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Using Data Analytics to Make Informed Decisions About Retail Inventory

When it comes to inventory decisions, there’s often a lot of guesswork involved. Guesswork informed by historical data—sometimes too much data to completely wrap your head around. Sometimes you might wonder if you’re not missing some key piece of information that could mean the difference between overstocking or understocking and getting it just right. And even if you’re good at analyzing the data and making inventory decisions yourself, you don’t have to spend valuable hours doing it all yourself anymore, not with all of the data analytics tools available today.

The Four Types of Data Analytics

  • Descriptive Analytics: The raw numbers of your business, the data that describes how much you bought, how much you sold, how many customers you served, how many units you moved, etc. In the past, you would have recorded this data in spreadsheets or even by hand, but today, there is software that can do this for you.
  • Diagnostic Analytics: This is where new technology really comes in handy. Software using statistics and algorithms can draw connections between different data points that are too complex or time-consuming for a human analyst to find.
  • Predictive Analytics: Statistical models that can gather lots of historical data and make predictions about the future. Most small business owners are doing this already, though in a limited capacity, and with more limited data: We ran out of this item last February, so I will order extra this February. But by bringing together thousands of data points through descriptive analytics and using diagnostic analytics to make connections that a human observer might miss, predictive analytics can make more accurate and nuanced predictions about the future.
  • Prescriptive Analytics: This is the cutting edge of data analytics, incorporating algorithmic and machine learning models that might not be accessible to every business. Prescriptive analytics combines the previous three types of analytics to give suggestions about what you should do next.

Together, these four types of analytics come together to give you a clearer picture of your business, showing you trends and forecasts you wouldn’t have been able to see using more traditional methods like Excel spreadsheets, statistical models, customer surveys, or economists’ predictions.

Why Use Data Analytics for Inventory?

As the above section shows, data analytics does more than tell you how much is coming in and how much is going out. It can also gather data on different types of consumers, helping you develop ideal customer profiles and buyer personas. You will get a clearer picture of who is buying from you as well as when, how much, and why they buy from you. This information will allow you to create separate marketing campaigns for each type of customer.

It will also help you to better understand seasonal patterns, new trends, and changes in consumer behavior gleaned from the large amounts of data your analytics software is working with. You’ll be able to make more accurate predictions and decisions about when to restock and how much of an item you should keep on hand. Acting with that kind of confidence is particularly empowering in a time of supply chain disruptions, an ongoing global pandemic, war, inflation, and changing consumer behavior.

There are risks that come with collecting the data necessary for this kind of analysis. It is best practice these days, when more and more consumers are becoming concerned about privacy, to make your data collection process as transparent as possible. Make sure you are adhering to all applicable laws concerning data collection, inform visitors to your website about cookies, and let subscribers to your email list know how their data will be used. It’s not so much that people are against sharing their information; they will share it with companies that they trust, so make sure you are working to earn their trust.


Deploying retail analytics does not mean you leave your business in the hands of computers and the people who know how to use them, however. You still have to know how to interpret the analysis, and you still have to use your own expertise, experience, and intuition to make the final judgment. But with retail analytics, you can be more confident that you are making the most informed decision possible.