Feature Product Placement Decision

Business Context

ShopHub is an e-commerce marketplace deciding whether a product should stay in the homepage hero slot or be moved to a secondary carousel lower on the page. The merchandising team wants a metric-driven framework because featured placement drives large traffic volume but uses limited premium real estate.

Metric Scenario

Last week, Product A received 1.8M homepage impressions in the hero slot, 216K product detail page clicks (12.0% CTR), 25.9K add-to-carts (12.0% of PDP visits), and 7.8K purchases (30.1% of carts; 0.43% of impressions). Its average order value was $48, gross margin was 22%, and 14-day return rate was 11%. In a prior 2-week period when Product A was in a secondary slot, it received 620K impressions, 49.6K clicks (8.0% CTR), 7.4K add-to-carts (14.9%), and 2.6K purchases (35.1% of carts; 0.42% of impressions). Meanwhile, Product B currently in a secondary slot has 700K impressions, 70K clicks (10.0% CTR), 11.9K add-to-carts (17.0%), and 4.8K purchases (40.3% of carts; 0.69% of impressions), with $61 AOV, 28% margin, and 6% return rate.

Stakeholders ask whether Product A deserves prominent placement, whether Product B should replace it, and which metrics should govern future placement decisions.

Requirements

1. Define the primary metric you would use to decide featured vs secondary placement.
2. Show how you would compare products with different traffic levels and funnel performance.
3. Identify the key decomposition cuts needed before making a final decision.
4. Recommend whether to keep Product A featured, demote it, or test alternatives.
5. Name guardrail metrics to avoid optimizing for clicks alone.

Data Available

• homepage_impressions
• product_clicks
• product_page_events
• orders
• returns
• product_catalog
• inventory_snapshots