Detect ad fatigue & automate scale/stop suggestions for Meta ads

Executive Summary

Ad fatigue on Meta manifests as rising CPM/CPC, falling CTR and conversion rates, and wasted spend when creative performance decays—this problem is acute for SMBs and mid-market advertisers as well as agencies that manage thousands of ad sets but lack the bandwidth to monitor creatives at scale; roughly 20 million advertisers could benefit from automated fatigue detection and remediation. Teams today often rely on raw metrics or manual rules, which miss subtle decay patterns and lead to either premature pausing or prolonged waste. You could build a lightweight SaaS that ingests Meta Marketing API data, scores creative health with explainable models, predicts time-to-degradation, and generates prescriptive scale/stop suggestions with confidence levels and automated execution or human-in-the-loop workflows. Core features would include cohort-level monitoring, automated A/B or rotation experiments, ROI-focused rulesets, and integrations with common reporting and ad management stacks so recommendations can be actioned rather than just reported. This market is attractive now: estimated TAM of $10.0B (20M advertisers × $500 ACV), a market score of 90/100 and revenue potential rated 80/100, driven by rising ad costs, the shift to AI-driven prescriptive tools, and SMB demand for consolidated martech. To stand out versus a medium-competition field you must focus on closed-loop outcomes (measurable spend efficiency), transparent explainability, low-friction integrations, and safety guardrails; challenges include Meta API limits, model drift, and the need to prove trust through early agency partnerships and conservative rollout strategies.