Automated budget pacing and anomaly detection across ad accounts: overspend and underspend alerts, performance breach detection, and alerting that reaches a human.
Found on the 3rd, spent on the 28th
Pacing problems are discovered during month-end review, which is the one moment when nothing can be done about them. The structural reason is that no human can watch three hundred accounts daily, so the check happens on a monthly cadence while the spend happens hourly. Underspend is the quieter version of the same failure: budget the client agreed to that simply never went out the door, invisible because nothing looked broken.
What the system watches
- Pace-to-budget tracking per account and campaign, projected to month end rather than reported to date
- Overspend and underspend thresholds with client-specific tolerances
- Statistical anomaly detection on CPC, CVR, ROAS, and impression share that accounts for day-of-week and seasonality
- Zero-delivery and campaign-stall detection, which catches the failures that produce no data at all
- Alert routing to the owning account manager in Slack or email, with the account, the metric, and the size of the gap
- A pacing dashboard covering the whole book, so leadership can scan exposure in one view
How we work
Model pacing against real budgets and flight dates, including mid-month changes
Baseline each metric on your own history so thresholds reflect normal variance, not round numbers
Route alerts to the person who owns the account, not a shared channel nobody reads
Replay against past months to prove the system would have caught real incidents before going live
Typical stack
Frequently asked questions
Platform alerts are per-platform, per-account, and threshold-based, so a client running four channels needs four sets of rules and still has no combined view. This watches the full book in one place, projects to month end rather than reporting spend to date, and detects statistical anomalies rather than only fixed thresholds.
Only if the thresholds are wrong, which is why baselining comes from your history and includes seasonality. We validate by replaying past months: the system should have caught the incidents you remember and stayed quiet the rest of the time. We tune until that holds.
It can, and that is the campaign optimization agents work. We usually recommend starting with detection and alerting so the team builds trust in the signal, then automating the safe, well-understood responses once it has proven itself.
It runs off your warehouse rather than per-account API polling, so the marginal cost of another account is negligible. We have run this pattern across thousands of accounts.
Go deeper
Stop finding overspend at month end
Tell us how many accounts you run and how pacing is checked today, and we will scope the monitoring layer.
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