Michael Hickins | Content Strategist | March 17, 2023
Retailers used to rely mostly on their instincts and hunches, honed through years of experience, to make decisions about which items to sell, which locations will likely draw the most demand, how much inventory to carry, and when to adjust prices. And while retailers are often proud of their acumen, instincts no longer are enough, especially in an industry with narrow profit margins. Consumers are too fickle and market conditions are too numerous for humans to accurately account for all those variables. Data analytics software can help make decision-making more precise and profitable for retailers by augmenting—and, in some cases, correcting—those well-educated hunches.
Retail analytics involves using software to collect and analyze data from physical, online, and catalog outlets to provide retailers with insights into customer behavior and shopping trends. It can also be used to inform and improve decisions about pricing, inventory, marketing, merchandising, and store operations by applying predictive algorithms against data from both internal sources (such as customer purchase histories) and external repositories (such as weather forecasts). In addition, retail analytics can measure customer loyalty, identify purchasing patterns, predict demand, and optimize store layouts so that, for instance, retailers can place items on store shelves that are often bought together or offer personalized discounts to frequent shoppers that will result in higher average basket sizes and more frequent visits.
Retail analytics is the science of collecting, analyzing, and reporting on data related to a retailer’s operations. It complements the art of retail.
Retail analytics can apply to analyzing customer behavior, tracking inventory levels, measuring the effectiveness of marketing campaigns, and more. For example, by analyzing data from a variety of sources, such as customer purchase histories, call center logs, and POS systems, retailers can gain valuable insights into their customers’ habits and preferences so they can adjust their product offerings, pricing, return policies, and even their physical and online store layouts accordingly. Analytics also helps retailers make better decisions about which promotions to run and which marketing strategies to focus on, as well as when to staff up and down. Ultimately, data analytics helps retailers increase sales, reduce costs, and improve customer satisfaction and loyalty.
Simply put, retail analytics takes the guesswork out of many types of decisions. Experienced employees are often a font of wisdom, but as the baby boomer generation ages out of the workforce, less experienced employees will have fewer insights to share. And even the most experienced and savvy retail executives must wade through a plethora of internal and external data points on factors that include labor strikes, merchandise trends, and weather forecasts. Analytics helps retailers synthesize such data and take steps to anticipate future events.
Retail is a highly competitive business complicated by the relative novelty of online commerce, and retail profit margins have always been thin, leaving little room for error. Even slight adjustments in product selection and inventory management can greatly reduce stockouts or, at the other end of the same spectrum, the need for steep discounts. Those adjustments, in turn, can have an enormous impact on the bottom line. For example, fashion retailers can use data analytics to decide which styles and sizes to order for different locations and in what quantities, based on demographic and purchasing trends at each location.
Retail analytics is a set of tools that retailers use to help them increase revenue, reduce overhead and labor costs, and improve their margins. Some of the ways retail analytics can accomplish these goals are by:
There are four main types of retail data analytics: descriptive analytics that reflect and explain past performance; diagnostic analytics to determine the root cause of a given problem; predictive analytics to forecast future results; and prescriptive analytics to recommend next steps. Below is more detail on each of the four approaches.
Descriptive analytics is the foundation for more sophisticated types of analytics, including those that follow in this list. It addresses fundamental questions of “how many, when, where, and what”—the stuff of basic business intelligence tools and dashboards that provide weekly reports on sales and inventory levels.
Diagnostic analytics helps retail organizations identify and analyze issues that may be hindering their performance. By combining data from multiple sources, such as customer feedback, financial performance, and operational metrics, retailers gain a more comprehensive understanding of the root causes of problems they face.
Predictive analytics helps retailers anticipate future events based on several variables, including weather, economic trends, supply chain disruptions, and new competitive pressures. This approach often takes the form of a what-if analysis, which, for example, would let a retailer map out what would happen if it offered a 10% discount versus 15% on a product, or estimate when it would run out of stock based on a given set of possible actions.
Prescriptive analytics is where AI and big data combine to take those predictive analytics outcomes and recommend actions. Prescriptive analytics can, for example, provide customer service agents with suggested offers they can pass along to customers on the fly, whether that be an upsell based on previous purchase history or a cross-sell to satisfy a new customer inquiry.
Companies use retail analytics to explain past operational and financial performance, diagnose what might have gone wrong, suggest alternative approaches that would have been more productive, forecast demand, and offer suggestions, sometimes in real time, that store associates, customer service agents, and others can use to cross-sell, upsell, or improve the customer’s experience. In all cases, the tools are intended to help retailers boost sales, profits, and customer satisfaction.
Retail analytics relies on data captured through a variety of means, both at physical store locations and on websites. The following are some of the tools used:
Customers provide a lot of explicit and implicit information about their desires and intentions, and the best practitioners of retail analytics use that data to identify trends and better understand those customers. Leading retailers blend customer data from their own loyalty programs with data they collect from ecommerce, POS systems, and other sources, as well as with data purchased from brokers.
Experts often categorize customer data as a blend of demographic, transactional, behavioral, and even psychographic points. Collecting, consolidating, and capitalizing on those varieties of customer data often follow a progression, starting with the broad demographic variety. Retailers also make a distinction between “customers” (people who have already done business with them) and “consumers” (who include those who might make good prospects). Consumer data can help inform “lookalike modeling”—for example, a retailer identifies Mark as a great customer, so it looks for more people with similar attributes and targets them with special offers.
Visualization tools such as charts, graphs, and dashboards, common in BI software, are essential for understanding data and making informed decisions. They are a much more effective way of grokking information than simply staring at rows and columns of data. BI visualization tools also put analytics into the hands of business users, rather than forcing them to wait for IT to generate reports and run queries.
Analyzing multiple data sources, including sales data, historical customer data, and inventory data, can help retailers gain a more nuanced view of the business, especially as metrics are often interdependent. For example, retailers can correlate in-store analytics with merchandise attribute analytics to determine how to optimize the layout of a physical store to help turn shoppers into paying customers. Inventory analytics can help ensure the retailer has enough goods on hand to support the merchandising layout. (Retailers should also be mindful that different applications may have different definitions for data types, which could lead to incorrect analyses if not corrected. This is an argument in favor of using a single platform for retail analytics as opposed to adopting so-called best-of-breed applications.)
Tracking key performance indicators helps retailers measure their performance and identify areas for improvement. Most successful retailers have adopted weekly KPI summaries (known as balanced scorecarding), comparing the latest metrics to those from the prior week. That usually starts with a review of what happened (for example, sales dropped for certain items), followed by a deeper analysis into why it happened (for example, because of stockouts).
Not everything that can be measured should be measured. New analytic tools and an ocean of data are available to retailers, but they need to be judicious about what they measure or risk drowning decision-makers with recommendations. Retailers should start by identifying high-priority opportunities that can have an immediate impact on the business. The best analytics solve a particular business problem and achieve a measurable outcome, according to McKinsey.
Mark Lawrence, a retail analytics expert, suggests that all five of the best practices above tie together. His recommendation: Start with a goal, then perhaps two or three underlying objectives. The KPIs that inform progress at this level are “leading” KPIs, he says. If one goal is to “get closer to the customer,” he says, KPIs could be to “increase customer lifetime value by 20%,” “achieve 15% year-on-year consumer conversion,” and “optimize inventory levels to support customer-centricity objectives.” Visualization tools let business leaders review progress toward meeting those objectives and spur corrective actions, such as new promotions and changes to product assortments.
In the coming years, retail analytics will become more widespread, less visible, and, most certainly, less discussed. Users and applications will leverage analytics continuously, often unknowingly—not unlike the way smartphones constantly use location tracking to quickly meet users’ needs.
For business users, retail analytics will become less about producing or reviewing weekly reports and become more embedded into their daily workflows. More people will have access to the fruits of AI in their regular business activities, even without being aware of it. AI-based data analyses will be normalized, no longer hyped.
When choosing retail analytics tools, consider ones that can ingest and correlate data from a variety of internal and external sources, use AI to produce deep insights, and scale to grow with your business. Oracle Retail’s integrated suite of cloud services includes analytic tools for merchandising, inventory planning and management, and customer engagement across channels and can be fully deployed within just a few months.
What are examples of analytics?
Retailers use analytics for a variety of reasons: predict demand, guide managers to buy and allocate sufficient inventory to meet that demand, help understand customer behaviors, optimize pricing, and make staffing decisions.
What kind of data is used in retail analytics?
Retail analytics uses a variety of data from internal and external sources, including customer purchase histories, call center logs, ecommerce-site navigation, POS systems, in-store video, and customer demographics.
Which kinds of decisions does retail analytics help retailers make?
Retail analytics helps take the guesswork out of retailing by providing industry executives with guidance on how much to order of a given item, where to store it, how much to charge for it, and what types of goods tend to be bought in conjunction with each other.
See how Oracle solutions, with built-in AI and machine learning features, help fashion retailers deliver an efficient shopping experience that meets consumers' needs.>