Industries · Auto & Parts
In auto parts, the wrong result costs you twice.
A buyer who orders a part that doesn't fit doesn't just cost you the order. They cost you the return, the shipping, and the trust. Catalog depth, fitment data and search behaviour make this one of the most technically demanding stores you can run. We measure where that costs you revenue and translate it into euros per month.
Familiar?
Signals we see in Auto & Parts
Search returns the wrong part number, or nothing at all
Returns from wrong-fit orders eat the margin on the order
Filtered category pages with thousands of variants crawl on mobile
The fitment or year/make/model tool is a third-party app that blocks the page
Product data arrives from a supplier feed nobody fully trusts
AI assistants name competitors' part numbers instead of yours
Who this is for
Is this you?
- You run tens of thousands of SKUs with real fitment data behind them
- You already buy traffic on part numbers and want it to land on a page that loads
- Returns from wrong-fit orders are a line on your P&L, not an incident
- You sell a handful of universal accessories: your leak almost certainly doesn't sit in catalog architecture
Auto & Parts
A catalog this deep only earns money if search can find the right part.
What we fix
What we go after first
- Speed on deep category, filter and search pages, not just the homepage
- Fitment and search tooling that runs without blocking the page
- Catalog and variant architecture that can carry the SKU count
- Supplier feed handling that fails loudly instead of silently
- Part numbers, OEM references and fitment attributes structured for search and AI assistants
- Checkout measured on mobile, on the device your buyer actually uses
Where to start
How we'd approach it
Same route as always: measure first, then fix what demonstrably costs the most.
Step 1
Revenue Leak Audit
We measure where your revenue leaks, across all five layers, and translate it into euros per month and per year.
Step 2
Stack Rebuild
When optimizing inside your current stack is no longer enough. Focused on structural recovery of performance and conversion.
Step 4
Agentic Readiness
Preparing commerce infrastructure for AI agents and new buying interfaces: product data, structured data, feeds, APIs, and transactional readiness.
Questions
What people in this sector ask us
Next step
Find out what wrong-fit traffic costs you.
The Revenue Leak Audit measures all five layers on your catalog and translates them into an amount per month and per year.