Meta Ads - Performance Analysis Workflow

Metric definitions (CTR, CPM, ROAS, CPL, CPR, conversion rate, account-type → PCM mapping, performance benchmarks, Pareto definition) live in meta/tool-fundamentals and auto-load with this skill. Do NOT redefine them here. This skill is the workflow for performance audits after the metrics are loaded.

Step 1: Identify Account Type & PCM

Read promoted_object.custom_event_type from the ad set to determine the Primary Conversion Metric (PCM). The mapping table is in tool-fundamentals → Account Type → Primary Conversion Metric.

Where to read it:

• facebook_get_details_of_ad_account → returns account_structure.adsets with promoted_object for top spending ad sets. Use this first.
• facebook_get_adset_details → use when you need conversion events for ad sets not in the account structure.

PROHIBITED: Never extract or infer conversion events from names. Ad set names frequently don't match the actual config.

Mixed accounts: group ad sets by their custom_event_type. Each group gets its own PCM. Do not mix conversion types.

If promoted_object is missing: ASK the user which conversion event is the business outcome.

Step 2: Pareto Pull (90% Spend)

1. Get ad-level insights sorted by spend descending.
2. Calculate cumulative spend percentage for each ad.
3. Keep ads where cumulative % ≤ 90% — these are your Pareto ads.
4. Focus analysis on Pareto ads.

Query for Pareto ads (MUST include active status filter):

Tool: facebook_get_adaccount_insights
level: "ad"
fields: ["ad_name", "ad_id", "adset_id", "adset_name", "spend", "impressions", "cpm", "ctr", "clicks", "actions", "action_values", "purchase_roas"]
date_preset: "last_30d"
filtering: [
  {"field": "impressions", "operator": "GREATER_THAN", "value": 0},
  {"field": "ad.effective_status", "operator": "IN", "value": ["ACTIVE"]}
]
sort: "spend_descending"

CRITICAL: Always include ad.effective_status (or adset.effective_status) filter. Without it, the API returns data for paused/deleted entities that had historical spend, producing analysis that includes inactive entities. Fetch ALL pages before analyzing — ad-level results paginate at 25 per page (see tool-fundamentals → Pagination).

Step 3: Establish Baselines

From the Pareto set, compute:

• avg_cpm = mean CPM across Pareto ads
• avg_ctr = mean CTR across Pareto ads
• avg_pcm = mean ROAS (e-commerce) or mean CPL/CPR (lead gen / custom) across Pareto ads

These become the comparison points for individual-ad assessment. Apply the variance benchmarks from tool-fundamentals → Performance Benchmarks.

Step 4: Diagnose Issues

High CPM + Low PCM:

• Expensive audience, not converting
• Action: pause ad or test different targeting

Low CTR + Low PCM:

• Ad not resonating with audience
• Check comments for negative sentiment
• If comments fine: test copy/creative variations with stronger CTA

Good metrics but declining trend:

• Check frequency — see fatigue signal in tool-fundamentals → Diagnostic Signals
• Action: new creative variations, not just budget changes

Step 5: Generate Recommendations

MANDATORY: After completing the analysis, generate specific, actionable recommendations for each Pareto ad and for the account overall.

Per-Ad Recommendations

For each Pareto ad, based on its diagnostic profile:

• What to do: specific action (pause, scale, create variation, change targeting)
• Why: metrics-based justification (e.g., "CPM 40% above Pareto avg with 0.3× ROAS")
• How: concrete next steps

Account-Level Recommendations

Summarize the top 3–5 strategic recommendations:

1. Budget reallocation (shift spend from poor → good performers; respect guardrails)
2. Creative strategy (what's working, what to test next)
3. Targeting adjustments (if CPM issues are widespread)
4. Structure changes (campaign / ad set consolidation if needed)

Output Format

## Performance Summary

| Ad | Format | Spend | CPM | CTR | ROAS/CPA | Status |
|----|--------|-------|-----|-----|----------|--------|
| [Name] | Image | $X | $X | X% | X | [Scale/Pause/Test] |

**Benchmarks (Pareto avg)**: CPM: $X | CTR: X% | ROAS: X

## Recommendations

### Per-Ad Actions
1. **[Ad Name]**: [Action] — [Justification]
2. **[Ad Name]**: [Action] — [Justification]

### Strategic Recommendations
1. [Actionable recommendation with metrics basis]
2. [Actionable recommendation with metrics basis]
3. [Actionable recommendation with metrics basis]

If creative analysis is needed (video performance, hook rates, ad copy evaluation):

• The Creative Analysis skill should already be loaded for audits.
• If not loaded, use the meta-creative-analysis skill.