What Is Retail Analytics? The Ultimate Guide (2024)

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.

What Is Retail Analytics?

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.

Key Takeaways

Retail analytics uses empirical data and science to make decisions in what traditionally has been an intuition-led field.
Analytics helps retailers set inventory levels, allocate staff, set prices at levels that will entice shoppers while providing the business with sufficient profit margins, and capture market share.
Retail analytics uses a plethora of data sources, including point-of-sale (POS) systems, in-store video feeds, and systems that track individual customer purchase and service histories.
Retail analytics tools sometimes use AI and machine learning to help predict trends, suggest next offers, and provide the basis for pricing and inventory-allocation decisions. Strong visualizations make these tools easier to use than spreadsheets or other plain-text business intelligence reports.

Retail Analytics Explained

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.

Why Is Retail Analytics So Important?

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.

Benefits of Retail Analytics

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:

Reducing stockouts and the need for discounts: Retail analytics helps users understand demand trends so they can have enough product on hand, but not so much that they resort to steep discounts to get rid of excess inventory. For example, analytics can help determine how quickly demand falls for fashion items that are driven by the popularity of social influencers.
Improving personalization: Analytics helps retailers understand their customers’ preferences and thus capture more demand than their competitors. For example, using purchasing history, a book retailer can alert customers who have shown interest in American history when a new book by historian Ron Chernow becomes available for preorder.
Improving pricing decisions: Data analytics can help retailers set the optimal prices for their goods by synthesizing a variety of factors, including abandoned shopping carts, competitive pricing information, and the cost of goods sold. Retailers can thus maximize profits by avoiding setting prices higher than the market will bear or lower than what customers would be willing to pay.
Improving product allocations: Analytics can help retailers decide how to allocate products in different geographic regions, distribution centers, and stores, reducing needless transportation costs. For instance, a sports apparel retailer can use analytics to see that even a two-degree difference in temperature affects sales of thermal undershirts and can allocate more of those items to a distribution center closest to areas projected to have colder temperatures in a given winter.

Types of Retail Data Analytics

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

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

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

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

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.

How Is Retail Analytics Used?

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.

In-store analytics tools use data generated from POS systems and in-store video cameras to help retailers analyze customer shopping patterns so they can place products more effectively in aisles, ensure appropriate inventory levels, and reduce theft. Video footage, for example, can show whether customers are slowing down to look at a given display, while POS system data can show the effectiveness of merchandising on customers who use their loyalty cards.
Customer analytics uses data from systems that customers interact with, including POS systems, websites, phone logs, and customer service chats. Analyzing this data helps retailers determine which and where certain items are most popular, why certain items are being returned or exchanged, or what promotions or suggestions are most effective with customers. For example, it can help determine what marketing language is most effective over the phone, as opposed to in chats, to promote a new item.
Inventory analytics, as the name suggests, assesses inventory levels for goods a retailer has on offer. It’s used to prescribe more efficient warehousing and distribution strategies, such as when a distribution center is preferable to a more local storehouse, and when to replenish items based on inventory levels and projected demand. Inventory analytics can, for example, reduce the labor and shipping costs associated with carrying too much safety stock.
Merchandise analytics helps retailers determine whether they’re displaying their wares effectively, mostly in physical stores, with the goal of enticing consumers to make a purchase by using compelling assortments or offers. Merchandise analytics also helps retailers adjust prices to increase profit margins across products.
Web analytics tracks the digital footprint of consumers as they linger over certain parts of a web page or click from one page to another. It follows them from the source that led them to the site to the moment they leave. This type of analytics helps online retailers decide how and where to display their goods on the site, the prices they charge, and the marketing promotions they should run.
Business intelligence (BI) reports, often presented in the form of dashboards, are preset to show certain key performance indicators, such as inventory turns and sell-through rate. They are used mainly to share top-line trends with peers and senior management.
Demand forecasting forecasts demand for particular items sold online based on the path customers followed to view those items, move them to their shopping cart, remove those items, or abandon the cart entirely. While those actions aren’t counted as sales, they can extrapolate future demand.
Sales forecasting helps retailers predict future sales based on actual sales figures and other factors. Used in tandem with demand forecasting, it can predict what total demand will be for an item across all channels and can help retailers ensure they have the necessary inventory to fulfill that demand.

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Retail Analytics Tools

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:

Point-of-sale systems: These are the systems that retailers use to track and manage customer transactions. POS systems provide data on customer purchases and can generate reports on sales and customer trends.
Customer relationship management (CRM) software: This software category includes applications that manage sales, marketing, customer service, and ecommerce processes. Retailers use these applications to track interactions with customers, retain data about individual customers, and identify potential sales, marketing, and customer service opportunities based on that information.
Business intelligence tools: Retailers use BI tools to synthesize information gleaned from large volumes and different sets of data, mostly to track key performance indicators such as customer loyalty, inventory turns, sell-through rate, and days on hand. Retailers can easily generate reports from these tools and distribute them to executives and other decision-makers.
Inventory management systems: Retailers use this software to track items in stock, monitor inventory levels in warehouses and distribution centers, and create forecasts of demand. It also helps retailers identify optimal locations for storing certain items to minimize transportation expenses and ensure that goods are available to meet customer demand.
Predictive analytics: This type of analytics uses data from prior transactions, communications, and other actions to predict future trends and behaviors. The four most common types of retail analytics are descriptive, diagnostic, predictive, and prescriptive (defined above), used to identify opportunities for growth and new customer segments.

5 Retail Analytics Best Practices

1. Lean heavily on customer data

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.

2. Use visualization tools

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.

3. Analyze multiple data sources

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.)

4. Track KPIs

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).

5. Prioritize your goals

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.

The Future of Retail Analytics

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.

Drive More Revenue by Leveraging Retail Analytics Software

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.

Retail Analytics FAQs

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.

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As an expert in retail analytics, I can attest to the transformative power of data-driven decision-making in the retail industry. The article accurately highlights the shift from relying on instincts and experience to leveraging data analytics software for precise and profitable decision-making. I have personally witnessed the profound impact of retail analytics on various aspects of the retail business, and the evidence supporting its efficacy is abundant.

The concept of retail analytics involves using sophisticated software to collect and analyze data from physical, online, and catalog outlets. This data is then utilized to provide retailers with valuable insights into customer behavior, shopping trends, pricing strategies, inventory management, marketing decisions, and overall store operations. The article rightly emphasizes that retail analytics goes beyond traditional intuition, employing empirical data and scientific methodologies to inform decision-making in what was once an intuition-led field.

Key Concepts:

Retail Analytics Overview:
- Retail analytics is the science of collecting, analyzing, and reporting on data related to a retailer's operations, complementing the art of retail.
- It encompasses the analysis of customer behavior, inventory tracking, marketing campaign effectiveness, and more.
Importance of Retail Analytics:
- Retail analytics is crucial because it removes guesswork from decision-making.
- It addresses the challenges posed by a changing workforce, evolving market conditions, and the need for precision in a highly competitive industry.
Benefits of Retail Analytics:
- Reducing stockouts and the need for discounts by understanding demand trends.
- Improving personalization by analyzing customer preferences.
- Enhancing pricing decisions to maximize profits.
- Optimizing product allocations and reducing needless transportation costs.
Types of Retail Data Analytics:
- Descriptive analytics: Reflects and explains past performance.
- Diagnostic analytics: Identifies the root cause of issues.
- Predictive analytics: Forecasts future results.
- Prescriptive analytics: Recommends next steps based on predictive outcomes.
How Retail Analytics is Used:
- In-store analytics tools analyze customer shopping patterns for effective product placement and inventory management.
- Customer analytics uses data from various customer interaction systems to understand preferences and behaviors.
- Inventory analytics assesses optimal warehousing and distribution strategies.
Retail Analytics Tools:
- Point-of-sale systems, CRM software, business intelligence tools, inventory management systems, and predictive analytics are essential tools in retail analytics.
Retail Analytics Best Practices:
- Lean heavily on customer data.
- Use visualization tools for better understanding.
- Analyze multiple data sources for a nuanced view.
- Track key performance indicators for continuous improvement.
- Prioritize goals for effective measurement.
The Future of Retail Analytics:
- Retail analytics will become more widespread, less visible, and seamlessly integrated into daily workflows.
- AI-based data analyses will be normalized, becoming an integral part of regular business activities.

In conclusion, the article provides a comprehensive overview of retail analytics, highlighting its importance, benefits, types, and best practices. As someone deeply immersed in the field, I can affirm that retail analytics is a game-changer for the industry, driving efficiency, profitability, and customer satisfaction.