“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. According to McKinsey & Company, retail data analytics drives personalised marketing, enabling retailers to achieve 20% better results compared to traditional approaches.
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 from multiple sources, aggregate it and create custom analysis.
This is especially useful to retailers nowadays because the retail space has become more complex. Retailers no longer only sell through a brick and mortar store, they also usually have an online store, sell their products through Amazon, Walmart and Target.
Management of multiple sales channels requires collection of data from different systems: POS, Shopify, Facebook Ads, Amazon Seller Central, etc. This is exactly what Power BI is good at: combining the data from different sources into a single management report.
Our data analysts have created dozens of retail Power BI dashboards for our clients. You will examples of our dashboards below that help our clients managing different sales channels in their retail business: their brick-and-mortar store, online store, Amazon store, Target and Walmart sales.
We will also demonstrate how retail data analytics can help inventory management and explain how these dashboards are used by our clients.
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.
We have worked with a consultancy that helped their clients place their products into Walmart and Target. They then helped their clients to manage Walmart and Target sales effectively by using data analytics. We helped our client to identify KPIs and build Power BI retail dashboards for Walmart seller analytics.
The important KPIs for Walmart data analytics are:
We started by analysing current week sales and comparing them vs last week, same week last year and an 8 weeks average.
We then analysed the same metrics by channel: brick and mortar stores and online store. This helped our client to identify whether a particular channel needed optimization such as changing the product packaging, taking better images for the online product listing, etc.
We also analysed the inventory in Walmart on-hand and total number of week of sales that this stock covers. If we saw that a certaim product was covering less number of weeks than the target, our client would contact Walmart and highlighted this problem to them leading to additional sales.
We also calculated how many items of each product were sold every week and what is the average out of stock percentage by product. This analysis drove the conversations with Walmart about increasing the order size.
We have worked with many online retailers helping them to create Power BI dashboards to analyse their online sales. These project usually revolved around analysing data from sources like Shopify, Woocommerce, Big Commerce, Magento and similar sources.
The data from these sources is great for analysing the customer purchasing patterns which helps to find new marketing opportunities and create new offers. If you want to see examples of this analysis, you might be interested in reading about Power BI ecommerce dashboards that we created.
However, running an online retail store is also largely about marketing your business through Google, Facebook, Bing and other channels.
We helped one of our clients combine the data from all of these sources together and see the daily sales through Shopify, Facebook, Google and Amazon. This made it easy to track the profitability of the business with all the marketing costs combined.
We then analysed each marketing source in more detail to help our client find optimization opportunities. This included analysing the effectiveness of every ad, campaign, thumbnail and video that were used.
The Shopify data specifically was used to calculate the sales, discounts and refunds for every product.
When retailers sell through Amazon, they have the organic listings and also pay-per-click ads. The organic data comes from Amazon Seller Central while the sponsored ads data is coming from Amazon Ads. When building a Power BI Amazon dashboard, it is important to analyse both of these parts of the business.
The important KPIs that are specific to Amazon Ads analytics are:
We have built the dashboard above to analyse the business performance on Amazon for one of the retailers we worked with.
The top of the dashboard shows the key high-level KPIs helping the retailer to analyse the profitability of their Amazon operation. The key goal of this analysis is to ensure that after all costs the sales are profitable enough to justify the sales on Amazon.
The advertising summary table shows the performance by campaign highlighting where the advertising cost of sales is highest.
Finally, this dashboard is showing the organic and ad sales per week, sales by country and product. The product-level analysis is especially useful to identify fast-growing products highlighting opportunities to group them with other products.
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.
