Machine Learning Helps Food Distributor Reach Its Customers

Wholesaler JJ Food Service's ecommerce portal left it a little disconnected from its customers, so it's using its data and Azure-based machine learning to bring back the personal touch.

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Azure ML is a fully managed cloud service for building predictive analytics solutions. It features visual workflows and startup templates for common machine learning tasks, many of them based on the algorithms Microsoft developed for its own products like Xbox and Bing.

The company started working with the Microsoft Azure team, first writing code for the website to capture customer behavior and then leveraging three years of transactional data to train an Azure ML predictive model. They then integrated the recommendations from the model into both their call center environment and their website (both powered by Dynamics) so that phone-based customers get the exact same recommendations (via JJ Food Service's call center representatives) as online customers.

In all, Ahmed says the system took three months to implement. The end result? When customers call in or log in, they system uses its analysis of past purchases to automatically pre-fill customer shopping carts with the items they intended to purchase. Ahmed says nearly 80 percent of the items customers intend to order are already pre-filled in carts when customers call or log in.

"We can predict what the customers are likely to buy today," Ahmed says. "Rather than leaving it open to the customer and letting them search product by product and it to the shopping cart, we wanted to make the shopping experience very focused and very quick. Our intention was to keep customers on our website as little as possible."

Knowing What the Customers Want Before They Do

Making the ordering process more efficient has been a big hit with customers, Ahmed says. And the new predictive analytics capabilities allow JJ Food Service to provide customers with tailored recommendations for related items they may want to order. For instance, if a fish and chips shop orders batter, the system might recommend specific spices to go with it. And just prior to checkout, the system analyzes the cart to determine if the combination of products suggests that something might be missing.

So far, about five percent of items recommended this way get added to customer carts, though Ahmed believes that percentage may go down as the system gets better at anticipating customer needs in the first place. The important thing, Ahmed says, is that many of the products added in this way are items that customers didn't know JJ Food Service carried.

"The wow factor is huge," he says. "Customers are amazed that we can predict so accurately what they need."

JJ Food Service is only at the beginning of its machine learning journey. Based on its initial success, Ahmed says the company is looking for other ways to leverage the technology beyond increasing customer satisfaction and incremental sales.

"We're now thinking of expanding into other areas," he says. "Dynamic campaign management, a price optimization service for catalogs so that we can price products based on the time of the year, season and demand — if we can sell at the most optimized price point we won't overprice or underprice."

The company may also use the technology to optimize their warehouse stock by using forecasts to determine what customers, in aggregate, are likely to buy in the near future.

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Copyright © 2015 IDG Communications, Inc.

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