Attribute filters on collection pages: +10.1% CR but -10.3% RPV
Test run byClean CommitCRO agency
Commentary byLiftArchiveOur analysis, not theirsAdding a filter panel for style, colorway, text layout, text arch and city lifted conversion rate by 10.1% but cut revenue per visitor by 10.3%, so we treat the result as inconclusive.
Clean Commit ran this test for a Shopify store and published it in its A/B test library.
The conversion gain had an 84.6% probability of being real, short of significance, and smaller orders offset the extra buyers.
- Conversion rate: +10.1%, with an 84.6% probability of beating the original. Not significant.
- Add-to-cart rate: +12.8%, and orders +6.0%. Bounce rate fell 11.5%, so shoppers engaged more with filtered pages.
- Average order value: -16.1%, and -38.2% on desktop's 1,234 visitors. Negative.
- Revenue per visitor: -10.3%. Negative, which makes the net effect on sales unclear.
The change
An attribute filter panel added to fixed collection pages
Simplified mockup drawn by LiftArchive from the shared screenshots.
Control
Collection pages relied on fixed collections, with no way to filter by product attributes.
Variant
A filter panel appeared on the left on desktop and as a collapsible drawer at the top on mobile. Shoppers could pick several values per filter, incompatible combinations were hidden, and choices were kept when shoppers came back from a product page.
Why it likely came out this way
Our reading of the result, not the practitioner's claim.
- Filters let shoppers jump straight to the exact configuration they wanted, which helps explain more add-to-carts.
- Shoppers who found one exact match may have bought a single item instead of browsing into extra items.
- The large desktop order value drop rests on only 1,234 visitors, so part of the revenue loss may be noise.
Caveats: Total visitors and test length were not shared, and the conversion lift did not reach significance. The same change showed a loss in a separate run (the 'attribute filters' test), so results did not repeat.
Who ran it
Clean Commit ran this test for a Shopify store (anonymized) and published the results (Clean Commit A/B test library). The numbers are theirs; the summary and commentary on this page are ours.
Read the original case study on Clean Commit →
How to run this test on your store
- Hypothesis
- If we let shoppers filter collections by the product attributes they care about, then conversion rate will rise because they find the exact item they want faster.
- Primary metric
- Revenue per visitor
- Guardrails
- Conversion rate
- Average order value
- Items per order
- Who should test it
- Stores with large catalogs of configurable or near-identical products, where browsing fixed collections is slow. Watch order value closely, and skip it if your catalog is small enough to scan in one screen.
Questions
Do collection filters increase conversion rate?
In this test, conversion rose 10.1% but revenue per visitor fell 10.3%, and a repeat run showed a loss.
Filters can help shoppers find items, but they may also shrink order size.
Why would filters lower average order value?
One likely reading is that shoppers found one exact match and stopped browsing.
Fewer chance discoveries can mean fewer add-on items.
Which metric should I use for a filter test?
Use revenue per visitor as the main metric, not conversion rate alone.
This test shows conversion and revenue can move in opposite directions.
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