客户重复购买的组合预测方法

A Composition Forecasting Approach of Customer Repeat Purchasing

SMC模型是由Schmittlein等人提出,用于描述非契约客户关系情景下客户的重复购买行为。该模型假设客户重复购买行为服从泊松过程,客户一旦流失则不会被赢回。HIPP模型是由马少辉等人提出,该模型假设客户不会永久流失,而是在活跃和不活跃的状态之间转换。然而在现实中,永久流失和暂时流失的客户都是可能存在的。因此,SMC模型可能会低估重复购买的概率,而HIPP模型可能会高估重复购买的概率。本文提出一种客户重复购买的组合预测方法。该方法利用SMC模型和HIPP模型分别对客户重复购买进行预测,通过遗传算法寻找这2个模型的最优组合权值。通过实证分析,验证了组合预测方法的优越性。

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 inac-tive state.In reality however , both permanent and temporary defecting may exist .Therefore, SMC model may underestimate cus-tomer’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 .