A comparative study of hybrid machine learning techniques for customer lifetime value prediction

Kybernetes ◽  
2013 ◽  
Vol 42 (3) ◽  
pp. 357-370 ◽  
Author(s):  
Chih‐Fong Tsai ◽  
Ya‐Han Hu ◽  
Chia‐Sheng Hung ◽  
Yu‐Feng Hsu
Keyword(s):  
Machine Learning ◽  
Comparative Study ◽  
Customer Lifetime Value ◽  
Machine Learning Techniques ◽  
Value Prediction ◽  
Customer Lifetime ◽  
Learning Techniques ◽  
Lifetime Value ◽  
Hybrid Machine

Customer Lifetime Value Measurement using Machine Learning Techniques

2014 ◽  
pp. 565-573
Author(s):  
Tarun Rathi ◽  
Vadlamani Ravi
Keyword(s):  
Machine Learning ◽  
Customer Value ◽  
Customer Lifetime Value ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Customer Lifetime ◽  
Learning Techniques ◽  
Lifetime Value ◽  
Additive Regression ◽  
Or Organization

Customer Lifetime Value (CLV) is an important metric in relationship marketing approaches. There have always been traditional techniques like Recency, Frequency and Monetary Value (RFM), Past Customer Value (PCV) and Share-of-Wallet (SOW) for segregation of customers into good or bad, but these are not adequate, as they only segment customers based on their past contribution. CLV on the other hand calculates the future value of a customer over his or her entire lifetime, which means it takes into account the prospect of a bad customer being good in future and hence profitable for a company or organization. In this paper, we review the various models and different techniques used in the measurement of CLV. Towards the end we make a comparison of various machine learning techniques like Classification and Regression Trees (CART), Support Vector Machines (SVM), SVM using SMO, Additive Regression, K-Star Method and Multilayer Perception (MLP) for the calculation of CLV.

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 Optimization of Distribution Control System in Oil Refinery by applying Hybrid Machine Learning Techniques

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Al Jlibawi A. Hussein ◽  
Mohammad Lutfi Othman ◽  
Aris Ishak ◽  
Bahari S. Moh Noor ◽  
Al Huseiny M. Sattar Sajitt
Keyword(s):  
Machine Learning ◽  
Control System ◽  
Oil Refinery ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Hybrid Machine

A Comparative Study of Machine Learning Techniques for Gesture Recognition Using Kinect

2020 ◽  
pp. 1096-1117
Author(s):  
Rodrigo Ibañez ◽  
Alvaro Soria ◽  
Alfredo Raul Teyseyre ◽  
Luis Berdun ◽  
Marcelo Ricardo Campo
Keyword(s):  
Machine Learning ◽  
Comparative Study ◽  
Gesture Recognition ◽  
Microsoft Kinect ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Natural Interaction ◽  
Computer Communication ◽  
Human Computer Interfaces ◽  
Learning Techniques

Progress and technological innovation achieved in recent years, particularly in the area of entertainment and games, have promoted the creation of more natural and intuitive human-computer interfaces. For example, natural interaction devices such as Microsoft Kinect allow users to explore a more expressive way of human-computer communication by recognizing body gestures. In this context, several Supervised Machine Learning techniques have been proposed to recognize gestures. However, scarce research works have focused on a comparative study of the behavior of these techniques. Therefore, this chapter presents an evaluation of 4 Machine Learning techniques by using the Microsoft Research Cambridge (MSRC-12) Kinect gesture dataset, which involves 30 people performing 12 different gestures. Accuracy was evaluated with different techniques obtaining correct-recognition rates close to 100% in some results. Briefly, the experiments performed in this chapter are likely to provide new insights into the application of Machine Learning technique to facilitate the task of gesture recognition.

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