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Celect analyzes the context of each customer choice


Combined with an understanding of each transaction for every customer

The Celect Choice Engine

To build the world’s first data-driven customer choice modeling suite

Celect Solutions Built on the
Celect Optimization Platform

Assortment Optimization

Through optimized assortments, retailers can build robust inventory selections specifically optimized for the foot traffic in each individual store - resulting in happier customers.

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Predictive Personalization

Just as no two people are exactly alike, no two customers shop exactly the same way. Employ the best assortment of products to recommend to a customer via digital channels.

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Markdown Optimization

Understand the impact of markdown decisions on the entire assortment and overall revenue. Eliminate the spiral-down effect often associated with discounting efforts.

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Choice Modeling, Explained

Celect is founded on the concept that there is a better way to understand how customers choose. Celect's founders, Dr. Vivek Farias and Dr. Devavrat Shah, have pioneered the next-generation of innovation in Choice Modeling with their award winning research.

Customer decisions are governed by choice: what a customer purchases is both constrained and influenced by what that customer is offered. Celect uses comparisons between products derived from transactional, inventory, and online browse data to model choice. The more comparisons, the richer the model. As a simple example, if you purchased a red jacket when you were shown jackets in red, white, and blue, you're indicating that you prefer red to white or blue.

There are valuable and continual experiments going on with each customer interaction, with data that is generally thrown away - that is, what products a customer did not select but were also available. When we aggregate these individual preference models, the result is a much more clear and precise understanding of buying patterns across the chain, and down to a particular store / location, and even at the individual customer level.

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