scholarly journals Customer lifetime value prediction by a Markov chain based data mining model: Application to an auto repair and maintenance company in Taiwan

2012 ◽  
Vol 19 (3) ◽  
pp. 849-855 ◽  
Author(s):  
C.-J. Cheng ◽  
S.W. Chiu ◽  
C.-B. Cheng ◽  
J.-Y. Wu
Keyword(s):  
Data Mining ◽  
Markov Chain ◽  
Customer Lifetime Value ◽  
Model Application ◽  
Value Prediction ◽  
Customer Lifetime ◽  
Lifetime Value ◽  
Mining Model

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.

Data Mining Techniques to Enhance Customer Lifetime Value

2011 ◽  
Vol 225-226 ◽  
pp. 3-7
Author(s):  
Chia Chia Lin ◽  
Dong Her Shih
Keyword(s):  
Data Mining ◽  
Clustering Analysis ◽  
Critical Factor ◽  
Department Store ◽  
Customer Lifetime Value ◽  
Data Mining Techniques ◽  
Customer Lifetime ◽  
Rfm Model ◽  
Lifetime Value

It is proved by many studies that it is more costly to acquire than to retain customers. Consequently, evaluating current customers to keep high value customers and enhance their lifetime value becomes a critical factor to decide the success or failure of a business. This study applies data from customer and transaction databases of a department store, based on RFM model to do clustering analysis to recognize high value customer groups for cross-selling promotions.

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