509 / 2024-04-25 17:24:20

Unveiling Consumer Preferences: A Two-Stage Deep Learning Approach to Enhance Accuracy in Multi-Channel Retail Sales Forecasting

Consumer Group Preference,Sale Forecasting,Design Science,Heterogeneous Graph Neural Network,Machine Learning

In the dynamic landscape of the business environment, sales forecasting for multichannel retailers has become increasingly intricate, particularly with the shift from traditional brick-and-mortar channels to a diverse range of distribution platforms, including online avenues. This transition not only complicates forecasting challenges but also highlights the crucial role of utilizing online traceable consumer purchase data to discern consumer preferences for stores and products and enhance sales forecasting accuracy. This paper proposes a two-stage deep learning approach based on Online Channel Consumer Preference Heterogram and Multi-Head Attention mechanism (OCCPH-MHA). 

In the first stage, the model identifies potential consumer group preferences based on individual purchasing behavior. In the second stage, it seamlessly integrates this identified feature with time-series demand data using a global-local attention mechanism, thereby facilitating multi-step forecasting.

The study's robust validation involves testing the model on a dataset from a multichannel retail restaurant company, showcasing its prowess in significantly improving the precision of sales forecasting. This not only substantiates the model's effectiveness but also underscores the collaborative nature of research, as it contributes to a comprehensive framework. This framework, focused on tracking the preferences of potential consumer groups, emerges as a shared resource within the research community—a valuable tool that collectively refines and optimizes the sales forecasting process for industry practitioners and researchers alike.