What is retail analytics and its use cases

use cases retail analytics

The retail landscape has undergone a change unmatched in the recent past. This change has brought about a number of challenges and opportunities for retailers ranging from complex customer behaviour and stiff competition to newer retail channels like e-commerce and the latest one Q-commerce.

In this complex retail landscape, a static, one-time carved strategy is not enough. The strategy, plans, and execution needs to be agile and evolving based on customers or rather market sentiments. That’s where retail analytics comes into the picture.

What Is Retail Analytics?

Retail analytics is the collection of all the retail business information, digitising it if need be, to put it through the analytics engines. Now what are different sources of data — it is the data contained in cash registers, point-of-sale devices, ERP, sourcing and inventory data, pricing and discounts, financial ledgers and so on.

Retail analytics feeds on this information to generate insights to make the right decisions as well as uncovers answers to hidden questions, unearths bottlenecks in your retail business and further helps in better planning and preparedness.

Here’s a retail analytics reference architecture:

Source: Salesforce

Note: It’s a reference architecture and the tech-stack can be changed.

What Are Different Types Of Retail Analytics Use Cases?

The customer’s attention span has gone down while they are bombarded with a flood of information from social media, ads spaces, chats and messages, emails, and so on. New age retail is all about cutting through all the noise and reaching their customers and capturing their attention by providing a unique & personalised experience.

Leading retailers are creating unprecedented business value & competitive edge by investing heavily in technology solutions like retail analytics, Cloud computing, and customer experience (CX) management. With this Technology-driven evolution in retail, these companies are constantly listening to what customers want, how they are reacting to new product launches, and what the competition is doing.

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With all this information, analytics in retail follows naturally. It gives them the insights to be rightly positioned for the next wave of transformation in the retail industry. These insights are empowering key decision-makers at different levels in the retail value chain to formulate an optimum strategy for their business along with monitoring & changing them if needed.

Here’s a pictorial representation of the retail value chain:

Let’s Dive Into Retail Analytics Use Cases Across Each Function And Nodes In The Retail Value Chain.

Sales-Profitability & Demand Forecasting

The use of retail analytics to analyse sales performance and optimise processes is very critical for any business. With the help of a Business Intelligence platform, a company can dive deeper into the retail sales data, analyse patterns & correlations & identify outliers through visually appealing & intuitive retail dashboards. These tools offer the following retail analytics use cases pertaining to the sales function:

The retail analytics process starts with collating all the retail sales data, be it ERP, cash registers, point-of-sales transitions and so on, thus helping break the data silos prevalent in a retail business and accumulates all of it in a single repository be it a data warehouse or a data lake. The analysis of all these sales-specific data, also termed as ‘Sales Analysis’ draws a complete picture of every transaction like mode of purchase, payment details, customer demographics information, discount coupon/promotional offer applied, return queries and refund status.

Sales Personnel/ store Performance & Profitability Analysis gives insights into how each salesperson or a store is performing vis-a-vis their peers or other stores in the territory. A retailer can drill down into these Business Intelligence (BI) dashboards to get insights on area/category/product-wise sales performance, map it with the respective sales personnel & identify improvement opportunities to enhance profitability. Using these insights, retailers can manage both the sales force performance as well as their overall profitability.

Losing a sales opportunity because of stock-outs not just hurts the retailers’ sales but also the buyers’ experience takes a beating. A retail analytics system could project sales of different items based on past trends and minimise the opportunity cost. There are some very comprehensive ‘Demand Forecasting Models’ adopted by leading retailers that factor into multiple drivers of change. These systems can model scenarios by changing the variables and enable retail decision-makers to choose the best course of action. It even helps with effective sales goal setting, Incentive and compensation planning for the sales team.

Must Read: Retail Analytics Use Cases



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