Customer equity (CE henceforth) is a powerful new paradigm to evaluate the firm's value and to optimally allocate marketing resources. This paper is focused on the relationship between customer acquisition and CE. We attempt to answer the following four questions: 1) How should customer acquisition channels be categorized to make them meaningful to managers and academics?; 2) How do we measure the effects of different acquisition channels on the firm's performance?; 3) How do we disentangle

more »

... t-run effect and long-run effects?; and 4) How should the manager allocate a limited budget among the acquisition channels so as to maximize customer equity? We first propose a way of categorizing customer acquisition channels according to their level of contact and intrusiveness. A vector-autoregressive (VAR) model is used to examine the dynamics of acquisition channels and the firm's performance, and an empirical illustration on a surviving Internet company will be provided. The results show that each cohort (i.e., customers from different acquisition channels) has different short-run and long-run effects on the firm's performance by the subsequent login and purchasing behavior. Building on previous research on optimal resource allocation, we develop a Marketing Decision Support System (MDSS) to help managers allocate the acquisition budget among different channels with the objective of maximizing customer equity. We illustrate the consequences of naively maximizing the short-term profit and not accounting for differences in the margin contribution of different cohorts. THE IMPACT OF ACQUISITION CHANNELS ON CUSTOMER EQUITY Hence, there is an urgent need to develop models capable of measuring the long-run effects of different acquisition strategies, and provide systems to help managers optimally allocate their acquisition spending among different channels. These models should be able to disentangle the long-run from the short-run effects, incorporate the risk associated with future payoffs, and take into account the costs associated with different acquisition channels. This is the main objective of the current paper. Moreover, we depart from "soft" metrics of communication effectiveness (e.g., brand awareness) to "hard" metrics of profitability (Greyser and Root 1999) , in that we measure the effectiveness of each acquisition channel with respect to its contribution to the CE of the firm 4 . Once these long-run effects have been measured, we can optimally allocate the acquisition budget among the various channels. In doing so, we do not measure the expected CLV of a customer, but rather her CE contribution. In this way a customer is worth not only her own expected CLV but also all the indirect impacts that she has on the firm's performance over time (e.g., by bringing new customers to the firm through word-of-mouth). 2 3 For example, streams of research include the use of multivariate time-series techniques (e.g., Dekimpe and