Food producers, retailers, and restaurants are using data analytics to better understand customer needs and uncover important food industry market trends.

The food industry is one of the world's largest and most important business sectors. The field encompasses everything from producers and shipping companies to retailers and restaurants.

Food is nothing less than an essential part of life and a major global economic force. Therefore, it makes perfect sense for the food industry to follow the path already taken by many financial and marketing firms and use sophisticated analytics tools and methods to better understand consumers and uncover emerging market trends.

Big data-driven analytics supports food industry businesses with critical decision-making capabilities in the areas of pricing, product promotion, product development, and demand forecasting. Benefits include improved product innovation, greater sales effectiveness, enhanced margins and profitability levels, extended customer reach, increased marketing ROI, and greater customer satisfaction and loyalty.

"To stay competitive in the industry, food and beverage companies should highly consider implementing data analytics tools," says Lori Mitchell-Keller, global general manager of consumer industries for analytics technology provider SAP. "Companies that have unbiased, analytical insight into their consumers and overall operations will have a serious advantage over their competitors."

Data mining farm-fresh insights

Oberweis Dairy, headquartered in North Aurora, Ill., operates a chain of dairy-related shops and restaurants, a direct-to-home delivery service, and a wholesale dairy business in the Midwestern U.S. Like a growing number of food industry businesses, Oberweis decided several years ago that it needed to get inside customers' minds in order to fully understand their needs and preferences. "Our primary goal for analyzing data is to understand our customers better," says Bruce Bedford, the company's vice president of marketing analytics and consumer insights. "We want to identify our best customers for cross-selling and up-selling purposes, and identify those customers who are at risk for leaving so we can intervene."

Using SAS analytics tools, Oberweis gathers data from numerous sources, including store point-of-sale transactions and delivery service records, as well as data generated by its wholesale shipments to neighborhood grocery stores. The company also taps into third-party datasets to understand evolving situations and events that could potentially hinder deliveries or store traffic. "For example, weather data comes to us through a web interface supported by the Midwestern Regional Climate Center," Bedford says. Additionally, a significant amount of potentially useful data is still entered manually into spreadsheets by staff located throughout the company. "SAS helps us read data from all of these sources, regardless of how it is entered, and process it into common, consistent, and structured formats that can be used for reporting and analyses of all types," he notes.

Denmark’s largest grocer transformed an IT bottleneck into an engine of in-store retail analytics and business agility, resulting in increased revenues and reduced waste. Read how

Bedford views analytics as an essential business technology. "Whether you're selling wholesale or serving customers in a retail model, the strategic use of analytics is essential for any food business that wants continued success in this competitive industry," he says.

A growing number of restaurant operators have realized that analytics is critical to success in competitive markets. Darden Restaurants, which operates Olive Garden, LongHorn Steakhouse, Bahama Breeze, and several other restaurant brands, relies on analytics to detect fraud, optimize menu prices, and study the length of customer visits. The Cheesecake Factory has relied on analytics for several years to develop customer-pleasing dishes and ensure a better overall customer experience. In the fast-food sector, Subway is one of several players that use analytics to study daily operations, spot opportunities for improved efficiency, and increase revenue.

Bedford notes that the recent Amazon-Whole Foods Market merger is spurring an even deeper interest in analytics among food industry players. "What they're going to offer consumers ratchets up the stakes for everybody in the food business," he says. "Becoming a data-driven culture that values the insights analytics can reveal is key for remaining relevant in this landscape."

Big data in the food industry

Among major U.S. food retailers, Cincinnati-based supermarket giant Kroger was an early and enthusiastic analytics adopter. The company now has its own in-house data analytics firm in the form of a wholly owned subsidiary, 84.51°, which it uses to gain deep insight into customer preferences and ordering patterns. Last year, 84.51° expanded its capabilities by acquiring Market6, a predictive analytics specialist. “Every decision we make focuses on engaging customers where, when, and how it matters most to them,” CEO Stuart Aitken said in a statement at the time of the Market6 acquisition.

A key way Kroger uses analytics to drive sales is by generating personalized offers and tailored pricing to customers through its MyMagazine direct marketing initiative. Leveraging analytics from 84.51°, Kroger was able to deliver in the first quarter of 2017 more than 6 million unique and customized offers to its Plus Card members through MyMagazine. “Kroger has more data than any of our competitors, which leads to deep customer knowledge and unparalleled personalization,” observed Rodney McMullen, Kroger's chairman and CEO, during the company's 2017 second-quarter earnings call. "We have a history of evolving to meet our customers’ ever-changing needs. The key is to proactively see where the customer is going and to proactively address the changes."

In Denmark, Dansk Supermarked Group (DSG) is using analytics to match its inventory needs to customer preferences, ensuring that it never misses a sale yet never overstocks items, a practice that leads to waste and needless costs. Shoppers benefit by always having access to a wide selection of fresh food products.

A mass-market retailer that serves up to 1.4 million store customers a day, DSG uses SAP HANA analytics technology to predict the types of food consumers will purchase by analyzing recent sales data trends. The approach generates accurate and timely insights into each store’s overall shopping history.

DSG’s systems continuously inhale massive amounts of transactional data generated by point-of-sale systems located in stores scattered across Europe. The information is rapidly analyzed to deliver information-rich, actionable reports to key decision-makers throughout the company. Store managers, from the moment they arrive in the morning, can view in detail exactly what customers purchased the day before. That helps the company make the best possible inventory stocking decisions. At the top executive level, DSG has the insights it needs to plan for future growth, including opening additional stores, introducing a new convenience-store format, and pursuing promising e-commerce opportunities.

Viewing the big picture

Data analytics offers another important benefit: the ability to reveal clues to larger and potentially significant market trends. The recent spike in avocado prices provides a prime example of how analytics can help retailers and restaurants manage costs, says Nic Smith, SAP's global vice president of product marketing for cloud analytics. Over the past few years, the U.S. consumption of avocados has significantly spiked. Meanwhile, retailers and restaurants have seen avocado prices more than double over the past 12 months. "With the ability to review recent purchase history and consumer sentiment, businesses can predict consumer demands well in advance," Smith says.

Smith notes that as soon as consumers began purchasing avocados in much larger quantities, restaurants with access to that data should have incorporated more avocado-based options on their menus. "Grocers should have stocked up and provided a range based on price and variety, and producers should have invested in methods to ensure a constant flow of fresh, ripe avocados year-round," Smith says. "By interpreting this trend with analytics, restaurants, grocers, and retailers could have experienced a significant boom in business, without the stress of not being prepared for the increased demand for avocados."

A priceless tool: Vision

Bedford notes that analytics gives food industry businesses a priceless tool: vision. "By analyzing data, you're in a much better position to make sound predictions about customer preferences and behavior," he says. "If you can tap into customer sentiment and offer products and services before they even realize they need or want it, you’re as close to bulletproof as any company can be."

Smith agrees. "Soon, all successful businesses will rely on analytics to inform company decisions around production, sales, and marketing," he says. "While larger companies were the first to invest in the technology, smaller food companies are now following suit, as they see the value of having access to data analytics at a moment's notice."

Big data in the food industry: Lessons for leaders

Use data analytics tools to understand customer preferences in order to stock or serve the right products at the right time.

Carefully analyze collected data to uncover and address trends that may soon help or hurt the business.

Look for and evaluate promising new data analytics technologies and methods to keep pace with competitors and customer demands.

Allow managers to access data and make fast and decisive changes based on the insights they receive.

Related links:

From infrastructure to workloads: Full stack support with HPE Adaptive Management Services

HPE GreenLake Big Data: Unwrapping the Gifts Your Data Brings

How server memory advances real-time analytics