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.

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.

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.

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:

1. 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.
2. 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.
3. 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.
4. 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.
5. 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.