Retail analytics explained: The ultimate guide

What is retail analytics: Four types, tools and AI implementation

Today’s retailers sit on mountains of data. But turning that into action? That’s still a challenge.

Luckily, there are tools, such as retail analytics software, to help. But what exactly is retail analytics, and how can data analytics be used in retail to give businesses a competitive edge?

Let’s explore how it’s used in modern retail to find out.

What is retail analytics?

Retail analytics is the process of collecting, analyzing and acting on data generated throughout retail operations to drive better business decisions. It typically interprets:

And it consistently brings results. Especially when combined with AI and machine learning, retail analytics is producing insights faster than ever, helping today’s retail leaders better manage inventory, pricing, staff scheduling and customer engagement. More than just a passing fascination, its importance is only predicted to grow. While in 2023, the global retail analytics market was valued at $7.56 billion, by 2032, it’s expected to soar to $31.08 billion.

In fact, retail analytics represents a fundamental shift from relying on instinct-based decision-making to leveraging data-backed strategies, redefining how retailers operate.

What are the four types of retail analytics: Descriptive, Diagnostic, Predictive, Prescriptive

Analytics is plural for a reason. There are many different types that serve distinct purposes, and you need to understand them to choose the right approach for each business challenge.

Descriptive analytics

Descriptive analytics summarizes historical data to reveal patterns and trends, which can explain what happened. For example, descriptive analytics might reveal that winter coat sales peaked in December, helping retailers track seasonal demand patterns. It’s often seen as the baseline for understanding other forms of analytics.

Diagnostic analytics

Diagnostic analytics helps you understand why something happened. This analysis digs deeper into descriptive data to identify the root causes of trends, patterns and anomalies in retail performance. So, beyond tracking a dip in sales, diagnostic analytics investigates potential causes such as competitor pricing changes or supply chain disruptions, enabling retailers to address underlying issues rather than just reacting to the symptoms.

Predictive analytics

Predictive analytics builds on important information found by descriptive analytics and makes informed predictions. This forward-looking analysis uses historical data patterns to forecast future trends, demand levels and business outcomes. It could even forecast increased demand for specific products during forecasted weather events or local festivals.

Prescriptive analytics

Prescriptive analytics is the practice of analyzing data to identify patterns to make predictions and determine the best course of action. This most advanced form of analytics not only predicts what will happen but also recommends specific actions to achieve previously stated outcomes.

Another way to think about it is that descriptive and diagnostic analytics will help you understand past interactions, while predictive and prescriptive analytics help you more effectively plan for the future. But you need to know where you’ve been to know where you’re going.

How data analytics is used in retail

Since store operations are so complex, just to improve store performance and customer satisfaction, retailers need to apply analytics to multiple different areas and disciplines. Some of the most common uses of analytics are as follows.

Customer behavior analysis

To learn more about customer behavior, analytics platforms track purchase patterns, browsing behavior and demographic preferences to create detailed customer profiles and segments. This analysis helps retailers to:

Additionally, advanced retail analytics solutions can even help retailers by predicting individual customer lifetime value and churn probability.

Inventory management

Retailers can use analytics to analyze historical sales data, seasonal trends, supplier lead times and external factors to optimize stock levels across all locations. With this information, retailers specifically can:

Depending on the solution, analytics platforms can even account for factors such as weather patterns, local events, promotional activities and economic forces, making it easier to order stock proactively instead of just reactively.

Pricing strategies

Pricing is one of the easiest ways to encourage or discourage customer spending. With dynamic pricing, retailers leverage real-time market data, competitor pricing, inventory levels and demand patterns to optimize sales and margin. It does this by:

This intelligence helps retailers identify opportunities to boost market share or improve profitability just through pricing alone.

What is retail analytics software?

Retail analytics software platforms, such as Power BI, Google Data Studio, Tableau or purpose-built tools, aggregate and visualize large datasets. These solutions use different types of analytics to help track key performance indicators (KPIs), measure campaign performance and model “what‑if” pricing or inventory scenarios.

Additionally, many leading platforms offer cloud-based solutions that scale with business growth and integrate seamlessly with existing retail technology stacks, allowing retailers to get a better view of their store operations.

AI in retail analytics

While AI may be a recent hot topic, its use within retail analytics is nothing new. Still, artificial intelligence has revolutionized retail analytics by enabling more sophisticated analysis and automated decision-making capabilities. For example, machine learning algorithms can:

Additionally, AI‑powered retail analytics systems continuously learn from new data, improving their accuracy and effectiveness over time. These systems can process vast amounts of structured and unstructured data to generate insights at a rate that would be impossible for human analysts.

But AI is not just about machine learning. Add to the flow other forms of AI, such as generative AI and agentic AI, and you can create autonomous agents that not only interpret raw data but also summarize and work with traditional qualitative data, such as call transcripts, to identify problems and make recommendations.

The newest type of AI, agentic AI also fuels automated decision-making, reducing the need for managerial approvals and enabling autonomous actions.

The missing piece in retail analytics

All of this might sound easy to implement on an e‑commerce site, but what about in a brick-and-mortar store? The truth is that due to the nature of in‑store shopping, most existing store systems don’t produce the right kind of data that allows retailers to run descriptive, let alone prescriptive, analytics to the same level as they can with online sales. The moment customers decide to shop without their loyalty card, retailers know nothing about their shopping journey.

But does it have to be this way?

The case for smart retail communications

Luckily, retailers can bridge the knowledge gap between online and in‑store shopping with an advanced AI‑enhanced retail headset system, like x‑hoppers. Designed to equip associates with voice-activated headsets, trackable smart notifications and agentic AI assistance, x‑hoppers provides retailers with a way to empower their staff while also giving them a window into what’s really happening on the shop floor by:

This is more revolutionary than regular retail headsets. With x‑hoppers, it’s not just about connecting frontline staff to back office data analysts but putting them at the center of the store ecosystem, giving them real‑time access to system alerts and insights that help them make better decisions in the moment.

And that is the future of retail analytics. Not only uncovering more in‑store insights, but delivering them directly to associates in real time.

Want to learn how real-time communications and analytics can help your teams make faster, data‑driven decisions?

Check out our latest white paper, “Empowering smart decisions with in‑store communications analytics,” to find out how leading retailers are bridging the gap between insights and frontline implementation. Inside, we cover:

Download the white paper now and discover how you can unlock the full potential of your retail analytics.

Kathryn Yarnot

Kathryn Yarnot is a copywriter and content marketer who draws on her decade of retail experience to share industry insights and trends. Born and raised in Pennsylvania, she is now based in the UK where she keeps an eye on shopping habits on both sides of the pond.

Kathryn Yarnot

What is retail analytics: The ultimate guide to data‑driven success

What is retail analytics: Four types, tools and AI implementation

What is retail analytics?