Adelino started the discussion with a post on customer lifetime value modeling. Jim Novo continued it here. I’ll add a few thoughts. In measuring or modeling customer lifetime value, marketers need to:
1) Incorporate measures of risk. As Adelino states, CLV boils down to a single number. But there are a number of variables and assumptions that feed that number — variables whose values: a) are estimated at the time of calculation, and b) change over time.
Channel behavior is a good example of this. In banking, a customer who requires a lot of branch service is more expensive to serve (and thus less profitable, all other things equal) than a customer who relies heavily on the online channel for service. But this behavior can change over time — and in both directions. Younger consumers, who may rely heavily on the online channel today, may have more sophisticated needs in the future, and change the channel mix of their interactions over time. And older consumers can be trained and incented to use the online channel, even if they don’t today.
The key point is that CLV — which Jim rightly notes is a calculation at a certain point in time — incorporates assumptions about future behavior. The “risk” that this behavior could change should be built into the CLV calculation.
2) Use activity-based costing. I used channel behavior as an example of a factor impacting customer profitability. But understanding actual channel behavior is a challenge for most firm, as is understanding the true cost of serving customers.
Without ABC, costs are often allocated based on product ownership because service behavior is accepted as an unknown. In some firms — even where service activity is incorporated into CLV estimates — differences in the costs of providing service across channels and even the differences in costs of different types of services is washed over.
Without ABC, marketers cannot get an actionable estimate of CLV.
3) Use CLV to drive customer relationship strategies. Somewhere along the line, it became fashionable to say that firms should “fire” unprofitable customers. Although Adelino mentions “dropping” unprofitable customers, his prescriptions lean more towards “managing” their behavior — through support charges or restocking fees, for example.
Firing unprofitable customers is a flawed concept. As I alluded to in point #2, few marketers can be 100% sure that their CLV calculation is accurate in the first place. But a customer — unprofitable or not — contributes to meeting fixed costs. If you drop unprofitable customers, you (negatively) affect the profitability of other customers — potentially pushing them in front of the “firing squad.” And the cycle continues. A ridiculous notion.
In the end, marketers cannot simply use the CLV calculation as the only dimension upon which they segment customers. Even good-old RFM metrics can help better segment customers in order to drive marketing strategies. In the financial services world (where purchase frequency is low), I advocate using customer engagement measures to help provide a qualitative perspective on customer behavior and strategic directions.