摘要
大数据技术推动了成品油零售行业客户管理模式的变革,但在加油站管理实践中仍存在"大而不精"的弊端。要实现对客户的精准管理,客户识别和定位是关键。本文构建了加油站持卡客户的基础分类树,通过改进RFAT模型对不同偏好客户进行K-means聚类,从当前价值和增值潜力两个维度勾画客户价值定位方格,并结合加油站个人卡客户数据进行测算。结果证明:加油站客户基础分类树模型对加油站的客户价值定位发挥积极作用,有利于更精确地实施对应的价格策略和其他营销管理手段。
Big data technology is accelerating the revolution of the traditional CRM model in refined oil retailing industry. However, it is still inaccurate when masses of information are collected in management practices of gas station. The recognition and position of customer is the key to manage numerous clients accurately. Based on the foundational classification tree, clients with different preference were classified by using improved RFAT method and K-means algorithm. After a measure of a sample of individual consumers, the value of customer was described by two dimensions: current value and appreciation potential. Therefore, this model has a positive impact on consumer position for the gas station,and be more helpful to implement corresponding price policies and other marketing management tools.
出处
《价格理论与实践》
CSSCI
北大核心
2018年第11期158-161,共4页
Price:Theory & Practice
基金
中央高校基本科研业务费专项资金资助,(项目编号:27R1606047B)
关键词
RFAT模型改进
成品油零售业
客户价值分类
精准营销策略
定价模式
RFAT Model Improvement
Refined Oil Retail Industry
Customer Value Classification
Precision Marketing Strategy
Pricing Model
