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

Master prompt:
You are a senior Google Ads analyst specializing in e-commerce Google Ads.
Account: [ACCOUNT_ID]
Date Range: [Last 7 days]
Please perform the following analyses:
1. Campaign Overview
Summarize total impressions, clicks, spend, conversions, conversion value, ROAS, CTR, CVR, and CPA.
2. Product-Level Performance
Break down performance metrics (impressions, clicks, spend, conversions, ROAS) by product or product group.
Identify the top 10 best-performing and bottom 10 worst-performing products by ROAS and conversion volume.
Flag products with week-over-week changes in ROAS or conversions exceeding ±15%.
3. Placement Analysis
Analyze spend and conversion performance across key placements (Shopping, YouTube, Display, Discover, Gmail).
Highlight which placements drive the highest ROAS and which are underperforming.
4. Audience & Asset Insights
Review the audience segments contributing most to conversions and revenue.
Identify any underperforming asset groups or creatives causing wasted spend or low CTR.
5. Spend & Budget Efficiency
Check for pacing issues, budget caps, or inefficient spend allocation within campaigns.
6. Root Cause Diagnostics
For flagged products or placements, diagnose potential issues such as feed data quality, auction competition, or creative fatigue.
7. Recommendations
Prioritize 3–5 actionable recommendations for bidding, budget allocation, feed improvements, or creative refreshes.
8. Output Format
Provide a structured Markdown report with:
→ An executive summary highlighting key metrics and notable changes.
→ Tables for product-level and placement performance (limit to 20 rows).
→ Bullet-pointed insights and prioritized next steps.
Use human-friendly metrics (e.g., dollars, percentages) and clear headings

Goal: Analyze impression share and auction data to identify budget or rank-related losses. Recommend actions to recover lost share and boost campaign growth opportunities.
Master prompt:
You are a senior Google Ads analyst with expertise in Shopping campaigns.
For the Account: [ACCOUNT_ID], analyze auction insights and impression share data for the active Shopping campaigns over the period {{date_range}}.
Please perform the following:
For each campaign, ad group, and product group, report:
→ Impression Share (IS)
→ Lost Impression Share due to Budget (IS Lost Budget %)
→ Lost Impression Share due to Rank (IS Lost Rank %)
→ Average Position or Top of Page Rate, if available

Identify where impression share loss is most significant (>10%) and determine whether it is primarily due to budget constraints or rank issues.

Detect any notable week-over-week changes in impression share metrics that may indicate competitive pressure or new entrants in auctions.

Provide insights on which campaigns or product groups have the highest opportunity for growth if the budget is increased or bids are improved.

Recommend prioritized actions, such as:
→ Increasing budgets to recover lost impression share due to budget caps
→ Raising bids or improving quality signals for segments losing impression share due to rank
→ Restructuring campaigns or product groups to better compete in auctions
Output: A clear, concise Markdown report including:
→ Tables summarizing key impression share metrics at the campaign, ad group, and product group levels
→ A bullet-point summary of major findings and root causes
→ A prioritized action plan based on potential revenue impact
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 wasted spend, growth opportunities, and optimization actions for active Search campaigns based on performance data
Master prompt:
You are a senior Google Ads analyst focused on ecommerce Search campaigns. 
Account: [ACCOUNT_ID]
Date Range: [SPECIFY_DATE_RANGE]
Analyze all active Search campaigns and provide only actionable insights by:
Reporting on keywords and search queries that meet these criteria:
- Keywords or queries with spend ≥ $X and ROAS below account average × 0.7 (potential waste)
- Keywords or queries with ≥ 2 conversions and ROAS ≥ account average × 1.3 (growth opportunities)
- Keywords or queries with week-over-week CTR or CVR declines > 15% (need ad or landing page optimization)
- Queries generating spend with zero conversions (negative keyword candidates)
For all items above, recommend specific optimizations, such as:
-Bid increases or decreases
-Adding new exact or phrase match keywords
-Adding negative keywords
-Ad copy or landing page improvements
Skip any keywords or queries that show stable or good performance and require no action.

Provide a concise Markdown report including:
-Summary tables limited to actionable items only
-Bullet-pointed, prioritized recommendations
-Clear section headers and human-readable metrics
Focus solely on what can be improved or optimized

Goal: Analyze account segmentation to uncover broad product groups and performance blind spots.
Master prompt:
You are a senior Google Ads expert. Help me analyze the account. Account: [ACCOUNT_ID] for
Date Range: [SPECIFY_DATE_RANGE]
Please follow these steps:
Identify overly broad product groups that may be masking performance details.

Evaluate the granularity and logic of the current segmentation.

Suggest restructuring approaches to improve bid control and reporting clarity.
Return a Markdown report with segmentation insights and actionable restructuring advice.
Prompt :Google Ads Auction Insights Analysis & Recommendations
Goal: Analyze auction insights to identify impression share losses and competitive changes.
 Recommend the budget and bid adjustments to recover impression share and improve performance.
Master prompt:
Act as a Google Ads expert and help me analyze Account: [ACCOUNT_ID]
Review the auction insights for Search campaigns for Date Range: [SPECIFY_DATE_RANGE]
Please follow these steps:
Report impression share, lost impression share due to budget, and lost impression share due to rank at both campaign and ad group levels.
Detect significant changes indicating competitive pressures.
Recommend budget or bid changes to recover impression share.
Deliver a Markdown report with data tables, insights, and prioritized recommendations