Changes in today’s market are happening at breakneck speed, new brands are replacing familiar products, and the consumer is waiting for something special, a newly invented bicycle, if you will. All these changes should be monitored and controlled, in general, analyse the situation.

Retail analysis is necessary for any business, as it allows you to assess the success of a particular group of goods to predict the growth or decline in sales, etc. Without having detailed information, it is impossible to conduct a comprehensive analysis.

To consider sales from the side of volume, dynamics, structure, and assortment will help widely known methods of sales analysis.

What is Retail Analytics?

Retail data analytics is the process of collecting and analysing retail data (such as sales, inventory, pricing, etc.) to identify trends, predict results, and make more efficient and profitable business decisions. When done right, data analytics allows retailers to better understand the performance of their stores, products, customers, and suppliers and use that insight to improve profitability. Almost all retailers use data analytics in one way or another, even if it is just looking at sales figures in excel. This analytics makes your business profitable. Without it, you will not be able to adjust your assortment, optimise your advertising promotion, and perform many other useful improvements.

Why is Retail Analytics Important for Business?

One of the most important reasons to use data analytics to make decisions is to make sure your conclusions are based on the real truth (cold, hard numbers) and not someone else’s view of reality.

Analytics can also help you understand your firm in much more detail than you might otherwise.

In practice, a store can use data analytics to:

•   Understand the cost of a typical order and the number of products sold.

•   Determine which products sell the most, which sell the least, and everything in between.

•   Identify your most valuable customers.

•   Recognise your actual demand as well as previous missed sales.

•   Determine the best-suggested order quantities and make recommendations for purchase quantities and allocations.

•   Determine the best price for a particular product in each given area.

These (and other) insights will help you better understand your company’s performance and develop strategies that will get you where you want to go.

Data analytics should become a vital component of your company as it grows to improve decision-making and develop effective retail tactics.

It is no surprise, then, that the retail analytics solutions sector is large and rapidly growing.

Benefits of Retail Analytics

Retail analytics is a set of tools retailers use to increase sales, minimise overhead and labour costs, and increase profits. Retail analytics can help achieve these goals in several ways, including:

Reducing shortages and the need for discounts: Retail analytics helps users understand demand trends so that they have enough product, but not so much that they have to resort to steep discounts to get rid of excess inventory. Using analytics to determine the rate of product consumption is a common practice.

Improved personalisation: Analytics allows retailers to better understand the preferences of their consumers and, as a result, generate more demand than competitors. A book retailer, for example, can use purchase history to notify consumers who have expressed interest in American history when a new book by historian Ron Chernow becomes available for pre-order.

Improving pricing decisions: By synthesising several metrics such as abandoned baskets, competitive pricing information, and the cost of products sold, data analytics can help businesses set ideal prices for their products. Retailers can maximise profits by not charging prices higher than the market can bear or lower than customers are willing to pay.

Improving product distribution: Analytics can help retailers determine how to distribute products across geographic regions, distribution points, and storefronts, thereby eliminating unnecessary transportation costs. For example, a sportswear store can use analytics to see how a two-degree change in temperature affects sales of thermal underwear, and direct more of those products to the distribution centre closest to locations where colder temperatures are expected during a given winter.

The Future of Retail Analytics

The future of retail is uncertain, but the current state of retail is not. Users and applications will use analytics constantly, often unintentionally, similar to how smartphones constantly use location tracking to fulfil user needs.

Retail analytics will become more integrated into the day-to-day processes of business users, rather than simply being used to create or view weekly reports. More and more people will enjoy the fruits of AI in their daily business activities, even if they do not realise it. Data analysis with AI will no longer be advertised.

To Sum Up

Retail analytics is very extensive and takes into account almost all internal areas of the company. However, it is important to remember that all processes affect sales and profits in one way or another. Analytics has long driven retail development. The analytics market size in the retail industry is expected to grow from USD 5.26 billion in the current year and is expected to reach USD 13.76 billion by 2028. The more qualitatively the sales analysis is conducted, the higher the probability for the company to reach higher performance indicators. After all, it is sales that determine the revenue and profit of the organisation.