How to Read a PPC Audit Report: Data Labels, Confidence Tiers, Quality Scores | Docs

For · Operators reading their first Catalyst Audit
Time · 7 min read
Next · Audit evidence and citations

A Catalyst Audit report is designed to be read, not decoded. Every number is labeled so you know where it came from, every recommendation includes the case against it, and a quality score tells you how much confidence to place in the overall analysis.

The Short Version

Every data point carries a label: [FACT] for measured data, [PROJECTED] for estimates, [INFERRED] for pattern recognition, or [INSUFFICIENT DATA] when the sample is too small.
Confidence tiers gate what Catalyst Audit is willing to recommend based on your conversion volume. More data means tighter estimates.
A quality score (0-100) rates the audit itself across six dimensions, from data accuracy to recommendation quality.
Attribution verification and COGS sharpen the analysis, but Catalyst Audit still works without them and tells you when gaps limit its conclusions.
Start with the highest-risk recommendations. Use the conservative scenario for projections. Check the monitoring plan’s threshold before acting.

Navigating the Report

When an audit completes, click into it from the Audits page to see the full report. The detail view has three main sections:

The markdown report is the narrative analysis: findings, context, and recommendations organized by topic (bid strategy, budget allocation, keyword performance, conversion tracking). Each section leads with the data, then interprets it.

Structured recommendations are pulled out separately so you can scan them without reading the full report. Each recommendation includes the evidence basis, a risk score, a credible alternative with its flip condition, and a monitoring plan.

The quality score appears at the top of the report and reflects how much confidence to place in the analysis as a whole. Reports are also available as downloadable PDFs.

Data Labels: Know What You’re Looking At

Every number in a Catalyst Audit carries a label that tells you how much weight to place behind it.

Label	What It Means	Example
[FACT]	Measured directly from your ad platform or order data. No estimation involved.	”Your CPA was $18.40 over the last 30 days.”
[PROJECTED]	Calculated from facts using stated assumptions. The math is shown.	”Eliminating $120 in wasted spend could recover 3-5 conversions per month.”
[INFERRED]	Derived from your data combined with known industry behavior. Always given as a range.	”Quality Score improvement of ~15-25% based on landing page alignment.”
[INSUFFICIENT DATA]	Not enough information to draw a reliable conclusion.	”Conversion volume is below the floor for this finding. Collect more data before acting.”

A good rule of thumb: act on [FACT]-tagged findings first, verify the assumptions behind [PROJECTED] estimates, and treat [INFERRED] insights as areas to watch on the next audit. When you see [INSUFFICIENT DATA], the recommendation is always the same: wait, collect more data, then revisit.

Confidence Tiers

Catalyst Audit gates its recommendations based on how much conversion data is available. The same percentage CPA shift means something different on a high-volume campaign than on one with only a handful of conversions.

Tier	What It Means	What You Should Do
Very High	Small changes are detectable and reliable	Act with confidence
High	Moderate changes are statistically meaningful	Act on clear signals
Medium	Only larger shifts are distinguishable from noise	Act with monitoring plan
Low	Only very large swings are meaningful	Hold. Collect more data before acting
Insufficient	Too few data points for reliable analysis	Gather more data before deciding

The tier climbs as your conversion volume grows, because more data means tighter estimates. These tiers directly affect what Catalyst Audit recommends. A high-volume campaign might receive a specific bid adjustment recommendation, while the same percentage shift on a very low-volume campaign gets flagged as [INSUFFICIENT DATA] with a monitoring plan instead.

This gating exists because reacting to noise is one of the most common and most expensive mistakes in ad management.

Quality Score

Every audit receives a composite quality score from 0 to 100, reflecting the depth and reliability of the analysis. The score is built from six dimensions, with Data Accuracy and Analytical Depth weighted most heavily:

Dimension	What It Measures
Data Accuracy	Conversion counts verified against actual order data
Analytical Depth	Whether findings reflect causal analysis, not surface-level reporting
Recommendation Quality	Recommendations are executable, evidence-backed, and risk-scored
Structure	Report is organized and logically flows from data to conclusions
Estimation Policy Compliance	Proper use of data labels and confidence tiers throughout
Cross-Validation	Findings checked across multiple data sources

A score of 70 or above indicates a reliable audit with actionable recommendations. Scores below 70 typically reflect data gaps (low conversion volume, missing order data, or unverified attribution) rather than problems with the analysis itself.

The score isn’t a grade on your advertising performance. A well-run campaign can produce a low-scoring audit if the data feeding it is incomplete.

How Attribution and COGS Sharpen the Analysis

Two data sources improve every audit when available:

Attribution verification compares what your ad platforms report as conversions against what your store actually processed as orders. Tracking accuracy 80% or above is Validated, and the audit proceeds with full confidence. 60-80% is Warning: the audit raises a tracking_accuracy_degraded banner and tags affected recommendations with [TRACKING CAUTION X%]. Below 60% is Unvalidated, and recommendations still ship but carry [TRACKING UNVALIDATED X%] caveats so you can apply judgment item by item while fixing the tracking at the source.

Product cost data (COGS) shifts the analysis from revenue to real profitability. A campaign generating $5,000 in revenue at 3x ROAS looks strong, until you factor in that it’s primarily driving sales of low-margin products at 25% gross margin. With cost data, Catalyst Audit catches this and reclassifies the campaign accordingly.

Neither is required, but both sharpen the picture. When they’re missing, the audit tells you what it can’t see.

Acting on Recommendations

Each recommendation in a Catalyst Audit includes everything you need to decide whether to act:

Evidence basis: the specific data points supporting the recommendation
Risk score (1-5): how difficult to reverse and what could go wrong
Credible alternative: a plausible different read of the same evidence, plus the flip condition (what you would need to observe to switch to it)
Monitoring plan: the metric, threshold, and date to check

Start with the highest-risk recommendations; they represent the biggest potential impact and the most urgent decisions. For any [PROJECTED] estimates, use the conservative scenario rather than the optimistic one. And before acting on any recommendation, check whether recent account changes are still in their evaluation window. Layering new changes on top of unsettled ones makes it harder to know what’s working.

What Catalyst Audit Doesn’t Do

Catalyst Audit does not guarantee outcomes. Projections include ranges and stated assumptions. No audit claims a specific result will happen.
It does not make recommendations when conversion data is insufficient. You’ll see [INSUFFICIENT DATA] labels and monitoring plans instead.
It does not extrapolate trends into the future. It reports what the data shows and where the patterns point, not what next quarter’s revenue will be.

What’s Next

Business Context and Your Economics: how your margins, seasonality, and CLV shape every recommendation
Audit Evidence and Citations: how every recommendation cites its evidence
Testing Timelines and Evaluation Windows: documented evaluation periods for different change types