most basic of human needs. Activities in this category reflect a desire to ensure current and short-term survival (e.g., ability to pay bills).
• Security. Activities in this category represent the need to create a longer-term safety net. Examples include creating a will or starting a retirement account, etc.
• Personal development. At the highest level in Maslow’s famous hierarchy of needs, personal development involves a desire to better oneself. Investing in a better future, not for a sense of security but for pursuit of delight and better things, would belong to this personal development need.

Along the other dimension of the matrix, we identify four different types of activities:

• Knowledge/information acquisition, which means to learn something new and obtain information that one does not have before.
• Existing resource maintenance, which involves tallying and in some cases adjusting the resources that one already has.
• Adding new resources, such as buying a new house or obtaining other resources that one does not currently possess.
• Removing old resources, which involves shedding one’s current possessions such as selling an old house or donating money to charity.

4×3 View in Practice

Putting the 4×3 view above into practice, the table below shows some sample activities in each of the 12 cells.

I want to note that the classification of each activity into this matrix is not absolute. Depending on the nature of some activities, they may fall into different categories. For example, buying a car can be typically considered as adding new resources for short-term survival needs. But if you are a luxury car dealership, buying a car from you is more likely to belong to the personal development+adding new resources category instead. This means that the classification of activities requires a clear understanding of your customers and your products.

How to Leverage the 4×3 View in Your Business

To implement this 4×3 view of significant life events in your business, the first step is to catalog all of the observable activities you have or can collect from your customers on a fairly continuous basis. Then these activities need to be classified into the 12 cells as shown in the table above. These function as vital signs of the customer. Create a system to regularly monitor the level of activities in each area. Significant changes in activity levels in one or more of these areas likely indicate a pending or recent significant life event.

For certain events, there may be common activity signals. For example, a person planning a big wedding may monitor his or her savings accounts more frequently to avoid overspending (survival+existing resource maintenance). Or someone who just had a baby is likely to think more seriously about long-term security and create a will (security+adding new resources) or update an existing insurance coverage (security+existing resource maintenance).

In the meantime, my research does indicate distinct segments of people who show different patterns of activities for the same event. Therefore, how these activities will manifest in your business may be different depending on the nature of your business and the types of customers you have. The best approach is to identify past significant life events you already know among at least some of your customers, and use their activity levels before and after the events to build a good predictive model of those events. This will require a combination of business acumen and data crunching skills. Alternatively, if there are insufficient data to build such a predictive model, qualitative studies of a sample of your customers through in-depth interviews and focus groups may also be helpful. These studies can help understand how your customers deal with significant life events through each of the 12 areas.

I hope you find this discussion helpful in monitoring and anticipating significant life events among your customers. If you need help with deploying this framework in your business, my research team at the Collaboratory for Customer Insights and Strategy (CASC) would be happy to work with you. Just drop me a note. Until next time, happy reading!

Author’s note: With this post, I am restarting a previous tradition on my blog to report from time to time the findings from a single research paper. Now named “Research Focus”, these slightly shorter posts will summarize each research paper in accessible and practically relevant lingo. The focus is on what the research findings mean to practice. The research papers featured will be chosen from recently published or soon-to-be-published work as well as significant working papers that I consider to be highly relevant to loyalty marketing practice. The goal is to facilitate the dissemination of scientific research, build practice knowledge, and speed up the scientific discovery to practice cycle time. I hope you will enjoy the feature!

Loyalty program is everywhere, yet engaging consumers in a loyalty program remains elusive to businesses. Look at your own wallet or key ring, and you are likely to see at least a few loyalty cards that you no longer use. You are not alone. The 2017 Colloquy Loyalty Census reports that as many as 54% of loyalty program memberships in the US are inactive. A more recent loyalty program engagement survey of 1000+ consumers by CodeBroker shows that 65% of those surveyed are engaged with less than half of their loyalty programs.

The loyalty program (dis)engagement problem has various possible solutions. One solution is gamification, that is, introducing gaming into a loyalty program. Examples of this approach abound in practice, offered by familiar brands such as Victorial’s Secret, Domino’s Pizza, and Starbucks (see the picture at the beginning of this post for a screenshot of the currently running Starbucks Bonus Star Hopscotch game). In today’s Research Focus paper, to be published in Journal of Business Research, Professor Jiyoung Hwang and her colleague studied when and how gamification helps in a loyalty program context.

What Did They Do?

The researchers created descriptions of four different versions of a coffee shop loyalty program:

• A no-gamification program + reward points redeemable for free coffee and food;
• A gamified program with a bingo challenge board + reward points redeemable for free coffee and food;
• A no-gamification program + reward points redeemable for a donation to the local community;
• A gamified program with a bingo challenge board + reward points redeemable for a donation to the local community.

They randomly showed one of the four versions to each of 200+ consumers and asked their opinions about the program they saw.

What Did They Find?

Program loyalty and participation ratings were higher for the gamified programs than for the no-gamification programs. Gamification increased program loyalty and participation through two ways: (1) by making the program seem playful to consumers; and (2) by making people have more favorable feelings toward the program. Interestingly, the importance of playfulness doubles when the reward involves self benefits (i.e., free coffee and food) versus when it involves benefits for others (i.e., donation to the community). Not surprisingly, people preferred self-benefit rewards than other-benefit rewards.

What Does This Mean for Marketing Practice?

The biggest lesson learned from this research is how important playfulness is in driving people’s reaction to gamification. Playfulness makes people concentrate on what they are doing, makes them curious and gives them an overall enjoyable experience. An important part of that playful experience is a mind state called flow, where one loses oneself in a fully engrossed experience with the task at hand. (You can read more about flow in *this book by Dr. Csikszentmihalyi.) In order to generate the flow state, a game needs to provide just the right amount of stimulation, interaction, and challenge. Too simplistic and people will be bored; too hard to win and people won’t be motivated to continue. From a game design perspective, one needs to consider *the story, the mechanics, the aesthetics, and the technology behind the game. A touch of playfulness can be added to each of these components to create an overall fun experience.

Caution

Although gamification can be quite powerful, previous research also finds that games do not necessarily create a stronger connection with the brand. The trick is to provide sufficient integration between the game and the brand, and to give consumers the autonomy to interact with the game the way they want to and however long they want to, without feeling pressured or forced.

Reference

Hwang, Jiyoung and Laee Choi (in press), “Having Fun While Receiving Rewards?: Exploration of Gamification in Loyalty Programs for Consumer Loyalty,” Journal of Business Research.

If your business offers a tiered loyalty program with an annual cycle, beginning of the year may be a “heartbreaking” time for customers who failed to earn enough and was demoted from their previous premium tier status. Some argue that this is one of the downsides of a hierarchical loyalty program. What can you expect from these customers? What can you possibly do to earn back the love (or at least not lose the respect) of these demoted customers? I looked into research in this area to find some answers.

Negative Impact from Demotion

Just like anyone who have flown first class may feel quite miserable going back to the economy cabin, losing one’s premier tier status in a loyalty program is likely to have some damaging impact. This negative impact can have a more “rational” source and a more emotional source. On the rational side, getting demoted means the loss of important benefits such as higher point earning ratio and free perks. So objectively speaking, consumers lose something concrete when they get demoted. But that objective loss is only part of the story. The most important impact comes from the emotional effect of demotion. It is common for demoted customers to feel frustrated, disappointed, uncomfortable, or even angry.

Whether the demoted customer is driven by rational loss or by an emotional response, the ultimate outcome is higher dissatisfaction, lower loyalty toward the company, and a higher likelihood to switch brands. Not surprising? Here comes the kicker. Not only is demotion bad, the damage from demotion (e.g., silver to base tier) is much stronger than the positive effect from an equivalent promotion in the opposite direction (e.g., base tier to silver). In one study, researchers found that even after a demoted customer is restored to the lost premier tier at a later point, the overall loyalty level is still lower than before the customer was demoted. This is quite a serious issue. Unless most of your customers are on an upward trajectory in terms of how much they spend with your business, your gain from rewarding customers with premium tiers may never quite compensate for the loss you will suffer when consumers lose their premium tiers.

Who Suffer the Most?

The negative impact from demotion is not the same across customers though. Knowing which customers tend to respond more strongly can help you take proactive measures to avoid really negative consequences, e.g., keeping some customers in the old tier even if they have not quite spent that much last year. Below are some factors suggested by previous research, many of which should be fairly intuitive.

• Exclusive or polygamous loyalty? Exclusively loyal customers to your business will react more negatively.
• Relationship history? Consumers who have been with the business longer will react more negatively.
• Previous satisfaction with the business? Satisfaction with the business prior to demotion can cushion the negative impact of demotion.
• Involvement/engagement in the program? People who are more engaged and involved in the program will react more negatively.
• Costly to switch? A higher switching cost can soften the blow from demotion.
• Power distance? Consumers from higher power distance cultures (e.g., Malaysia, Mexico, China) will react more negatively to demotion.
• The lost tier? Really high-status program members (e.g., platinum) will be more upset than lower-status premium members (e.g., silver).
• Need for status? People who enjoy and appreciate special status and treatment will respond more negatively to demotion.
• Gender? Men are likely to respond more negatively than women.
• Who is in control? Consumers who believe themselves (rather than other people/things/situations) to be in control will respond less negatively.
• Who pays? Consumers who travel on their employer or someone else’s dime will be less upset about the demotion.

How Can You Mend a Broken Heart?

To answer this question, Professor Wagner and his colleagues compared things that businesses can do to make demoted customers feel better. Three types of intervention approaches were considered:

1. It’s you, not me! Make the customer realize his/her action was the real cause of the demotion. This involved one of two things: either tell the customer the requirements for the premium tier (method 1), or provide them information about their declining spending (method 2).
2. I didn’t have any choice! Shift the blame to factors uncontrollable by the company. This can be achieved, for example, by explaining to consumers the competitive pressure the company faces (method 3).
3. I am sorry! Compensate for the harm done. The compensation can be either monetary (a gift certificate for future purchases with the company; method 4) or non-monetary (a simple apology; method 5).

Using an airline frequent flier program as the context, the researchers compared the loyalty intentions of demoted consumers receiving one of the interventions versus not receiving any communication. Out of the five methods, three were able to reduce the negative impact of demotion: offering premium tier requirements information (method 1), offering consumer spending information (method 2), and offering an apology (method 5). Unfortunately, none of these interventions was quite enough to fully restore the demoted customers’ loyalty compared with those people who were not demoted. In other words, while these three communication tactics are certainly helpful, don’t expect them to magically erase the trauma of demotion.

Proactive Measures to Prevent Demotion

It should be obvious by now that member demotion is not something you want for your program. Its negative consequences are far reaching and quite severe in some cases. How can you avoid it? One thing you can do is to nudge consumers toward their previous tier. I wrote before about how offering the right progress feedback to loyalty program members can motivate them to try harder. Based on my research, you can nudge individuals who are close to their target tier by highlighting how close they are (e.g., you are only one flight away from reaching the gold tier!). Throwing in some bonus points to help consumers reach their previous tier could also be helpful.

Besides nudging consumers to make sure they make it to their last year’s tier level, you should also consider the ironic effect of tier promotion. If a consumer is gradually climbing up the ladder through solid growth in his/her spending, you can reasonably expect that consumer to stay there. But if tier promotion is because of some sporadic increase in spending all of a sudden that is unlikely to continue in the long run, it would have been better off for the customer not to be promoted in the first place. So be careful when you issue bonus points in your program that may unintentionally propel some consumers to a new height they are not likely to sustain. Also if you see a sporadic increase in spending by a consumer that is unlikely to continue, get creative. For example, maybe you can try to buy out the consumer’s earned higher tier with immediate incentives with the tradeoff that the consumer stays where he/she was. This may involve some guesswork and may be complex to manage. But it may prove useful in the long run.

Finally, do you really need a tiered loyalty program? This is a loyalty program design decision you should not take lightly. The cost of wooing the cream of the crop may not be worthwhile in certain situations. So the decision is dependent on what you really want to achieve with your program. Tread carefully to avoid broken hearts!