Chapter 11 Forecasting, Projections, and Customer Value
Future-oriented estimates are useful only when their assumptions and uncertainty are visible. This chapter distinguishes forecasts, projections, scenarios, and targets; compares major forecasting approaches; develops utilization-based projections; and builds customer lifetime value from revenue, gross margin, retention, churn, acquisition cost, and discounting.
Learning outcomes
After completing this chapter, you should be able to:
- distinguish forecasts, projections, scenarios, and targets;
- compare moving averages, regression trends, exponential smoothing, ETS, and ARIMA;
- validate forecasting methods using time-ordered data;
- construct population-utilization and scenario projections;
- calculate and interpret revenue-based and margin-based CLV; and
- incorporate retention, churn, acquisition cost, discounting, and uncertainty into customer-value analysis.
Key terms
Forecast: An estimate of a future value based primarily on patterns learned from observed data.
Projection: A conditional estimate of a future quantity under stated assumptions.
Scenario: A coherent combination of assumptions used to examine one possible outcome.
Forecast horizon: The number of future periods covered by a forecast.
Seasonality: A recurring pattern associated with a regular calendar or operational cycle.
Utilization rate: The number of events or services used relative to a defined population and period.
Retention rate: The proportion of eligible customers who remain active from one defined period to the next.
Churn rate: The proportion of eligible customers who end or fail to continue the relationship during a defined period.
Customer acquisition cost (CAC): The attributable cost of acquiring a new customer under a stated allocation rule.
Customer lifetime value (CLV): The estimated economic value produced by a customer over the relationship, under a stated revenue, margin, retention, cost, and discounting model.
Discount rate: The rate used to convert future amounts into present value.
Sensitivity analysis: An examination of how results change when important assumptions or inputs change.