“With Converge, we now have multiple attribution models, core website metrics, and ad platform data all in one table. That's been huge for deciding what to cut or scale.”
Rove Concepts is a Vancouver-based luxury furniture brand specializing in modern, design-forward home furnishings sold direct to consumers across North America.
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.
Converge implemented a unified measurement layer designed to replace unreliable platform attribution with a complete and accurate view of performance. The Rove team used Converge to fix unreliable Meta attribution, unlock product-level performance insights, and build a unified data foundation to scale profitably.
From biased in-platform attribution to full performance visibility on Meta
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.
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.
Server-side tracking with multi-touch attribution
Converge replaced fragmented in-platform attribution with server-side tracking and a multi-touch attribution layer that compares first touch, last touch, and participation models side by side. For the first time, Rove could see how Meta campaigns actually contribute to new customer acquisition independently of platform-reported numbers.
Impact:
- Full visibility into new customer ROAS for Meta spend
- Discovered DPA ads consistently outperform assumptions on furniture, shifting budget accordingly
From blind product spend to ad-to-product performance visibility
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.
Result: Flying blind on product performance meant spend was systematically misallocated.
Product-level analytics tying ads to actual purchases
Converge’s product analytics layer tracks sessions by product view, revenue per session per product, and ad-to-product conversion paths. This gives Rove a direct line of sight from each ad to what customers actually buy, so they can kill low-converting product ads and scale winning ones with confidence.
Impact:
- 20% lift in revenue per session for product-focused ad spend
- Shifted spend away from underperforming ads toward products with proven conversion rates
- Made product CAC calculable for the first time at scale
From siloed storefronts to one unified customer graph
Multi-brand data was completely siloed
The Rove Group operates multiple brands and storefronts: RoveConcepts.com, RoveLab.com, and Rove Outlet. With no shared data infrastructure, cross-brand marketing was impossible. Email flows could not target Rove Concepts customers with Rove Lab products. Each brand was essentially a data island.
A unified customer graph stitched across every storefront
Converge acts as a unified measurement layer across all Rove storefronts. User profiles are stitched across brands in a single customer graph, with identification rates improving continuously. This creates the data foundation for cross-brand retargeting, email flows, and Meta audiences sourced from the full customer base rather than a single brand’s silo.
Impact: Foundation for reducing CAC across new brand launches by leveraging the existing customer base.