Product-aware ranking for marketplace advertisers

The product problem

Marketplace advertisers have deep catalogs, uneven product liquidity, noisy buyer intent, and multiple conversion surfaces. The product challenge is to help delivery systems understand which product, merchant, audience, and destination combinations are most likely to create value.

How I frame it

• Start with the marketplace system, not a single ad unit: buyer intent, catalog structure, merchant constraints, and post-click paths all interact.
• Treat product data as ranking fuel only when quality, freshness, and measurement can support it.
• Separate growth hypotheses into product liquidity, audience fit, signal isolation, and measurement confidence.
• Use experiments to learn which layer is binding before scaling a broad platform bet.

Transferable lessons

Great marketplace ranking work is rarely just model quality. It is a full-stack product problem: incentive design, data contracts, catalog operations, advertiser trust, GTM, and measurement all determine whether ranking improvements become durable business impact.