Response Modeling with Nonrandom Marketing-Mix Variables

Abstract

Sales response models are widely used as the basis for optimizing the marketing mix. Response models condition on the observed marketing-mix variables and focus on the specification of the distribution of observed sales given marketing-mix activities. The models usually fail to recognize that the levels of the marketing-mix variables are often chosen with at least partial knowledge of the response parameters in the conditional model. This means that contrary to standard assumptions, the marginal distribution of the marketing-mix variables is not independent of response parameters. The authors expand on the standard conditional model to include a model for the determination of the marketing-mix variables. They apply this modeling approach to the problem of gauging the effectiveness of sales calls (details) to induce greater prescribing of drugs by individual physicians. They do not assume a priori that details are set optimally, but instead they infer the extent to which sales force managers have knowledge of responsiveness, and they use this knowledge to set the level of sales force contact. The authors find that their modeling approach improves the precision of the physician-specific response parameters significantly. They also find that physicians are not detailed optimally; high-volume physicians are detailed to a greater extent than low-volume physicians without regard to responsiveness to detailing. It appears that unresponsive but high-volume physicians are detailed the most. Finally, the authors illustrate how their approach provides a general framework.

Gratitude versus Entitlement: A Dual Process Model of the Profitability Implications of Customer Prioritization

“…First, we considered customer firm size and customer-supplier relationship duration as covariates that may influence the financial outcomes of an exchange relationship. Second, we accounted for the possibility that prioritization tactics are endogenous given that the firm might strategically set the intensity of each tactic directed toward an individual customer according to the tactic's anticipated effectiveness for triggering a behavioral response from the customer (Boulding et al 2005;Manchanda, Rossi, and Chintagunta 2004). We controlled for this form of endogeneity using the procedure Garen (1984) suggests, 4 which previous marketing research has applied to similar problems (e.g., Grewal, Chakravarty, and Saini 2010;Landsman and Stremersch 2011).…”

The effects of detailing on prescribing decisions under quality uncertainty

“…4 The majority of the studies in this industry use product level data because they are the least expensive data that could be purchased from IMS. Recently, there are a few studies which use proprietary individual level data to study the demand for prescription drugs (e.g., Gonul et al 2001, Wosinska 2002, Manchanda et al 2004, Crawford and Shum 2005, Dong et al 2006, Narayanan and Manchanda 2006. In particular, Crawford and Shum (2005) and Narayanan and Manchanda (2006) model how an individual physician/patient learns his/her own match with different drugs.…”

Modeling Indivisible Demand

“…Examples include the work of Anderson and Simester (2004) who assume that the number of units ordered by a customer from future catalogs follows a Poisson distribution, and Manchanda et al (2004) adopt an NBD distribution to account for the number of new prescriptions written by a physician. Statistical models are frequently assumed for count data and data thought to be generated from a censored continuous model such as in cut-point models in customer satisfaction research (Rossi et al (2001), Bradlow and Zaslavsky (1999), Gupta (1988)).…”