Top 10 Data Science Use Cases in Retail Industry

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( — June 22, 2021) —

A characteristic feature of the retail industry is a significant fragmentation, and thus a large number of data sources regarding the company’s condition, customers, or the company’s immediate environment. So how can you integrate distributed data, process it, and maximally use it in the company’s management? Data Science solutions provide the answers.


Nowadays, retail chain managers need more than a monthly summary of what has been sold and for how much. Retailers handle data on many elements of a company’s operations, from the supply chain to a single sales operation in a specific store. As a result, it is challenging to keep up-to-date with the data, let alone judge which data is relevant to the decision-making process. Data science solutions dedicated to the retail industry allow for reporting, analyzing, and monitoring large amounts of data, which entails several business benefits, and above all, maximizing the value of information. Retailers can use data science solutions in many ways, increasing the efficiency and quality of their business. So let’s take a look at the top 10 use cases of data science in the retail industry.


1.    Customer profiling


Although every company entering the market should start business by determining the target group they want to reach with their products or services, sometimes only the analysis of the customer behavior and purchase history allows answering all the questions relevant to the company. Getting to know this information will enable you to prepare a personalized product offer and adjust the marketing tools more effectively. Such activities lead to increased profits from sales and gaining loyal customers.


Moreover, customer profiling allows you to recognize the most profitable customers and those who may be insolvent. Data science solutions will enable you to identify trends in consumer behavior, strategically plan marketing campaigns, acquire, retain and segment customers, which translates into building long-term relationships.


2.     Stock management


Data science in the retail industry enables the delivery of the right products to the right stores at the right time. Flexible analytical capabilities allow you to compare product performance and evaluate promotion effectiveness across items, categories, locations, and sellers. Analysts can monitor and forecast all aspects of the merchandising process. In addition, the system allows you to improve logistics, reduce goods shortages, and improve inventory management.


3.     Supplies management


The ability to monitor inventory helps avoid product losses due to changing economy, technology, and consumer conditions. The thorough analysis of warehouse activity also enables reducing storage costs. Thanks to the inventory rotation forecast, it is possible to save and optimize the delivery cycles depending on the estimates so that the volume of transport and its frequency are as effective as possible. Thanks to data science, tracking inventory throughout the entire order, delivery, and in-stock process is also possible.


4.     Cart analysis


The cart analysis allows you to identify what products customers are buying more. Advanced cart analysis will enable you to understand buyers’ needs by monitoring purchasing patterns and increasing sales by attracting new buyers. Additionally, creating customer profiles and transaction history helps in identifying cross-selling and up-selling opportunities.


5.     Supplier performance management


Data science enables retailers to evaluate supply chain activities, logistics operations, and supplier performance across a wide range of variables. Relational analysis of supplier performance for quality, delivery, and price isolates bottlenecks that require immediate attention and automatically alerts managers concerning potential pitfalls and emergencies.


6.    Price optimization


In the face of growing consumer incomes and an increase in the number of sales channels available, price optimization becomes an attractive source of revenue growth for retailers. However, pricing requires precision on many levels of detail–between stores, customer segments, sales channels. It also requires considering key strategic factors such as competition, category roles, and brand image. Analytical algorithms, using various factors (e.g., product supply, sales targets, margins, traffic in the store, product popularity), constantly adjust the price level to the market situation. Thanks to data science, price optimization can become an integral part of the daily business of retailers.


7.     Work schedule


To optimize their business, retailers need to monitor staffing needs and employee performance. Advanced analytical capabilities enable you to identify the hours, days, and seasons that require the most staff and streamline employee performance by sales, working hours, store, and other variables.


8.    Planned store display


Data science can turn out to be an excellent tool for planning a store display to increase sales. It is about, for example, placing products that are most popular in the most exposed points in the store so that consumers can be sure that they will quickly find their favorite things in it. Or, it can revolve around drawing special attention to the assortment that performs worse to encourage visitors of the store to purchase it.


9.     Increasing the sale of the selected assortment


The operation of the American hypermarket chain Target is an excellent example of using data science to increase sales of a selected product range. By analyzing the behavior of a selected group of customers (women) and how their shopping cart changes periodically, the company has developed a method of recognizing pregnant women. The collected data allowed not only to determine what future mothers buy most often, but even estimate the expected date of delivery, and thus to prepare a personalized product offer for customers who have started a new stage of life and have changed their shopping needs.


10. Analysis of operations taking place in stores


Store managers can easily and quickly receive personalized sales or marketing reports and scorecards comparing individual stores while ensuring that the data is always up-to-date. The unified system architecture allows you to monitor the store’s performance and increases the efficiency of sales, promotion, marketing, operations, and budgeting management.


Data science in the retail industry allows you to obtain valuable information based on indicators, not on assumptions reflected in reality, translate into actual sales increase, or optimize the costs of running a business. Now more than ever, data also offers retailers a competitive advantage in a saturated market. A flexible basis for open-standard analytics enables retailers to process data from various sources and fully exploit their potential.


However, whether a retail company is successful in the market is determined by adequate analysis and the effective use of data. Therefore, retailers who do not want to be left behind by the competition should invest in consulting and data analytics services to turn their data into insights. A professional data science partner ( allows you to use the acquired information to make effective decisions and increase profits.