Customer Lifetime Value Prediction

Customer lifetime value, or CLV, is an estimate of the value a customer may generate over a stated future period. A practical calculation may use expected transactions, average order value, gross margin, retention, discounting, and service or acquisition costs. The precise definition matters: revenue CLV is not the same as profit CLV, and estimates over different horizons cannot be compared without context. Probabilistic customer-base models such as BG/NBD estimate repeat-purchase behavior and the chance that a customer has become inactive, given transaction histories and model assumptions. They do not directly predict profit unless spending and costs are added. Results are sensitive to transaction definitions, observation windows, seasonality, changing prices, and whether a customer makes repeat purchases in the modeled way. For a new customer, uncertainty is high because there is little observed behavior. Marketers can use CLV to compare budget scenarios or set acquisition limits, but should not treat an individual score as a permanent trait. If targeting affects prices, services, or access, fairness and privacy deserve review. Evaluation should compare predictions with later value over the same horizon and use appropriate baselines. Cohort and subgroup checks can reveal systematic over- or under-estimation. A CLV forecast supports planning; it does not prove which intervention caused value or what a customer will do. A randomized campaign test is needed to measure incremental effects of a marketing action.