What Is Retail Analytics?

Retail analytics solutions help retailers make better sense of their data so they can make more-informed decisions with it.

Using the mountains of data most retail businesses generate, retail analytics solutions extract insights that drive better results. These technologies can be used to better understand areas such as sales patterns, customer trends, inventory levels, or marketing ROI. This understanding empowers retailers to improve customer experiences, optimize stock levels, enhance pricing strategies, and boost their bottom line. They can quickly or even proactively respond to market trends, personalize customer engagements, and sharpen their competitive edge.

Retail data analytics platforms feature software tools that collect and analyze data. These platforms are supported by on-premises data center resources and cloud and edge computing. In some cases, the cloud is the best fit—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.

Today, edge computing plays an especially critical role in enabling retail store analytics that brings together the digital and physical worlds. In-store edge servers and other connected devices, such as cameras or sensors, can process and analyze data closer to where it is generated, providing retailers with access to near-real-time information about their customers. Data analytics and sensor technologies combine data streams from point of sale (POS) devices, digital signs, inventory, video, and loyalty programs to deliver a deeper understanding of stores and shoppers.

Additionally, artificial intelligence (AI) is being applied across the retail landscape to enable intelligent, automated, and predictive capabilities. 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 also use computer vision to keep an accurate count of what’s on store shelves.

Benefits of Retail Analytics

Without data, retailers can only guess what their customers want. When fed into the right retail analytics solutions, data unlocks insights that help improve business results.

Here are just a few of the ways analytics helps retailers:

• Creating hyperpersonalized experiences: About 77 percent of consumers believe that the customer experience is just as important as the quality of products and services.¹ By analyzing a customer’s past purchases and reactions to previous campaigns, retailers can create offers and experiences uniquely tailored to their preferences. These hyperpersonalized experiences often make use of AI-powered recommendation engines.
• Transforming brick-and-mortar stores: Retail store analytics facilitates rich customer engagements in near-real time. Imagine responsive digital signage that delivers the right message to target audiences or POS systems that integrate with loyalty programs.
• Running effective marketing campaigns: Retail predictive analytics helps retailers identify which customers will most likely buy which products in the coming months.
• Optimizing inventory and supply chain management: AI and predictive analytics help retailers anticipate which products or features will have the highest traction. Computer vision or smart shelves can monitor inventory to help minimize out-of-stock occurrences. Analytics can predict the right orders for new stock, ensuring stores won’t have too few or too many items.
• Enhancing merchandising and product placement: By using technologies such as retail traffic analytics to create a heat map of store foot traffic, retailers can make better decisions about where to place products so customers will notice them.
• Fueling an omnichannel strategy: Analytics plays a critical role in unified commerce. By using data from all available channels, retailers can optimize operations while improving customer experiences.

Challenges of Retail Analytics

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. Many retailers possess the relevant data for AI analytics, but adoption can be hindered by operational technology and IT systems that have not been connected in the past.

Similarly, 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 to create new experiences with speed and ease.

Incorporating AI into retail analytics introduces a range of complex challenges, from ensuring the trustworthiness of the model being employed to right-sizing hardware investments to powering distributed AI capabilities, like automated loss prevention detection using computer vision. Integrating advanced technologies like AI and edge computing often requires significant investment and expertise.

Data privacy is an important concern for both AI-enhanced and traditional analytics. Adding a new digital tool, device, or partner means sharing customer data to some degree and creating new sources of potential breaches. Security is a top concern when choosing a technology partner or implementing a new capability.

Types of Retail Analytics

At a high level, several different techniques are applied by retail analytics platforms to help enable business results:

• Descriptive analytics uses historical sales data to provide visibility into past performance and trends. For example, a retailer may use descriptive analytics to identify top-performing products in the last quarter or to spot top seasonal trends.
• Diagnostic analytics examines data to determine the root causes of past outcomes. A retailer could use retail diagnostic analytics to investigate why a marketing effort delivered underwhelming results in particular regions.
• Predictive analytics uses historical data and statistical models to forecast future trends and behaviors, such as predicting product demand.
• Prescriptive analytics suggests strategies to achieve defined goals based on previous events and data. This form of analytics often incorporates AI capabilities. A retailer can use prescriptive analytics to pinpoint the best promotions and stocking strategies to maximize profits during the busiest times.

Retail Analytics Use Cases

With a footprint from edge to cloud and a plethora of customer data at their disposal, retailers can use analytics to unlock a number of powerful use cases. Whether customers are online or in store, these technologies allow retailers to understand their behavior, preferences, and demographics and then build tailored experiences and targeted marketing strategies to match.

For example, in-store analytics can help retailers understand customer movement, dwell time, and engagement to help store staff adjust layouts and improve shoppers’ experiences. These kinds of in-store capabilities employ hardware such as Wi-Fi sensors, cameras, and beacon technology that integrates with software for real-time analysis and visualization. Retail customer analytics also extends to online engagements, where retail web analytics can analyze user behavior to improve site performance, merchandising, and conversion rates.

Additionally, enabled by capabilities such as POS system and supply chain integration, inventory analytics can help retailers maintain ideal stock levels by analyzing purchases, restocking times, and logistics across all channels. This capability can incorporate technologies such as RFID tags, barcode scanners, warehouse management systems, and inventory management software with ERP integration.

Across use cases, AI capabilities are being applied to help retail analytics platforms predict and forecast customer behavior and market demands, including retail demand and sales forecasting. Using these predictions, retailers can plan and prioritize more effectively. These forecasting efforts are augmented by data platforms that constantly bring in near-real-time information from various edge environments. On the operations side, AI can find and correct inventory distortion, optimize supply chains, or improve product development.

Retail Analytics Tools

Software plays an integral role in powering retail analytics use cases. The specific types of software tools can vary greatly based on the use case but can include POS systems, customer relationship management platforms, business intelligence tools, inventory management systems, and predictive, AI-enabled analytics solutions.

These tools are supported by underlying technology infrastructure from edge to cloud, from edge servers and self-service kiosks in stores to advanced compute nodes in cloud data centers that handle the most-intensive computations for complex tasks like retail demand forecasting or trend analysis.

The Future of Retail Analytics

As AI continues to evolve, it will have a sizable impact on retail businesses. Sophisticated AI capabilities will unlock increasing levels of intelligent automation, driving new levels of efficiency and personalization for retailers and their customers. Increasing integration between various data sources will allow retail analytics solutions to deliver deeper and more-useful insights as retail digital transformation continues to progress. The continued integration of data, AI, and analytics tools will allow retailers to extract powerful insights across their omnichannel business model.