الفهرس 
Maximizing Customer Engagement using Deep Reinforcement Learning
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Abstract
In direct marketing, Customer Engagement Value (CEV) is a comprehensive measure
that captures the total engagement of a customer within a _rm. It consists of purchasing
and non-purchasing components. Customer Lifetime Value (CLV) is its purchasing
component. While, Customer Referral Value (CRV), Customer Influencer Value (CIV),
and Customer Knowledge Value (CKV) are its non-purchasing components. Many researchers
competed in developing models either for CEV or each of its components
separately. Meanwhile, the previous CEV models were very few, theoretical, and none
of these models focused on maximizing CEV’s value. The majority of the researchers
went to developing models to calculate, analyze, or simulate the components of CEV separately.
Few of those researchers tried to predict or maximize CLV. Also, few researchers
tackled each of the non-purchasing components with different theoretical models mainly
for the sake of analyzing these components not to maximize their values.
The goal of this research is to maximize CEV through maximizing each of its components.
For CLV, three models are developed to maximize CLV (Double Deep Reinforcement
Learning (DDRL), Fuzzy Q Learning (FQL), and Neutrosophic Q Learning (NQL).
DDRL utilizes two deep networks (one to select, and the other one is to evaluate) a single
crisp Q value that represents CLV. While, Fuzzy logic and neutrosophic logic are used
to search for a stochastic value of Q that maximizes the long-term reward (i.e., CLV).