Matching search intent with relevant products is critically important for retailers today. In order for products to be discoverable, however, their online data including descriptions, categories, and more, need to be cleanly structured and relevant to buyer intent. A Big 4 retailer with millions of third-party listings partnered with Invisible to close the gap on other eCommerce giants by optimizing their SKU data for optimal relevance.
At the crux of the retailer's challenge was machine limitation. The process of enriching its data requires both human judgment and tasks beyond the scope of full automation. Yet, the team did not have the resources to implement the manual workflow internally.
The solution required a well-coordinated process operated by a scalable workforce of human operators. To test the hypothesis, Invisible was brought on as a strategic partner to verify, enrich, and update tens of thousands of SKUs and measure performance against listings with low SKU fidelity.
In just 30 days, Invisible hired and trained a team of 70 agents to complete the 6-step process of product data enrichment in 16 days.
Invisible applied generative AI to perform the heavy lift data enrichment steps at an unparalleled scale and also to flag data containing errors. Our platform wove in these AI-driven steps with tasks performed by skilled operators that required a finer touch.
Our client saw near-immediate ROl. In 30 days, 3,100+ dead-stock items generated nearly $1M in revenue. The next 10,000 products outperformed in their category by 139.54%, netting $21K in daily sales. The 44% of SKUs entered page one for search results, increasing visibility and sales velocity.
