Merchandising Analytics: Using Tracking Data to Optimise Product Placement

What Is Merchandising Analytics?

Merchandising analytics connects product placement decisions — which products appear where on your store — to revenue outcomes. In physical retail, this is called visual merchandising and category management. In ecommerce, it is about which products appear first on category pages, in carousels, in recommendation widgets, and in search results.

Without data, merchandising decisions are made by intuition ("this product looks popular") or by historical sales rank alone. With ecommerce tracking data, you can measure the impact of placement directly.

The Three Data Sources for Merchandising Analytics

1. Product List Performance (view_item_list + select_item)

Implemented correctly, list tracking shows you:

• Click-through rate by product within a list: which products attract clicks from category pages?
• Position effect: do products in positions 1-5 get dramatically more clicks than positions 6-10?
• Revenue per impression by position: is position 1 generating disproportionate revenue vs its traffic share?

Use this data to: move high-converting products to top positions, remove low-converting products from prominent positions, and test whether new products benefit from "featured" placement.

2. Promotion Tracking (view_promotion + select_promotion)

Track all homepage banners, category banners, and featured sections:

• Which banner creative gets the highest CTR?
• Does the summer sale banner drive more revenue than the new arrivals banner?
• Is the sidebar promotion section ever clicked, or should it be redesigned?

3. Site Search Analysis

Products that appear frequently in search but are not prominent in navigation are candidates for better placement. If "office chair" is your top search term but chairs are buried under "Furniture then Seating then Office", the category navigation is not surfacing this demand effectively.

A/B Testing Placement with GA4

To test whether placing Product A vs Product B in position 1 of a category page affects revenue:

1. Run the test for 2-4 weeks (split visitors using your CMS or A/B testing tool)
2. Both variants push view_item_list events with the same item_list_name but different item index values
3. In GA4, compare transactions and revenue for Product A vs Product B across the test period
4. Also compare overall category page revenue in both variants (a winning product in position 1 should lift the whole page, not just one product's metrics)

Inventory-Driven Merchandising

Pass stock status as an item-scoped custom dimension on all list and view events. This enables: when stock is running low ("only 3 left"), does featuring that product more prominently accelerate its sale? Does the scarcity signal increase or decrease conversion rate for that product?

Summary

Merchandising analytics requires three tracking implementations: product list events (view_item_list, select_item) for position and list-level analysis, promotion tracking for banner and feature section performance, and site search analysis for navigation gap identification. Together they provide an objective data foundation for product placement decisions that can otherwise be dominated by opinions and brand preferences.

See our Enhanced Ecommerce Tracking service for merchandising analytics implementation.

Need merchandising analytics set up? Contact Adslytics.