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.

Using Machine Learning to Improve Food distribution

For B2B organizations, the ongoing shift from call center-based services to ecommerce portals has been a huge boon, decreasing costs and increasing efficiency. But it has also resulted in a loss of interaction with customers, and in many cases that lost interaction has cost them cross-sell and upsell opportunities. The U.K.'s JJ Food Service has responded by leveraging its data and machine learning.

JJ Food Service is one of the largest independent food delivery companies in the U.K., with 60,000 customers in the form of cafes, restaurants, schools, office canteens and the like. The family-owned company has been in business for 26 years and has nationwide operations with eight distribution centers across the U.K. It offers all sorts of food products — fresh, frozen and packaged — as well as related products, like cleaning supplies. In total, the company's catalog lists more than 4,500 products.

"Our products are bigger pack sizes and predominantly focused to restaurants and catering organizations," says Mushtaque Ahmed, COO of JJ Food Service. "We try to be a one-stop shop and serve all of their needs."

The company is no slouch at what it does. Last year, The Grocer, a weekly U.K. magazine focused on the fast-moving consumer goods (FMCG) industry, named JJ Food Service the Wholesaler of the Year.

Until about five or six years ago, Ahmed says, JJ Food Service was almost entirely dependent on call center agents for orders. It accepted orders from other channels like Fax and email (and still does), but the vast majority of orders came through the call center.

"A lot of our customers are not quite tech-savvy," Ahmed notes.

Then the company built its ecommerce portal. It now takes about 60 percent of its 5,000 daily orders via ecommerce and about 40 percent via call center. The other channels are negligible. On the whole, the move has been very good for JJ Food Service Ahmed says, but the loss of personal contact with customers has hurt.

"Before we had the opportunity to talk to customers and do some upselling, cross-selling, soliciting of new products and telling them about the market segment they were in," he says. "When we moved to the ecommerce portal, we lost that capability of talking to the customer. There was nobody to tell them about anything new that we were doing or to suggest some of the products they weren't buying. We were missing out on those opportunities."

But what the company did have was data. In 2004, JJ Food Service implemented Microsoft Dynamics for ERP and CRM. For the past 10 years it has been refining its operations, and Microsoft Dynamics AX now powers the company's entire operations, from HR, procurement and sales to warehouse management and order processing.

Ahmed felt that rich vein of data could be the answer to capitalizing on those lost opportunities by using predictive analytics to anticipate customer orders and recommend relevant products to them at the point of sale. To do it right, the company needed to tailor insights based on each customer's past order patterns; for instance, the fact that a particular customer orders salad greens every day, flour about every two weeks and cooking oil once a month.

Machine Learning in the Cloud

The costs associated with staffing and implementing an advanced analytics project like this seemed daunting, but because JJ Food Service is a Microsoft shop, Ahmed was aware of a new Azure service in preview: the machine learning service Azure ML.

"Machine learning was quite new to us," Ahmed says. "We didn't have any clue what was happening out in the market. We thought it must require a big budget with big data scientists involved and we thought we weren't prepared for that investment. We also didn't have any internal resources to do a small proof of concept or prototype. But we were already on the Microsoft stack. Azure ML took away a lot of the fear factors. That's why we went down the Azure ML path."

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