零售业的智能定价
公司规模
Large Corporate
地区
- Europe
产品
- Dataiku Data Science Studio (DSS)
技术栈
- Python
- HDFS
- R
- Spark Streaming
实施规模
- Enterprise-wide Deployment
影响指标
- Cost Savings
- Productivity Improvements
- Revenue Growth
技术
- 分析与建模 - 预测分析
适用行业
- 零售
适用功能
- 销售与市场营销
服务
- 数据科学服务
关于客户
客户是欧洲一家领先的零售商，拥有 3,500 多家门店以及提供送货上门服务的电子商务部门。这家零售商拥有数十万名员工和遍布多个国家的客户，始终走在大数据技术的前沿，以在不断增长的市场中保持竞争力。
挑战
欧洲一家领先的零售商拥有 3,500 多家门店和电子商务部门，由于价格被竞争对手压低而亏损。他们还发现，他们的客户群倾向于等到季节结束时才进行大幅降价，而且只会购买某些季节性产品，这扭曲了他们对未来如何进货的预测，并延续了定价问题。此外，他们很难有效地调整价格并保持门店和线上的一致 - 通常，这会导致定价不一致，尤其是当各个门店经理自行决定销售时。该零售商希望通过了解客户购买特定产品的决策因素以及哪种价格最能引起共鸣、轻松实时了解所有竞争对手提供的价格以及在门店和线上一致更新定价来改进他们的定价策略。
解决方案
该零售商将 Dataiku 数据科学工作室 (DSS) 引入其数据团队的流程，以大规模整合预测分析。他们与 Dataiku 合作制作了一个最终数据项目，该项目考虑了竞争对手的定价，并将其用于预测模型中，以确定对于特定产品，整体业务是否可以支持该产品的激烈价格竞争。该解决方案利用 Dataiku 的 REST API 根据一组特定的预定义功能自动调整生产中的定价。它使用生产中的模型的实时监控来确保定价模型性能不会漂移，并且生产中的价格变化随时间推移都有充分的记录。该解决方案还包括一个基于预测定价模型的强大定价仪表板，可提醒并允许实体店对建议的价格变化或在线价格变化做出反应。
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