Choice Modeling & Rewards Redemption
Written by Amit
Steve Shu had quoted his wife (Suzanne Shu, a Professor of Marketing at Southern Methodist University) on the issue of indefinitely delaying reward redemptions.
Shu’s comment is interesting from a behavioral model for rewards redemption point of view. A credit card holder will be interested in those redemptions that suit his choice and utility function. True- a rewards program in itself manages to alter the choice function available for the credit card holder.
The linked article and another US News article highlight some interesting attributes of rewards redemption by consumers
- Incentives –
- Leading to purchase acceleration
- Guilt/Excitement of opting for something that they would not otherwise
- Indefinitely delaying an aspirational redemption for a special occasion
- Cost of Card
- Benefits from Reward
- Cost of payments/credit card usage
The closest we can come to creating a consumer choice/utility function in the database driven world is by collecting and integrating 5 different sources of information –
- Demographic Data – Age, Gender, Ethnic Background, Education level, etc. Available through census, application forms filled by customers, etc.
- Economic Background – Income levels, saving and investment data. Available through application forms, relationship level view of a customer, etc.
- Behavioral Data – attitude, interests, etc. Can be collected through surveys and lifestyle data bureaus.
- Bureau Data – e.g. risk bureaus such as Experian, FICO score, or lifestyle data from Claritas.
- Transaction Data – prior credit card usage data, payment data, etc.
Demographic and behavioral data together would be ideal to define a person’s wants, while to build a choice model, it would be a good idea to include Economic and Bureau data as well. The transaction data can be used for validation and refinement of the choice model.
Back to the articles, a choice model can help us explain and manage all three aspects–
- Incentives – through a greater understanding of choice limitations and utility functions, we can customize offerings that maximize the utility for a customer at the lowest possible cost
- Disincentives – any alternative outside the “want†area and significantly beyond the choice area suffers from possibility of being delayed, as pointed out by Suzanne Shu.
- Cost-Benefit Analysis – an economic model of choice can bake in the tangible costs of a reward program to optimize profitability.
Some of our other posts on credit card analytics are here
Hi Amit - Thanks for picking up on the blog post and the point about consumers not redeeming “special occasion” rewards. The quote was based on a research project I’ve been working on that specifically looks at this behavioral tendency to match rewards with special occasions, which often leads to long delays (since no occasion is special enough). A substantial part of the project has been to figure out what types of rewards/gifts/etc fall into this behavior. The nutshell answer is that rewards that are outside the consumer’s normal spending and/or have some sentimental value are the most likely to be delayed. So, for example, if I normally buy $15 bottles of wine and someone gives me a $75 bottle of wine, I’m much more likely to save it for a future special occasion. You could apply the same rule to airline tickets (a more expensive vacation flight than I normally buy), restaurant gift certificates, etc.
The good news about this finding is that if you have access to people’s normal credit card activity you may be able to determine what their normal spending levels are on such goods and design rewards that are appropriately above that normal spending. If you wanted to make sure they use the reward, which is better for customer satisfaction, then you want it slightly but not too far above their normal levels (e.g., a $40 bottle of wine).
The other thing I should mention that might be useful to you is that people respond well to deadlines for consuming these rewards, since they can no longer indefinitely delay. Shorter deadlines are better. A related research project I have looks at the effects of short vs long time windows on positive-utility activities. For example, we survey tourists and residents of major cities and find that the tourists see more sights in a few weeks than most residents see in a few years. It’s not that residents don’t want to go see the Sears tower or Millenium Park, it’s just that it’s easier for them to postpone it into the future when they think they’ll have more free time. Credit card rewards can work the same way.
Sorry for the long comment - this is a favorite topic of mine!
Suzanne Shu
17 Jul 07 at 1:44 pm
Suzanne – Thanks for the great comment and insights. And don’t worry about the long comments! The more, the merrier!
From a behavioral point of view, it makes sense that people tend to make the special a little more special (the whole idea of delaying “that†reward for a special occasion). And if the reward becomes way too special, it supresses the effect of the occasion itself.
However, I would not completely agree with the credit card spending data part. An average American has more than 3-4 cards in his pocket. While using the approach suggested by you may help the “most important card in the pocket†to come up with the right reward strategy (maybe), it would also limit the creativity of the under utilized credit cards’ rewards program. If reward programs are to be used as an incentive to increase spending, then its important that we do not look at the past spend only, but also at the potential spend across all cards! For instance– will a $75 bottle of wine be special for someone who is not only buying a $15 bottle on his Discover card, but also a $200 wine on his American Express card? Which is why, instead of using the transaction data per se, I would advocate the use of behavioral studies, market research and lifestyle bureau data.
And your comment on the deadline is a fine observation. Reminds me of a project we did for one of the largest Museums, where one of the fundamental dimensions of customer segmentation was their location, given that tourists to the city were frequent visitors across all weekdays, while city residents had the option of delaying the visit, and were more heavily concentrated towards weekends!
It seems like a lot of interesting work you are leading in this field. I would love to interact on this and hear more about the results. It’s a topic which is very close to my heart too!
Amit
18 Jul 07 at 2:03 am
In the years gone by, a Retail Store used to acquire customers for their Private Label Credit Cards primarily through walk-in customers or through mass mailings. Over time, these traditional methods have started giving very low ROI and hence businesses are now demanding much more sophisticated methods to target the right prospect through the most cost effective channel and increase the response of these prospects to convert them to customers.
The introduction of various customer touch points has opened up various opportunities to target in a more cost effective manner. Yet customer acquisitions are much harder than it might appear.
Zaheen
13 Apr 09 at 5:33 am