Uses of Lifetime Value - Part 4: Optimization and What-If Modeling

Yesterday’s post on forecasting the values of LTV components may have been a little frightening. Most managers would have a hard time translating their conventional business plans into LTV terms. The connections between the two are simply not intuitive. And how would you know if you got the right answer?

Part of the solution is technical. Given a sufficiently detailed LTV model, it is possible to plug in the expected changes in customer behavior and have a system calculate the corresponding values for the LTV components. Such a model needs three things:

- relationships among different kinds of transactions (for example: there are 0.15 customer service calls for each item sold)

- inventory of existing customers (purchase history, segment membership, etc.—whatever predicts future behavior)

- assumptions about future inputs (promotions sent, new customers added, etc.)

These can be combined to project future results, which can in turn be summarized into lifetime value components. A projection using current values gives baseline LTV components. A projection using the behavior changes expected from a particular project gives the revised LTV components.

Translation from a conventional project plan cannot be completely mechanical because some inputs are needed that a conventional plan will not include. To stick with yesterday’s example of a retention program, a typical project plan might project that 5% more customers will be retained during the year the plan is in effect. But it probably won’t estimate those customers’ behavior in the following year: will they continue to be retained at a higher rate, revert to the previous rates, or leave faster until aggregate rate returns to normal? A LTV forecast requires managers to make that prediction or, perhaps, to rely on a company-wide policy so all such predictions are consistent. Either way, it’s more work for somebody and introduces new subjectivity into the process.

This need not be an overwhelming problem. Because the LTV model will break out its projections by time period, it is possible to focus analysis on current year results (forecast vs. actual). A separate analysis can look at a longer time horizon.

The other main issue cannot be solved technically. It is the challenge of estimating the true incremental impact of a project, on its own and in combination with other projects. The LTV approach highlights the importance of this by looking at changes in all behavior components across all projects. Yet, in fact, any project justification should be based on incremental changes and should consider the effects of other projects. So these objections are less a problem with LTV than a complaint about the cruel nature of world itself. Get over it.

Let’s get back to that magical lifetime value model I mentioned earlier. It’s really a conventional business simulation model: you define the relationships among business inputs (new customers, product prices, retention rates, costs, etc.) and it comes up with projected results. These results are broken down by period so each period’s output can become the next period’s input. Typical outputs include income statement, cash flow, and balance sheet. Once you have all that, it’s not much more work to create the estimated values for the LTV components. The LTV itself is nothing other than a Net Present Value figure from a discounted cash flow analysis.

I’m not saying this model is easy to create. Getting it right means understanding the subtle relationships between components. For example, although we know intuitively that better customer service should result in improved retention, what is the exact relationship between those elements and how do you build it into the model? Most people would argue for an intervening variable such as a customer satisfaction score. But that just adds another level of complexity: what creates that score, and how does the score itself relate to retention? Ultimately you need to look at attributes of the customer service transactions themselves, such as response time and resolution rate. These may not be captured in a conventional business simulation since they are not standard financial measures. And they are only one set of contributors to ultimate customer satisfaction, which is why an intervening variable for customer satisfaction score may actually make sense.

So clearly some additional effort is required beyond what’s needed for the models typically used by corporate finance. Considerable research may be needed to accurately understand the relationships that drive model results. The model may also include non-financial measures like customer satisfaction. But both of these are valuable requirements: companies really should understand what drives their results, and linking non-financial measures to financial results allow them to be incorporated into financial models. In other words, the added research needed for the LTV model is worth the effort.

The LTV model can be applied to traditional business forecasting: given this set of assumptions, what results will we get? It can also be used for what-if scenarios: calculate the results of different sets of assumptions either to find optimal resource allocations or for risk analysis of different contingencies. The forecasts can be applied to strategic decisions such as a major investment or to tactical choices such as alternative marketing campaigns and business rules. Of course, more tactical decisions require increasing levels of detail in the model itself.

Forecasts can also be help to understand the implications of a scenario: since more sales will mean more calls to customer service, do we have the call center capacity to handle them? This information can be used for resource planning and to highlight potential bottlenecks. A sophisticated system would incorporate capacity figures for such resources and issue warnings when they are be exceeded. A more sophisticated system would project the results of exceeding capacity (diversion of customers from the call center to the Web in the short term; lower satisfaction and higher attrition in the long run). An even more sophisticated system would look at all these factors and identify optimal investment decisions.

Sophisticated modeling systems of this type do exist, although they’re rare. But much simpler models can also create forecasts of LTV components. This is enough to generate forecast values to compare with actual results. Even if the predictions are less than precise, they’ll help managers understand what the LTV components mean, how they fit together, and how their actions affect them. This in turn will build a deeper understanding of the business, a shared frame of reference, and continued focus on building customer value: the key benefits of an LTV-oriented organization.