We present a case study and methodological developments in large-scale hierarchical dynamic modeling for personalized prediction in commerce. The context is supermarket sales, where improved forecasting of customer/household-specific purchasing behavior informs decisions about personalized pricing and promotions on a continuing basis. This is a big data, big modeling and forecasting setting involving many thousands of customers and items on sale, requiring sequential analysis, addressing information flows at multiple levels over time, and with heterogeneity of customer profiles and item categories. Models developed are fully Bayesian, interpretable and multi-scale, with hierarchical forms overlaid on the inherent structure of the retail setting. Customer behavior is modeled at several levels of aggregation, and information flows from aggregate to individual levels. Forecasting at an individual household level infers price sensitivity to inform personalized pricing and promotion decisions. Methodological innovations include extensions of Bayesian dynamic mixture models, their integration into multi-scale systems, and forecast evaluation with context-specific metrics. The use of simultaneous predictors from multiple hierarchical levels improves forecasts at the customer-item level of main interest. This is evidenced across many different households and items, indicating the utility of the modeling framework for this and other individualized forecasting applications.
翻译:超市销售,改进对客户/家庭特定采购行为的预测,使关于个人化定价和晋升的决定能够持续地在超市销售;这是涉及数千个客户和销售物品的大数据、大模型和预测设置,需要先后进行分析,处理不同时间的信息流动,并处理不同层次的信息流动,客户概况和项目类别不一; 开发的模型是完全的巴伊西亚、可解释和多级的,以等级形式覆盖零售环境的固有结构; 客户行为以几个层次的汇总为模型,信息从综合到个人层次; 单个家庭一级预测,推断价格敏感度,为个人化定价和促销决定提供信息; 方法创新包括延长巴伊西亚动态混合模型,将其纳入多层次的系统,并预测对具体指标的评价; 使用多个等级的同步预测器,改进客户项目主要层次的预测; 在许多不同住户和项目中都证明了这一点,表明模型框架的实用性,并表明这种模型应用的效用。