scholarly journals Customer Lifetime Value of Supplementary Health Insurance: An Analytical Model

2021 ◽  
Author(s):  
Roohollah Dehghani Ghale ◽  
Farzad Karimi ◽  
Hassan Ghorbani Dinani
Keyword(s):  
Impact Factor ◽  
Structural Equation ◽  
Deep Understanding ◽  
Customer Lifetime Value ◽  
Load Test ◽  
Insurance Companies ◽  
Structured Interviews ◽  
Cross Sectional ◽  
Customer Lifetime ◽  
Lifetime Value

Background: With the number of insurance customers growing, insurance companies are trying new ways to retain customers and streamline communication channels to avoid loss of revenue. The present study set out to develop a model for a reliable analysis of customer lifetime value. Methods: The present study was exploratory mixed method in design. The study took place in Jundishapur University of Medical Sciences, located in Ahvaz, Iran. A total of 402 insurance experts and university staffers participated in the study. A cross-sectional data collection was done using semi-structured interviews (n = 22) and a questionnaire (n = 380). The latter was validated via a panel of content area experts, criterion-dependent validity (second-order confirmatory factor analysis), and divergent validity (cross-sectional load test and Fornell-Laker). Cronbach's alpha and combined reliability were - 0.8 and 0.8, respectively. A structural equation approach was employed to analyze data using Smart PLS software. Results: Customer loyalty with an impact factor of 0.60 and T-statistic of 5.79, profitability with an impact factor of 0.55 and T-statistic of 3.75, customer co-creation with an impact factor of 0.28, and T-statistic of 2.7 have been identified as dimensions of customer lifetime value. Conclusion: Measuring customer lifetime value to implement various strategies requires a deep understanding of such value dimensions as loyalty, profitability, and value creation.

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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