Next Level Performance
Auto parts · Shopify · 180K+ SKUs · 40K sessions/mo
Traditional analytics shows the drop-offs. Not the reasons.
Tom (CEO, Next Level Performance) had 40K sessions and a 0.07% conversion rate. He could see the funnel leaking but couldn't tell if it was the product pages, the checkout, mobile UX, or something else entirely. Two agencies had looked at it. Neither found the answer.
First, we gave him data he could trust
Before looking at the store itself, we needed to understand who was actually visiting it. UserApproved cross-referenced Tom's GA4 and Shopify data and started flagging patterns: clusters of sessions lasting under two seconds, zero add-to-carts, zero transactions, and geographic concentrations that didn't match his customer base. Individually, any one of those could be a bad day. Together, they pointed to a significant chunk of traffic that wasn't human. GA4 was counting all of it the same, so the headline numbers looked healthy, but the real shopper count was much lower than it appeared.
Data cleaning
Cross-referenced GA4 and Shopify to separate real shoppers from noise.
With the noise filtered out, we could focus on what real shoppers were experiencing.
UserApproved simulated what real shoppers would experience
We ran AI personas through the store the way real customers would browse it. Session replays with emotion shift markers showed exactly where people went from engaged to frustrated. None of this would show up in GA4.
A Honda CR-V shopper scrolled for two minutes because there was no vehicle filter. On mobile, the Add to Cart button was hidden below the fold. The homepage promised free shipping, but checkout charged $45. And forced account creation at checkout was killing 92% of purchases. Of 380 who started checkout, only 28 finished, an estimated $85,000/mo lost. The fix took five minutes.
Session Replay
1 of 3Conversion Funnel
Last 30 daysEvery problem quantified. Tom took the findings to the copilot.
Copilot helped him fix it. Fast.
Tom took the findings to the copilot. It already had context on every issue from the audit, so he could ask about specific problems and get answers grounded in his actual store data. Simple fixes shipped in minutes.
Your product titles already contain vehicle data (e.g. "2017 Honda CR-V Brake Pad"). Here's how to approach it:
- •Parse your catalog: Extract Year/Make/Model from your 180K product titles and metadata
- •Build the filter UI: A 3-step dropdown (Year → Make → Model) on your collection pages
- •Deploy to theme: Generate the Liquid component and add it to collection.liquid
Would you like me to help draft a plan for auditing your current product data and scoping this out?
Based on your current data, three clear priorities are holding back revenue:
- •Checkout account wall: With only 7.4% of checkout users completing purchase, the forced account creation is your biggest leak. Est. $85K/mo.
- •Vehicle fitment filter: 62% of collection page visitors drop off without viewing a product. A Y/M/M filter would help them find what they need.
- •Shipping promise mismatch: Homepage says free over $99, checkout charges $45. This creates sticker shock at the worst moment.
Would you like to prioritize one of these for a deeper look?
Here is the breakdown of your checkout funnel by device for the last 30 days.
Your mobile traffic is significantly more valuable, converting at a rate 7x higher than desktop. The desktop numbers are heavily inflated by bot traffic, which is why you see high session counts but a much lower conversion rate.
| Device | Sessions | Add to Cart | Checkout | Purchases | Rate |
|---|---|---|---|---|---|
| Mobile | 14,800 | 620 | 168 | 22 | 0.15% |
| Desktop | 24,600 | 680 | 204 | 5 | 0.02% |
| Tablet | 800 | 40 | 8 | 1 | 0.13% |
Focusing your optimization efforts on the mobile checkout experience will yield the most direct return on your human traffic.
Six weeks later
Before, Tom relied on educated guesses. GA4 showed numbers but not reasons. Agencies gave recommendations but couldn't explain why. Now every decision is backed by real shopper behavior, quantified impact, and a system that keeps finding new things to improve.
Ongoing proactive monitoring
Tom's store doesn't sit unattended between audits. The system runs continuously, watching for the things that quietly break conversion without anyone noticing.
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Last 7 days"Think of it like having an AI e-commerce expert reviewing your store 24/7. Identify problems, implement solutions, and ship faster."
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