Scaling listing accuracy without burning out the team
Sneaker Reseller is a fast-moving business with tens of thousands of live listings monthly across platforms like Amazon. That kind of volume helps them stay competitive—but it also opened the door to small listing issues that created big headaches.
Because the company lists under brand-owned product pages (like Nike or Adidas), they don't have direct control over visuals or metadata. And sometimes… things don't match. A red sneaker shows up labeled "Midnight Navy." A size 9 listing accidentally includes a size 10 image.
"We started seeing more support tickets, more returns but the main problem was negative 1 star reviews because the product received did not match what they thought they were buying. The inconsistency wasn't coming from us, but it was impacting us. Which means we need to be proactive to work with Amazon to solve it."
But how do you manually review tens of thousands of listings every month, within budget?
The team reached out to Audio Bee with a simple ask: "Can we get all of the listing data through web scraping that we can run our analysis to review?"
Rather than treat it as a simple data scraping job, we asked them to help us understand what they were trying to achieve—to see if we could solve their problem end-to-end.
Audio Bee worked closely with Sneaker Reseller to create a complete solution:
"The accuracy of this was amazing. We really loved how you solved the entire problem rather than just giving us scraped data which we would need to figure out how to process it."
After just two monthly reviews, the number of listings with errors dropped dramatically. Since new listings are constantly added, some natural error rate was expected—but the improvement was significant:
"Audio Bee helped us protect the buyer experience—without burning out our team. It saved us time, but more than that, it saved our sanity."
Let's discuss how Audio Bee can help your team work smarter, not harder.
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