Modeling Customer Lifetime Value

By Sunil Gupta, Dominique M. Hanssens, Bruce G. S. Hardie, Wiliam Kahn, Viveka Kumar, N. Lin, Nalini Ravishanker, S. Sriram. 2006

739 citations recorded by the source corpus.

Subfields: Marketing, Organizational Behavior and Human Resource Management

View Paper: https://www.semanticscholar.org/paper/f2ab7d284fcf45b68b8eedf6bd9fc246042ae1a7

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Citations in the corpus

Customer Complaints and Recovery Effectiveness: A Customer Base Approach (2014, background): “Collecting self-reported perceptual measures, such as customer satisfaction, and merging them with longitudinal purchase and complaint data (Gupta et al. 2006; Gupta and Zeithaml 2006) could comprise the backbone of any company’s service-quality information system and allow for a more detailed asses”
The Role of Big Data and Predictive Analytics in Retailing (2017, background): “Imagine a day where online and offline retailing data provide a complete view of customer buying behavior, and even better if the data is linked at the level of the individual customer to enable “true” customer lifetime value calculations (Gupta et al., 2006; Venkatesan and Kumar, 2004).”
Customer Lifetime Value: Empirical Generalizations and Some Conceptual Questions (2009, background): “…age might be associated positively with CLV for financial services, but negatively for music downloads. models used to estimate CLV (e.g., see Berger and Nasr, 1998; Dwyer, 1989; Fader and Hardie, 2009; Jain and Singh, 2002; Gupta et al., 2006; Pfeifer and Bang, 2005; Pfeifer and Carraway, 2000).”
Enhancing Marketing with Engineering: Optimal Product Line Design for Heterogeneous Markets (2011, background): “Chen & Hausman proposed a binary programming formulation solvable by nonlinear programming techniques.”
Applied Predictive Modeling (2013, background): “As another example of classification probabilities as inputs to a subsequent model, consider the customer lifetime value (CLV) calculation which is defined as the amount of profit associated with a customer over a period of time (Gupta et al. 2006).”
Handling class imbalance in customer churn prediction (2009, methodology): “ature and the needs of the leading edge industry practitioners, Gupta et al. (2006) state that understanding how to model rare events is one the issues that represent opportunities for future research:”