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."
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."
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.
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.
FAQs
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.
How big data is used in fast food industry? ›Improved Efficiency
For instance, the fast food chain uses big data analytics to optimize the drive-thru experience based on three factors: design, information provided on the menu and the types of customers coming through.
Data analytics in the food and beverage industry helps brands and restaurants recognize recurring patterns and forecast ongoing food trends. During the initial days of the lockdown, for instance, restaurant sales were at an all-time low. Eating and dining habits of consumers changed significantly during this time.
What is the need of big data analysis explain the different types of analysis techniques? ›Big data analytics benefits
Quickly analyzing large amounts of data from different sources, in many different formats and types. Rapidly making better-informed decisions for effective strategizing, which can benefit and improve the supply chain, operations and other areas of strategic decision-making.
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 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 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 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 extremely competitive. New entrants have to match the low pricing, attractive packaging, and flavor standards of consumers. Not to mention that big players like Coca Cola and Anheuser Busch already control a significant share the of non-alcoholic and alcoholic beverage markets.
Why is it crucial for hotels to integrate data analytics in its operation? ›
Data analytics in the hotel industry is key to marketing strategy, building customer loyalty, and enhancing productivity. It enables hotels to personalize experiences for their guests, introduce better hotel pricing strategies, and expand their customer base.
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.
What is the market size for analytics? ›...
| Report Coverage | Details |
|---|---|
| 2028 Value Projection | USD 549.73 Billion |
| Base Year | 2020 |
| Big Data Analytics Market Size in 2020 | USD 206.95 Billion |
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.
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.
What are current trends in the food industry? ›The Rise of Plant-Based
Plant-based diets have increased 300% for Americans in the last 15 years, and global retail sales of plant-based food alternatives may reach $162 billion by 2030 — up from $29.4 billion in 2020. If so, the projected plant-based food market would make up 7.7% of the global protein market.
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 five great techniques in food production? ›Boiling, broiling, frying, grilling, steaming and mixing. Pasteurization. Fruit juice processing.
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.
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.
Why is big data important in the hotel industry? ›Big data is a key concept to be aware of within the hospitality industry, and can help hotel owners and other business leaders to identify important patterns and trends. As a result, it can help to improve revenue management, optimise marketing efforts and enhance the customer experience that is being delivered.
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.
What are the challenges of the F&B industry? ›- Enforcement of Plastic Ban. An Optimized Supply Chain.
- Stringent Regulatory Landscape. Use of Modern Technologies.
- The Pervasive Presence of e-Commerce. Transparency, Sustainability, and Waste Reduction.
PepsiCo is currently the world's largest beverage company by sales and market value. It is one of the world's largest soft drink companies that offers products on categories including juice and smoothies, carbonates, and bottled water.
What is big data analytics in hospitality industry? ›“Big data” is the term used to describe the large volume of structured and unstructured data that a business collects every day. By analyzing big data, businesses can gain insights that lead to better business decisions, beat competitors, learn about their customers in-depth, and grow strategically.
What is the data analytics in hospitality and why it is important? ›Data Analytics can assist the hospitality industry hotels in analyzing demand, customer behavioral patterns and effectively handle the customer base.
How does analytics be useful in tourism sectors? ›
Tourism analysis is an important tool for optimizing your city's tourism marketing programs. Today's analytics-based approaches can leverage new alternative datasets to identify who your visitors are, pinpoint where you should focus your marketing efforts, and answer other important questions about your tourism sector.
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? ›The Rise of Plant-Based
Plant-based diets have increased 300% for Americans in the last 15 years, and global retail sales of plant-based food alternatives may reach $162 billion by 2030 — up from $29.4 billion in 2020. If so, the projected plant-based food market would make up 7.7% of the global protein market.
Farm service sector, producers sector, processors sector and marketers sector are the major sectors of food industry.
What are the five great techniques in food production? ›Boiling, broiling, frying, grilling, steaming and mixing. Pasteurization. Fruit juice processing.
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.
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.