A 2015 McKinsey study reported that food retailers can improve their operating margins by up to 60% simply by harnessing the power of Big Data. In order to keep pace with consumers’ fickle buying habits, food and beverage companies need to begin combining raw point-of-sale data with the Big Data that is now available. Analytical capabilities then can transform this data into meaningful intelligence that can inform management decisions. Those decisions will boost sales and improve their overall bottom-line performance. For example, food and beverage retailers, suppliers, and trading partners can share Big Data to ensure they offer the right products, in the right quantities, in individual stores and online.
Big Data Helps Drive In-Store Revenues
Food and beverage companies can use Big Data to increase traffic to their brick and mortar stores. The GPS location capabilities of most mobile phones provide a channel for retailers to display “pop-up” promotional messages that are highly relevant to an individual’s specific location and past purchasing history. A shopper, for example, standing in a frozen food aisle can receive a text offering a discount for a certain ice cream flavor nearby that she has bought in the past.
Big Data Helps Schedule Food Deliveries
Big Data can optimize on-time deliveries of orders to restaurants, food chains, and home customers. Big Data will collect recent information from various sources about road traffic, weather, temperature, routes, etc. and provide an accurate estimate of the orders’ times. This data analysis helps ensure that food/beverage companies don’t waste their resources transporting stale products. They will deliver perishable food items when they are fresh.
Big Data Helps Allocate Food Across the Country
By using Big Data to track purchasing decisions from wholesalers down to the customer level, food and beverage companies can learn what products are being purchased and where. For example, a company might learn that customers in the Pacific Northwest are purchasing 15% more of a diet beverage than the nationwide average. Further, they may learn that the Midwest is purchasing 15% less of that same beverage. This knowledge allows the company to know to ship more of the diet product to the Pacific Northwest and less to the Midwest.
Big Data Helps Maintain Consistent Food Quality
Big Data allows restaurants to maintain consistent quality of their products. Consumers expect the same taste in food at the chain restaurants they love. The taste of food not only depends upon the proper measurement of ingredients, but also on their quality, storage, and season. Big Data analytics can analyze such changes and predict the impact of each on the food quality and taste. The insights from these analyses will be used to identify pain points and suggest measures for improvement.
Big Data Analyzes Customer Sentiment
Big Data can analyze customer sentiment by monitoring customer emotions expressed on social media networks. Food companies use sentiment analysis to track their customers’ emotions. They can assess negative reviews and take appropriate preventive steps before the word spreads. Large food retailers like McDonald's, KFC, and Pizza Hut have found this particularly valuable.
Big Data Has a Good Idea What Customers Will Purchase Next
Food and beverage companies use Big Data for “market basket analysis.” Market basket analysis is a technique which predicts the most obvious item that a customer is likely to purchase next based on her purchase history and the items already in her cart. Food retailers and restaurants use these projections to create effective combo deals and improve their marketing messages. For example, if the market basket analysis identifies that a customer prefers a muffin with her coffee, then it can create a combo to help her enjoy them together.