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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A revolutionary model to optimize promotion pricing – Georgia Perakis

MIT Sloan Prof. Georgia Perakis

MIT Sloan Prof. Georgia Perakis

From The Huffington Post.

Grocery stores run price promotions all the time. You see them when a particular brand of spaghetti sauce is $1 off or your favorite coffee is buy one get one free. Promotions are used for a variety of reasons from increasing traffic in stores to boosting sales of a particular brand. They are responsible for a lot of revenue, as a 2009 A.C. Nielsen study found that 42.8% of grocery store sales in the U.S. are made during promotions. This raises an important question: How much money does a retailer leave on the table by using current pricing practices as opposed to a more scientific, data-driven approach in order to determine optimal promotional prices?

The promotion planning tools currently available in the industry are mostly manual and based on “what-if” scenarios. In other words, supermarkets tend to use intuition and habit to decide when, how deep, and how often to promote products. Yet promotion pricing is very complicated. Product managers have to solve problems like whether or not to promote an item in a particular week, whether or not to promote two items together, and how to order upcoming discounts ― not to mention incorporating seasonality issues in their decision-making process.

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Using optimization to improve bundle recommendations and pricing – Georgia Perakis

MIT Sloan Prof. Georgia Perakis

MIT Sloan Professor Georgia Perakis

From The Huffington Post

When you shop online, it is common for retailers to offer additional items in a bundle to try to increase sales. For instance, if you are buying towels, the seller may offer matching washcloths. Or if you are buying an airline ticket, you may be asked if you also want to purchase inflight Wifi and premium seating. If this “bundle” is appealing to you in terms of the items offered and the price, you might be motivated to buy it all. If not, the items or services are left on the table, eventually getting marked down even more.

With the online market projected to grow 57% from 2013 to 2018, retailers have the potential to significantly increase their profits through bundling. This strategy can be beneficial for customers too if they are presented with desirable items they otherwise may have missed — and at better prices. The key is creating an attractive enough bundle to incentivize the buyer to click “add to cart.”

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