U.S. retail chains often rely on intuition in choosing which products, from a vast inventory, will sell best at stores across the nation. Now MIT spinout Celect is refining this process with novel data analytics, revealing interesting insights into how retailers can optimize their shelf space.
Co-founded by CSAIL principal investigator Devavrat Shah and MIT professor Vivek Farias, Celect develops software that crunches a store’s sales and inventory data — and, sometimes, online buying data — to determine which products local customers want to buy.
Powered by the professors’ algorithms for improving Netflix’s recommendation engine and predicting trending Twitter topics, the software compares items located near each other in an individual store, and statistically finds which will sell better, based on sales records. Analyzed at scale — over thousands or millions of product comparisons — this shows the buying preferences of customers, as a population.
“We basically create a bag of comparisons and convert that into a black box … known as our customer-choice engine,” says Shah, an associate professor of electrical engineering and computer science, and chief science officer at Celect.
More at MIT News: http://bit.ly/1LcGdqu