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Pacing & anomaly detection

Budget pacing and anomaly detection across every account.

Overspend and broken campaigns are only expensive because they are found late. We build the monitoring layer that watches pacing and performance across all your accounts continuously, and tells the right person while there is still time to act.

  • Pace-to-budget tracking
  • Anomaly detection
  • Routed alerts

What you get

Found while there is still time to act.

Projected to month end

Pace-to-budget per account and campaign, not spend to date.

Anomalies, not just thresholds

CPC, CVR, ROAS and impression share against your own seasonality.

Alerts to the owner

Routed to the account manager in Slack or email, with the gap sized.

The whole book in one view

Leadership can scan exposure across every account.

The problem

Caught on the 3rd, not the 28th.

Pacing problems are discovered during month-end review, 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

What's included.

  • 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

How we run it.

  1. Step 1

    Model pacing against real budgets and flight dates, including mid-month changes

  2. Step 2

    Baseline each metric on your own history, so thresholds reflect normal variance, not round numbers

  3. Step 3

    Route alerts to the person who owns the account, not a shared channel nobody reads

  4. Step 4

    Replay against past months to prove the system would have caught real incidents before going live

What we use

Tools, platforms and methods.

  • Python
  • dbt
  • Airflow
  • Snowflake
  • BigQuery
  • Slack API
  • Looker Studio

FAQ

Common questions.

How is this different from platform budget alerts?

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.

Will this generate alerts our team learns to ignore?

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.

Can it act on the problem, not just report it?

It can, and that is our AI bid and budget agent 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.

How many accounts does this scale to?

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.

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.

Talk to an engineer