A Composition Forecasting Approach of Customer Repeat Purchasing

doi:10.3969/j.issn.1006-2475.2015.05.014

Abstract

Abstract: SMC model was proposed by Schmittlein et al., which is very popular in customer base analyses under non-contractual settings. The SMC model assumes that customers’ purchasing follow Poisson processes until they defect. The HIPP model was proposed by Ma et al., which assumes that customers do not defect, but instead they switch constantly between active and inactive state. In reality however, both permanent and temporary defecting may exist. Therefore, SMC model may underestimate customer’s probability of re-purchasing, while HIPP model may overestimate that. Based on two models, in this paper, we propose a composition forecasting approach which combines the forecasts from both SMC and HIPP. The optimal weights of compositing are calculated by a genetic algorithm. Empirical studies show the superiority of the proposed composition approach.

Key words: SMC model, HIPP model, composition forecasting, genetic algorithm

CLC Number:

TP391

SHU Fang, MA Shao-hui. A Composition Forecasting Approach of Customer Repeat Purchasing[J]. Computer and Modernization, doi: 10.3969/j.issn.1006-2475.2015.05.014.

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