Omni-channel Retail: Leveraging Machine Learning for Personalized Customer Experiences and Transaction Optimization

Abstract

In the contemporary retail business model, timely fulfillment of customer needs as well as the
supremacy of customer experience is of paramount importance. As such, a win-win model seems
to be crucial for both the retailer and the customer alike, while at the heart of the retailer is the
mission of enhancing customer relationships. Retailers make efforts to ensure that every gainful
interaction is as meaningful and valuable as possible. However, personal recall of customer
preferences, the urgency of fulfillment, and the salesperson’s ability to bundle the 'right' items
are all strong constraints. The customer’s transaction history is the only reliable resource that can
be depended on to be persistent and recognized. The aim is to identify possible data analytics
opportunities in personalizing these transactions as well as to optimize sales opportunities. We
also explore the use of a particular artificial intelligence technique, a group of algorithms widely
known as machine learning, with task clustering and segment labeling.