Database Marketing: Analyzing and Managing Customers

By Robert C. Blattberg, Pyong Kim, Scott A. Neslin. 2008

342 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/3a7ebc61c6791e22dc07c6a73e7560f5442d02f1

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Citations in the corpus

Customer Lifetime Value: Empirical Generalizations and Some Conceptual Questions (2009, background): “The argument for marginal costing (summarized by Blattberg, Kim, and Neslin, 2008, Chapter 6), is that full costing can lead to the rejection of customers (either by not acquiring them or by “firing” them) who would clearly increase profits if they were served.”
Customer Complaints and Recovery Effectiveness: A Customer Base Approach (2014, background): “Customer complaints are important nontransactional events because they represent critical turning points in the company’s relationship with its customers (Blattberg, Kim, and Neslin 2008).”
Building with Bricks and Mortar: The Revenue Impact of Opening Physical Stores (2015, background): “These factors include channel attributes, customer characteristics, marketing, and shopping situation (see Blattberg et al. 2008, Chapter 25; Neslin et al. 2006 for complete reviews).”
Loyalty Programmes: Current Knowledge and Research Directions* (2012, methodology): “Third, a customer-tier LP must induce customers to increase their loyalty over time (Blattberg et al. 2008; Drèze and Nunes 2009; Kumar and Shah 2004).”
The effectiveness of different forms of online advertising for purchase conversion in a multiple-channel attribution framework (2016, background): “However, these FICs (defined as any contact with a customer that is initiated by the firm) are increasingly unwanted (Blattberg et al. 2008).”
The Role of Big Data and Predictive Analytics in Retailing (2017, background): “…could analyze market baskets crosssectionally - item co-occurrences, complements and substitutes, cross-category dependence etc (see, e.g., Blattberg et al. 2008; Russell and Petersen 2000); analyze aggregate sales and inventory movement patterns by SKU (e.g., Anupindi, Dada and Gupta…”
The Predictive Ability of Different Customer Feedback Metrics for Retention (2015, neutral): “Because we are less interested in the size of the effects and more in the model (in- and out-of-sample) fit, omitting the other CFMs is not a concern (Blattberg et al., 2008).”