scholarly journals Estimating Customer Lifetime Value in the Gaming Industry Using Incomplete Data

2021 ◽  
Author(s):  
Ridda Ali ◽  
Sophie Abrahams ◽  
Anna Berryman ◽  
Collin Bleak ◽  
Nor Aishah Hamzah ◽  
...  
Keyword(s):  
Customer Lifetime Value ◽  
Machine Learning Algorithms ◽  
Large Dataset ◽  
Gaming Industry ◽  
Customer Lifetime ◽  
Lifetime Value ◽  
Key Variables ◽  
Definition Of ◽  
Gambling Patterns ◽  
Base Data

We were asked by Innovation Embassy to work with a large dataset centred around gambling investment, with the task of making a predictive function for computing Customer Lifetime Value (CLV), and also to see if there are ways of detecting fraudulent financial practices and addictive gambling patterns. We had moderate success with the data as it stands, but we were partly held back for two main reasons: the ability to discern a solid definition of CLV due to highly inconsistent data and data that contained many large and incomputable gaps. Different machine learning algorithms were used to find CLV functions based on key variables. We also describe a short and explicit list of ways where the base data can be improved to support effective calculation of CLV. Our key findings suggest that the average customer's CLV is 1035 and ~80% of revenue is brought in from ~10% of the clients.

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.

scholarly journals Cognitive analytics management of the customer lifetime value: an artificial neural network approach

2021 ◽  
Vol 34 (2) ◽  
pp. 679-696
Author(s):  
Marco De Marco ◽  
Paolo Fantozzi ◽  
Claudio Fornaro ◽  
Luigi Laura ◽  
Antonio Miloso
Keyword(s):  
New Technology ◽  
Customer Lifetime Value ◽  
Customer Segmentation ◽  
Machine Learning Algorithms ◽  
Self Organizing Map ◽  
Neural Network Approach ◽  
Content Type ◽  
Customer Lifetime ◽  
Lifetime Value ◽  
Promotional Strategies

PurposeThe purpose of this study is to show that the use of CAM (cognitive analytics management) methodology is a valid tool to describe new technology implementations for businesses.Design/methodology/approachStarting from a dataset of recipes, we were able to describe consumers through a variant of the RFM (recency, frequency and monetary value) model. It has been possible to categorize the customers into clusters and to measure their profitability thanks to the customer lifetime value (CLV).FindingsAfter comparing two machine learning algorithms, we found out that self-organizing map better classifies the customer base of the retailer. The algorithm was able to extract three clusters that were described as personas using the values of the customer lifetime value and the scores of the variant of the RFM model.Research limitations/implicationsThe results of this methodology are strictly applicable to the retailer which provided the data.Practical implicationsEven though, this methodology can produce useful information for designing promotional strategies and improving the relationship between company and customers.Social implicationsCustomer segmentation is an essential part of the marketing process. Improving further segmentation methods allow even small and medium companies to effectively target customers to better deliver to society the value they offer.Originality/valueThis paper shows the application of CAM methodology to guide the implementation and the adoption of a new customer segmentation algorithm based on the CLV.

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.

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