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Retail analytics: The power of data for brick and mortar stores

In-store, online, hybrid, omnichannel. These are just a few faces of the retail industry. People need products and they love to shop. However, the competition is fierce and retailers are looking for new and engaging ways to attract customers. 

One of the most important things nowadays in many industries is data. Without it, businesses are not able to understand their customers and predict the next steps. In this article, we are going to focus on retail analytics. How important is data in retail? How can retailers collect and analyze data? The answer to these questions and many more are below. 

What is retail analytics?

Let’s begin with the basics. What does retail data analytics mean? 

Retail analytics is the process of collecting, monitoring, and analyzing retail data, such as sales, inventory, foot traffic, and pricing. This process helps retailers predict outcomes, discover new trends, and make more profitable business decisions. By analyzing retail data, retailers can also understand why their shoppers select certain products and see different shopping patterns. 

Plus, data analytics offers retailers a full overview of their customer, store, and product performance. Normally, every retailer does some type of data analysis, even if it's done on a spreadsheet or with the help of the latest tools and technologies. 

Types of retail data analytics

There are four types of retail data analytics that can offer key insights to retailers:

  • Descriptive Analytics
  • Diagnostic Analytics
  • Predictive Analytics
  • Prescriptive Analytics

1. Descriptive analytics

Just like its name suggests, descriptive analytics helps retailers describe “what” is actually happening with their business. This is the most common type of analytics and it focuses on using raw data to discover the state of the business. 

2. Diagnostic analytics 

The second type of retail data analytics focuses on answering the “why”. When correlated with descriptive analytics, retailers can figure out certain trends and patterns. This type of data analytics focuses on putting a “diagnostic” to a business problem. Why are sales dropping in one of our stores? Why are customers shopping more in certain periods of time?

3. Predictive analytics

With the help of predictive analytics, retailers can figure out future trends. Discovering a pattern and understanding how it can impact the business is extremely important. Predictive analytics shows “what’s next”. However, this can only show a simple forecast, without offering specific insights.

4. Prescriptive analytics

Finally, the most advanced type of analytics is the prescriptive one. Once retailers find the “what”, “why”, and “what’s next”, with the help of prescriptive analytics they can find “what they should do next”. 

Algorithmic AI and machine learning programs can identify patterns and offer recommendations and possible outcomes.  

Collecting data in-store 

Collecting data is one of the biggest challenges for physical retailers. While eCommerce platforms can easily collect data and analyze it, physical retailers have a hard time gathering data from customers. Here are a few things retailers can do to collect more customer insights and raw data.

Point of sale data collection

A POP system offers valuable information about metrics like profit margins, sales trends, and customer counts. This will help you forecast purchases and manage inventory.

Foot traffic

This data helps you find out the real number of customers and their activity in your store. You can find out the conversion rate, the times of day when your store has the most activity, and certain shopping trends during special occasions.

Market research

Performing market research from time to time helps you discover new trends and how the industry has changed. Plus, you discover the impact of those new trends on your business.

Surveys and feedback forms

Surveys and feedback forms are great ways to receive insightful quantitative data. Customers will be able to answer your questions directly and you will be able to figure out what you can change to better fit their needs.

Proximity marketing tools

Proximity marketing tools such as beacons and geofencing can help you collect data from customers and offer them a better, more personalized experience in-store.

The importance of retail data analytics

Collecting and analyzing data helps you in many ways because it offers a clear overview of the industry and of your business performance. Here are some benefits of retail data analytics:

1. Optimize inventory and procurement

One of the most common use cases of retail data analytics is for inventory optimization. By discovering how many products are actually needed, retailers can optimize inventory and procurement.

2. Identify customer trends and buying patterns

With the help of retail data analytics, retailers can spot customer and industry trends. Also, retail data analytics surfaces buying patterns which can also impact the inventory or the business strategy of retailers.

3. Improve marketing campaigns 

Finally, once retailers discover buying patterns and customer trends, it’s easier to create better, more targeted marketing campaigns that will attract shoppers in-store. 

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