Uses of Lifetime Value - Part 5: Trend Analysis

This series of posts has followed what should seem like a logical progression: using the Lifetime Value figure; using components within that figure; comparing component values over time, across segments, and against forecasts; creating the forecast values with models; and using the models for simulation, planning, and optimization. Since optimization is the ultimate goal of management, that should be the end of the discussion.

But it isn’t.

You probably noticed that the focus of the discussion shifted midstream from LTV to modeling. That’s not wrong: you do need models to predict the customer behaviors that determine LTV and to do the simulations for planning and optimization. But the discussion of forecasts can also lead in another direction, centered not on models but on building forecasts from the LTV components themselves. So let’s backtrack a bit.

The first point, which may seem a bit pedantic, is that I’ve been somewhat sloppy when describing the comparison of current values against forecasts. As the context makes clear, the forecasts I had in mind were estimates of future component values prepared during a planning process. Most businesspeople would probably refer to those as “plan” values, leaving the term “forecast” to refer to revised estimates based on the latest available data.

I don’t think my ambiguity caused any serious harm. But it does highlight the potential for confusion when the term “forecast” is used in conjunction with lifetime value. This confusion exists on several levels.

The first is simply that LTV sometimes refers to the value since the start of a customer relationship, sometimes to future value, and sometimes to a combination of previous and future value. These distinctions were described earlier and are easy to understand. They only create confusion when people don’t know which definition is under discussion. For most applications, future LTV is the appropriate one, but I (and others) often don’t bother to state that explicitly.

Of course, the future LTV is a forecasted value, even though it’s based on current data. This is the second type of confusion: not recognizing that even “current” LTV figures incorporate projections of future activity. Again, no particular harm done, except to perhaps give an impression of more certainty than actually exists.

The third level of confusion relates to which set of customers is being measured. The average LTV (past, future or total) of your existing customers is almost certainly not the same as the LTV you’d calculate using current component values. Put another way, historical results yield different component values than recent results. Easy to understand, once you think about it. But which numbers do you define as “current” when comparing them against the plan or forecast?

As all these examples make clear, there is no one right answer to the question of what’s the “real” LTV. Different calculation methods will make sense in different circumstances. This isn’t a problem, but it does mean you have be conscious of which method you’ve chosen, ensure comparative calculations are consistent, and document your method in case anybody wants to know the details. It’s true that few casual users will ever actually ask. But that just means you have to try still harder to use a method that is consistent with their intuitive expectations.

To help understand the calculation options more clearly, let’s step back and take a look at where LTV figures come from. The basic definition of LTV is the net cash flows associated with a customer. This is usually limited to a specific time period and discounted for net present value. Thus, you can think of LTV as a series of buckets, one for each year, where the level of water in the bucket represents the cash brought in during that year. The buckets themselves may be subdivided—perhaps, like the Internet, they are a series of tubes—into marketing cost, revenue, cost of goods, service expenses, and so on. (It’s not exactly clear how you deal with expenses in this metaphor. Perhaps they are holes beneath the buckets, or perhaps some kind of water-absorbing material. Let’s not get too literal.)

The critical thing to realize is that each group of customers who start during a given period has its own series of buckets. Buckets that relate to past years are “filled” with actual values; buckets for future years are “filled” with estimates. Every year, one bucket switches from estimated to actual. To calculate a group’s LTV, you combine the values of all buckets for that group.

Even though each group gains just one “actual” bucket per year, there are many “actual” buckets that get filled (belonging to different groups). For example, customers in the group that started one year ago get their “year 1” actual bucket filled; customers who started two years ago get their “year 2” bucket filled, and so on. One definition of “current” LTV is the combination of the values in all the “actual” buckets that have just been filled. This makes sense because it uses the most recent data, but it does involve mixing results from different starting groups.

Such mixing can be problematic, particularly if the nature of your customers has changed over time. One partial solution is to identify homogeneous segments within the customer base and track results for each segment within each start group. You can them combine the most recent results for members of the same segment with different start years to calculate an estimated LTV at the segment level. Segment values can then be combined in a weighted average to get an over-all LTV. Of course, now you have to decide what quantities to use for your weights.

Hopefully this clarifies why there is no one “right” method to calculate LTV. But there’s a further lesson: LTV figures are always changing. New buckets are always being added and some buckets are always changing from estimated to actual. So LTV is dynamic indeed.

In a way, this is no different from other business measures. “Profit” is also always changing as new transactions are recorded. LTV is a little worse because profit stays fixed once the books for a period are closed, whereas LTV for a previous period includes forecasted values (depending of course on the calculation method) that could require later adjustment as subsequent actuals are received. As with profit, you may choose to restate the previous period figures if there is a major discrepancy, or you may choose to book an adjustment in a subsequent period. Either way, it’s important to realize just how fluid an LTV figure can be.

That was a long digression but hopefully it clarified the roles of forecasts in LTV calculations. Now we can turn to using LTV components as forecasting tools.

This is not the same as estimating future business results using the current LTV component values. That would be done with the simulation models described earlier. Rather, this is about estimating the future of the component values themselves based on trends in their changes to date. For example, you might find that this year’s acquisition cost is $50 per customer and it has been increasing $5 per year over the past three years. After controlling for source mix, volume, and whatever other factors you can identify, you might expect that trend to continue. You would therefore estimate next year’s LTV using an acquisition cost of $55 per customer. Trends in other components would yield similar estimates for next year’s values. From these, you would derive other figures (income statement, cash flow, etc.) and estimate the LTV itself.

The advantage of this approach is that you don’t need an elaborate simulation model or trend identification system. Simply identifying the changes in LTV components lets you calculate the impact of those changes on aggregate lifetime value (LTV per customer x number of customers). You can then rank those impact values to determine which changes are most important to examine more deeply. Even the most sophisticated simulation model doesn’t do this, since it can only calculate the outcomes based on the assumptions you feed into it.

The first step in examining LTV trends is controlling for known factors. A change in source mix could easily change the aggregate component values even if behavior within each source remained stable. (A stable aggregate value could also mask significant changes within particular segments—another reason to do segment-level analysis.) Some changes might also reflect discernable causes, such as a price increase, whose future impact can be estimated directly. But even after the known factors are considered, there may be other changes which cannot be explained. If these represent significant trends, their continuation should be built into estimates of future behavior.

To get back to the progression of applications I mentioned earlier: we can now revise it to be LTV; LTV components; comparisons of components across segments; and forecasts based on trends in components. Simulation modeling for planning, optimization and to estimate LTV component values represents a stream of related activity, but perhaps is not an LTV application in itself.

Then again, tomorrow is another day.