Meta Advantage+ Campaign Strategy & Optimization Framework
Advertising & Paid MediaintermediateClaude 3.5 Sonnet or GPT-4o. Both excel at structured problem-solving and can handle the multi-part framework (diagnosis, testing logic, ROI connection). Claude is slightly stronger at connecting operational friction to business outcomes; GPT-4o is faster for rapid iteration if you're testing multiple campaign scenarios.
When to Use This Prompt
Use this prompt when you're running Meta Advantage+ campaigns and hitting a performance plateau or unclear optimization path. It's especially valuable when your team is spending cycles on manual testing without clear ROI signals, or when you need to justify the next budget increase to finance by connecting campaign improvements to pipeline outcomes.
The Prompt
You are a Meta Advantage+ campaign strategist helping me optimize performance and reduce operational overhead in my paid media workflow.
## Campaign Context
I'm running a Meta Advantage+ campaign with the following parameters:
- **Campaign Objective:** [e.g., conversions, lead generation, traffic]
- **Current Budget:** $[amount] per day
- **Campaign Duration:** [dates]
- **Target Audience:** [brief description]
- **Product/Service:** [what you're promoting]
- **Current Performance:** [CTR, CPC, ROAS, or conversion rate if available]
- **Main Challenge:** [e.g., high CPC, low conversion rate, scaling plateau]
## What I Need
Provide a concrete optimization strategy that:
1. **Diagnoses the bottleneck** - Identify which lever (creative, audience, bid strategy, or conversion tracking) is most likely causing underperformance
2. **Recommends 3 specific tests** - For each test, specify:
- Exact change to implement (creative angle, audience adjustment, bid strategy shift)
- Expected impact on the metric that matters most
- How to measure success (specific KPI threshold)
- Timeline (how long to run before deciding)
3. **Reduces operational friction** - Suggest one workflow automation or process change that eliminates manual coordination on this campaign (e.g., automated reporting, audience sync, creative rotation logic)
4. **Connects to revenue** - For each test, explain the path from improved metric to pipeline impact and estimated ROI lift
## Output Format
Structure your response with clear sections for each test, including rationale, implementation steps, and success criteria. Avoid generic advice—be specific to Advantage+ mechanics and my stated challenge.
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Tips for Best Results
- 1.Fill in all bracketed fields with real data from your campaign. Vague inputs produce generic outputs. Specific metrics (current CPC, ROAS, conversion rate) enable precise recommendations.
- 2.After getting the initial strategy, ask a follow-up: 'Which test should I run first to prove ROI fastest?' This forces prioritization and prevents analysis paralysis.
- 3.Use the 'revenue path' section to build your CFO deck. Connect each optimization to pipeline impact, not just metric improvement. This is how you prove AI-driven testing moves the needle.
- 4.Test one variable at a time. If you implement all three tests simultaneously, you won't know which lever worked. Run Test 1 for 7 days, measure, then layer in Test 2.
Example Output
## Diagnosis
Your challenge is likely **creative fatigue combined with audience overlap**. Advantage+ is showing your ads to the same high-intent users repeatedly, driving up CPC while conversion rate stays flat. The bid strategy is working, but you're exhausting your addressable audience.
## Test 1: Creative Angle Rotation
**Change:** Replace 2 of 3 creative assets with new angles focused on [specific pain point] instead of [current angle]. Keep the top performer.
**Expected Impact:** 15-20% reduction in CPC, potential 8-12% lift in conversion rate as fresh creative reaches fatigued users.
**Measurement:** Run for 7 days, target 500+ conversions before deciding. Success = CPC drops below $[X] while ROAS stays ≥ [Y].
**Revenue Path:** Lower CPC directly reduces CAC. If conversion rate holds, 20% CPC reduction = 20% margin improvement on each conversion, flowing directly to pipeline margin.
## Test 2: Audience Expansion via Lookalike Refresh
**Change:** Create a new 1% lookalike audience from converters in the last 30 days (vs. your current 90-day window). Run this as a separate ad set.
**Expected Impact:** Access to fresher, higher-intent signals. Expect 5-10% lower CPC on new audience, potentially higher conversion rate.
**Measurement:** Run for 10 days, compare CPC and conversion rate to your control ad set. Success = new audience outperforms on ROAS by 10%+.
**Revenue Path:** Fresher audience = more qualified leads, shorter sales cycle, higher close rate downstream.
## Test 3: Bid Strategy Shift (Advantage+ Automation)
**Change:** Move from manual bid cap to Advantage+ automatic bidding with a conversion value goal of $[X]. Let the algorithm optimize for 5 days.
**Expected Impact:** Algorithm finds pockets of high-intent inventory you're missing. Expect 10-15% volume increase with stable or lower CPC.
**Measurement:** 5-day test, measure volume lift and ROAS stability. Success = 12%+ volume increase with ROAS ≥ [Y].
**Revenue Path:** More conversions at same or better efficiency = direct pipeline volume increase.
## Operational Friction Reduction
**Workflow Change:** Set up automated daily reporting via Meta's native dashboard alerts + Slack integration. Remove manual daily reporting pull. This saves 3-4 hours per week and ensures real-time decision-making.
**Impact:** Your team shifts from reporting to strategy, reducing coordination overhead and enabling faster test iterations.
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