The analysis of customer value in direct marketing typically combines customer timing and quantity data into a single statistic that is used to compute lifetime values, rank-order customers for differential action, and identify prospects for cross-selling. However, current models assume that purchase timing and quantity decisions are independently realized (i.e., uncorrelated) over time given individual-level parameters. In this article, the authors show that customer value calculations can be severely biased in these models when timing and quantity are dependently related. The authors propose alternative models that lead to substantial gains in profitability in two direct-marketing data sets. The results indicate that the commonly held assumption of independence leads to an over-valuation of customer value.
