Pillar
Bach AI was built to detect revenue leaks — overspend, frequency saturation, attribution gaps, ROAS regression. This pillar inventories the patterns and shows how to spot them in your own account.
Bach AI was built to do one specific thing well: detect revenue leaks in Meta Ads accounts. After auditing hundreds of D2C accounts, we found the same eight patterns recur across spend levels, verticals, and account ages. The leaks are not random. They have signatures. Once you know what to look for, an audit takes ten minutes.
This pillar inventories those patterns. Each cluster article takes one leak and goes deep: how the leak manifests in the data, the threshold at which it becomes material, the fix, and how to verify the fix worked without losing the learning phase.
The framing word matters. Most ads coverage talks about "optimisation" — a vague aspiration. We talk about leaks — a specific, quantifiable $-amount escaping from your account every day until you plug it. Bach AI quantifies the leak in your currency on every audit.
Eight specific revenue-leak patterns Bach AI looks for in every Meta Ads audit — frequency saturation, attribution gaps, audience fatigue, geo/placement waste, CPC spikes, account-level overspend, ROAS regression, conversion drop-off. Each with a worked example.
A 10-minute Meta Ads account audit checklist — what to check, in what order, and what each signal means. Designed for D2C founders and performance marketers who need a fast, repeatable diagnostic.