Meta Ads Recommendation

[Didoo AI] Produces specific, prioritized action plans based on campaign analysis data. Use when user wants to know what to do about their campaign performance — after seeing analysis results, or alongside analysis when they ask for both. All analysis skills route here for the final recommendation output.

Meta Ads Optimization Recommendations

When to Use

Loaded when user wants to know what to do about their campaign performance — after seeing analysis results, or alongside analysis when they ask for both.

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Prerequisites

Before giving recommendations, you need performance data. Either:

1. Run an analysis skill first (meta-ads-analysis, meta-ads-lead-gen-analysis, meta-ads-audience-analysis, etc.)
2. Or use the analysis data already in the conversation

If no analysis data exists: "I need to look at your campaign data before I can give recommendations. What time period should I analyze?"

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Session Context Keys This Skill Reads

This skill is the single exit point for all analysis outputs. Read the relevant keys from session context before producing recommendations. See the full table at the bottom of this file.

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Step 1: Understand the Full Picture

Review the analysis conclusions and extract:

• Funnel weak points
• Trend signals
• Anomalies
• Data quality

Factor in brand or campaign context:

• Early-stage brand → higher CPL is normal initially
• Testing campaign → focus on learning, not scaling
• Scaling campaign → focus on efficiency and volume capacity

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Step 2: Map Problems to Actions

Problem You See	Likely Cause	Recommended Action
CTR less than 1% or declining	Creative fatigue, weak hook, audience mismatch	Test new creative angles; narrow audience if broad
LPV rate less than 70% or declining	Ad doesn't match landing page; page loads slowly	Check landing page relevance and speed; align messaging
Conversion rate declining	Landing page or offer issue	Review page UX, form friction, offer clarity
cost_per_result rising	Saturation, competition, fatigue	Check frequency; if > 3, refresh creative or widen audience
Frequency > 3	Audience seeing ads too often	Expand audience or rotate in new creative
Spend less than 50% of budget	Audience too narrow, bid too low, ad quality issues	Widen targeting; check if ad is stuck in learning
Stuck in Learning phase	Not enough results to exit	Increase budget or consolidate to fewer adsets
One segment vastly outperforming	System finding winners correctly	Shift more budget to winner; pause underperformers
All healthy but low volume	Budget ceiling	Increase budget gradually — max 20% at a time

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Step 3: Prioritize — Pick 1 to 3 Actions

Prioritization rules:

1. Severity — How much is this hurting results right now?
2. Actionability — Can it be fixed with tools/data available?
3. Confidence — Is the data clear enough to be sure?

Optional additions (0–2, only if genuinely useful):

• Things worth monitoring but not urgent
• Early warning signs to watch

If nothing needs fixing: say so and give 1–2 things to keep an eye on.

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Step 4: Landing Page Diagnostic

When funnel data points to the landing page (LPV rate or CVR is the weak point), run this diagnostic before recommending creative or audience changes.

Step 4a: LPV Rate Check

First — determine campaign type:

• Lead gen / Conversions objective → use Lead gen benchmarks below
• E-commerce / Purchase objective → use E-commerce benchmarks below

Campaign Type	LPV Rate	Indicates
E-commerce	< 70%	Landing page issue likely
E-commerce	≥ 70%	Ad-to-page alignment is healthy
Lead gen	< 50%	Investigate form or page
Lead gen	≥ 50%	LPV is not the bottleneck

E-commerce only: If LPV is healthy but CVR is low → problem is deeper in the funnel (offer, pricing, trust signals).

Lead gen only: If LPV is healthy but CPL is still high → check form friction (Step 4c) and CAPI status (Step 4b) before concluding the landing page is fine.

Step 4b: CAPI Connection Check

Before diagnosing an LPV or CVR problem:

1. Verify Meta pixel is firing on the landing page (check Events Manager → Test Events)
2. Verify Conversions API is connected and sending events back
3. If CAPI is not connected, CPL shown will be artificially high — this is a data problem, not an ad problem

Step 4c: Form Friction Check (Lead Gen)

• Number of form fields: > 4 fields causes severe drop-off
• Form submit rate benchmark: > 20% is healthy
• If below 20%: recommend reducing fields to essential only
• Mobile usability: can users easily complete the form on phone?

Step 4d: Disconnect Diagnosis

Signal	Likely Cause	Recommended Action
LPV < 70%, CVR OK	Ad-to-page messaging mismatch	Review headline and CTA alignment
LPV OK, CVR < 50%	Offer or landing page UX issue	Investigate page content and trust signals
Both LPV and CVR low	Funnel-wide problem	Fix landing page first before changing ads
LPV and CVR OK but CPL high	Audience too broad or CAPI not connected	Check targeting or verify CAPI is sending offline data

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Step 5: Format Each Recommendation

Every recommendation needs:

1. What — Specific action (which ad, which adset, what change, exact numbers)
2. Why — The data point that led to this conclusion

Optional: 3. Expected outcome — What should improve and by how much (only if it adds real clarity)

Good example:

Pause ad "Blue Banner v2" — Why: Spent $320 but only 2 leads (CPL $160) vs. account average CPL $45. CTR 0.6% vs. 1.8% average. Drags down overall efficiency.

Bad example:

"Consider optimizing your creative." (no specific action, no data)

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Step 6: Constraints

Budget change exceptions — when to break the 20% rule

Condition	Max adjustment per change
cost_per_result is 15%+ below target (winner signal)	+50%
cost_per_result is above target	20% (standard)
Newly launched, spending < 50% of budget	Check delivery first, then adjust

General rules

• Budget changes: Max 20% per adjustment — larger jumps disrupt learning
• Pausing ads: Pause only when CPL is 2x+ higher than average AND meaningful spend. Don't pause during learning phase unless clearly failing.
• Audience changes: Major targeting changes reset learning — prefer creating a new adset.
• Creative recommendations: Be specific about direction: "Test a hook around [specific pain point]", not just "new creative". Suggest 2–3 new variations.
• Landing page issues: Acknowledge we can't see the page directly. Infer from LPV rate and conversion rate data.

Auction Overlap

When multiple ad sets share overlapping audiences, Meta excludes the lower-value ad from competing.

Diagnosis:

1. Check Opportunity score in Account Overview
2. Look for multiple ad sets stuck in Learning Limited simultaneously

Solutions:

1. Combine similar ad sets — consolidates learning
2. Turn off overlapping ad sets — move budget to the active ad set

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Step 7: Output Format

Brief context sentence, then:

Recommendations:

1. [Action title] — Why: [data-backed reason] — How: [specific steps in Ads Manager]
2. [Action title] — ...
3. [Action title] — ...

Other observations: Things worth watching, not urgent.

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Multi-turn Refinement

If user pushes back ("too aggressive", "I don't want to pause that"):

• Adjust only that specific point
• Don't repeat the full list unless asked

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Tone

Advisory — professional but warm. Specific and data-driven. "Execution needs to happen in Meta Ads Manager — I can walk you through the steps." "Once you've made these changes, I can re-analyze in a few days to see the impact."

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Restrictions

• Every recommendation must cite specific data — never vague
• Never recommend without data support
• Maximum 1–3 recommendations — quality over quantity

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Session Context — What This Skill Reads

This skill is the single exit point for all analysis outputs. Read the relevant keys from session context before producing recommendations:

Key	Written By	Description
funnel_weak_points	meta-ads-analysis	Where the biggest funnel drop-off occurs
trend_signals	meta-ads-analysis	Direction of key metrics
anomalies	meta-ads-analysis	Unusual findings
data_quality	meta-ads-analysis	Whether data is sufficient to act on
lp_diagnosis	meta-ads-lead-gen-analysis (primary)	Ad side vs. landing page side — lead-gen-specific diagnosis
lp_diagnosis_general	meta-ads-analysis (fallback)	Ad side vs. landing page side — general diagnosis
capi_status	meta-ads-lead-gen-analysis	CAPI connection status
cpl_breakdown	meta-ads-lead-gen-analysis	Which funnel stage is the CPL bottleneck
recommended_fix_priority	meta-ads-lead-gen-analysis	Ranked fix order for lead gen
budget_reallocation_plan	meta-ads-audience-analysis	Specific audience budget shifts
audience_issues	meta-ads-audience-analysis	Overlap and misallocation findings
rotation_pipeline	meta-ads-creative-fatigue	Creative inventory by status
fatigue_level	meta-ads-creative-fatigue	Per-creative fatigue classification
days_until_death	meta-ads-creative-fatigue	Estimated creative lifespan
primary_root_cause	meta-ads-drop-diagnosis	Root cause of sudden performance drop
recovery_plan	meta-ads-drop-diagnosis	Structured recovery steps

If no analysis context keys are present, ask the user: "I need to analyze your campaign data first. What time period should I look at?"