Category: Research Insight

Building Loyalty Program Partnerships Wisely

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In less than two weeks from today, Plenti, a coalition loyalty program, will officially shut down. Created by American Express three years ago, Plenti had an impressive roster of partners, at one point including Macy’s, ExxonMobil, Rite Aid, Hulu, Expedia, among others. Despite its high-profile start, the failure of Plenti shows the many challenges associated with loyalty program partnerships. If you ever consider loyalty program partnerships, it is important that you do so strategically and judiciously.

The Business Case For Loyalty Program Partnerships

On the surface, loyalty program partnership is a great idea. It allows consumers to earn points from different businesses, making reward earning easier and more relevant to more consumers. This expands the potential market for the program. Running a joint program reduces the operational cost for each business. In the case of a dominant business-peripheral business partnership (such as the partnerships airline frequent flyer programs form with smaller businesses), the dominant business can make good money selling its program currency to its partners. Airlines, for example, are estimated to make between 1.5 and 2.5 cents per mile. With all these benefits, what could possibly go wrong? Continue reading “Building Loyalty Program Partnerships Wisely”

Reducing the Loyalty Gap

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Although loyalty programs seem to be everywhere, many question whether such programs are indeed effective marketing tools. Strengthening the case for the skeptics, recent news report a restaurant that shut its doors in just a few months because it offered a ridiculously generous loyalty program. This is of course an extreme case. In reality, loyalty program providers can fairly easily keep program costs under control through smart program design involving the right combination of program currency, point ratio and reward structure.

Some loyalty program problems are not so straightforward however and can be much more subtle and challenging to deal with. One such example is the concern that loyalty programs foster loyalty to the program rather than to the company. That is, “I love Starbucks Rewards” may end up ringing more true than “I love Starbucks”. In the long run, this is bad news for the company because it makes the business overly dependent on the loyalty program. If a competitor decides to offer a more attractive program, customer attrition is likely to happen. What can businesses do to reduce this loyalty gap and make loyalty program members more loyal to the company rather than just to the program? I want to share with you some insights from a research project I conducted with a colleague in Belgium. Continue reading “Reducing the Loyalty Gap”

How to Measure Habit Strength Using Customer Data

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Since my recent series on building customer habits through customer relationship management, I have received a few inquiries about how we can figure out customers’ habit strength based on their transactional data. I will offer a brief explanation in this article. The approach I am discussing here assumes that you have individual customers’ purchase or product usage data available. If that’s not the case, you can refer to the last section for alternative measures of habit strength using customer surveys.

Two Key Components of Habit

To properly measure habit, we need to first understand what it is. In psychology and marketing, a habit is most commonly thought of as an action that is frequently repeated under the same situations. If we break this down, there are basically two ingredients to habit:

  • Frequency: how frequently does someone take that action (e.g., watches TV, uses the mobile app, etc.)?
  • Stability: when the person engages in the action, is it often under the same circumstances (e.g., on the way home, on Sunday morning, etc.)?

So to measure how strong a customer’s habit is, you need to find out about these two ingredients. The higher the frequency and the higher the stability, the stronger the habit is.

habit formula

How to Measure Frequency

Measuring frequency is pretty straightforward. If you are dealing with purchase data, frequency means how many times on average someone buys your product in a given time window (e.g., per week, per month, per year, etc.). If you have usage data such as customer use of your mobile app, frequency means how many times someone uses the product in a given time length. Let’s say if someone buys your product 36 times a year, the average frequency per month would be 36/12 = 3 times. Continue reading “How to Measure Habit Strength Using Customer Data”