Retail analytics

What Is Retail Analytics?

With intelligence from edge to cloud, retailers are using data to understand customers, fine-tune inventory, and so much more.

Getting Smart with Retail Data Analytics:

  • Retail analytics helps you create hyperpersonalized experiences and improve inventory, marketing, and merchandising efforts.

  • By collecting data from more points, you deliver better experiences, make more accurate predictions, and validate that your strategies are working.

  • Intel® technologies power retail analytics solutions in the cloud and at the edge to give you a continuum of intelligence you can use to make better business decisions.



For retailers, data has sparked a new revolution. It helps you understand your customers and give them what they’re looking for. It tells you which products will fly off the shelves and when. It makes it possible to personalize experiences and offers, whether a customer is shopping in the store or online. By analyzing their data from multiple touchpoints, retailers could potentially capture an additional USD 94 billion in revenue, according to a Microsoft report.1

However, while data can drive sales in the short term—and customer loyalty over the long term—retailers must first make sense of the overwhelming amount of data available across their business. And with new data coming in from new sources all the time, it’s critical for retailers to have a strong foundation for analytics that can bring everything together and let them create new experiences with speed and ease.

Benefits of Retail Analytics

Without data, retailers can only guess about what their customers want. When fed into the right retail analytics solutions, data gives you insight to improve your inventory, marketing, and merchandising strategies. Here are just a few of the ways analytics helps retailers:

  • Create hyperpersonalized experiences. About 77 percent of consumers believe that the customer experience is just as important as the quality of products and services.2 By analyzing a customer’s past purchases and reactions to previous campaigns, you can create experiences and offers that are uniquely tailored to their preferences.
  • Transform brick-and-mortar stores. Retail store analytics facilitates rich customer engagements in real time. Think responsive digital signage that delivers the right message to target audiences or point of sale (POS) systems that integrate with loyalty programs.
  • Run more-effective marketing campaigns. Predictive analytics helps a retailer identify which customers are most likely to buy which products in the coming months.
  • Optimize inventory and supply chain management. Artificial intelligence (AI) and predictive analytics in retail help you forecast which products or features will have the highest traction. Robots equipped with AI can “see” what’s on shelves to help minimize out-of-stock occurrences. Analytics can predict the right orders for new stock, making sure stores won’t end up with too few or too many items.
  • Enhance merchandising and product placement. By using analytics to create a heat map of a store, you can make better decisions about where to place products so customers will notice them.
  • Fuel an omnichannel strategy. Analytics is the secret ingredient to unified commerce. By using data from all available channels, you can optimize your retail operations while improving customer experiences.

Retail analytics can run either in the cloud or at the edge. In some cases, the cloud is a better environment for analytics—for example, when bringing together data from multiple sources. In other cases, running analytics at the edge—in the store itself—makes better sense because of the need for low latency and data locality.

About 77 percent of consumers believe that the customer experience is just as important as the quality of products and services.2

Analytics and AI in the Cloud

Aggregating and analyzing data in the cloud is ideal for predictive analytics, merchandising analytics, and other types of customer analytics. These strategies help retailers understand long-term trends and make predictions about the future. For example, retailers can feed data into cloud-based predictive analytics software to help inform localized, targeted marketing campaigns; achieve precise demand planning; and understand the differences between store-to-store performance and preferences.

One growing trend is the use of AI to gain insights from big data in retail to improve both customer experiences and business operations. AI helps retailers segment customers or make product recommendations based on customer data analytics. It tracks data from customer interactions with online channels to improve e-commerce strategies. On the operations side, AI finds and corrects inventory distortion, optimizes supply chains, or improves product development.

Analytics and AI at the Edge

Today, you don’t need to rely on the cloud alone for the horsepower to run retail data analytics. The latest edge computing technologies are bringing analytics and AI to brick-and-mortar stores. In-store analytics give retailers the ability to use new types of data that until now only online channels could collect—for example, how customers explore products and which marketing messages catch their interest.

Benefits of Edge Computing

With edge computing, data is collected, stored, and analyzed locally. This offers several advantages. For one, it keeps sensitive data at or near its source. This helps retailers comply with data locality and privacy regulations such as the EU General Data Protection Regulation (GDPR). Second, it reduces the cost of bandwidth for large-scale data transmission to the cloud. Finally, edge computing reduces latency, enabling real-time responses.

Computer Vision at the Edge

One of the most important technologies for edge analytics is computer vision, a type of AI that “sees” and interprets visual data. This technology is enabling a range of exciting new use cases. With computer vision embedded into digital signage, retailers can capture audience impressions, measuring which types of customers looked at a marketing message and for how long. Smart cameras around the store can capture foot traffic, product movement, and other customer activity. Inventory systems can use computer vision to keep an accurate count of what’s on store shelves.

At NRF 2020, Intel demonstrated a data-driven use case for intelligent loss prevention from Flooid and other partners. The solution uses computer vision and data from multiple sensors to accurately identify the product a customer is purchasing at self-checkout. It integrates components from several vendors, including a point-of-sale real-time transaction log, computer vision‒based object detection, scale solutions, and RFID capabilities. An open-source middleware from EdgeX Foundry provides the common framework needed to integrate the data from these different components.

More Analytics for Better Results

Retailers can amplify their digital transformation by applying analytics at multiple touchpoints. In doing so, they’ll create a greater number of personalized experiences and will be rewarded with a more comprehensive view of their business.

For example, the Intelligent Retail Experience demonstrates how edge-to-cloud technologies can help retailers provide better experiences while gaining real-time insights. When walking into the environment, a Microsoft Surface Studio powered by an Intel® Core™ i7 processor detects whether a person is a first-time or returning customer so it can customize the experience immediately. To power AI inferencing, the system is supported by a local Azure Stack Edge appliance powered by Intel® Xeon® Scalable processors and Intel® FPGAs.

At checkout, a mobile POS system running customer loyalty software captures additional information that can be used to personalize future visits. In the background, cameras collect data about store traffic and inventory, which is shared with the cloud so that managers can see what is happening across multiple stores.

Intel® Technologies for Retail Analytics

Ultimately, software is the brain behind retail analytics. However, before retailers can put software to use, they must be able to capture, store, and process their data, which often comes from many different sources.

With compute, storage, and networking technologies that span edge to cloud, Intel enables a data-centric world—one that lets retailers collect and manage data from any touchpoint, be it in the store or online. Our technologies make it possible for retailers to analyze data on the spot, right where it’s created, or bring it all together in one place. The results? Highly curated experiences, inventory and supply chain efficiencies, and the ability for customers to get what they want, where they want it, with less friction.

To find trusted solutions through Intel’s partner ecosystem, you can explore the Intel® Solutions Marketplace.

Enabling Analytics through the Open Retail Initiative

While retail analytics has the potential to unlock new business value, interoperability challenges often make it difficult for retailers to deploy new solutions.

Intel is leading the Open Retail Initiative (ORI) to encourage a common, open framework that enables an ecosystem of interchangeable components and accessible retail solutions. The goal of ORI is to accelerate the scalable deployment of data-rich solutions optimized for physical retail, from the edge to the cloud. This makes it easier for retailers to put their data to work and harness the full potential of analytics and AI from multiple applications.

Data-Driven Transformation in Retail

Now more than ever, data can give retailers a competitive advantage in a crowded market. A flexible foundation for analytics based on open standards empowers retailers to consolidate data from many sources so that it can be used to its full potential. Intel®-based solutions at the edge and in the cloud help retailers collect, move, store, and analyze data in the right places, at the right time, for valuable insights.