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How Retail Analytics Helps Improve Sales?

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Most retail businesses have significant data inventories that require constant monitoring to make informed decisions about their operations, marketing, and sales strategies. Retail analytics helps leverage this data to identify areas for improvement and make data-driven decisions that can increase sales performance.

When the pandemic hit Southeast Asia, retail businesses were one of the hardest impacted. After losing over 300,000 workers due to mall closures, the Malaysian retail industry is eager to bounce back. Some merchants are banking on retail analytics to not only give them a head start but to keep them ahead.

Let’s look at how retail analytics can improve sales in retail stores.

Analytics in Sales–What does it do? 

Retail analytics is a process that turns data on sales, inventory, and consumers into actionable insights. By using these insights to make critical business decisions, retailers can optimize their operations, forecast demand and capitalize on it.

There are four key components to analytics in retail. 

First, descriptive analytics contextualizes a store’s performance based on past data, while diagnostic analytics outlines possible causes for a specific issue. Predictive retail analytics is more advanced as it forecasts sales, demand and so on. Finally, prescriptive analytics, the most complicated component, combines machine learning and artificial intelligence (AI) to prescribe optimal business strategies.

Examples of retail analytics 

Major retail corporations worldwide such as IKEA, Domino’s and more, have successfully used analytics to increase sales. The most successful case, however, is Amazon Go.

Retail data analytics case study

Amazon Go is known for allowing customers to shop and check out of their stores immediately after, without needing to line up at a cashier’s counter or use self-checkout counters. The stores achieved this by replacing their cashiers with cameras in the ceiling and putting weight sensors on product shelves, which automatically detect which items a customer has added to their cart. It also requires customers to download an app linked to their Amazon account, both to enter the store and pay for their purchases. These technological advances streamlined the customer experience a lot, which experts estimate can bring Amazon Go $4.5 billion in sales revenue per year.

How do analytics help retailers improve their sales performance goals? 

Despite businesses opening digital shops or adding digital websites to their catalogs, 91% of the Southeast Asian retail market is still offline.

Contrary to popular belief, consumers still prefer trying on products physically before making a purchase.

Yet, sales assistants have a hard time keeping track of daily transactions and providing satisfactory customer support. To relieve their burden, some merchants have turned to retail analytics, which mines and simplifies big data to help store managers make better decisions regarding product catalog and inventory management.

Retail analytics gathers information on the purchase history, shopping behavior, age, gender, and preferences of customers. It analyzes what causes a customer to pick one item over the other.

Is the item an absolute household essential? Is the item merely an aesthetically pleasing product the customer can’t help but want? Why did the customer enter one retail store over the other?

Could it be because the visual merchandising of a certain store was more pleasing? Did the smart retail outlet make customers think it’s for high-end products or inexpensive products from the outside?

Any and all retail-centered question is answered through retail analytics. After mining big data, the analyzed data is turned into bullet points for store managers and sales assistants to study before implementing any changes. 

Here are some ways business owners can benefit from analytics in retail:

Better customer analysis

In-store retail analytics can help businesses understand their customers’ shopping habits, preferences and browsing activity. These insights can later be used to optimize product placement, understand preferences, segment customer base, and create personalized marketing campaigns to better meet demand and boost sales.

Improved inventory management

With real-time info on inventory levels, retailers can make more informed decisions about stock replenishment, demand forecasting and product performance. This can lead to reduced inventory costs, increased revenue and fewer lost sales due to out-of-stock products.

Optimized store layout and design

Retail analytics software can help business owners understand the way customers move through their stores, which products they are most interested in, and how long they spend in different areas. Their newfound knowledge of shopper patterns and user journeys can help to improve store layouts and product placements, which in turn enhance shopping experience and increase sales performance.

store layout and design
Photo by Maria Orlova

Better pricing strategies

As the market changes constantly, so does the optimal pricing for retail products. Using analytics in sales can help retailers price their products more dynamically, keeping them ahead of the competition while also maximizing their profit margin.

Personalized customer experience

Retail analytics companies like ComeBy help merchants provide a customized shopping experience. Based on the customer’s in-store location and browsing data, retailers can direct experienced staff to assist customers as needed. This improves the customer’s perception of the business and boosts customer retention rate.

Customer segmentation

Retail analytics can be used to categorize customers into groups based on factors such as demographics, geo-location, purchase history, and behavior. It allows businesses to create targeted marketing campaigns that are more likely to resonate with specific customer groups.

purchase history and behavior
Photo by Tim Douglas

Also read: How to Increase Sales and Expand Your Retail Business?

Challenges for retailers using big data

Although big data is an integral part of retail analytics, there are several limitations that prevent businesses from accessing their full potential. Here are some challenges they might face, and solutions to overcome them.

Data privacy

While some customers appreciate personalized shopping experiences, others may avoid stores that they feel invade consumer privacy. Therefore, it is important to work with these consumers’ boundaries to earn their trust. For example, retailers can give consumers the choice to opt out of certain data collection processes, thus catering to their preferences regarding customer data protection. 

Another alternative is to be upfront about the data collection process, like telling customers the purpose of collecting browsing data. This will not guarantee a customer’s consent, but the transparency and respect for their boundaries will prevent them from leaving out due to  privacy concerns.

Skilled staff to perform retail analysis 

A business can have all the tools it needs to succeed and still fail if the business owner has yet to learn how to use them. Retail analytics is one such tool, and retailers must constantly adapt to any technological advancements in the field or risk falling behind.

There are several solutions to this challenge. Firstly, merchants can send their employees for retail software training to help them make full use of analytics for sales. Another option is to hire a retail analyst or outsourcing the work to retail analytics companies.


To conclude, retail analytics can turn big data on sales, inventory and more into valuable insights that help boost sales performance. 

Given the rapid growth of machine learning and AI, retail analytics software will only become more sophisticated and accurate in its demand and sales forecasting. That means the potential of retail analytics in improving retail sales is practically limitless.

Retail analysis can not only help sales but change the entire method through which brands conduct business. Currently, the tech behind retail analytics is infinitely complex, especially for offline systems. However, those who employ it notice a huge improvement in their business management.

This is why retailers can benefit significantly by using a retail analytics service like ComeBy. Contact us for more information on pricing, and choose the best plan for your business today.

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