Michael Hickins | Content Strategist | July 31, 2023
Data analytics is an increasingly important tool for grocery retailers, helping them understand their customers better and increase the efficiency and profitability of their stores.
First and foremost, analytics provides grocery retailers with valuable insights into customer behavior. By analyzing data on customers, such as purchase history, demographics, and social media activities, grocers can gain a better understanding of what motivates them. Grocers can use this knowledge to tailor marketing strategies and drive sales. For example, a grocery store may use analytics to find out that its most loyal customers are seniors and then create a special promotion targeting them to increase revenues.
Grocers also analyze sales data to identify slow-moving products and adjust their inventory accordingly, helping them minimize costs and maximize profits. Such data analytics is especially important for perishable goods, such as dairy, eggs, and meat. Grocers also use analytics to evaluate checkout processes and identify areas for improvement. For example, a store may use it to determine the optimal number of checkout lanes or to identify areas where cashiers may need additional training.
Finally, by analyzing sales data across different stores, grocery retailers can identify emerging industry trends and changes in customer preferences. Such analyses can inform new product launches and changes to the product mix.
Grocery data analytics is the process of analyzing purchase history, transactions, inventories, and other kinds of raw data—using advanced software applications—to make better marketing, stocking, merchandising, staffing, and other decisions.
For example, analytics software can use algorithms against shopping histories found in customer records to predict the types of goods an online shopper might want to buy in conjunction with an online search they typed. Analytics can also delve into call center logs and alert customer service agents about a problem with a particular product and provide them with a solution or alternative for customers who call about it, perhaps changing the customer’s experience from one of frustration to one of surprise and delight. Grocers often use analytics software combined with historical sales data to make savvier purchasing decisions for their stores, helping them meet customer demand.
The grocery retail business is getting ever more complex and competitive. Grocers must quickly identify new consumer trends, such as increased awareness of food allergies and diets, as well as demand for sustainably grown and organic products—all in the context of price inflation, continued supply disruptions, climate change, and changes in customer behavior in the aftermath of the COVID-19 pandemic.
Data analytics software provides grocers with the tools they need to sift through and correlate reams of empirical data to make better business decisions in a timely manner. Analytics changes the role of data from being merely a way of explaining a grocer’s financial performance in a previous week, month, or quarter to being a tool for predicting the future and prescribing actions to maximize opportunities. For example, data analytics can help grocers detect subtle shifts in consumer purchasing patterns at specific locations—such as a sudden heightened demand for cauliflower pizza at stores near universities—and suggest that those stores carry more of those items than at other locations.
Grocery retailers use data analytics in a cyclical manner, collecting data from both internal and external sources on the status of goods as they arrive in inventory, inventory levels as goods are sold or returned to suppliers, the types of consumers shopping in their stores, and prices charged by competitors. Grocers continually enrich this data and apply analytics to refine their marketing and promotional offers, pricing levels, reorders, returns, and more.
Inventory management is one of the most challenging aspects of the grocery business because of the sheer volume of perishable goods. Data analytics helps grocery retailers ensure that they have sufficient stocks of dairy, meat, fish, and other perishables; it also helps them determine when to pull products off the shelves.
Analytics also helps grocers reduce carrying costs by determining which distribution centers to use and the most efficient trucking routes. In addition, analytics helps grocers manage their inventories of nonfood items and ensure that seasonal goods (such as holiday decorations) are available for display at the right times.
Grocers use data analytics to detect fraud and theft by identifying anomalous customer purchase patterns and tracking inventory levels. For example, analytics based on point-of-sale data can help grocers identify customers making unusually large purchases or frequent returns, which could indicate they’re trying to commit refund fraud. Analytics can also show if a cashier is colluding with a friend or relative to commit fraud by improperly discounting some goods (known as “sweethearting”). Grocers also use data analytics to identify any discrepancies between their actual inventory levels and the inventory recorded in the store's system.
Data analytics tools generate predictive models that can help grocers anticipate when certain foods, beverages, and other items are likely to spoil, enabling them to take action to avoid or minimize the spoilage. Grocers also analyze data to identify areas where spoilage is occurring—such as in transit, with a given logistics company, or within a given area of a particular store—so they can mitigate those occurrences. Additionally, analytics provides grocers with insights into the factors that contribute to spoilage, such as temperature, humidity, and light, dictating changes to how they store and display food items.
Data analytics can help grocers measure the performance of their digital ordering systems. That analysis includes tracking the number of orders processed and how quickly orders are filled, as well as customer satisfaction scores. Additionally, analytics helps grocers identify patterns in online ordering behavior, letting them adjust their processes and offerings accordingly.
Grocery store managers use data analytics to chart their high-traffic times and low-traffic times so they know when to shift their people from low-traffic store functions, such as restocking shelves and cleaning aisles, to higher-traffic functions, such as checking out customers.
They also use analytics to improve inventory management, identifying at a granular level where and when to deploy people to restock specific items—especially important at supercenters and other grocers with very large footprints.
Grocers use loyalty programs to collect data on customers at both physical stores and through online memberships, and they analyze that data to generate personalized coupons and other promotions. These loyalty programs are exceedingly popular with customers—only 11% of customers surveyed say such programs rarely or never influence them. Data analytics also helps grocers correlate affinities to cross-promote goods that are often bought together, particularly by people in the same demographic group. For example, analytics might reveal that people who buy premium coffee also tend to buy organic butter, prodding grocers to group promotions of those high-margin items.
By providing insights into which products are selling well and which aren’t, data analytics helps retailers adjust their inventories and prices accordingly, improving overall operational efficiency. Analytics also helps grocers optimize their staffing levels (see the employee productivity section above) and their store layouts to make the most efficient use of space. For example, reports generated by analytics software can render heat maps showing where customers are lingering the longest. Additionally, grocers can use analytics to identify cost savings opportunities, such as places to reduce waste and energy consumption.
Data analytics can help grocers adjust their prices by providing insights into consumer behavior, pricing trends, and the prices at direct competitors. Analytics also helps grocers understand the impact of discounts and other promotional activities on profits.
Analytics can help grocers gain visibility into their supply chains by providing insights into inventory levels, demand trends, and bottlenecks in supply routes. Analytics can also help grocers identify opportunities to reduce supply chain costs and speed deliveries by optimizing transport routes. Additionally, grocers can analyze data to detect fraud in the supply chain, including noncompliance with product provenance and other regulations.
Grocers use data analytics both on the revenue and cost sides to improve their profitability. By providing insights into customer behavior and preferences, analytics helps grocers develop targeted marketing strategies to increase sales. Analytics also helps grocers identify their most profitable product categories, dictating potential changes to their product mix. On the cost side, analytics can help grocers identify opportunities to improve supply chain efficiency (see section 9) and identify lower-cost suppliers, as well as reduce outlays for energy, labor, materials, and other inputs.
Grocery data analytics software collects, stores, and analyzes industry data. Grocers use it to enhance their understanding of customer behavior, product trends, and purchasing patterns. Grocers also use the software to analyze customer purchase history to identify cross-selling opportunities, and they use it to analyze customer demographics to segment target markets. What’s more, grocery retailers use analytics software to track product sales to determine inventory needs. Other uses include tracking sales performance, analyzing competitors’ pricing strategies, and evaluating promotional effectiveness.
Oracle Retail analytics software provides insights into customer behavior, helping grocers make better decisions about pricing and merchandising. It helps them identify sales trends and opportunities, forecast demand, segment customers into different target groups, and optimize inventory levels. The software also provides visibility into the performance of store operations, helping grocers improve areas such as customer service and store layouts.
Grocers use Oracle Cloud ERP applications to take advantage of Oracle analytics capabilities that help them tailor offers, pricing, and assortments, retain customers through savvy use of loyalty programs, and even allocate the right number of stores per geographic region. The applications also help grocers determine how customers make purchasing decisions, so as to make useful predictions and prescriptive suggestions.
How do grocery stores use big data?
Grocery stores analyze big data to identify customer needs and preferences, adjust their prices, inform promotions, improve customer service, and personalize their offerings. By collecting and analyzing data from a variety of sources, including customer loyalty cards, surveys, websites, point-of-sale (POS) systems, and store-level video, grocers gain insights into customer behaviors and buying patterns. This information helps grocers determine which products to carry, how much of them to stock, and how to best promote and display them.
What data collection methods do supermarkets use?
Supermarkets use a variety of methods and systems to collect data, including online and email surveys, POS systems, loyalty programs, website cookies, video systems, social media analytics, and third-party data providers.
How is data analytics used in retail?
Retailers use data analytics to better understand customer preferences, optimize their pricing and promotions, improve inventory management, analyze the effectiveness of marketing programs and store layouts, and improve customer service.
How do grocery stores use customer transaction data?
Grocery stores use customer transaction data to help them optimize their promotions, identify new product opportunities, develop targeted marketing campaigns, adjust their prices, and manage their inventories.