Meta Ads

Master prompt:
 
Create a comprehensive campaign-level Facebook ads report with visual storytelling for a client without media buying experience.
Account: [ACCOUNT_ID]
Date Range: [SPECIFY_DATE_RANGE]
Please follow these steps:
1. Pull the following data points from the account:
   - Account-level performance data for the specified period and previous period
   - Campaign-level performance breakdown
   - Platform performance (Facebook vs Instagram)
   - Demographic performance (age and gender)
   - Daily/weekly trends
2. Calculate key performance metrics:
   - ROAS (Revenue divided by Spend)
   - Cost per Purchase
   - Average Order Value
   - Conversion Rate (Purchases/Clicks)
   - Week-over-week percentage changes
3. Create a visual storytelling dashboard with these chapters:
   - Chapter 1: The Bottom Line (headline revenue, sales, AOV, and ROAS metrics)
   - Chapter 2: Where Our Revenue Came From (platform and campaign breakdowns)
   - Chapter 3: Who Our Customers Are (demographic analysis by age/gender)
   - Chapter 4: Comparing Our Campaigns (performance metrics for sales campaigns only)
   - Chapter 5: Our Recent Progress (week-over-week performance trends)
4. Include these specific elements:
   - Use "our" instead of "your" throughout to maintain a collaborative tone
   - Create clear data visualizations for each chapter
   - Include "The Story" summary boxes that explain insights in plain language
   - Highlight the top-performing customer segments
   - Add campaign comparison cards showing ROAS, Cost per Sale, and ROAS change
   - Check if the current week is the best performing period in the last 30 days and 90 days and highlight if true
5. Focus on these business outcomes for a non-technical audience:
   - Revenue generated from advertising
   - Number of sales/purchases
   - Return on advertising investment
   - Customer acquisition cost
   - Which audiences and platforms are most valuable
Do not add any recommendations in the visual dashboard
Please make the dashboard visually engaging, easy to understand, and focused on the story behind the numbers rather than technical advertising metrics

Goal: To evaluate ad performance across campaigns specifically for e-commerce businesses, focusing on revenue metrics and purchase behavior.
Master prompt:
Create a comprehensive e-commerce performance analysis for our Meta advertising account.
Account: [ACCOUNT_ID]
Date Range: [SPECIFY_DATE_RANGE]
Please follow these steps:
1. Extract these key data points:
   - Overall account metrics (Spend, Purchases, Revenue, ROAS)
   - Campaign-level purchase metrics and ROAS
   - Product catalog performance by category
   - Top-performing ads by purchase conversion
   - Funnel conversion breakdown (Impressions → Clicks → Add to Cart → Initiate Checkout → Purchase)
2. Calculate critical e-commerce metrics:
   - Return on Ad Spend per campaign
   - Average Order Value
   - Purchase Conversion Rate
   - Cost Per Purchase
 
3. Create these analysis sections:
   - Revenue Overview (total revenue, ROAS, week-over-week growth)
   - Product Performance Matrix (showing best/worst performing products)
   - Purchase Journey Analysis (conversion rates at each funnel stage)
   - Campaign Efficiency Comparison (which campaigns deliver highest ROI)
   - Creative Performance (top ads driving purchases)
4. Highlight these specific insights:
   - Identify products with highest purchase intent
   - Flag underperforming products with high ad spend
   - Compare dynamic vs. static creative performance
   - Analyze mobile vs. desktop purchase behavior
Present all data using clear visualizations with minimal technical jargon, focusing on business impact rather than ad metrics. Format as a visual dashboard with clear section dividers

Goal: To identify the most valuable customer segments and audience targeting opportunities based on Meta ads performance data.
Master prompt:
Generate a comprehensive audience analysis report to identify our most valuable customer segments in Meta Ads.
Account: [ACCOUNT_ID]
Date Range: [SPECIFY_DATE_RANGE]
Please follow these steps:
1. Extract and analyze these audience dimensions:
   - Demographic breakdowns (age, gender, location)
   - Platform performance (Facebook, Instagram, Audience Network)
   - Placement analysis (Feed, Stories, Reels, etc.)
   - Detailed targeting categories performance
   - Lookalike audience performance vs. interest targeting
2. Calculate these audience efficiency metrics:
   - Cost per Result by audience segment
   - Conversion Rate by demographic group
   - Frequency and Reach efficiency
   - Audience overlap percentage (if available)
   - Return on Ad Spend by audience type
3. Create these audience insight sections:
   - Audience Performance Overview (which segments drive best results)
   - Demographic Sweet Spots (best performing age/gender combinations)
   - Platform Preference Analysis (where our audience converts best)
   - Targeting Strategy Comparison (interest vs. lookalike vs. custom audience)
4. Highlight these specific insights:
   - Identify underserved high-performing audience segments
   - Compare cold audience vs. retargeting performance
   - Show which creative types resonate with which audience segments
   - Flag audience saturation issues (high frequency, declining performance)
Present all data using clear visualizations with minimal technical jargon, focusing on business impact rather than ad metrics. Format as a visual dashboard with clear section dividers

Goal: To analyze which creative elements and messaging strategies are driving the best performance across campaigns.
Master prompt:
Create a comprehensive creative performance analysis for our Meta Ads account.
Account: [ACCOUNT_ID]
Date Range: [SPECIFY_DATE_RANGE]
Please follow these steps:
1. Extract these creative performance data points:
   - Ad-level metrics across all active campaigns
   - Creative format performance (image, video, carousel, collection)
   - Video engagement metrics (average watch time, video completion rate)
   - Copy performance by headline and primary text
   - Call-to-action button performance
2. Calculate these creative efficiency metrics:
   - Click-Through Rate by creative type
   - Conversion Rate by creative format
   - Cost per Click by creative elements
   - Engagement Rate (reactions, comments, shares)
   - Video Retention Curve data (if applicable)
3. Create these creative analysis sections:
   - Creative Format Comparison (which formats drive best performance)
   - Copy Element Analysis (which headlines and text drive action)
   - Visual Element Breakdown (which images/videos perform best)
   - Creative Fatigue Assessment (performance trends over time)
   - A/B Test Results Summary (if multiple variants exist)
4. Highlight these specific insights:
   - Identify creative patterns in top-performing ads
   - Compare static vs. video performance
   - Analyze emotional appeals that resonate best
  

Do not give any recommendations about moving budgets. Just state facts.
Present all data using clear visualizations with minimal technical jargon, focusing on business impact rather than ad metrics. Format as a visual dashboard with clear section dividers

Goal: To analyze spending efficiency and identify opportunities to reallocate budget for maximum return on ad spend.
Master prompt:
Generate a comprehensive budget allocation and ROAS optimization analysis for our Meta Ads account.
Account: [ACCOUNT_ID]
Date Range: [SPECIFY_DATE_RANGE]
Please follow these steps:
1. Extract these budget and performance data points:
   - Campaign-level spend vs. results
   - Ad set budget utilization
   - Day-of-week and time-of-day performance patterns
   - Budget pacing throughout the month
   - Platform and placement cost efficiency
2. Calculate these budget efficiency metrics:
   - Return on Ad Spend by campaign
   - Cost per Result by ad set
   - Spend-to-Result correlation
   - Marginal ROAS (returns on incremental spend)
   - Budget utilization percentage
3. Create these budget analysis sections:
   - ROAS Overview (total investment vs. return)
   - Budget Allocation Map (where we're spending vs. results)
   - Spending Efficiency Matrix (identifying high/low performers)
   - Diminishing Returns Analysis (where more spend isn't helping)
4. Highlight these specific insights:
   - Identify campaigns with budget constraints but high ROAS
   - Flag campaigns with high spend but poor returns
   - Show correlation between spending patterns and results

Do not give any recommendations about moving budgets. Just state facts.
Present all data using clear visualizations with minimal technical jargon, focusing on business impact rather than ad metrics. Format as a visual dashboard with clear section dividers

Goal: To analyze historical Meta ad performance patterns, identify seasonal trends, and provide insights for future campaign planning and forecasting.
Master prompt:
Create a comprehensive seasonal trend analysis using our Meta Ads historical data.
Account: [ACCOUNT_ID]
Date Range: [SPECIFY_LONG_DATE_RANGE - at least 12 months]
Please follow these steps:
1. Extract these historical performance data points:
   - Monthly performance trends over the full period
   - Day-of-week patterns across campaigns
   - Holiday/seasonal event performance spikes
   - Year-over-year comparison for recurring time periods
   - Creative fatigue cycles and refresh patterns
2. Calculate these trend metrics:
   - Seasonal performance index by month/quarter
   - Weekly performance patterns (best/worst days)
   - Holiday performance lift percentages
   - Yearly growth/decline rates by metric
   - Creative performance decay rate over time
3. Create these seasonal analysis sections:
   - Annual Performance Calendar (visual heat map of performance)
   - Monthly Performance Comparison (identifying strongest/weakest months)
   - Weekly Pattern Analysis (optimal days for different objectives)
   - Holiday & Event Impact Assessment (quantifying seasonal effects)
   - Creative Refresh Timing Analysis (optimal creative rotation schedule)
4. Highlight these specific insights:
   - Identify predictable seasonal patterns in performance
   - Calculate the impact of major shopping events/holidays
   - Determine optimal campaign timing based on historical data
   - Recommend budget allocation timing based on seasonal opportunity
Present findings with clear seasonal visualization charts showing performance patterns across time periods. Include month-by-month performance heat maps and year-over-year comparison charts. Focus on actionable timing insights for future campaign planning

Goal: Quickly identify critical issues or opportunities in the account that need immediate attention.
Prompt:

Run a rapid Meta Ads account health check and highlight urgent issues or opportunities.
Account: [ACCOUNT_ID]
Date Range: Last 14 days
Identify and report on:
1. Any campaigns with spend but zero conversions
2. Any significant week-over-week performance drops (>20%)
3. Campaigns approaching budget caps with strong ROAS
4. Ads with high frequency (>3) but declining CTR
Format response as a bulleted alert list with specific metrics and campaign names. Sort issues by estimated revenue impact

Goal: Extract detailed cost metrics across all campaigns.
Prompt:
Extract and organize cost efficiency data from our Meta Ads account.
Account: [Ad Account Name]
Date Range: [SPECIFY_DATE_RANGE]
Pull and organize these specific metrics by campaign:
1. Total spend
2. Cost per result (by campaign objective)
3. Cost per click
4. Cost per thousand impressions (CPM)
Present data in a simple table format sorted by highest to lowest spend

Goal: Identify which specific ads are showing performance decay and need refreshing.
Prompt:
Identify ads experiencing creative fatigue in our Meta Ads account.
Account: [ACCOUNT_ID]
Date Range: Last 21 days
For all active ads running >7 days:
1. Calculate daily CTR and conversion rate trend
2. Measure frequency increase correlation with performance decline
3. Identify point where performance began decreasing (in days since launch)
4. Compare current 3-day performance average against first 3-day average
List ads showing >15% performance decline, sorted by estimated revenue impact if not refreshed. Include specific ad names, current metrics, and days until recommended replacement