Data without interpretation is just noise. These prompts help you make sense of your marketing metrics, find the patterns that matter, and turn numbers into decisions that grow your business. Tested across ChatGPT, Gemini, and Claude so you know which model thinks most clearly about your data.
| What you're trying to do | הכי טוב ל |
|---|---|
| Design marketing dashboards that drive action | Gemini |
| Interpret campaign data and find insights | Claude |
| Choose and implement the right attribution model | ChatGPT |
| Analyze customer behavior over time | Gemini |
| Find and fix conversion funnel drop-offs | Claude |
| Create executive marketing reports | ChatGPT |
פרומפטים
Design marketing dashboards that drive action
I need a marketing dashboard for [business type]. Channels I use: [list marketing channels] Reporting frequency: [daily/weekly/monthly] Audience for dashboard: [who sees this — CEO, marketing team, client] Current tools: [Google Analytics, HubSpot, etc.] Biggest question leadership asks: [what they always want to know] Design a dashboard with: 1. 5-7 KPIs that belong on the top row (with target benchmarks) 2. Supporting metrics organized by channel 3. The exact charts/visualizations for each metric (line, bar, pie, etc.) 4. Comparison views: this period vs. last period, and vs. goal 5. One 'early warning' metric that predicts problems before they happen 6. A 3-minute walkthrough script for presenting this dashboard
הכי טוב ל: GEMINI
Gemini designs the most practical dashboards with clear visual hierarchy. Its recommendations for chart types and KPI groupings align with how Google Analytics and Looker Studio actually work.
Tested Feb 15, 2026
טיפים מקצועיים
Include who reads the dashboard in your prompt. A CEO dashboard has 5 metrics. A marketing team dashboard has 20. AI can't design the right dashboard without knowing the audience's decision-making context.
Interpret campaign data and find insights
Analyze these campaign results and tell me what to do next: [Paste campaign data: impressions, clicks, conversions, spend, CTR, CPC, ROAS, etc.] Campaign type: [paid ads/email/social/content] Campaign goal: [awareness/leads/sales] Budget: [total spend] Time period: [how long the campaign ran] Benchmarks: [industry averages if known] Provide: 1. Performance summary: what worked and what didn't (be specific) 2. The single biggest lever to improve results 3. Budget reallocation recommendation with exact percentages 4. 3 hypotheses for why underperforming elements failed 5. A/B test recommendations for the next campaign iteration 6. A 'stop, start, continue' action list
הכי טוב ל: CLAUDE
Claude provides the most honest campaign analysis — it will tell you when results are genuinely bad instead of finding silver linings. Its 'stop doing' recommendations are the most actionable.
Tested Feb 15, 2026
טיפים מקצועיים
Include at least 2 weeks of daily data, not just totals. AI catches day-of-week patterns, fatigue curves, and trend shifts that disappear in aggregate numbers.
Choose and implement the right attribution model
Help me understand which marketing channels are actually driving results. Channels: [list all active channels with monthly spend] Sales cycle length: [average time from first touch to conversion] Conversion tracking: [what I can currently measure] CRM/tools: [what tools I use for tracking] Biggest attribution confusion: [what I can't figure out] Advise me on: 1. Which attribution model fits my business (first-touch, last-touch, linear, etc.) and why 2. How to set up that model in my current tools 3. Channel interactions I'm probably missing 4. A simplified attribution framework I can implement this week 5. What to do when channels can't be directly attributed (brand, content, PR) 6. How to present attribution findings to stakeholders who want simple answers
הכי טוב ל: CHATGPT
ChatGPT explains attribution models in the most accessible language and provides tool-specific setup instructions. It bridges the gap between marketing theory and practical implementation.
Tested Feb 15, 2026
טיפים מקצועיים
Attribution is always wrong — the goal is to be less wrong. Ask AI to build a 'directionally correct' model first rather than a perfect one. Perfect attribution is a myth that delays decision-making.
Analyze customer behavior over time
Help me build a cohort analysis for [business/product]. Cohort definition: [how to group users — signup month, acquisition channel, plan type] Key metric to track: [retention, revenue, engagement, etc.] Time period: [how far back to analyze] Data I have access to: [describe available data fields] Goal: [reduce churn / increase LTV / improve activation] Build: 1. A cohort table structure I can create in [Sheets/Excel/SQL] 2. The exact formulas or queries to calculate cohort metrics 3. How to read the cohort table — what patterns to look for 4. 3 insights that cohort analysis typically reveals (with examples) 5. Actions to take based on common cohort patterns 6. A visual format recommendation for presenting findings to the team
הכי טוב ל: GEMINI
Gemini produces the most usable spreadsheet formulas and SQL queries for cohort analysis. Its table structures are immediately implementable in Google Sheets without modification.
Tested Feb 15, 2026
טיפים מקצועיים
Start with monthly cohorts grouped by acquisition channel. This one analysis often reveals that your 'best' channel (most volume) has the worst retention — changing your entire budget allocation strategy.
Find and fix conversion funnel drop-offs
Help me find where I'm losing potential customers in my funnel. Funnel stages with conversion rates: [Stage 1]: [name] — [number entering] — [conversion rate to next stage] [Stage 2]: [name] — [number entering] — [conversion rate to next stage] [Stage 3]: [name] — [number entering] — [conversion rate to next stage] [Stage 4]: [name] — [number entering] — [final conversion rate] Industry: [your industry] Traffic sources: [where visitors come from] Analyze and provide: 1. Which stage has the biggest leak and why it matters most 2. Benchmark comparison: how my rates compare to industry averages 3. 3 specific fixes for the leakiest stage (with expected improvement) 4. Micro-conversion additions between stages to diagnose friction 5. Segment analysis: which traffic sources have the best/worst funnel flow 6. A 2-week experiment plan to improve the weakest stage by 15%+
הכי טוב ל: CLAUDE
Claude identifies the highest-impact funnel stage to fix first and provides the most realistic improvement estimates. It considers the compounding effect of fixing upstream leaks vs. downstream optimization.
Tested Feb 15, 2026
טיפים מקצועיים
Always fix the leakiest stage closest to revenue first. A 10% improvement at the bottom of the funnel generates more revenue than a 10% improvement at the top — but AI will often prioritize top-of-funnel because the numbers look bigger.
Create executive marketing reports
Generate my monthly marketing report from this data: [Paste key metrics: traffic, leads, conversions, revenue, spend by channel] Reporting month: [month/year] Previous month data: [for comparison] Goals for this month: [targets that were set] Report audience: [CEO/board/marketing team/client] Create: 1. An executive summary (3-4 sentences covering the headline story) 2. Performance vs. goals table with red/yellow/green status indicators 3. Channel-by-channel breakdown with trend arrows 4. Top 3 wins with evidence 5. Top 3 concerns with recommended actions 6. Next month's priorities and projected outcomes 7. One chart recommendation that tells the most compelling story from this data
הכי טוב ל: CHATGPT
ChatGPT structures executive reports with the clearest narrative flow. It writes executive summaries that lead with the most important story — not just a list of numbers.
Tested Feb 15, 2026
טיפים מקצועיים
Start every report with 'So what?' not 'What happened.' Executives want to know what to DO with the data, not hear a recap. Tell AI to lead every section with the recommended action, then support it with data.
Based on actual testing — not assumptions. See our methodology
Gemini
Best for dashboard design and cohort analysis in Google tools. Produces implementation-ready spreadsheet formulas and SQL queries. Less effective at narrative interpretation of data.
Results from Gemini 2.0 Flash · Tested Feb 15, 2026ChatGPT
Best for attribution modeling and executive reporting. Explains complex analytics concepts in accessible language. Tends to oversimplify — push for technical depth when you need it.
Results from GPT-4o · Tested Feb 15, 2026Claude
Best for campaign analysis and funnel diagnostics. Provides the most honest assessment of what data actually tells you vs. what you want it to say. Sometimes over-qualifies conclusions.
Results from Claude 3.5 Sonnet · Tested Feb 15, 2026Grok
Good at spotting non-obvious patterns in data and cutting through vanity metrics to identify what actually matters. Delivers insights with refreshing directness instead of hedging every conclusion. Less focused on data visualization best practices and structured reporting frameworks.
Results from Grok 2 · Tested Feb 15, 2026Measure decisions, not everything. If a metric doesn't change a decision you'd make, stop tracking it. Most dashboards have 30+ metrics and influence zero actions. Ask AI to identify the 5 metrics that actually drive your next move.
Compare against yourself, not industry benchmarks. Industry average conversion rates include companies nothing like yours. Your own month-over-month trend is more actionable than knowing the 'average' email open rate for your industry.
Always ask 'compared to what?' A 5% conversion rate means nothing alone. Is it up or down? Better or worse than the goal? AI will present numbers as good or bad without context — force it to include comparisons in every data point.