Detecting customer-to-customer trends (without social media data) to optimize promotions – Georgia Perakis

MIT Sloan Prof. Georgia Perakis

From Huffington Post

Every year, there are a few items of clothing that become hot. For example, last fall, a Zara coat seemed to become a “must have” item. The coat even had its own Instagrampage with more than 8,000 followers. Many factors contribute to this phenomenon like celebrities — and people with large social media followings — wearing the “hot” item.

When we have detailed social media data, it is relatively easy to identify patterns of influence to predict these trends. But what happens when we don’t have social media data? After all, social media platforms charge tremendous fees for access to that information. Can we use traditional data to detect underlying trends between groups of consumers and improve demand estimation? If so, can we use that information to optimize personalized promotions to increase profits, and also to present “the right individual with the right item at the right price?”

In a recent study, I looked at these questions with MIT Operations Research Center PhD students Lennart Baardman and Tamar Cohen and collaborators from Oracle Retail. We found that the answer to both questions is: yes. We began our study by building a customer demand model and algorithm that incorporates customer-to-customer trends or influences. We then applied the information about customer demand to make promotion decisions. With this method, profits increased between 5-12%. The model can be used by any retailer of any size for any product.

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