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A Machine and Deep Learning Framework to Retain Customers based on their Lifetime Value

-, Kannan Kumaran (2022) A Machine and Deep Learning Framework to Retain Customers based on their Lifetime Value. Masters thesis, Dublin, National College of Ireland.

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Customer Lifetime Value (CLV) measures the average revenue generated by a customer over the course of their association with the firm. CLV is measured by RFM-Recency, Frequency, and Monetary factors using their previous purchasing history. This research proposes a Machine and Deep Learning Framework to predict the Customer Lifetime Value in order to retain customers through targeted product promotions. The proposed framework combines clustering and regression models to analyse the significant variables for predicting the value of customers. Customers are grouped based on that value into levels such as high medium and low profitable customers. To identify the optimum model, this research compares Deep Neural Network and Machine Learning to probabilistic models Gamma-Gamma and Betageometric/ negative binomial in order to predict the level of profitable customer class of following years by segmentation with help of K-means and Hierarchical ML clustering algorithms. Results of the five models are presented in this paper based on accuracy(R2), Mean Squared Error and Mean Absolute Error. This research shows promise for Deep Neural Network(R2-71%) in projecting the CLV. Considering the predicted CLV, the e-commerce decides on which customer group to invest for achieving a long-term Customer Relationship Management strategy.

Item Type: Thesis (Masters)
Subjects: Q Science > QA Mathematics > Electronic computers. Computer science
T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science
H Social Sciences > HF Commerce > Marketing > Consumer Behaviour
Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
Divisions: School of Computing > Master of Science in Data Analytics
Depositing User: Tamara Malone
Date Deposited: 20 Feb 2023 15:26
Last Modified: 02 Mar 2023 09:55

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