Reflection on Design Thinking

David Perez

June 7, 2020

The challenge:  In order to persuade prospects for a warehouse membership purchase, Sam’s Club uses  various incentives.  The company has found that many consumers will move from the consideration  state to the purchase state faster if incentives are offered.  While the purchase price would remain the  same at $45, additional incentives after purchase help hundreds of thousands of households annually  commit to an annual membership.  However, no incentives have universal appeal.  Equally important is  the fact that warehouse clubs sell products at a price that covers the overhead of club operations;  organizational profits are derived from membership fees.  Costly incentives essentially defer any  membership income to year two members up on renewal.  The challenge is to develop incentive  programs with a lower cost and in a new manner that do not require half a year to get measurements  on effectiveness.

The tool I have implemented is Learning Launch.  It’s important to note that this is not a hypothetical or  theoretical test.  An actual learning launch was launched a few weeks ago.  This tool was chosen  because it is an approach our team has not tried before.  In the past, we have executed large-scale  national campaigns making assumptions provided from our team.  The appeal is in the concept of “not  knowing what we don’t know.”

Instead of a large, national, multi-million dollar ad campaign, we have identified a test market and a  random audience and just launched a new program for the sake of learning.  We are testing five vastly  different offers with two different messaging themes.  For application, since we don’t know what would  be most appealing, we are pushing a variety of incentives and campaigns and starting off by giving each  of them equal funding in the test market.

The insights have thus far been fascinating, but we are still so early on in the findings that we don’t  know what we don’t know yet.  By launching multiple tiny programs with a very different incentive, we  are measuring real-time responses and social media shares.  Within the week, we will have enough data  to determine which ad messaging has been most effective in the test market, and will invest only in the  most productive 50% of the offers and messages.  We will repeat this process until we determine which  strategy or strategies are most effective.

We do not know what we will do different next time as we don’t have the data back to measure what is  a success this time.  The next iteration will just reflect what we learned as we are considering an  unsuccessful ad element as not a failure, but a learning toward optimizing what is more of a success.

