“Data is the new oil, and analytics is the combustion engine. Retailers leveraging data analytics see a 10–15% increase in revenue and a 20% reduction in operational costs.”
McKinsey’s Retail Analytics Report 2023
Data is far more than just sales figures; it’s a powerful asset. Retailers are increasingly relying on data analytics to make smarter decisions, meet growing customer expectations, and streamline operations. With rising consumer demands and intense competition from online giants like Amazon and Temu, effectively leveraging data has become essential for success.
Retail data analytics plays a key role in aligning your business with sustainable growth and innovation, with key components like customer analytics, supply chain and revenue analytics.
In this article, we’ll explore what retail data analytics is, its core components, and the tools and technologies that can help you implement it effectively.
Power BI is an powerful tool for retail data analytics since it enables brands to automatically extract the data and create custom analysis. It is easiest to understand the power of it with a real world example.
We created the dashboard above for a jewelry brand which mostly sold their products through retail stores. The data was extracted automatically from the Shopify POS using our Shopify connector (it is anonimized in the dashboard above).
Our Power BI consultants helped the client to clearly see the sales by outlet, collection, sub collection and profit. This made it easy to analyse the success of newly launched products and collections vs the core product line.
They can use the slicers above to select the metric for all of the charts (Sales, Revenue, Average Order Value, Number of Orders) or the breakdown for this metric (product category, collection, sub collection, etc).
The management can also click on a store name in the bar chart and all the other graphs would be filtered to this store. This helps to analyse which product categories are most popular in every store. The filters above help make this analysis interactive.
Another use case for retail data analytics is inventory analysis. This helps the businesses to ensure that they have sufficient stock to meet the demand and that they are investing into the right level of stock.
For example we created the dashboard above for a luxury watch retailer. Every piece of their inventory is expensive and traps their cash if it is unsold for a long time.
By analysing the speed of movement of their inventory we found that Rolex watches stay in the inventory the longest while Connoisseur watches are sold within 30-60 days. This analysis informed the client’s investment decisions into additional inventory.
Large retailers might also benefit from our warehouse dashboard which helps to analyse how effectively deliveries are being handled and orders are being shipped out.
Retail sales and inventory analytics is probably what most retailers should start with. However, there are many other analytics techniques that could be relevant for retail businesses.
| Analytics Technique | Description |
| Demand Forecasting | Retailers can use linear regression to predict the demand for their products in the coming months. Anticipating demand helps to ensure that there is sufficient inventory to meet it. |
| Customer Basket Analysis | Retailers can benefit from analysing which products are frequently bought together. This may inform strategies such as placing products close together on the shelf or bundling them into a special offer. |
| Footfall Analytics | We worked with an entertainment venue that installed the sensors with a company called Vcount. We automated the data extraction from these sensors and analysed the footfall by hour into the venue. While this project was outside of retail industry, we believe this analytics technique is useful for retailers. It helps to identify busy hours and measure the conversion of store visitors to customers. |
| Shelf Analytics | IoT sensors and RFID tags enable real-time monitoring of product placement and availability, ensuring shelves are never empty and products are easily found. |
| Customer Lifetime Value | If your customers register an account with you, their email can be used as a customer ID. You can then analyse how often they shop with you and what their lifetime value is. As a result you will have more confidence knowing how much on average you can spend to acquire a customer to still be profitable in the long term. |
Think of it like this: every time a customer buys something, visits your store, or browses your website, they create data. Retail analytics takes all this information and turns it into useful insights that help you:
Simple Example: If you notice that customers always buy chips when they buy sandwiches, you can place these items near each other to boost sales. That’s retail analytics in action, using data to make your business work better.
In short, retail data analytics helps store owners make decisions based on facts rather than just guessing, leading to better profits and happier customers.
Retailers today use many tools and technologies to understand their business better and make smarter decisions. These tools help them analyse customer behaviour, manage inventory, predict trends, and visualise data easily. In this article, we will explain some popular tools and technologies used in retail analytics and how they help stores grow.
You need to collect the data before you can analyse it. Therefore deciding on your future data source for retail analytics is an important strategic decision.
Point-of-sale systems and ERPs are commonly used as data sources for retail analytics. Whatever data source you pick, make sure that it allows to automatically extract the data from it by offering an API.
Common POS systems that we frequently work with during our retail analytics projects are:
We also often analyse the data from ERPs which helps to capture more information about product costs and broader business operations. We have worked previously analysed the data from the following ERPs:
We are huge advocates of business intelligence software because they give retailers the flexibility they need to create custom analysis based on their needs. There are 2 BI tools that we recommend:
Tableau: A popular tool for creating interactive charts and dashboards. It helps retailers see trends and patterns in their data quickly.
Power BI: Microsoft’s data visualisation tool is affordable and easy to use. It connects to many data sources and creates beautiful reports. You can check out this comprehensive guide to Power BI to help you understand how the tool works.
You can read about how Tableau compares to Power BI in our guide. We recommend these tools because they are:
Retailers today use data analytics to improve their business, but it’s not always easy. There are some common challenges that stores face when working with data. Understanding these problems and finding solutions can help retailers use data better and grow faster. Let’s look at three big challenges: data silos, privacy compliance, and skill gaps.
Data silos happen when information is stored in different systems that don’t talk to each other. For example, customer data might be in one system, while sales data is in another. If these systems don’t connect, it’s hard to get a complete picture.
They are a problem because:
There are multiple strategies to solve this
Skill gaps happen when a retailer doesn’t have enough people with the right knowledge to analyse data or use analytics tools. Data analytics needs special skills in technology and statistics.
The implications of a skill gap are:
The strategies to solve this problem are:
We have understood that retail data analytics is transforming the way businesses understand their customers, manage inventory, optimise sales, and improve operations. Core components like customer analytics, inventory and supply chain insights, sales performance tracking, and workforce analysis provide a strong foundation for data-driven decisions. Powered by advanced technologies such as AI, machine learning, and user-friendly tools, retailers can unlock powerful use cases like real-time promotions, fraud detection, and seamless omnichannel integration.
Looking ahead, future trends like AI-powered visual analytics, voice commerce, blockchain transparency, and ethical AI will further revolutionise the retail landscape.
