Poor TikTok ad ROI — smarter campaign tracking + AI automation for reports

Brands and agencies reallocating significant spend to TikTok and Reels increasingly report poor ad ROI because platform-level metrics, short-form engagement dynamics, and privacy-driven signal loss make campaign-level attribution noisy and slow. With roughly 600,000 digital advertisers in play and an $18.0B addressable market, many measurement teams are stretched thin and cannot rapidly validate or optimize investments on these platforms. A focused SaaS could combine server-side event ingestion, deterministic matching where available, probabilistic attribution models for cookieless contexts, and LLM-driven automated reports that translate signal into prescriptive optimization recommendations. Packaged integrations with TikTok APIs, MMPs, data warehouses and a white-label agency dashboard could aim at an ACV near $30K while cutting manual report generation from days to minutes. This is an attractive moment: short-form-video ad budgets are rising, platform APIs have matured, and privacy shifts (IDFA/cookie deprecation) are forcing server-side and modeled measurement approaches, creating immediate demand for platform-specific solutions. Simultaneously, advances in AI enable automated anomaly detection and natural-language reporting that accelerate time-to-insight into this $18B opportunity. To stand out you must deliver a hybrid measurement stack tailored to TikTok/Reels, transparent modeling with validated lift studies, and productized AI that gives prescriptive actions rather than raw charts. The core challenges are medium competition, reliance on platform API access and creative-level signals, and the need to prove modeled attributions against business outcomes — all addressable but requiring focused engineering, legal compliance, and strong early references.