| name | investigation-methodology |
| description | Structured hypothesis-driven investigation framework for diagnosing Google Ads performance issues. Auto-invoke when user says "investigate," "why is [account] performing poorly," "dig into," or asks to diagnose a performance issue. |
Investigation Methodology
Structured framework for diagnosing Google Ads performance issues. Follow this process for every investigation — no exceptions.
Step 1: Define the Problem Statement
Start with the specific question. Do not generalize.
Bad: "Account is performing poorly"
Good: "CPA increased from $37.97 (December) to $59.04 (January) — why?"
Bad: "Spend is off"
Good: "Account is 22% under monthly budget pace as of day 15"
If the user gives a vague problem statement, ask clarifying questions:
- What metric changed?
- What was it before vs. now?
- Over what time period?
- Which campaigns or the whole account?
Step 2: Generate Hypotheses BEFORE Analysis
List 5-8 possible causes BEFORE pulling any data. This prevents confirmation bias.
Always include hypotheses from these categories:
Internal Changes (Things You Control)
- Budget changes (increases, decreases, shared budget reallocation)
- Targeting changes (geo, audience, device, demographics)
- Keyword changes (additions, pauses, match type changes)
- Ad copy changes (new RSAs, paused ads, asset performance shifts)
- Bid strategy changes (switching strategies, target changes)
- Landing page changes (new pages, broken pages, speed issues)
- Negative keyword additions (too aggressive, blocking good traffic)
External Factors (Things You Don't Control)
- Seasonality (industry cycles, holidays, weather)
- Competitive shifts (new competitors, competitor promotions)
- Market changes (demand shifts, economic factors)
- Google algorithm or auction changes
Measurement Issues (The Data Itself)
- Conversion tracking changes (new actions, removed actions, tag issues)
- Attribution model changes
- Data lag (conversions that haven't reported yet)
- Comparing different date ranges or campaign structures
Assign initial probability estimates to each hypothesis (gut feel is fine at this stage):
Hypothesis 1: Budget was reduced — 30% likely
Hypothesis 2: New competitor entered auction — 20% likely
Hypothesis 3: Conversion tracking broke — 15% likely
...
Step 3: Gather Evidence One Layer at a Time
Pull data sequentially, not all at once. Each layer narrows the investigation.
Layer 1: Performance Metrics (Top-Level)
- Cost, conversions, CPA by month or week
- Compare the same campaigns across time periods (apples-to-apples)
- If campaigns were restructured, combine them for fair comparison
After Layer 1, update hypothesis probabilities.
Layer 2: Traffic Quality
- Impressions, clicks, conversion rate
- Is traffic volume down, or is conversion rate down?
- If traffic is down: budget or targeting issue
- If conversion rate is down: landing page, ad relevance, or tracking issue
After Layer 2, update hypothesis probabilities.
Layer 3: Segmentation
- Device breakdown (mobile vs. desktop)
- Geographic breakdown
- Campaign / ad group breakdown
- Day-of-week or hour-of-day patterns
After Layer 3, update hypothesis probabilities.
Layer 4: Change History
- Keyword additions / pauses
- Targeting changes
- Bid strategy changes
- Budget changes
- Ad changes
After Layer 4, update hypothesis probabilities.
Layer 5: Attribution and Tracking (If Relevant)
- Conversion action changes
- Tag health (are tags firing correctly?)
- Attribution model differences
- Data lag for recent conversions
After Layer 5, you should have a clear diagnosis.
Step 4: Present Findings
Structure the conclusion as:
Root Cause
State the primary cause clearly. One sentence.
Evidence
List the 3-5 data points that prove it.
Hypothesis Scorecard
Show which hypotheses were confirmed, eliminated, or remain uncertain:
✅ Confirmed: Budget was reduced 15% on Jan 3 (60% → 95% confidence)
❌ Eliminated: No competitor changes in auction insights
❌ Eliminated: Conversion tracking verified working
⚠️ Uncertain: Seasonal demand shift (possible contributing factor)
Recommendation
What to do about it. Be specific:
- Bad: "Consider increasing the budget"
- Good: "Increase daily budget from $50 to $58 (15% increase to restore previous spend level). Monitor CPA for 7 days before further changes."
Rules
Do Not Skip Steps
Even if you think you know the answer at Step 1, go through the hypothesis generation. You might be wrong, and the structured process catches what assumptions miss.
Do Not Dump Data
Each layer should produce a concise finding, not a raw data table. "Conversion rate dropped 30% on mobile while desktop held steady" is a finding. A 200-row table is not.
Update Probabilities
After each layer, explicitly update which hypotheses are more or less likely. This keeps the investigation focused and prevents wandering.
One Root Cause
The conclusion should identify the PRIMARY cause. There may be contributing factors, but lead with the main one. If there are genuinely two independent causes, state that explicitly.
Ask When Stuck
If the data doesn't point to a clear root cause after Layer 4, say so. "The data is inconclusive — here's what I've eliminated, and here are the remaining possibilities that need manual investigation" is better than making up an answer.
