Modeling Customer Lifetime Value, Retention, and Churn

2021 ◽  
pp. 1001-1033
Author(s):  
Herbert Castéran ◽  
Lars Meyer-Waarden ◽  
Werner Reinartz
Keyword(s):  
Customer Lifetime Value ◽  
Customer Lifetime ◽  
Lifetime Value

Customer Lifetime Value-Berechnung beiMarkov-Prozessen

2010 ◽  
Vol 39 (10) ◽  
pp. 521-528
Author(s):  
Alexander Baumeister ◽  
Thomas Alt
Keyword(s):  
Customer Lifetime Value ◽  
Customer Lifetime ◽  
Lifetime Value

Customer lifetime value prediction for gaming industry: fuzzy clustering based approach

2021 ◽  
pp. 1-10
Author(s):  
Ahmet Tezcan Tekin ◽  
Tolga Kaya ◽  
Ferhan Cebi
Keyword(s):  
Machine Learning ◽  
Fuzzy Clustering ◽  
Customer Lifetime Value ◽  
Gaming Industry ◽  
Value Prediction ◽  
Customer Lifetime ◽  
Lifetime Value ◽  
Membership Value ◽  
Different Characteristics ◽  
Ensemble Algorithms

The use of fuzzy logic in machine learning is becoming widespread. In machine learning problems, the data, which have different characteristics, are trained and predicted together. Training the model consisting of data with different characteristics can increase the rate of error in prediction. In this study, we suggest a new approach to assembling prediction with fuzzy clustering. Our approach aims to cluster the data according to their fuzzy membership value and model it with similar characteristics. This approach allows for efficient clustering of objects with more than one cluster characteristic. On the other hand, our approach will enable us to combine boosting type ensemble algorithms, which are various forms of assemblies that are widely used in machine learning due to their excellent success in the literature. We used a mobile game’s customers’ marketing and gameplay data for predicting their customer lifetime value for testing our approach. Customer lifetime value prediction for users is crucial for determining the marketing cost cap for companies. The findings reveal that using a fuzzy method to ensemble the algorithms outperforms implementing the algorithms individually.

Customer Lifetime Value: A Review

2012 ◽  
Vol 40 (7) ◽  
pp. 1057-1064 ◽  
Author(s):  
Wen Chang ◽  
Chen Chang ◽  
Qianpin Li
Keyword(s):  
Customer Relationship Management ◽  
Critical Review ◽  
Development Process ◽  
Relationship Management ◽  
Customer Lifetime Value ◽  
Customer Relationship ◽  
Review Of The Literature ◽  
Customer Lifetime ◽  
Lifetime Value

The concept of regarding customers as assets that should be managed and whose value should be measured is now accepted and recognized by academics and practitioners. This focus on customer relationship management makes it extremely important to understand customer lifetime value (CLV) because CLV models are an efficient and effective way to evaluate a firm's relationship with its customers. Assessment of CLV is especially important for firms in implementing customer-oriented services. In this paper we provide a critical review of the literature on the development process and applications of CLV.

Netflix, Inc.: The Customer Strikes Back

2012 ◽  
pp. 1-7
Author(s):  
Daniel Shively ◽  
Rajkumar Venkatesan
Keyword(s):  
Customer Lifetime Value ◽  
Financial Analyst ◽  
Customer Lifetime ◽  
Lifetime Value

This case is an updated version of “Netflix Inc.: DVD Wars” (UVA-M-0763), and was written as a replacement for it.A financial analyst is asked to appraise the value of Netflix’s stock at a time of unprecedented turmoil for the company. This case introduces customer lifetime value (CLV) as a useful metric for subscription-based businesses.

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