However, we show that the firm cannot credibly commit to such a strategy once given access to multiple dimensions of customer data. When this result holds, the firm may want to adopt a single-dimensional targeting strategy. Therefore, they may be less willing to exert a costly effort of clicking the ad and making a purchase decision. This is because, with multidimensional targeting, targeted consumers may face greater uncertainty about on which specific dimension(s) they can expect to enjoy the advertised product. We show that the firm can be worse off under multidimensional targeting than under single-dimensional targeting, in which the firm targets consumers based only on a single component of their utility. This paper provides a theoretical analysis of multidimensional targeting under which consumers can draw inferences about multiple components of their utility from the advertised product. Exposed to more targeted ads, consumers are becoming increasingly aware of being targeted and respond accordingly. 2 highlights the various methods in terms of their relative citation percentages over time.Īdvancements in targeting technology have allowed firms to engage in more precise targeting based on several aspects of consumers’ preferences. 2012) and hidden Markov models (e.g., Netzer et al. 20), and models that allow for evolving customer response coefficients such as state space models (e.g., Van Heerde et al. 2016), hazard models for predicting customer behaviors with limited information especially in non-contractual CRM settings (e.g., Reinartz and Kumar 2003 Fader et al. 2004 Park and Park 2016), VAR models for addressing marketing's short-and long-term effects (e.g., Dekimpe and Hanssens 1995a Pauwels and Neslin 2015 Hewett et al.
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