Defection Detection: Measuring and Understanding the Predictive Accuracy of Customer Churn Models

By Scott A. Neslin, Sunil Gupta, Wagner A. Kamakura, Junxiang Lu, Charlotte H. Mason. 2006

591 citations recorded by the source corpus.

Subfields: Information Systems, Marketing

View Paper: https://www.semanticscholar.org/paper/d9740fb0fc3d857c4c8f9ff6a84aec5e197fe8cb

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Citations in the corpus

Gaining Customer Experience Insights That Matter (2018, background): “The customer experience can be conceptualized as holistic, comprised of multiple touchpoints (Frow and Payne 2007) in an end to end journey (Neslin et al. 2006), involving the customer’s cognitive, affective, emotional, social and sensory elements (De Keyser et al. 2015; Lemon and Verhoef 2016;…”
Customer Equity: Measurement, Management and Research Opportunities (2007, unknown): “combination of decision trees (Neslin et al., 2006).”
Bagging and Boosting Classification Trees to Predict Churn (2006, unknown): “…top-decile lift, γ is the success rate of the incentive among the churners, LVC is the lifetime value of a customer (Gupta, Lehmann, and Stuart 2004), δ is the incentive cost per customer, and ψ is the success rate of the incentive among the nonchurners (for more details, see Neslin et al. 2006).”
Understanding Customer Switching Behavior in a Liberalizing Service Market (2007, methodology): “Second, we estimate a logistic regression model with the switching intention variable as the dependent variable and the derived principal components (PCs) as explanatory variables, as is common for explaining customer switching or churn (Kumar and Reinartz 2005; Neslin et al. 2006).”
Customer Metrics and Their Impact on Financial Performance (2006, unknown): “Neslin et al. (2005) describe such models which were submitted by several academics and practitioners as part of a “churn tournament”. Models emanating from machine learning, such as bagging and boosting, have also been recently used for modeling customer churn (Lemmens and Croux 2005). The “always ”
New insights into churn prediction in the telecommunication sector: A profit driven data mining approach (2012, methodology): “The average values of the retention rate and the CLV that are used in the study are based on values found in the scientific literature (Neslin et al., 2006; Burez and Van den Poel, 2007), and based on information provided by data mining specialists of several telco providers.”
Handling class imbalance in customer churn prediction (2009, methodology): “Logistic regression Logistic regression modelling is very appealing for four reasons: (1) logit modelling is well-known, conceptually simple and frequently used in marketing (Bucklin & Gupta, 1992), especially at the level of the individual consumer (Neslin et al., 2006); (2) the ease of interpretat”
Modeling Customer Lifetime Value (2006, background): “Research in the social network theory can be very useful for exploring these issues (e.g., Newman 2003; Wasserman and Faust 2005; Watts 2004).”
Domain knowledge integration in data mining using decision tables: case studies in churn prediction (2009, methodology): “…the result from the best performing model in the calculation of CLV. Neslin et al (2006) analyse the results from different data mining techniques used to calculate churn and conclude that logistic regression and decision trees performed well and are good techniques to be used when analysing churn.”