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Life Time Value for “Do Leaders”

Calculate your ROI with this Excel-based Return on Customer Retention Estimator
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We prefer to use the concept of a 3 x 3 ‘Value Map’ to guide our marketing strategy and execution, as we discussed in this previous post on customer value . The concept of ‘captured customer value’ is pretty straightforward for most clients, but the ‘uncaptured value’ can get people to scratching their heads.

So before we move onto the promised discussion of  ‘marketing allowable’ for each Value Map cell, I’d like to briefly discuss how to calculate ‘Uncaptured customer value’.

In financial services markets, the example given in the initial post is a good approach. With (typically) contract based products and services, comparing the product holdings (and therefore typical income) of a customer with ‘best customers’ like her, allows you to determine what products she could reasonably buy from you. She either does not have these accounts (for example) or has them with a competitor, but these are uncaptured potential value for you for this customer.

In markets where no contractual relationship exists between you and your customer, it is a little harder to calculate ‘uncaptured value’, ironically because it is harder to determine when a customer has left you – stopped buying your type of product or has switched to buying from your competitor.

In financial services, a customer typically leaves you, ‘attrites’ or ‘churns’, by cancelling the account or service they have with you (leaving the more common ‘silent attrition’ where customers simply stop using your services without explicitly cancelling, to one side for a moment). In retail they buy less over time then disappear.

Sometimes they even come back!

This plays havoc with calculations of uncaptured value unless you set some consistent hurdles / triggers and focus on relative values, trends, consistency and not fall for the illusion of precision. In a world of limited marketing resources, the objective is to spend them on the right customers in the right way to optimise returns. For this purpose, relative measures between groups of like customers are enough to start.

In the spirit of ‘Do Leadership’ we set some informed but arbitrary hurdles that we tune and refine as we learn more about our customers. One of the most important of these hurdles is the behavioural measure that indicates when a customer has left, churned, attrited.

Why is it important to have a measure of churn? Because very few organisations have a long term, data driven view of their customer’s life cycle. That’s why debates about Lifetime value (LTV) usually start with the question ‘but how long is our customer lifetime?’.

If you know the churn rate in a particular group of customers, you can take a snapshot in time (just as a balance sheet does) and say ‘at this moment, these customers have a lifetime of x (weeks/months/years) based on a current churn rate of y.’ If I also know the captured value for this group of customers for this period I can calculate the net present value of their future cash flows.

Then, just as you compare balances sheets over time to see increases in company value, you can do the same with this predicted cash flow to determine if you are making things better of worse, customer value wise. But in the meantime I can rank customer groups by their uncaptured, future value. And populate the Value Map to drive marketing execution.

If I can measure churn.

The formula for calculating the NPV of future cash flow for a segment of customers is not scary;

NPV of customer future cash flows = M(R/(1+i-R))

  • M is the captured value (e.g. margin) from this customer group this period
  • R is the retention rate – the inverse of churn
  • i is the discount rate used by your CFO to price money internally

So intuitively, you can increase the uncaptured value of a group of customers; by selling each one more, getting more of them and by keeping them buying longer.

The challenge to determining that a retail customer has gone is caused by the fact that customers shop at different intervals, they have different ‘rhythms’ to how long they rest between buying. So a large monthly shopper has not left you when she is absent for 2 weeks, but a daily shopper most likely has. Different latencies should be taken into account when calculating whether a customer has gone.

There are 2 ways to approach these individual latency differences when setting hurdles for churn Y/N decisions;

  • Score individual customers on the likelihood they have churned. A simple ‘event-history’ calculation may be a good start. In its simplest form, the scoring formula is t to the power of n, where ‘n’ is the number of purchases made in the period (say 12 months) and ‘t’ is the fraction of the period represented by the time between her first purchase and her last one.

An example (from Reinhartz & Kumar 2002) ; Smith has made 4 purchases, the last in month 8, so n is 4 and t is 8/12 or 0.6667. Smith’s probability of still being active with you is (0.6667) to the 4th or 0.198. There is a 20% probability that Smith will keep on purchasing.

Jones also made her last purchase in month 8 so her t is 0.6667 as well, but as she bought only twice her probability is (0.6667) squared or 0.444, nearly 45%. Jones is more than twice as likely as Smith to remain an active customer.

You can then set an arbitrary hurdle and stick with it; let’s say that all customers with less than 10% probability of continuing with us are deemed to have churned at the point we calculate uncaptured value.

  • The second approach is simpler and takes advantage of the fact that in retail, the Recency, Frequency and Monetary attributes of RFM are not independent. Take the High, Medium, Low groups of customers on the ‘Captured Value’ axis of the Value Map and calculate the average time between purchases for customers in each group. List the average and the standard deviation. The high value group will typically have notably shorter times between purchases (latency) than the other groups.

Then set an arbitrary threshold that says, customers in this group who have not shopped for {average days between purchases plus 1 or 2 standard deviations, (use your judgement)} have churned.

This approach may lead to some apparent anomalies, for example I have a client with an empty High-High cell in their Value Map as high captured value customers churn quickly – but that is important to know!

These examples fit high transaction, non-contractual environments best, but they are effective. What do you think?

I've created an Excel based Retunr on Customer Retetnion Estimator so you can  perform ROI calculations on investments in Customer Retention. Download it Here

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