The Value of Purchase History Data in Target Marketing

By Peter E. Rossi, Robert E. McCulloch, Greg M. Allenby. 1996

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Citations in the corpus

A Hidden Markov Model of Customer Relationship Dynamics (2008, background): “Advances in data collection and statistical methods enable marketers to better track the transactions of their customers over time and target the marketing mix activities such as customized coupons (Bawa and Shoemaker 1987; Rossi, McCulloch and Allenby 1996), direct mail (Basu, Basu and Batra 1995; ”
A Practitioner's Guide to Estimation of Random‐Coefficients Logit Models of Demand (2000, methodology): “In cases where only consumer data is observed, usually estimation is conducted using either maximum likelihood or the simulated method of moments [for recent examples and details see Goldberg, (1995), Rossi et al. (1996) , McFadden and Train (2000), or Shum (1999)].”
A Bayesian analysis of the multinomial probit model with fully identified parameters (2000, background): “(16) In our view, this hierarchical model makes the most sense when applied to the identi"ed coe$cients (see for example Rossi et al., 1996).”
Optimal Advertising Budget Allocation in Luxury Fashion Markets with Social Influences: A Mean‐Variance Analysis (2018, background): “Rossi et al. (1996) show that ﬁrms can obtain signiﬁcant beneﬁts by properly setting their promotion target.”
The Effectiveness of Demographic and Psychographic Variables for Explaining Brand and Product Category Use (2003, background): “For example, Rossi et al. (1996) show that the information content contained in demographics for explaining brand preference is very low, and Bucklin et al. (1995) report weak to no evidence of a predictive relationship between demographics and brand preference.”
A PLS Model to Study Brand Preference: An Application to the Mobile Phone Market (2010, background): “In keeping with the evidences retrieved from the literature review (Rossi et al. 1996; Bucklin et al. 1995), the demographic profile in this study shows a small, but statistical significant, impact on brand preference.”
Embedded Premium Promotion: Why It Works and How to Make It More Effective (2007, methodology): “Consistent with standard reporting practice in marketing ( Rossi et al. 1996, Arora and Allenby 1999), we report the Bayesian equivalent of a p-value to indicate statistical significance.”
Do Loyalty Programs Really Enhance Behavioral Loyalty? An Empirical Analysis Accounting for Self-Selecting Members (2006, background): “Rossi, McCulloch, and Allenby (1996) show the value of using this information for target marketing to certain customer groups.”
A practitioner’s guide to Bayesian estimation of discrete choice dynamic programming models (2012, unknown): “…vectors for complicated static discrete choice models, making the posterior mean an attractive estimator compared with classical point estimates in that setting (Albert and Chib 1993; McCulloch and Rossi 1994; Allenby and Lenk 1994; Allenby 1994; Rossi et al. 1996; Allenby and Rossi 1999).”