Big data in the food industry: Developing a taste for analytics (2022)

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 supportsfood 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."

(Video) Farm to Table Meets Big Data Analytics | Erin Baumgartner | TEDxNatick

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.

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."

(Video) Impact of analytics on restaurant industry

Big data in the food industry

Among major U.S. food retailers, Cincinnati-based supermarket giant Krogerwas 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 statementat 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.

(Video) Data And The Food Industry | Deliveroo

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.

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This article/content was written by the individual writer identified and does not necessarily reflect the view of Hewlett Packard Enterprise Company.


How is big data used in food industry? ›

Many food companies are leveraging data analytics to design their inventory, boost business, reduce expenses, improve quality control, meet changing demands, improve consumer experience, reduce waste, and save resources. This paper provides several opportunities for big data applications in food industry.

How is analytics used in the food industry? ›

One way food and beverage manufacturers can take more control of their quality parameters is through data analytics. Multivariate data analysis provides a way to understand which elements will have the greatest effect on a product during manufacturing and predict the impact of these factors on quality and taste.

What are the data analytics in food and beverage industry? ›

Here are several areas in which data analytics is serving this industry:
  • Data democratization.
  • Revenue management.
  • Streamlining operational efficiency.
  • Risk management.
  • Market basket analysis.
20 May 2020

How big data is revolutionizing the food industry? ›

Everyone needs food for survival, and most of us thoroughly enjoy to eat. Thus, it makes sense that the industry would take advantage of the same big data services as financial firms and marketing departments to better understand their consumer, increase efficiency and even create new recipes to try.

What is the need of big data analysis explain the different types of analysis techniques? ›

Big data analytics helps organizations harness their data and use it to identify new opportunities. That, in turn, leads to smarter business moves, more efficient operations, higher profits and happier customers. Businesses that use big data with advanced analytics gain value in many ways, such as: Reducing cost.

How much is the big data industry worth? ›

The global big data and business analytics market was valued at 169 billion U.S. dollars in 2018 and is expected to grow to 274 billion U.S. dollars in 2022. As of November 2018, 45 percent of professionals in the market research industry reportedly used big data analytics as a research method.

What is meant by food technology? ›

Food technology is the application of food science to the selection, preservation, processing, packaging, distribution, and use of safe food. Related fields include analytical chemistry, biotechnology, engineering, nutrition, quality control, and food safety management.

What is computer data analysis? ›

Data analytics (DA) is the process of examining data sets in order to find trends and draw conclusions about the information they contain. Increasingly, data analytics is done with the aid of specialized systems and software.

How Data Science is used in healthcare? ›

Data Science helps in the recognition of scanned images to figure out the defects in a human body for helping doctors make an effective treatment strategy. These medical image tests include X-ray, sonography, MRI (Magnetic Resonance Imaging), CT scan, and many more.

How competitive is the food industry? ›

Food industry no longer slackers

Today, a single trip to the grocery store will tell you the food industry is a highly competitive market with new products coming out almost on a daily basis—at least around the perimeter of the store. Products in the inner aisles have seen changes, too.

What are the data analytics in lodging industry? ›

Data analytics in the hospitality industry can help hoteliers to develop a strategy for managing revenue by using the data gathered from various sources like the information found on the internet. Through analysis of these data, they can make predictions that will help owners with forecasting.

How competitive is the beverage industry? ›

The beverage industry is highly competitive. The principal areas of competition are pricing, packaging, development of new products and flavors and marketing campaigns.

What are the four types of big data analytics? ›

There are four main types of big data analytics: diagnostic, descriptive, prescriptive, and predictive analytics.

What is the purpose of big data analytics? ›

Big data analytics describes the process of uncovering trends, patterns, and correlations in large amounts of raw data to help make data-informed decisions. These processes use familiar statistical analysis techniques—like clustering and regression—and apply them to more extensive datasets with the help of newer tools.

Is big data the future? ›

Thanks to technological improvements such as greater access to massive volumes of data, big data has a bright future ahead of it, allowing organisations to gain more insights, increase performance, generate revenue, and evolve more swiftly.

What is big data analytics market? ›

Big data analytics analyzes the structured and unstructured database to understand and deliver insights based on hidden patterns, correlation, changing market trends, and more. Leading sectors focus on employing analytical tools to acquire consumer insights by evolving business intelligence.

How fast is business analytics growing? ›

The global big data and business analytics market size was valued at $198.08 billion in 2020, and is projected to reach $684.12 billion by 2030, growing at a CAGR of 13.5% from 2021 to 2030.

What technology is used in food industry? ›

Food innovations are using robots, GPS technology, and smartphone apps to improve food production processes. New technologies in various food industries improve and enhance customer experience.

What is the future of food technology? ›

Impact of food tech

The global food tech market was worth $220.32 billion in 2019, according to Emergen Research, and is estimated to grow to $342.52 billion by 2027. Food tech is increasing food production to help reduce the rate of hunger and feed the world.

What is the role of food technology in food industry? ›

More and better food through food tech

Technology helps food manufacturers to produce more efficiently for a growing world population. Improving shelf life and food safety revolves around technology, and greater use of machines and software ensures affordability and consistent quality.

Which is best tool for data analysis? ›

Top 10 Data Analytics Tools You Need To Know In 2022
  • R and Python.
  • Microsoft Excel.
  • Tableau.
  • RapidMiner.
  • KNIME.
  • Power BI.
  • Apache Spark.
  • QlikView.
22 Jul 2022

Why is data analytics important in healthcare? ›

Data analytics in the healthcare industry represents the automation of collection, processing, and analysis the complex healthcare data, to gain better insights and enable healthcare practitioners to make well-informed decisions.

What is an example of data analytics in healthcare? ›

Other examples of data analytics in healthcare share one crucial functionality – real-time alerting. In hospitals, Clinical Decision Support (CDS) software analyzes medical data on the spot, providing health practitioners with advice as they make prescriptive decisions.

What is a competitive analysis in food and beverage? ›

Used across many industries, competitive analysis is the process of studying your competition's strengths and weaknesses to understand your competitor's offerings and see how you can outperform them in the marketplace.

Is the food processing industry growing? ›

The global food processing equipment market size was valued at USD 55.50 billion in 2020 and is expected to expand at a compound annual growth rate (CAGR) of 6.1% from 2021 to 2028.

What are the benefits of having data analytics in hospitality industry? ›

Proper data analysis can allow the hotel industry to build a tailored marketing strategy plan that is targeted to specific customer types and maximize revenue. This enables advertisers to build a more personalized user experience. They can identify key consumer groups and develop more content to serve their needs.

Why do we need data analytics in hospitality industry? ›

Data Analytics has a wide range of applications in the hospitality industry and the hospitality and travel companies are leveraging it to enhance business operations, create unique marketing strategies, understand occupancy rates and yield, among others.

How do data analytics helps improve customer experience? ›

Big Data analytics removes the guesswork when it comes to understanding customer needs, pain points, goals, and interests, and it creates total visibility into the buying process. Companies can now review thousands of data points in real-time that help them understand their customers in context.

What is the fastest growing segment of the beverage industry? ›

The fastest-growing segment of the beverage industry is bottled water. Between 2021 and 2026, the bottled water segment is expected to grow at a 7.4% annual growth rate.

Why Coca-Cola still dominates the beverage market? ›

Coke is sold in more than 200 countries and territories worldwide. This diverse representation helps the company in steady growth. Coke also has one of the world's largest distribution networks and derives more than 40% of sales from developing and emerging economy with the growing middle class.

What is the meaning of food industry? ›

The term food industries covers a series of industrial activities directed at the production, distribution, processing, conversion, preparation, preservation, transport, certification and packaging of foodstuffs.

What is the meaning of exploratory analysis? ›

Exploratory Data Analysis refers to the critical process of performing initial investigations on data so as to discover patterns,to spot anomalies,to test hypothesis and to check assumptions with the help of summary statistics and graphical representations.

What are current trends in the food industry? ›

Trends that are gaining support are shifts away from fossil fuel-based energy sources, reduced water consumption, sustainable packaging (recyclable, biodegradable, or options that significantly reduce plastic use), and clean, eco-friendly detergent and cleaning solutions for end-products and equipment.

What are the four major sectors in the food industry? ›

Farm service sector, producers sector, processors sector and marketers sector are the major sectors of food industry.

What are the two goals of exploratory data analysis? ›

Purpose of EDA

The purpose of exploratory data analysis is to: Check for missing data and other mistakes. Gain maximum insight into the data set and its underlying structure.

What is an example of exploratory data analysis? ›

There are dress shoes, hiking boots, sandals, etc. Using EDA, you are open to the fact that any number of people might buy any number of different types of shoes. You visualize the data using exploratory data analysis to find that most customers buy 1-3 different types of shoes.

Why do we need exploratory data analysis? ›

Why Is EDA Important? Exploratory data analysis is essential for any business. It allows data scientists to analyze the data before coming to any assumption. It ensures that the results produced are valid and applicable to business outcomes and goals.


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