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.
- Predictive analytics uses data to predict future trends, asking the question, "what is going to happen?" By using statistical techniques like regression analysis, retail predictive analytics attempts to forecast the future based on patterns in historical data.
- Prescriptive analytics is the ultimate form of retail analytics. It goes beyond all the other types—it attempts to answer the question, "what should we do next?" Prescriptive analytics often makes use of advanced technology like machine learning to evaluate hypothetical scenarios and evaluate which course of action is likely to be most beneficial.
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.
Applications of Retail Analytics
You might think retail analytics is the domain of a specific team of data analysts. While it’s true that major retailers have whole departments dedicated to analyzing retail data, retail analytics can be used by virtually everyone in the company.
At the store level, floor staff can use retail analytics to track performance metrics, and store managers use analytics to make tactical decisions about labor, inventory, and sales targets. Marketing departments make great use of retail analytics to understand their audience and run effective campaigns. At the highest levels of the company, leaders use it to inform strategic decisions such as what products to carry and where to expand the business.
Here are a few examples of how different parts of a retail business use analytics:
Segmentation: Segmentation refers to grouping shoppers into categories based on shared qualities, such as price sensitivity, brand preferences, or demographics. Because different segments require different marketing tactics, this helps design marketing campaigns that speak as effectively as possible to potential customers.
Promotional Analytics: Marketing professionals use promotional analytics to evaluate customer response to their own and competitors' promotions. They look for correlations between promotions and sales to discover what strategies are most effective.
Recency, Frequency, and Monetary (RFM) Analysis: RFM analysis provides insight into customer engagement by ranking them according to the recency, frequency, and monetary total of their transactions. This data informs shopper segmentation and helps retailers to focus on the most high-value shoppers.
Category Development: Retail analytics are used to forecast the growth and decline of different product categories and identify new product niches—for example, the emergence of single-use coffee pods in the 2010s.
Range Planning: Range planning is the process of analyzing the past sales performance of product categories and current market trends to select exactly which products to sell. Retailers are constantly tracking sales data and using it to adjust their offerings for optimal sales performance.
People Diagnostics: Retailers use analytics to comprehensively understand the capabilities of their employees so they can see their strengths and where they need to improve. Manual, interpersonal, and technological skills can all be evaluated to determine where there are gaps that need to be closed through training or hiring. In addition, retailers can identify top performers and employees with underutilized skills who might be able to add more value than they do in their current roles.
Recruiting and Retention: Retail analytics can also help companies bring in and keep the best talent possible. Retail employers are increasingly using machine learning and advanced analytics to identify candidates who are the best fit for the job and the organization based on resume data, personality typing, and performance metrics.
Supply Chain Optimization: By using long-term sales data and forecasts, retailers can optimize their inventories and procurement processes to avoid over- and under-stocking. This data is often shared between retailers and suppliers to integrate the supply chain and optimize it at all levels.
Waste Reduction: In many retail segments, such as groceries, restaurants, and food brands, products have a limited shelf life. These retailers use analytics to compare sales by store, case sizes, and shelf life to identify store/product combinations that may lead to waste because of an inability to sell full cases of product within its shelf life.
Trends, Technology, and the Future of Retail Analytics
We are well into the information age of retail. Even the smallest retailers are tracking data about customers, sales figures, and operations and using it to improve and grow their businesses. It’s not a stretch to say that retail analytics are an absolute necessity for a retail business to succeed.
However, retail analytics is still a growing field. New technologies—and innovative uses of old technologies—are making more data accessible than ever before, helping retailers find new competitive advantages.
The rapidly evolving fields of machine learning and artificial intelligence offer unprecedented possibilities for retail data analysts. By learning and improving over time, these technologies can glean highly accurate insights from the massive amounts of data generated and collected by the retail industry.
For example, recommendation engines and chatbots driven by machine learning can collect extremely detailed information about customer behavior at an individual level. This detailed information is increasingly driving the creation of highly refined customer segments. This segmentation in turn allows retailers to provide hyper-personalized experiences tailored to individual customers, offering them exactly what they want and nothing they don’t.
One of the biggest obstacles most retailers face when attempting to implement data analytics is getting the data and the insights to the people who can use them. With so much data coming in, it can be a struggle to make good use of it.
As retailers collect and analyze more and more data, we are seeing a move toward a shared services model for retail analytics. A centralized platform accessible to all levels of the business helps ensure valuable insights don’t slip through the cracks by integrating data from multiple sources and putting it in front of the people who need it. A shared services platform can also provide data visualization—for example, charts and graphs—to help make sense of large and complex data sets.
Harness the Power of Retail Analytics for Your Business
You may already be taking advantage of retail analytics to inform your marketing, sales, and operational decisions. If so, great! With so much data available to retailers today, however, there’s still a wealth of valuable information out there just waiting to be discovered.
SimpliField’s retail analytics, communications, and operations solution is designed to help retailers get the maximum value possible from data analytics. In one simple platform, you can easily visualize complex data, turn it into valuable insights—and put that information in front of the people who can use it, whenever they need it.
Retail analytics are an essential ingredient for any retail business trying to stay competitive, grow, and optimize operations today. Contact us today for a live demo and see how SimpliField can supercharge your retail analytics and take your business to the next level.
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