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June 28, 2017

Celect: Predictive Analytics for Retailers

Celect is helping retailers reveal true demand, leverage existing data, and optimize their stock using predictive analytics and machine learning technology.

Bernadette Esposito

John Andrews

“Retail is clearly going through a transformation right now,” says John Andrews, CEO of Celect, a predictive analytics machine learning technology company focused on retail. “The reality is that stores are not going away. Customers are looking for experiences, and part of that experience is to touch the product and to try the product on,” he says. Improving the overall customer experience means understanding at a more precise and granular level how customers choose between an assortment of products. The Celect Choice Engine helps retailers identify and predict what a customer faced with an assortment of products is likely to buy.

Customer Choice Modeling
“The context of every customer’s decision delivers a very rich signal in terms of how individual customers choose,” says Andrews. At the core of this is customer choice modeling. When a customer walks into a store and buys a product, retailers use transaction-level information to personalize the experience and to optimize operations within their store. “But what if in addition to the customer’s purchase, you also knew what their options were?” asks Andrews. Said another way: What if you knew what customers didn’t buy?

That’s where the predictive piece comes in. One of the key challenges retailers have is around the problem of sparse data. In terms of identifying buying patterns of an individual customer over the course of a year, a retailer may only have one or two data points. Gathering data on a customer’s buying patterns, their browse history, and what they put in or remove from their shopping cart produces a very robust model that allows Celect to predict the likelihood of future behavior. “Based on this,” says Andrews, “what are the right products to put in front of an individual customer and in what quantity?”

Inventory Portfolio Optimization Challenge
At the highest level, the inventory portfolio optimization challenge is the problem Celect is solving: How much of an individual product does a retailer need to buy and how is that product going to interact with another product? “The complexity of that model can be baffling,” says Andrews. Until Celect came along, there was no real system that could handle such complexity. “We have a solution that can help merchants and planners identify what products they should be bringing into their assortment, and where they should be putting those particular products,” says Andrews. One of the core capabilities of the inventory portfolio is being able to identify how well a product will sell in the future based on history and purchases.

Inventory Portfolio Challenge
Around the idea of Inventory Portfolio Optimization is Celect’s longer-term vision. Be it online, direct, wholesale, or retail in-store experiences, Andrews sees a much bigger opportunity across the entire supply chain, from brand and manufacturers to distribution and retail. “At each step across that supply chain, we identify how much a retailer should be buying, at which distribution centers or fulfillment centers the product should be brought, how much of that product should go to each individual store, and in what assortment,” says Andrews.

Plan. Buy. Allocate. Fulfill.
Under the umbrella of Inventory Portfolio Optimization, Celect is focused on four core solutions, mapping directly to the process most retailers live by. The first, Plan Optimization, used in strategic planning and in merchandise financial planning, helps retailers identify how much they should be spending on specific departments, brands or styles, the demand for those products within their customer base, and in which stores they can sell those products.
The next module, Buy Optimization, helps retailers determine the demand for a product, whether they should be going big or buying small. “Getting that right early in the decision process is incredibly important in terms of what final revenue and markdown numbers are going to look like for a retailer at the end of the season,” says Andrews.

The third piece of optimizing inventories is Allocation Optimization. “Now, I’ve got an assortment of products. I know how much of each product I have. Where should I be allocating those products? Get the product into each store, in the right assortment and in the right number based on the buying patterns of customers in those stores,” says Andrews.

The final piece is the Order Fulfillment Optimization solution – the process of intelligently leveraging store inventories to fulfill online orders. Here, the retailer is trying to push as much of the inventory into their stores, use their stores as fulfillment centers, and then intelligently identify from which stores they should be shipping that product. Andrews says understanding the demand for a product over a course of a season can make for a much smarter decision in terms of which store to ship products from.

Using Your Data for Better Decisions
“Omni-channel—it’s probably an overused term—but it’s a real issue and challenge for retailers to figure out how to leverage every interaction point, every channel with a customer, and then be able to optimize across all of those different channels to provide the best experience to customers,” says Andrews. Whether customers want to buy something online and return it to a store, buy something online and pick it up in a store, or buy something in a store and mail it back, involves an enormous amount of complexity from an operational perspective. “At the end of the day,” he says, “it all comes down to getting the right product in front of the right customer at the right time.” As part of this transformation, retail is going evolve and change. Some retailers will have fewer stores; others will open more stores.

Andrews says you don’t need to be Amazon to use your data and make smarter decisions. “Predictive analytics and leveraging machine learning to supplement the decision-making is at the top of every retail executive’s priority list. They quite simply want to understand how to use it, how it gets integrated within their environment,” he says. As Celect has grown, so have the data points on how different retailers use information and science to help supplement decision-making, to help retailers make better decisions, to increase revenue, reduce stock-outs, and reduce markdowns within the customer experience. “The retailers who are able to truly understand how their customers are interacting with products and how the products are interacting with each other, and are then able to optimize on that are the ones who are going to win.”

About MIT Startup Exchange, STEX25, and MIT’s Industrial Liaison Program (ILP)
MIT Startup Exchange actively promotes collaboration and partnerships between MIT-connected startups and industry. Qualified startups are those founded and/or led by MIT faculty, staff, or alumni, or are based on MIT-licensed technology. Industry participants are principally members of MIT’s Industrial Liaison Program (ILP).

MIT Startup Exchange maintains a propriety database of over 1,500 MIT-connected startups with roots across MIT departments, labs and centers; it hosts a robust schedule of startup workshops and showcases, and facilitates networking and introductions between startups and corporate executives.

STEX25 is a startup accelerator within MIT Startup Exchange, featuring 25 “industry ready” startups that have proven to be exceptional with early use cases, clients, demos, or partnerships, and are poised for significant growth. STEX25 startups receive promotion, travel, and advisory support, and are prioritized for meetings with ILP’s 230 member companies.

MIT Startup Exchange and ILP are integrated programs of MIT Corporate Relations.