What Are the Different Types of Retail Analytics? 

There were times when the retail industry depended solely on guesswork and intuition to make decisions on how they could expand their profit margins and move towards growth. But in today’s uncertain environment, it is necessary to opt for insights based on data. Such analytical study based on data is becoming a game-changer in the retail industry.

With the advancement in technology, insightful solutions that offer a level of certainty during tough times are now available. The industry giants have access to better data sources, resulting in improved retail analytics that ultimately help them make informed decisions. There are various ways in which one can scrutinize data extracted from the market. But before going into the types of retail analytics, let’s first understand what retail data analytics means.

What is Retail Analytics?

Retail analytics is the process through which the people working in the retail sector gather data and find insights throughout their department. In other words, the retailers collect data like sales, pricing, inventory, etc., and study them to discover trends, make decisions, and predict outcomes in a particular scenario.

Many retailers are now opting for these latest trends to gather as much information as they can. It has become imperative for any company to be left behind in the race. There are several benefits of conducting retail analytics, two of which are enhanced profitability and strengthening the competitive positioning in the market.

Types of Retail Analytics

There are various approaches to conducting retail analysis, and all those have different objectives. Overall, there are five types of approaches that a business follows:

  1. Descriptive Analytics:

When it comes to descriptive analytics, the process gathers all the raw data from various sources like Enterprise Resource Planning (ERP), Point of Sales (POS) terminals, various inventory management systems, and much more for a detailed look at past and present performance. The analytics provides a performance summary of business processes and actions, which includes but is not limited to transaction history, promotional success, and inventory needs.

Descriptive analytics helps in attaining insight into current performance as compared to the last years, repeat customers, and changes in pricing. This kind of retail analytics is used to provide retailers with a deeper understanding of trends so that they can make informed decisions.

  1. Diagnostic Analytics:

The diagnostic analytics aims to find out what has happened. In other words, this simply answers the ‘why’ of a business problem. This kind of analytics behavior takes the same raw data as used in descriptive analytics and uses various algorithms, statistical analysis, and machine learning to dig deeper into the data and find correlations between different points of data.

  1. In-store Retail Analytics:

This kind of retail analytics includes data related to what is happening inside a store, which can include footfall, conversion, dwell time, and much more. In-store data provides the most crucial numbers that reveal the real story of what’s happening across different retail stores, which also includes customer behavior and team productivity.

The in-store retail analytics can help the business uncover trends and areas that can be improved. It can also provide information about staff and where a business can reshuffle staff to optimize business.

  1. Predictive Analysis:

This tool utilizes insight from past analysis and predicts the future course of the business. It helps forecast business performance by identifying the clusters and engaging complex algorithms. Businesses in the retail landscape can make proactive decisions, create effective allocation of resources, and stay ahead in the market.

  1. Prescriptive Analysis:

This analysis can simply tell a business what and why a certain thing is happening along with what will happen next. Prescriptive analytics is powered by algorithmic AI that guides the business among a range of scenarios based on different initial conditions, which can include pricing models, allocations, product assortments, etc. This analysis enhances profitability and overall success in a dynamic retail environment.


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