Benchmarking Meta ad performance for car-detailing businesses (ROAS/CPL/CPA)

Local car-detailing shops and the small agencies that serve them are running Meta ads blind to realistic ROAS/CPL/CPA benchmarks, forcing owners to guess acceptable performance or cut spend; that problem scales across an addressable set of roughly 2M local service businesses and supports a $6.0B opportunity when you assume a $3K ACV for benchmarking/optimization subscriptions. These businesses lack verticalized context—generic industry averages and platform dashboards don’t translate to a detailing shop’s cash-per-bay economics or local-seasonality patterns. A practical product would be a privacy-first SaaS that ingests aggregated Meta campaign signals, connects to booking/POS systems, and delivers peer benchmarks, LTV-adjusted ROAS, CPL/CPA targets, and campaign playbooks tailored to car-detailing units. Given rising SMB adoption of paid social and the shift to aggregated measurement after iOS changes, demand for contextualized, SKAdNetwork-friendly attribution and actionable optimization is growing now; I’d rate the market 88/100 and revenue potential 86/100 for a focused execution that can monetize at a high-enough ACV. To stand out you must be vertical-first: use domain-specific KPIs (revenue per bay, appointment-to-retention rates), ship out-of-the-box integrations for common booking systems, and provide conservative, privacy-compliant models rather than raw user-level attribution. Strengths include a large, underserved niche and predictable subscription economics, while realistic challenges are acquiring high-quality benchmark data, navigating platform API limits and privacy constraints, and winning distribution in a fragmented SMB channel—pursue this if you can secure early data partnerships and a low-cost SMB acquisition path.