A Short Title

Think about your favorite local mom-and-pop store. They know everything about their customers—their favorite foods, what size clothes they wear, and even what they’re most likely to buy in the future. They know exactly who their customers are and how to serve them best.

Of course, mom and pop have the advantage of being personally involved with every customer and every transaction. That just doesn’t scale to a large retailer with multiple locations and online shopping. So how can big retailers understand their customers at the same level?

The answer is retail analytics. By collecting and analyzing data about customer behavior, competition, and more, retailers of all sizes can achieve that same level of understanding. The insights gained from retail analytics allow you to identify trends, predict outcomes, and make informed decisions.

What Are Retail Analytics, and Why Are They Important?

Retail analytics is the process of tracking, collecting, and analyzing retail data to better understand business performance, identify trends, and drive decision-making. With the power of retail analytics, we can anticipate customer behavior and respond accordingly. We can replace guesswork with informed decision-making backed up by data.

Without retail analytics, retailers both large and small are flying blind. Making decisions based on lagging indicators and gut instinct is just not good enough in today’s changing world. What works today may not work tomorrow.

However, retail analytics only work when done correctly. When doing any kind of data collection and analysis, it’s important to understand what you’re doing. Let’s start by looking at the different types of retail analytics.

Types of Retail Analytics

The field of retail analytics encompasses a wide variety of techniques. There are many types of data and just as many ways to analyze it. In general, though, there are four types of retail analytics: descriptive, diagnostic, predictive, and prescriptive.

• Descriptive analytics is the most basic form of retail analytics. It uses data to describe past performance—in other words, it answers the question, "what happened?" For example, retailers use descriptive analytics when they track KPIs to make sure they are on track to meet their goals.
• Diagnostic analytics goes a step beyond descriptive analytics. Instead of asking, "what happened?," it asks, "why did this happen?" Where descriptive analytics looks for correlations and trends, diagnostic analytics attempts to determine the causes behind them.

As you can see, each type of analytics builds on and goes further than the previous one. However, that doesn’t mean one type is “better” than another. Retailers need to know the answer to all these questions—what happened, why it happened, what’s going to happen next, and what they should do—to fully understand their customers and optimize retail operations.