Rove Concepts operates at significant scale, driving millions of monthly site visits and investing heavily in paid media. However, their measurement infrastructure had not kept pace with that growth.

Unreliable Meta attribution

The most critical issue was Meta attribution. Only a small portion of spend was on Meta. This created a structural measurement disaster: the Meta pixel continuously targeted people already visiting the site organically, inflating reported ROAS consistently. Attempts to fix this through audience exclusions and existing tooling failed, leaving the team without a reliable source of truth.

The result: Rove's media buying team had no confidence in Meta performance data. Testing new strategies was impossible because results were never directionally trustworthy. New customer acquisition metrics could not be reported on at all.

No visibility into which products ad spend was actually selling

Rove lacked visibility into product-level performance. Their advertising relied heavily on top-of-funnel creative featuring multiple products, but there was no way to understand which products were actually driving revenue. A meaningful share of conversions occurred on different products than the ones shown in ads, making it impossible to accurately allocate spend or optimize merchandising decisions.

The result: Flying blind on product performance meant spend was systematically misallocated.
Zach put it directly: "There is no way to know which products people are buying from that spend. Which is kind of a crazy thing to think about, unless you have a tool like this."

Multi-brand data was completely siloed

Rove operates multiple storefronts: RoveConcepts.com, RoveLab.com, Rove Outlets, and additional micro-brands in development. With no shared data infrastructure, cross-brand marketing was impossible. Email flows could not target Rove Concepts customers with Rove Lab products. Meta audiences could not be seeded from cross-brand behavior. Each brand was essentially a data island.