Current Deposit Customer Lifetime Value Based On The Heuristic Algorithm

Nowadays customers'future purchase behavior has become a hot research in the environment of non-contractual in marketing industry. Academia use stochastic model such as Pareto/NBD, BG/NBD, etc to estimate client's future purchase behavior. However, due to the complexity of this model, this kind of method is not applied in industry. It is not the scholars' intention that the theory and practice mismatching. This paper uses heuristic algorithm identify customer churn on the deposit customers in commercial bank for the first time. In comparison with stochastic model, it's verified that the heuristic algorithm have same effective on the prediction of customers'future purchase behavior, and more simple. This method reduced the opportunity cost effectively and helps managers adjust marketing strategies quickly. It is really a convenient method for managers actual operating. The main contribution of this paperare are as follows:1. The application of heuristic algorithm in the field of current deposit customers in commercial bank. It's the first time applying the heuristic algorithm in the field of commercial bank's current deposit. This research consist of the following work:customer churn recognition; forecast the future number of transactions and forecast the amount of future transactions. In comparison with stochastic model, such as BG/NBD and Gamma-Gamma, verified the heuristic algorithm for the prediction of customers'future purchase behavior no less favorable than the complex stochastic models.2. According to the specific data characteristic of saving deposit customer in commercial bank, this paper employ the heuristic algorithm to improve the prediction model of future customer transactions. The research put forward a prediction model that in accordance with the business background of current deposit customers in commercial banks. The improved model is more suitable for measurement of customers'future transactions, and further improves the overall prediction precision of customers. Based on this model we can get a higher forecasting precision about the total number of future purchase.3. According to the calculated customer lifetime value, we selected 20% of the high value customers from the customer base directly, which out of the conventional research ideas about the classification of customers. Then we can provide the basis for managers to make good decision from the perspective of rational allocation of marketing resources.