A Connected In-Store and Online Customer Data Set for Omnichannel Retail Logistics Research

Omnichannel is a business strategy that aims to provide a seamless shopping experience so that a customer can buy, receive, and return their products using different channels. Omnichannel services include buy online pickup in store, return in store, ship from store, and home delivery. To better serve both online and in-store customers, innovations are needed in how facilities, business models, material handling equipment, and operational policies are designed and operated. In order to support this emerging research area, this work provides relevant connected in-store and online customer data sets for omnichannel retail research, focusing on a grocery retail environment. This work yields a mapped categorization of products into product families, and then uses this mapping to connect previously separate in-store and online customer data sets and collects additional data from publicly available websites. These connected data sets contain information about product family data on in-store and online customer demand values, impulse purchases, product dimensions, weight, and price. Additional data is provided on in-store and online customer arrival data. The data sets are made available on https://github.com/JoyjitBhowmick/Omnichannel-Retail-Datasets.