| 最优回报计划的设计与应用 |
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| 引用本文： | 李纯青,徐寅峰.最优回报计划的设计与应用[J].预测,2004,23(2):61-65. |
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| 作者姓名： | 李纯青 徐寅峰 |
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| 作者单位： | 1. 西安交通大学,管理学院,陕西,西安,710049;西安工业学院,经济管理学院,陕西,西安,710032
2. 西安交通大学,管理学院,陕西,西安,710049 |
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| 基金项目： | 国家自然科学基金资助项目(70028102)，国家自然科学基金优秀创新群体资助项目(70121001)，陕西省教育厅专项基金资助项目(02JK009,03JK176)，陕西省自然科学基金资助项目(02G11,03G07) |
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| 摘 要： | 利用随机博弈及可评估的结构动态规划技术，提出一种使公司及客户利益都最大的最优回报计划的设计方法。该方法可以根据客户的购买历史计算出最优回报计划的三个结构因素(回报率、回报极限、计划的时问范围)及最优营销组合策略。将该方法用于某超市的客户数据库中，验证了该方法的适用性及可行性。结果表明：合适的回报计划可以促进客户的购买、提高公司的利润及缓解价格竞争。回报极限应该比客户的平均购买水平偏高，回报率应该与回报极限的改变方向一致，计划的时问范围应定在一年左右比较合适：对于累积购买水平较高的客户一般不邮寄商品信息。在回报计划的初期与末期不用打折，中期对那些购买次数很少的客户可以实行相应的降价策略。讨论了实验应用结论对企业CRM实践的指导意义。
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| 关 键 词： | 最优回报计划 客户效用 客户全生命周期价值（CLV） 马尔可夫完美均衡 |
| 文章编号： | 1003-5192(2004)02-0061-05 |
The Optimal Reward Programs:Designing and Analysis |
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| LI Chun-qing.The Optimal Reward Programs:Designing and Analysis[J].Forecasting,2004,23(2):61-65. |
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| Authors: | LI Chun-qing |
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| Institution: | LI Chun-qing~ |
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| Abstract: | It presents a method of designing the optimal reward programs in a dynamic environment where customers and firm both maximize their benefit by using stochastic game theory and estimable structural dynamic programming technologies. This method can calculate the optimal of the three structural elements which is reward rate, reward threshold and program time horizon, and it also can calculate the optimal mixed marketing strategies. At the same time, the method is valid and feasible by applying it to the data from one supermarket. It is found that appropriate reward program is good at up-buying, improving firm's profits and relaxing the price competition. The reward threshold should be higher than the customer's average purchase. Reward rate should be adjusted in the same way as reward threshold. The right program time horizon is about one year. It is not necessary to discount with higher frequency and save the money to the customers who doesn't purchase frequently. It is not necessary to discount either at the beginning or ending of the reward program. However, it is necessary to depreciate to low frequency customers in the mid of program time horizon. The guidance significance of experimental research conclusions to CRM is discussed. |
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| Keywords: | optimal reward programs customer utility customer lifetime value Markov-perfect equilibrium |
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