6 examples of business intelligence (BI) in the retail industry

Retail generates enormous volumes of data across stores, e-commerce, distribution centers, customers, and a product assortment that can change by the week.

Business intelligence has traditionally helped retailers make sense of that data through dashboards, reports, and standardized metrics. Those tools still matter, especially when teams need a consistent view of performance.

But many of the highest-value retail analytics workflows now go further. Forecasting inventory requires modeling and scenario planning. Promotion analysis requires deeper statistical work. Merchandisers need to ask follow-up questions that no dashboard designer anticipated. And AI is making it possible for business users to explore data and build what they need in plain language.

So while these are all examples of BI in retail, they also show how retail analytics is expanding beyond traditional BI.

1. Demand forecasting and inventory optimization

Demand forecasting helps retailers decide how much inventory to buy, where to put it, and when to replenish it.

A dashboard can tell you how much inventory is on hand or how quickly a product sold last month. Forecasting goes further by combining historical demand with factors like seasonality, promotions, product characteristics, and new assumptions about what might happen next.

Large retailers have invested heavily here. Walmart has described an AI-powered inventory system that can reposition inventory based on changing regional demand. Target's FY2024 results also attributed part of its gross-margin improvement to lower book-to-physical inventory adjustments.

That becomes especially useful when planners can interact with the model rather than simply consume its output.

At Huckberry, the planning team built a forecasting data app covering more than 100,000 SKUs. Planners can adjust assumptions and run live scenarios before making purchasing decisions, rather than asking the data team to rerun every what-if. The workflow helped Huckberry save more than $1 million in inventory.

The important shift is from reporting inventory to helping someone make an inventory decision.

2. Customer segmentation and lifetime value analysis

Retailers rarely have one simple view of a customer. A shopper might buy online, return something in a store, participate in a loyalty program, respond to a promotion, and interact with customer support. Customer segmentation brings those signals together so teams can understand who their customers are and how their behavior changes over time.

Common analyses include:

• Customer lifetime value
• Recency, frequency, and monetary-value segmentation
• Churn or repeat-purchase propensity
• Acquisition cohorts
• Loyalty and engagement patterns
• Product or category affinities

The analytical challenge is that these questions rarely stop at the first segmentation. Marketing might want to know which high-value customers are showing signs of churn. Merchandising might want to understand which categories those customers buy. Finance might want to model how a change in retention affects revenue.

Modern retail analytics needs to support that kind of iterative exploration without every follow-up becoming another dashboard request.

3. Store performance benchmarking

Store performance analysis helps retailers compare locations, regions, formats, and teams using a consistent set of metrics.

The hard part is often less about building the visualization and more about making sure everyone means the same thing by the numbers.

Take comparable-store sales. Teams need agreement on which locations qualify, how openings and closures are treated, how returns are handled, and which time periods count. Revenue can create similar disagreements once discounts, taxes, gift cards, and refunds enter the picture.

That makes governed context especially important as retailers adopt conversational analytics. TDWI has highlighted how fragmented definitions and context can lead conversational BI systems to produce inconsistent or inaccurate results.

If a regional leader asks an AI agent why one store underperformed another, the answer needs to use the same definitions as the executive dashboard and the finance report.

Semantic models can be one part of that foundation, but context can also come from warehouse metadata, dbt models, documentation, endorsed data, and the analytical work teams have already created.

The goal is straightforward: a question asked in plain language should produce an answer grounded in the same business logic everyone else uses.

4. Self-serve analytics for merchandising and operations

Merchandisers and operations teams ask questions constantly.

Which products are selling faster than expected? Which sizes are about to stock out? How did a launch perform by region? Which products are customers buying together? Why did one category miss plan?

Traditional BI attempted to make those questions self-service through dashboards and drag-and-drop exploration. But when the answer wasn't already represented in the interface, the request usually went back to the data team.

AI changes that interaction. A merchandiser can ask a question in plain language, follow up on the answer, change the breakdown, or investigate an unexpected segment without learning how the underlying data model is organized.

With Threads, business users can explore questions conversationally while the underlying analysis remains connected to the same data and organizational context.

This is particularly valuable for smaller retail data teams supporting many functions. At Trade Coffee, a small data team supports ad hoc work across operations, merchandising, and marketing. Bringing exploration, analysis, collaboration, and sharing into the same environment helped the team move faster and reduced the errors that came from passing files and local notebooks back and forth.

The opportunity is growing quickly. In Hex's State of Data Teams 2026, the share of data leaders naming AI as a top goal jumped from 4% to 27% in six months. At the same time, 31% cited data quality and lack of trust as their biggest barrier to AI adoption.

That tension is what governed self-service needs to solve: make it dramatically easier for business users to get answers without giving the data team less visibility or control.

5. Promotion and markdown analysis

Promotion analysis is a good example of where retail analytics quickly moves beyond reporting. A dashboard can tell you how much a promoted product sold. It can't necessarily tell you what would have happened without the promotion.

To answer that question, teams may need to account for seasonality, baseline demand, stockouts, customer mix, selection bias, and other factors that affect performance.

Research on flash-sale retailer Rue La La illustrates the problem. Researchers from MIT and HBS used demand forecasting and price optimization to recommend prices for new products. In a controlled field experiment, the approach produced an estimated 9.7% revenue increase. The underlying research combined demand prediction, lost-sales estimation, and multi-product price optimization.

The same is true for markdown decisions. A merchandising team doesn't just want to know that sell-through is low. They want to understand what might happen if they change the price, how that varies by category or region, and what the implications are for margin and inventory.

That requires deeper analytical work, but the output still needs to be usable by the people making the decision. An analyst might build the underlying model with SQL or Python, then publish it as an interactive data app where a merchandising director can adjust a discount, category, or time horizon without touching the underlying logic.

AI compresses this workflow further. With Generative Apps, teams can increasingly generate the application itself from a prompt and refine it as the business question evolves.

6. Scaling analytics access across a growing retail org

As retailers grow, the number of analytical questions grows much faster than the data team. More stores, products, markets, channels, and teams create more dashboards and more requests. Eventually, adding another dashboard for every question stops scaling.

Research from IDC has highlighted operational efficiency and increasingly complex omnichannel environments as major priorities for retailers. KPMG's retail research similarly emphasizes the need to break down data silos and connect insights more directly to decisions.

When formal analytics channels are slow, people find workarounds. That's increasingly important as general-purpose AI spreads through the workplace. KPMG's AI trust research found that 58% of U.S. workers using AI rely on its output without thoroughly evaluating it, and 57% reported making mistakes as a result.

Retail teams therefore need governed paths that are also fast and easy enough for people to actually use. That means giving business users an easy way to ask and explore questions, while giving the data team a scalable way to manage the context, definitions, permissions, and answer quality underneath those interactions.

It also means letting analysts move quickly when more technical work is required.

At Whatnot, the retail marketplace brought analysts, machine-learning engineers, and data engineers into a more unified workflow with Hex and dbt. The company reported a 4–8x improvement in speed from idea to production and a 10x reduction in maintenance costs as it scaled its data organization.

That's an important part of scaling retail analytics: not every question should require the same workflow. A category manager might ask in plain language, an analyst might need SQL, and a forecasting problem might require a full model. Those different paths should still connect back to the same data and context.

Retail analytics is moving beyond dashboards

These six examples all point to the same shift. Dashboards remain useful for monitoring known metrics. But many important retail decisions don't follow a predefined reporting path.

Forecasting requires scenario modeling. Customer segmentation invites follow-up questions. Promotion analysis requires deeper statistical work. Merchandising teams need answers to questions that change every day.

AI makes it possible to support those workflows without forcing every question into a new dashboard.

With Hex, business users can ask and follow up on questions in Threads, analysts can go deeper with SQL and Python, and teams can turn recurring workflows into interactive data apps. Generative Apps make it possible to build those experiences from a prompt, while Context Studio gives data teams a way to manage and improve the context behind AI-generated answers.

The result is a broader model for retail analytics: one where reporting, exploration, modeling, AI, and applications can all work together instead of living in separate tools.

State of Data Teams 2026 shows why that matters: AI adoption is accelerating quickly, but trust remains the biggest barrier. Retail teams need both easier access to analysis and a scalable way to keep the answers reliable.

Ready to see it with your retail data? Try Hex free or request a demo.

Frequently Asked Questions

What is business intelligence in the retail industry?

Business intelligence in retail is the use of data to understand and improve areas such as sales, inventory, merchandising, customer behavior, store performance, and promotions.

Traditionally, retail BI has centered on dashboards and reports. Modern retail analytics increasingly combines those tools with forecasting models, conversational AI, interactive data apps, and other workflows that help teams move from reporting into deeper analysis and decision-making.

How do retail data teams prioritize which analytics use cases to invest in first?

Start with a business decision that happens frequently and where better information can materially change the outcome.

Inventory forecasting is often a strong example because retailers already have transaction and inventory data, planners make purchasing decisions constantly, and improvements can be measured through forecast accuracy, stockouts, inventory levels, and working capital.

The same principle applies elsewhere: start with a focused, repeated decision rather than trying to overhaul every analytics workflow at once.

How do retail teams measure the ROI of analytics?

Retail teams can measure analytics ROI through a combination of business outcomes and operating efficiency.

Common metrics include forecast accuracy, inventory reductions, stockout rates, margin improvement, promotion lift, time-to-insight, and the volume of recurring requests handled without data-team intervention.

Huckberry's forecasting workflow is a concrete example: improving the way planners made inventory decisions translated into more than $1 million in savings.