Merchant Innovation

New Startup Helps Retailers Track Buying Patterns

For retailers, being off the mark on buying patterns can be costly.

For one, understanding those patterns is key to keeping up the bottom line, and the consequence of being wrong can result in missed profit or a pile of merchandise that won’t sell.

But being on the mark can pay off big.

That’s where Celect, a Boston-based predictive analytics marketing firm, comes into play. And to help propel its mission, the startup that was born out of MIT, announced yesterday (June 25) it has raised $5 million to continue helping retailers pick out products that match their customers’ wants and needs.

The funding, which was led by Menlo Park-based venture firm August Capital and had help from Activant Capital Group in New York, will help the company expand its mission to more brick-and-mortar stores to aid them in understanding customers’ purchasing decisions. That’s with the help of Celect’s main product called Choice Engine.

“[Choice modeling] is the technique of using customer and transaction data to model out how a customer is likely to react to and choose from a set of options,” CEO John Andrews told VentureBeat in an email.

The co-founder of the company is MIT Professor Vivek Farias, and he explained that the technology takes a complex model and breaks it down into simple terms for the retailers to better understand how those customers are making their selections.

“But what if you also knew not just what a customer bought, but also what was available to them when they made their selection?” Farias told VentureBeat. That technology can not only help retailers understand which products sell, but also give them insight into what that customer’s preference is.

By using Celect’s tech, retailers can also better understand which products should be in stock, how many of those products should be ordered and how they can distribute the proper amount of products to each store. It also provides advice about how retailers can strategically mark down prices on specific products to boost sales of that item.

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