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Purpose The purpose of this paper is to propose a method to help firms assess lifetime profitability of customers whose buying behaviors are characterized by purchasing cycles, which are determined by both intrinsic purchasing cycles and cumulative effects of firms’ marketing solicitations.Design/methodology/approach This paper first proposes a probability model to predict customers’ responses to firms’ marketing solicitations in which a customer’s inter-purchase times are assumed to follow a Poisson distribution, whose parameters vary across customers and follow a gamma distribution. The paper then proposes a customer profitability scoring model that uses customers’ responses as an input to assess their lifetime profitability at a given point of time.Findings The paper illustrates the proposed method using individual-level purchasing data of 529 customers from a catalog firm. The paper shows that the proposed model outperforms the benchmark model in terms of both explaining and predicting customers’ purchases. The paper also demonstrates significant profit consequences to the firm if incorrect methods are used instead of the proposed method.Practical implications The proposed method can help firms select or eliminate customers based on their lifetime profitability so that firms can focus their marketing efforts in a more targeted manner to increase total profits.Originality/value The proposed Gamma-Poisson probability model and the profitability scoring method are easy to implement due to the attractive conjugacy property. It is valuable for firms’ customer relationship management applications from the standpoint of making customer selection and inventory management decisions. [ABSTRACT FROM AUTHOR]

We present an econometric technique for modeling endogenous selectivity in households' quantity outcomes as observed in scanner panel data. Simultaneous models of incidence, brand choice and quantity, that treat quantity outcomes as count data, ignore such self-selectivity considerations in quantity outcomes. Previously proposed approaches to modeling selectivity in continuous quantity outcomes do not apply to count data. Therefore, we adopt a recently proposed econometric technique to deal with selectivity in count data, and then appropriately extend it to handle correlations of quantity outcomes not only with incidence outcomes but also with brand choice outcomes. Our proposed methodology will be useful to researchers who want to estimate simultaneous models of whether, what and how much to buy decisions of households, treating quantity data as counts. [ABSTRACT FROM AUTHOR]