In retail, not all shelf locations generate the same results. Products placed at eye level, within easy reach, or near complementary items often sell significantly better than those placed on lower or less visible shelves. Yet many retailers still rely on historical practices or manual decisions when assigning shelf positions, leading to inconsistent execution and missed revenue opportunities.

The real business challenge is not creating a planogram - it is maximizing sales per square foot, improving category profitability, and ensuring every inch of shelf space contributes to revenue.

AI-powered software help retailers identify the shelf positions that consistently deliver stronger sales performance, allowing merchandising teams to make faster, data-driven decisions while reducing the time required to create and deploy planograms.

This blog explores how AI planograms identify high-performing shelf positions and improve merchandising decisions across retail stores.

The Cost of Poor Shelf Placement

Small placement mistakes can have a significant financial impact across hundreds or thousands of stores.

Common challenges include:

• High-margin products placed in low-visibility locations.
• Best-selling SKUs competing for limited shelf space.
• Slow-moving products occupying premium shelf positions.
• Inconsistent shelf layouts across stores.
• Low planogram compliance reducing merchandising effectiveness.
• Time-consuming manual shelf optimization.

These issues often result in:

• Lower category profitability.
• Reduced sales per square foot.
• Increased out-of-stock situations.
• Wasted shelf space.
• Slower promotional execution.

Instead of relying on assumptions, retailers need continuous insight into which shelf positions perform best.

What Makes a Shelf Position High Performing?

A high-performing shelf position is one that consistently drives better business outcomes. Rather than evaluating shelves based only on visibility, AI analyzes multiple performance indicators, including:

• Historical sales performance.
• Product velocity.
• Category contribution.
• Profit margins.
• Inventory turnover.
• Shopper purchasing behavior.
• Basket affinity.
• Promotional effectiveness.
• Seasonal demand.
• Store-specific buying patterns.

This enables retailers to understand why certain shelf locations outperform others instead of simply copying previous layouts.

How AI Identifies High-Performing Shelf Positions

1. Analyzes Historical Sales Data

AI studies sales history across thousands of SKUs and multiple stores. Instead of reviewing spreadsheets manually, it identifies patterns such as:

• Which shelves consistently generate higher sales?
• Which locations underperform?
• Which products perform better after repositioning?
• How shopper demand changes over time.

The system continuously learns from recent sales data, making future planograms more accurate.

2. Evaluates Shopper Buying Behavior

Customers rarely purchase products randomly. AI evaluates shopping behavior by analyzing:

• Frequently purchased products together.
• Customer traffic flows.
• Shopping missions.
• Product visibility.
• Purchase frequency.

For example, if customers regularly buy pasta and pasta sauce together, AI recommends placing them nearby to increase basket size. The goal is not simply better shelf organization—it is higher category profitability.

3. Identifies the Best Shelf Height

Shelf height plays a key role in purchasing decisions. AI determines which products deserve premium placement based on business value.

• Eye-level placement for high-margin products.
• Child-level positioning for family-focused products.
• Lower shelves for bulk items.
• Premium locations for new product launches.
• Checkout placement for impulse purchases.

Rather than assigning shelf space equally, AI allocates positions based on expected business impact.

4. Optimizes Shelf Space Allocation

Many retailers either over-allocate or under-allocate shelf space. AI continuously evaluates:

• Sales velocity.
• Inventory movement.
• Product demand.
• Shelf productivity.

This helps retailers:

• Increase facings for fast-moving products.
• Reduce space for slow-moving products.
• Improve product availability.
• Reduce empty shelf space.
• Increase space utilization.

The result is a more productive shelf without increasing the total store space.

5. Learns from Every Store

Not every store serves the same customers. AI recognizes differences between locations based on:

• Local demographics.
• Seasonal demand.
• Regional preferences.
• Store size.
• Sales trends.

Instead of deploying identical layouts everywhere, retailers can create localized planograms that better match customer demand while maintaining brand consistency.

Business Benefits That Matter to Retail Leaders

Retail executives focus on measurable business outcomes—not software features.

AI-powered planograms help improve key performance indicators such as:

• Sales per square foot.
• Category profitability.
• Shelf productivity.
• Planogram compliance.
• Space utilization.
• Planogram creation time.
• Deployment speed.
• Reduction in out-of-stocks.

Rather than spending hundreds of hours manually creating and updating planograms, merchandising teams can redirect their time toward category strategy, assortment planning, and faster execution.

Example: Before and After AI Shelf Optimization

Consider a national grocery retailer managing hundreds of stores.

Before AI

• Shelf space allocated using historical layouts.
• Manual planogram updates requiring weeks of work.
• Inconsistent execution across stores.
• Premium products placed in low-performing locations.
• Slow identification of underperforming categories.

After AI

• Shelf positions selected using sales and shopper data.
• Faster planogram creation and rollout.
• Improved merchandising consistency.
• Better allocation of premium shelf space.
• Continuous optimization based on changing demand.

While results vary by retailer, organizations that automate planogram creation and optimization often experience improvements in merchandising productivity, compliance, space utilization, and category performance.

Why AI Is More Effective Than Traditional Planograms?

Traditional planograms are static. Once created, they often remain unchanged until the next merchandising cycle. AI-powered planograms continuously adapt by incorporating:

• New sales trends.
• Inventory changes.
• Promotional performance.
• Seasonal demand.
• Shopper behavior.
• Category growth opportunities.

This enables retailers to optimize shelf layouts more frequently without increasing manual workload.

How Nexgen POG Helps Retailers Optimize Shelf Positions?

Nexgen POG uses AI-powered planograms to help retailers maximize the value of every shelf.

Instead of relying on manual merchandising decisions, the platform helps teams:

• Identify high-performing shelf positions using sales and merchandising data.
• Reduce the time required to create and update planograms.
• Improve shelf productivity and space utilization.
• Increase merchandising consistency across stores.
• Support faster rollout of seasonal and promotional planograms.
• Improve category performance through data-driven shelf allocation.

The result is a more efficient merchandising process that helps retailers improve sales, compliance, and overall category performance while making better use of existing shelf space.

Overview of Nexgen POG

Nexgen POG is a robust and user-friendly cloud-based visual merchandising tool. It is designed for quick and efficient planogramming with minimal effort. Planograms can be created using an intuitive drag-and-drop interface, while AI-driven Autofill automatically arranges products based on predefined rules, category roles, and sales data. This significantly reduces the time required to build accurate and high-performing planograms.

The platform is compatible across desktop, tablet, and mobile devices, allowing teams to create, edit, review, and share planograms from anywhere. It also supports store-specific planograms that improve product visibility, merchandising consistency, and overall sales performance.

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FAQs

1. How do AI planograms identify high-performing shelf positions?
AI analyzes sales history, shopper behavior, inventory trends, category performance, and merchandising data to recommend the shelf locations most likely to improve sales and profitability.

2. Can AI improve category profitability?
Yes. By allocating shelf space based on product performance, demand, and profit margins, AI helps retailers maximize the profitability of every category.

3. How do AI planograms reduce manual work?
AI automates much of the analysis, product placement, and shelf recommendation process, significantly reducing the time needed to create, update, and deploy planograms.

4. What business metrics improve with AI-powered planograms?
Retailers commonly measure improvements in planogram creation time, deployment speed, planogram compliance, sales per square foot, space utilization, category performance, and reductions in out-of-stocks.

5. Why is shelf position important in retail?
Shelf position directly influences product visibility and purchasing decisions. Placing the right products in high-performing locations helps increase sales, improve basket size, enhance category performance, and maximize the value of shelf space.