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9,000 orders. Order-level attribution. Full customer journey tracking. Here's what we found.
She wants comfortable heels for work. You sell them. Ziera. $200. 45% margin. Best product in your catalog.
She'll never find them.
Not because they're hidden. Because your navigation makes her work for it. Click into Women's. Click into Heels. Scroll past 4 pages. Hope "arch support" is a filter. It's not.
7 clicks. 6 minutes. She leaves.
This is the navigation tax. Every catalog pays it. The bigger the catalog, the worse it gets.
Search removes the tax. She types "comfortable heels for work." Ziera shows up first. 47 seconds. Done.
That's what we tested.
We installed semantic search. We tagged every order with attribution data. We tracked complete customer journeys from first search to purchase.
Two cohorts. Same store. Same period. Same products.
Search captured 17% of total store revenue.
Industry benchmark: Search typically drives 10-15% of e-commerce revenue. Collective is outperforming.
Search cohort: +9.1% AOV, +18% UPT.
Customer searches "navy loafers". Below the results she sees: leather care kit · matching belt · no-show socks.
She came for loafers. She leaves with three items.
Browse customers don't see contextual suggestions. They buy the loafers and leave.
Median time to convert: 1.2 minutes.
79% of search customers converted in under 5 minutes.
She types "comfortable work shoes for standing all day".
The results understand intent, not just keywords. First result: cushioned insole, arch support, slip-resistant. She clicks. She buys. 47 seconds.
Browse customers click through 6 pages looking for the same thing.
Search cohort LTV: +30% higher.
She searched "wide fit sandals". Found them in 2 minutes. They fit perfectly.
Three weeks later, she needs winter boots. She doesn't browse. She searches "wide fit boots". Same store. Same result.
When the first purchase works, she comes back. 65% more often.
Search cohort pays full price more often.
She searched "red heels for wedding". Found the exact pair. $179.
No hesitation. No discount code needed. This is what she came for.
Browse customer scrolls through 40 pairs. Not sure which one. Waits for the 20% off email.
Search customers find what they want. They don't need convincing.
Deeper sessions = higher AOV.
First search: "summer sandals". 30 results. Too broad.
She refines: flat heel. Then: leather. Then: wide fit.
Six searches later, she's looking at 4 perfect options. She buys two. $198.
Filtering isn't friction. It's confidence building.
NZ$119K from SKUs with zero browse conversions.
This is the most conservative incrementality metric. These 655 products generated zero revenue through browse navigation. Search was the only path to purchase.
Ziera orthopedic heels. $200+. Page 4 of "Women's Shoes."
Nobody browses to page 4. But someone searching "heels with arch support"? Ziera shows up first.
655 products only sold through search. They were always in the catalog. Browse couldn't surface them.
Revenue ROI Calculation
Conservative ROI Calculation (incremental revenue only)
Payback Period
Even using only incremental revenue (NZ$119K), payback occurs within 3 days.
What this pattern looks like across multiple brands.
Collective Shoes: NZ$1.4M quarterly revenue. 17% search attribution.
Assumes similar catalog complexity and customer intent patterns. Results vary by category and catalog depth. The bigger the catalog, the bigger the opportunity.
Search customers know what they want. They just can't find it. Give them a way to ask, and they:
Every metric traces to tagged transactions in Shopify. No projections. No modeled estimates. Actual orders.
Two customers. Same store. Same day. Same products.
One browses. Clicks around. Maybe buys. Maybe leaves.
One searches. Finds exactly what she wants. Buys. Comes back.
The search bar didn't change your products.
It changed how customers find them.