CU Employee Community Chair

Member lifetime value modeling

In his influential book, Customer Centricity, Peter Fader urges marketing analysts to identify and understand the traits of a company’s most valuable customers – and argues the merits of using that knowledge to guide the company's most rational use of resources and its attraction of high-value future customers.

His customer valuation model doesn’t limit itself to customers’ profitability at a particular point in time – but rather seeks to estimate (probabilistically) a customer lifetime value, acknowledging that some customers are likelier to remain loyal purchasers over longer periods than others and their purchase behaviors predictably yield different revenue streams over time.

Even though credit unions are cooperatives, these concepts seem relevant. Most of us who lead credit unions seek rationality in our product pricing as a matter of fairness. We look to encourage a level of member engagement with our products that presumably drives increased value to the individual and the collective. And, with occasional exceptions, we look to strengthen the franchise as we increase the size of our memberships, attempting to assure that growth delivers more shared benefits than burdens. A customer-centric approach seems likely to deliver insights to advance all of these.

Nonetheless, my own progress has been complicated by five practical challenges that arise as I start to compute member lifetime value in a credit union context:

  1. With financial institutions, revenues and costs accrue very differently than they do for the retail store scenario that Fader engages. I'm concerned that RFM analysis doesn’t quite fit.
  2. With many credit union products, the greatest source of profitability arises from members who make irrational choices that would be wrong for credit unions to optimize around.
  3. With other credit union products, the greatest source of unprofitability arises from our own irrational product pricing.
  4. Credit unions’ broadest source of member value/profitability is very often centralized in just one or two product categories (or so I'm finding).
  5. The data with which we might derive insights about our members’ future value is very uneven in its availability and that may bias our optimizations

Beyond those, there may also be opportunities to marginally improve on Fader's model, including recognition that many members' product usage morphs more or less reliably through different life stages.

I’ll try to expand on each of these issues in future posts to this thread, and perhaps together (in dialog) we can noodle our way toward a model that addresses them, even if it requires some greater sophistication than the simple foundational one that Fader proposes in his book. Many of you heard Nate Derby engage customer centricity as a topic at the 2018 CU Analytics Summit in Redmond. With luck, we'll coax him into contributing to this discussion as it marches along. I hope that many others will freely voice their thoughts, as well.

Dale Davaz
STCU R&D Strategist
CULytics Community Chair

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  • CU Employee


    Great topic.  I am interested to see where the conversation goes.  Below are a couple of my initial thoughts.

    • Here is a recent artile that argues it is time to move away from RFM models.  If nothing else, it has some interesting points.  For your purposes, your RFM model might still provide better insights as is, or with just a little tweaking, compared to excluding the model all toghether.
    • Have you defined "profitable"? Maybe members that meet a monthly card transaction minimum, have loans that produce interest revenue of a certain amount, generate regular fee income with limited risk / cost, have stable low cost deposits allowing the CU to make additional loans, a combination of these, or something else?
    • I like the statement that "...the greatest source of profitability arises from members who make irrational choices...".  I assume these are irrational choices from a data point of view.  Can the rationality be explained using additional datapoints; life stage, recent loan closure, recent loan application, cyclical buying behavior, etc.?



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