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Current Deposit Customer Lifetime Value Based on the Heuristic Algorithm

Author: ZhangMingZhu
Tutor: LiChunQing；LiGang
School: Xi'an University of Technology
Course: Management Science and Engineering
Keywords: Customer Lifetime Value Heuristic algorithm Stochastic model Optimal threshold
CLC: F224
Type: Master's thesis
Year: 2011
Downloads: 12
Quote: 0
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Abstract


Non-contractual environment , customers' future buying behavior gradually become a research focus of the marketing industry , academia, usually with a stochastic model for the future of customer buying behavior to predict , but this model is relatively difficult to use in the practice of community . How to explore a simple and practical method is the main task of this study . In this paper , a commercial bank demand savings the customer's historical transaction data for the study sample , to build heuristic algorithm and stochastic models to predict customers' future buying behavior proved heuristic algorithm is indeed simpler than the random model prediction method from an empirical point of view , efficient , effectively reducing the opportunity cost of the use of stochastic models to pay , to help managers to quickly adjust marketing strategies . The main contribution of this paper is as follows : 1 . Heuristic algorithm in the field of commercial banks demand savings . Heuristic algorithms applied, for the first time in the field of commercial banks demand savings , loss of recognition of customers future forecast of the number of transactions and the transaction amount forecast , the prediction results by a stochastic model for BG / NBD and Gamma-Gamma model predictions the comparison of the results to verify that the heuristic algorithm is applicable in the field of commercial banks demand savings , and shows once again predict customers' future buying behavior , heuristic algorithm is not inferior to the random model . Specific data characteristics . Combined the demand savings customers of commercial banks , the improved heuristic algorithm to predict customers' future number of transactions E [x] model . According to the characteristics of the customers of commercial banks demand savings proposed prediction model to meet customer business background of current savings , improved model is more applicable to estimated future number of transactions of China 's commercial banks demand savings customers , all customers to further improve forecast accuracy Total number of transactions . 3 out of conventional classification research ideas directly calculated according to the customer lifetime value of the customer base to select 20% of high-quality customers , then from the point of view of the rational allocation of marketing resources managers to provide basis for decision making .

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