Data-driven decisions have been defining the success of retailers long before machine learning and AI were readily available and applicable. More and more retailers track customer shopping habits through data sources such as CRM databases, loyalty programs, social media activity, purchase history, consumer demand, and market trends. But, the ability to apply complex mathematical calculations to big data automatically — iteratively and quickly — is now attainable with machine learning.
Research recently conducted by McKinsey found that “U.S. retailer supply chain operations who have adopted data and analytics have seen up to a 19 percent increase in operating margin over the last five years.” Data is clearly effective for retailers, but it’s about putting it to work in the right areas with predictive capabilities.
The term machine learning describes a method of data analysis that automates analytical model building and is a core sub-area of artificial intelligence that enables computers to self-learn without being explicitly programmed. Unlike traditional programming where an engineer tells a computer what to do, with machine learning, the engineer teaches a computer what to do, like teaching a child or training a dog. When exposed to new data, these computer programs learn, change, and develop to make more accurate decisions.
So why is this relevant to retail and how can it improve in-store sales? Retailers can now predict buying behavior with a greater degree of accuracy by understanding what products their shoppers are engaging with and how, whether they are trying on a product or simply picking it up. Machine learning principles can identify human actions of both shoppers and employees, including crouching, bending, reaching overhead, and the like, all the way down to analyzing what aids are being used – carts, bags, brooms, mops, and more.
Creating a more personalized shopping experience is high priority for retailers today. So much so, that research proves 86 percent of consumers — and 96 percent of retailers — said personalization has at least some impact on the purchasing decision, according to a study from Infosys.
Given the capabilities of AI and machine learning, retailers can now personalize product recommendations based on data about each shopper’s unique interest and buying propensity, resulting in targeted in-store promotions and layouts.
Instead of making intuitive guesses or laborious analyses to optimize operations, machine learning allows retailers to anticipate customer behavior and improve employee productivity by turning scheduled tasks into on-demand activities.
And if that’s not enough, these advanced systems are transforming the trade promotion process by automating operational tasks, such as setting shelf pricing, determining product assortments and much more.
Retailers can now capture shopper data with a greater degree of accuracy in a way that is both meaningful and actionable, resulting in more friction-free shopping experiences. From predictive recommendations, product pricing and staffing optimization, there is no doubt machine learning is here to stay.
Join the #retail, #inspiringretail and #SmartStore conversations on Twitter @TalithaLoftus & @RetailNext, as well as at www.facebook.com/retailnext.