AI Prompt Library by Windsor.ai: Ready-Made Prompts by Data Source and Use Case

With Windsor MCP, you can stream data from 325+ sources into your favorite AI chat or AI agent (ChatGPT, Claude, Cursor, Copilot, Gemini, Perplexity) and get insights or visual summaries in seconds.

From analyzing your cross-channel performance to optimizing budget allocation, you can ask any questions about your Windsor-integrated data to make smarter decisions.

In this AI prompt library, we’ve compiled a list of advanced, ready-to-use prompts focused on different data sources and use cases, so you can start analyzing your business data with AI right away. Just copy-paste a prompt into your AI chat (recommended to use the Claude Paid plan), review the answer, and ask the LLM to convert it into the required output (visual report, table, Excel file, etc).

⚙️ How to configure Windsor MCP for different LLMs: Setup Guides.

Facebook (Meta) Ads prompts

Prompt 1: Create a weekly performance report

Prepare an easy-to-understand weekly Meta Ads performance overview for our client.

Ad account: [Ad Account Name]
Date range: [SPECIFY_DATE_RANGE]
Comparison period: previous matching period

Please follow these steps:

1. Retrieve the account data

Pull the following data from the account for the selected date range and compare it to the previous period:

- Overall account performance (spend, revenue, purchases, new customers)
- Campaign-level results for all active campaigns
- Breakdown by placement (Facebook Feed, Instagram Feed, Stories/Reels, Audience Network)
- Key audience segments (age, gender, country)
- Day-by-day trends for the main KPIs

2. Summarize KPIs

Pull these metrics at the account and campaign level:

- ROAS (revenue ÷ ad spend)
- Cost per Purchase (CPP)
- Customer Acquisition Cost (CAC) for new customers
- Conversion Rate (purchases ÷ clicks)
- Click-Through Rate (CTR) and Cost per 1,000 Impressions (CPM)
- Period-over-period percentage change for spend, revenue, purchases, and ROAS

3. Structure the output as a visual report with sections

Build a clear reporting dashboard for a client composed of these sections:

- Section 1: Key results at a glance
A simple “scorecard” with total spend, revenue, purchases, ROAS, and CAC, plus how each changed vs the previous period.

- Section 2: Main drivers of our results
Show which campaigns, placements, and platforms (Facebook vs Instagram) generated the most revenue and purchases.

- Section 3: Our target audience
Summarize performance by age, gender, and top countries. Highlight which segments delivered the strongest ROAS and the highest purchase volume.

- Section 4: Campaign winners and laggards
Compare active campaigns based on ROAS, purchases, spend share, and CAC. Mark the top 3 “growth drivers” and the 3 weakest campaigns from a revenue perspective.

- Section 5: How this week compares
Show trends over time (e.g., last 2 weeks) for spend, revenue, and ROAS, and determine whether this period was above, below, or in line with recent performance.

Presentation rules:

- Use “we” and “our” language to keep the tone collaborative (e.g., “Our campaigns generated…”, “We saw…”).
- Create a visually compelling dashboard with a contemporary design.
- Include simple, intuitive visual summaries (tables, charts, or bullet lists) for each section.
- Add short “What this means” boxes in plain language explaining the data for a non-technical audience.
- Clearly highlight the top 3 customer segments and 3 top campaigns by revenue or ROAS.
- For each highlighted campaign, show: spend, revenue, purchases, ROAS, CAC, and change vs previous period.
- Check whether this reporting period ranks among the top 3 weeks in the last 90 days for revenue or ROAS, and emphasize that if true.
- Do not include optimization recommendations or tactical media buying advice.

Make the output feel like a weekly performance story that a non-technical client can read and understand in a few minutes.

Sample output:

Prompt 2: Analyze the cost efficiency of your campaigns

Retrieve and structure cost performance metrics from our Meta Ads account for cost efficiency analysis.

Ad account: [Ad Account Name]
Date range: [SPECIFY_DATE_RANGE]

Required metrics (campaign-level):
- Total advertising spend
- Cost per result (aligned with each campaign's objective)
- Cost per click (CPC)
- Cost per 1,000 impressions (CPM)

Output format
Display results in a table with campaigns ranked from highest to lowest total spend.

Sample output:

Prompt 3: Audit account for critical issues and opportunities

Audit my Meta Ads account and identify critical issues that need immediate resolution. Also, discover hidden growth opportunities.

Ad account: [Ad Account Name]
Date range: Last 30 days

Analyze and report on:

- Campaigns that are spending but generating zero conversions.
- Any week-over-week drops in performance greater than 20%.
- Campaigns nearing their budget limits while still delivering strong ROAS.
- Ads with high frequency (over 3) where CTR is trending down.

Also, suggest an action plan for the next week to fix the discovered problems and improve the results.

Output format
Summarize your findings in the visual report that includes campaign names and key metrics, ordered by highest estimated revenue impact first.

Sample output:

Prompt 4: Identify the best-performing creative elements and messaging

Analyze how our creatives are performing in Meta Ads and help clarify what’s working and what’s not.

Ad account: [ACCOUNT_ID]
Date range: [START_DATE – END_DATE]

1. Gather essential creative-level data

Pull performance data at ad level for all currently active campaigns, including:

- Results by creative format (single image, video, carousel, collection, etc.)
- Performance by headline, primary text, and description
- Results broken down by CTA button (Shop Now, Learn More, Sign Up, etc.)

2. Summarize creative effectiveness indicators

For each creative and format, retrieve these KPIs:

- CTR by format and placement
- Conversion Rate per creative (purchases or leads divided by clicks)
- Cost per Result (e.g., cost per purchase, cost per lead)
- Engagement Rate (reactions, comments, saves, shares ÷ impressions)
- Video engagement curve, where applicable (drop-off points across the video)

3. Organize the analysis as a visual reporting dashboard

Structure the output into the following sections:

- Summary of creative performance
High-level view of how creative performance contributed to overall results in the selected period.

- Format-level comparison
Compare images vs videos vs carousels vs collections on CTR, Conversion Rate, Cost per Result, and Engagement Rate.

- Messaging & copy review
Evaluate headlines and primary text variants: which wording drives the most clicks, conversions, and engagement?

- Visual & asset insights
Identify which specific visuals (images or video concepts) perform best, and how they differ from underperforming assets.

- Fatigue check
Show how key metrics (CTR, Conversion Rate, Cost per Result) change over time for top-spend creatives and indicate where performance started to decline.

4. Report on the most important findings

Within the report, clearly highlight:

- Recurring patterns among top-performing creatives (e.g., hooks, formats, lengths, CTA types)
- Differences in performance between formats
- Emotional or thematic angles (testimonials, urgency, discounts, storytelling) that resonate most
- Creatives showing consistent decline in key metrics over time, indicating potential fatigue

Output rules:

- Do not include any suggestions about shifting or redistributing the budget. Only describe what the data shows.
- Use straightforward, non-technical language and emphasize business impact (sales, leads, cost per result) instead of ad-platform jargon.
- Present the results as if they were a visual dashboard with contemporary design: clear sections, logical flow, and tables / chart-style descriptions where helpful.
- Keep the focus on what is working and what is fading from a creative standpoint, not on bidding or campaign structure.

Prompt 5: Detect creative fatigue

Spot ads that are showing signs of creative fatigue in our Meta Ads account.

Account: [ACCOUNT_ID]
Date range: Last 3 weeks

For all active ads running longer than one week, do the following:

- Track daily trends in CTR and conversion rate.
- Evaluate how rising frequency correlates with drops in performance.
- Pinpoint the moment (in days since launch) when results started to decline.
- Compare the latest 2-day average performance to the first 2-day average after launch.

Output format
Return a table of ads with more than a 20% decline in performance. Include the ad name, key metrics indicating creative fatigue, and the recommended number of days until the ad should be replaced.

Sample output:

Google Analytics 4 prompts

Prompt 1: Identify your most profitable channels

Compare the main channels in my GA4 [Your Property ID] account driving the most conversions and revenue.

Sample output:

Prompt 2: Identify your website conversion leaks

In GA4 for [Your Property ID], analyze how visitors enter the site, which pages they engage with, and how they move through the funnel.
Track page views, time on page, and scroll depth for key pages (landing pages, product pages, etc.).
Use Funnel Exploration to spot where users drop off in the checkout flow and review exit rates on cart and checkout pages. Recommend specific changes to reduce friction and improve completion rates.

Sample output:

To get deeper insights, you should:

1. Create Funnel Exploration in GA4 (Explore → Funnel Exploration)
2. Set up custom events for specific page interactions
3. Use Path Exploration to see actual user journeys
4. Review User Explorer for individual session playback

Prompt 3: Discover your top customer segments

In GA4 for [Your Property ID], review audience data to find high performing demographic segments.
Segment by gender, location, and age to compare conversion rate and average order value.
Flag underperforming groups and use Audience Builder to create behavior based audiences (for example, users who viewed high value products but did not convert).
Identify untapped opportunities by geo or demographic and suggest new segments for paid and remarketing campaigns.

Sample output:

Prompt 4: Reduce checkout abandonment (for e-commerce)

Make sure that e-commerce event tracking is properly implemented in your GA4 setup.

Using GA4 Funnel Exploration for <Your Property ID>, map the user journey from product page to cart, checkout, and purchase.
Measure exit and conversion rates at each stage and compare mobile vs desktop performance.
Based on the findings, recommend specific actions such as simplifying checkout, enabling guest checkout, or adding trust elements like security badges.

Sample output:

Prompt 5: 24-hour traffic health check

In GA4 for [Your Property ID], generate a traffic overview for the last 24 hours.
Segment by source (Direct, Organic Search, Paid Search, Social, Referral) and report sessions, bounce rate, engaged sessions, pages per session, and average session duration.
Break results down by device (mobile vs desktop) and highlight sources or devices with unusually high or low engagement.

Sample output:

Prompt 6: Analyze seasonality to plan traffic and revenue

For [Your Property ID], analyze traffic and revenue patterns over the last 2 years in GA4.
Identify seasonal peaks and dips to plan when to scale spend, adjust inventory or offers, and schedule promotions for maximum impact.

Sample output:

Prompt 7: Cut low-performing channels

In GA4 for [Your Property ID], review the last 45 days of traffic.
Report traffic, conversion rate, revenue, and ROAS by channel (Organic, Paid Search, Paid Social, Email).
Identify channels with high spend and low ROAS and recommend how to reallocate budget based on LTV and CAC insights.

Sample output:

Google Ads prompts

Prompt 1: Weekly Google Ads analysis for e-commerce (campaigns, products, placements, and optimization insights)

Perform an in-depth analysis of the Google Ads account according to the rules below.

Account: [ACCOUNT_ID]
Date range: [Last 7 days]

Imagine you are a senior Google Ads analyst with a focus on e-commerce accounts. Please complete the following:

1. Campaign overview

Provide a short summary of the total:

- Impressions
- Clicks
- Spend
- Conversions
- Conversion value
- ROAS
- CTR
- CVR
- CPA

2. Product-level performance

Break down performance by product or product group (impressions, clicks, spend, conversions, ROAS).

Identify:
- Top 10 products by ROAS and conversion volume
- Bottom 10 products by ROAS and conversion volume

Flag products where ROAS or conversions changed more than ±15% week over week.

3. Placement analysis

Compare spend and performance across key placements: Shopping, YouTube, Display, Discover, Gmail.

Highlight:
- Which placements generate the highest ROAS
- Which placements are underperforming or inefficient

4. Audience and asset insights

Identify audience segments that contribute the most to conversions and revenue.

Call out underperforming asset groups or creatives with low CTR or wasted spend.

5. Spend and budget efficiency

Check for pacing issues, budget caps, or poor spend allocation across campaigns and products.

Note any campaigns limited by budget or overspending with low return.

6. Root cause diagnostics

For any flagged products, audiences, or placements, assess likely causes such as:
- Feed data or tracking issues
- Increased auction competition
- Creative fatigue or weak ad messaging
- Misaligned targeting or bids

7. Recommendations

Provide 3 to 5 clear, prioritized actions related to:
- Bidding and bid strategies
- Budget shifts between campaigns, products, or placements
- Feed optimizations and product data improvements
- Creative or asset refreshes

Output format
Deliver a structured Markdown report that includes:

Executive summary with key metrics, trends, and notable changes

Tables for:
- Product-level performance (up to 20 rows)
- Placement performance (up to 20 rows)
- Bullet point insights and prioritized next steps

Use human-friendly metrics (currency and percentages) and clear, scannable headings.

Review auction insights and impression share data for all active Shopping campaigns.

Account: [ACCOUNT_ID]
Date range: [SPECIFY_DATE_RANGE]

Please complete the following:

1. Impression share by level

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, where available

Present this in a structured way, so it is easy to compare across entities.

2. Biggest impression share losses

Highlight where total impression share loss is greater than 10%.

Indicate whether the primary driver is:
- Budget limitations, or
- Rank-related issues (bids, quality, or competition).

3. Week over week shifts

Identify any notable week-over-week changes in impression share metrics.

Call out patterns that suggest:
- Stronger competitive pressure
- New competitors entering the auctions
- Sudden drops due to budget or bid changes

4. Growth opportunities

Pinpoint campaigns, ad groups, or product groups with high upside where:
- Increasing the budget could unlock more volume at an acceptable ROAS
- Improving bids or quality could gain more high-intent impressions
- Focus on segments with solid performance but constrained visibility

5. Recommended actions

Provide a prioritized set of actions, for example:
- Increase budgets where impression share is being lost, mainly due to budget caps
- Raise bids or improve quality signals where impression share loss is rank-driven
- Restructure campaigns, ad groups, or product groups where a cleaner structure would help win more auctions efficiently.

Prioritize based on expected revenue impact and feasibility.

Output format
Produce a clear, concise Markdown report that includes:

Tables summarizing key impression share metrics at:
- Campaign level
- Ad group level
- Product group level

A bullet point summary of the main findings and underlying causes.

A prioritized action plan tied to potential revenue impact.

Format the report like a visual dashboard with clear section dividers. Use simple language and focus on business impact rather than technical jargon.

Sample output:

Prompt 3: Improve under-performing and double down on high-potential keywords

Perform an in-depth analysis of the Google Ads account according to the rules below.

Account: [ACCOUNT_ID]
Date range: [SPECIFY_DATE_RANGE]

Imagine you're a senior Google Ads analyst focused on Search campaigns.

Review all active Search campaigns and provide only actionable insights by these points:

1. Identify priority keywords and queries

Report on keywords and search queries that meet at least one of these conditions:
- Spend ≥ $X and ROAS < account average × 0.7 (potential wasted spend)
- At least 2 conversions and ROAS ≥ account average × 1.3 (scalable growth opportunities)
- Week over week CTR or CVR drop > 15% (candidates for ad or landing page optimization)
- Spend with zero conversions (negative keyword candidates)

2. Recommend concrete optimizations

For each keyword or query you surface, suggest specific actions such as:
- Bid increases or decreases
- Adding new exact or phrase match keywords
- Adding negative keywords
- Improving ad copy or testing new variants
- Landing page changes that could improve conversion rate

3. Exclude stable performers

Do not report on keywords or queries with stable or strong performance that do not require any change.

Output format
Provide a concise Markdown report that includes:
- Summary tables with only the items that need action
- Bullet-pointed, prioritized recommendations

Use clear section headers and human-readable metrics (currency, percentages).

Keep the focus on what should be improved or scaled, not on reporting overall performance.

Sample output:

Prompt 4: Google Ads account segmentation audit and restructuring plan

Imagine you are a senior Google Ads specialist. Review and analyze the account setup.

Account: [ACCOUNT_ID]
Date range: [SPECIFY_DATE_RANGE]

Please:
- Identify product groups that are too broad and may be hiding important performance differences.
- Assess whether the current segmentation is granular and logical enough for effective optimization.
- Propose restructuring options that improve bid control, testing capabilities, and reporting clarity.

Return a Markdown report with your segmentation findings and clear, actionable restructuring recommendations.

Sample output:

Conclusion

Set up Windsor MCP once, bookmark your favorite prompts, and you’ll have a reusable AI “assistant” for ongoing analysis, optimization, and reporting across all your connected data sources.

Looking for specific prompt ideas we haven’t listed yet? Get guidance on the most effective use of Windsor MCP from our data expert: Book a Demo Now.

🚀 Get started with Windsor MCP today with a 30-day free trial: https://onboard.windsor.ai/ and experience the power of AI-driven analytics.