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Enhance Customer Retention Through Predictive Analytics

Optimize your resources, meet objectives, identify your customers’ trends and patterns—and even encourage their attitudes by applying Predictive Analytics to your Food & Beverage data.

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Optimize your resources, meet objectives, identify your customers’ trends and patterns—and even encourage their attitudes by applying Predictive Analytics to your Food & Beverage data.

Data is what makes the world go around, and in the food and beverage world, there may be no better way to uncover hidden patterns, meet customer expectations, improve marketing campaigns and yes, even gain advantage over competitors than with predictive analytics.

Certainly, the data an organization may have in its enterprise resource planning (ERP) system, such as inventory management, production control, warehousing, etc. is critical, but ultimately it is the customer who provides the most useful information. And as customers become increasingly influential, it is more important than ever to keep an ear to their wants, anticipate trends and garner the information necessary to make decisions that result in quantifiable advantages.

Inasmuch as the food and beverage industry encompasses many aspects – supply, transportation, quality control – each component will have a different set of requirements, but with all driven by the consumer.

Grocery stores, for example, may have a loyal relationship with regular suppliers, but at the same time must be able to react to a constantly fluid market. Special occasions, like the Super Bowl, Valentine’s Day, or Christmas place specific food items in high demand. Store owners may be aware of the products they need to stock up on but, in the absence of analytical data, may not know how much to have on hand—or if there are other items that should be offered for added value and customer retention.

Predictive analytics, sometimes referred to as “machine learning,” can help determine the needed quantity of existing products and the addition of new store items through the use of algorithms and statistical models. This subset of artificial intelligence (AI) relies on patterns and inference. Without getting too technical, predictive analytics builds a mathematical model from sample data in order to make predictions or decisions without being explicitly programmed to perform a specific task.

A machine learning system could help predict grocery store demand based on a particular event and then take that information and share it not only with one grocery store or chain, but also with distributors. Just because a grocery store ordered twice as many bags of chips for Super Bowl Sunday this year than last doesn’t mean the chip distributor will have enough inventory on hand to accommodate the increase. At blumshapiro, we believe this type of information cross-sharing should be more common among supply chain participants.

Machine learning can also play a role in transportation and logistics. Say you’re a large grocery distributor and need to ensure delivery of product to customers on a timely basis. To meet that demand, drivers must be available. And, it would be beneficial to know which routes they should take to minimize the number of stops and maximize the potential for on-time delivery. Machine learning can be applied to come up with optimized solutions.

Predictive analytics can also play a part for food goods producers. Boiled down, food producers are essentially process manufacturers. The manufacturer of a cake mix, for example, has to take all ingredients into account when making the final product. But depending on the time of year, the heat, cold, humidity, etc. can have an impact. Environmental factors can influence how a recipe pans out—and here is where the use of predictive analytics and anomaly detection can prevent costly bad batches.

Anecdotally, we are aware of one manufacturer who has created a new end-cap display that incorporates electronic price strip displays along with cameras. As customers approach, they are viewed by the camera to assess any number of inferences (gender, age, race, size, disposition) that might impact propensity to purchase. Through the use of this combination of machine learning and computer vision, the manufacturer, based on past purchase history and variety of customers, can automatically change the display to showcase a specific deal.

By applying Predictive Analytics to their data, those in the food and beverage industry can optimize their resources, meet objectives, identify their customers’ trends and patterns—and even encourage their attitudes!

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