Q: How does the budget optimizer work?

The budget optimizer uses Hill function response curves to model the spend-to-revenue relationship for each campaign. A Hill function captures saturation — the fact that doubling spend rarely doubles revenue. The optimizer then uses constrained numerical optimization (SLSQP) to find the spend allocation across all campaigns that maximizes total revenue while keeping total spend equal to your current budget. When marginal ROAS is equal across all campaigns, no reallocation can increase total output — that's the mathematical optimum. Recommendations are only surfaced when the suggested shift exceeds $500.

Q: What is a response curve and how is it built?

A response curve models how your revenue changes as you vary spend for a specific campaign. TrustData fits a Hill function — R(s) = R_max × sⁿ / (Kⁿ + sⁿ) — to your historical weekly spend and deduplicated revenue data. The three parameters are R_max (saturation ceiling), K (spend at half-max), and n (curve steepness). Curves are rebuilt every Monday using the last 12+ weeks of data. If an incrementality calibration factor exists for the channel, R_max is multiplied by the effective calibration factor before optimization runs.

Q: How are before/after verdicts calculated?

When you follow a budget shift recommendation, TrustData captures the current ROAS as a baseline snapshot. After 14+ days, it measures the actual post-change ROAS and computes the treatment delta. To filter out ambient market movement, it also measures the ROAS drift across all your other paid channels during the same period — the control. The adjusted delta is treatment_delta minus control_drift. verdict thresholds — winner (≥ 0.3 ROAS units), likely_winner (≥ 0.1), no_effect (≤ -0.1) — are in absolute ROAS units, not percentages, because percentages are misleading when baseline ROAS varies.

Question 1

What is the Paid Action Layer?

Accepted Answer

The Paid Action Layer is TrustData's system for turning paid media measurement into concrete, trackable actions. It has three components — anomaly detection (finding ROAS decay and CPA spikes in your deduplicated data), a budget optimizer (recommending spend shifts based on response curve equalization), and a verdict system (tracking whether each followed recommendation actually improved performance). Everything runs on your first-party deduplicated data, not platform-reported numbers.

Question 2

How is anomaly detection different from the tracking alerts?

Accepted Answer

TrustData's tracking alerts fire when your data collection infrastructure breaks — a pixel fires 50% fewer events than yesterday, a CAPI connector returns errors. The Paid Action Layer's anomaly detection fires when your paid performance metrics deteriorate — even if tracking is working perfectly. It monitors deduplicated ROAS and CPA trends against 30-day baselines and 14-day slopes, using statistical tests to separate real deterioration from normal variance.

Question 3

What are validation holds and why do they exist?

Accepted Answer

A validation hold is a waiting period for medium-severity anomalies before they surface as actionable cards. The problem with immediate alerting is that a one-day ROAS dip from a day with unusual traffic or a platform reporting delay creates noise. Validation holds wait 7–14 days and then re-run the anomaly detection on fresh data. If the anomaly is confirmed (still present), the card escalates to high urgency. If it has recovered, the card is auto-dismissed. High-severity anomalies (z-score < -3.0) bypass holds and surface immediately.

Question 4

How does the budget optimizer work?

Accepted Answer

The budget optimizer uses Hill function response curves to model the spend-to-revenue relationship for each campaign. A Hill function captures saturation — the fact that doubling spend rarely doubles revenue. The optimizer then uses constrained numerical optimization (SLSQP) to find the spend allocation across all campaigns that maximizes total revenue while keeping total spend equal to your current budget. When marginal ROAS is equal across all campaigns, no reallocation can increase total output — that's the mathematical optimum. Recommendations are only surfaced when the suggested shift exceeds $500.

Question 5

What is a response curve and how is it built?

Accepted Answer

A response curve models how your revenue changes as you vary spend for a specific campaign. TrustData fits a Hill function — R(s) = R_max × sⁿ / (Kⁿ + sⁿ) — to your historical weekly spend and deduplicated revenue data. The three parameters are R_max (saturation ceiling), K (spend at half-max), and n (curve steepness). Curves are rebuilt every Monday using the last 12+ weeks of data. If an incrementality calibration factor exists for the channel, R_max is multiplied by the effective calibration factor before optimization runs.

Question 6

How are before/after verdicts calculated?

Accepted Answer

When you follow a budget shift recommendation, TrustData captures the current ROAS as a baseline snapshot. After 14+ days, it measures the actual post-change ROAS and computes the treatment delta. To filter out ambient market movement, it also measures the ROAS drift across all your other paid channels during the same period — the control. The adjusted delta is treatment_delta minus control_drift. verdict thresholds — winner (≥ 0.3 ROAS units), likely_winner (≥ 0.1), no_effect (≤ -0.1) — are in absolute ROAS units, not percentages, because percentages are misleading when baseline ROAS varies.

Stop guessing where to move your media budget

Anomaly detection built on deduplicated data

Budget optimization via response curve equalization

Every recommendation includes a verdict

Anomaly cards through their full lifecycle

What's included

Frequently Asked Questions

Turn paid insights into actions that prove themselves