Ecommerce companies are spoiled for choice when it comes to analytics services that can help them track and optimize their product sales. What about physical stores?
Boston-based company Celect has launched a data-driven customer choice modeling suite that helps retail planners create optimized product assortments.
Celect says its platform helps stores figure out how to stock their shelves for increased inventory turnover and revenue. It uses machine learning to determine the best assortments by taking into account what was available or viewed when a customer purchased an item.
The technology is designed to give brick-and-mortar stores a chance to catch back up with ecommerce rivals.
Founder Devavrat Shah, the Jamieson Professor of Electrical Engineering and Computer Science at MIT, has been deeply involved in machine learning and artificial intelligence research.
In 2012, Shah teamed up with one of his students to create an algorithm that predicted Twitter trending topics up to five hours in advance with 95 percent accuracy.
Celect also closed a Series A funding round of $5 million led by August Capital.
The company offers its technology as a hosted service. Retailers can request a demo on Celect’s site.