Enter real campaign metrics and get AI-powered insights with industry benchmark comparisons and actionable recommendations
Overview
Key benefits
Input actual campaign metrics including budget, spend, impressions, clicks, conversions, and revenue. The tool automatically calculates CTR, CPC, CPA, ROAS, and conversion rate, giving you a complete performance picture without manual calculations.
See how your campaign performed compared to industry standards for your specific platform (Google Ads, Facebook Ads, LinkedIn Ads, etc.) and campaign objective (conversions, leads, traffic, awareness). Understand if your 3% CTR is good or bad for your campaign type.
Get an overall campaign performance score (0-100) with a clear performance rating (Excellent, Good, Needs Improvement, Poor). The AI evaluates all metrics holistically to give you a single number that summarizes campaign success.
Clear breakdown of successes and failures identified by the AI. Understand which strategies, tactics, and creative approaches drove results versus which ones underperformed. Learn specific lessons to apply to future campaigns.
Get actionable recommendations ranked by priority (critical, high, medium, low) with expected impact explained. Each recommendation includes specific implementation steps and projected improvement if executed. Focus on high-impact changes first.
Export your analysis as a markdown file to share with team members, clients, or stakeholders. The report includes all metrics, insights, and recommendations in a clean, professional format perfect for documentation and knowledge sharing.
How it works
Provide basic campaign information including campaign name, platform (Google Ads, Facebook Ads, etc.), objective (conversions, leads, traffic, awareness), and date range. This context helps the AI provide relevant benchmarks and insights.
Enter your campaign metrics: budget, actual spend, impressions, clicks, conversions, and revenue. You only need spend and impressions at minimum - the tool works with whatever data you have. More metrics = more comprehensive analysis.
Provide additional context like creative angles tested, targeting strategy, seasonality factors, or competitive landscape changes. This qualitative information helps the AI provide more nuanced insights beyond just the numbers.
The AI analyzes your data, calculates performance metrics, compares against industry benchmarks, identifies patterns, and generates a comprehensive report. This takes 15-30 seconds with Claude Sonnet 4.5 providing deep analysis.
Review your overall performance score, metrics breakdown with benchmark comparisons, what worked and what didn't, and prioritized recommendations. Click into each section for detailed explanations and implementation guidance.
Export the complete analysis as a markdown file to share with your team, add to campaign documentation, or include in client reports. Save insights to reference when planning future campaigns.
Use cases
Scenario
A marketing agency runs monthly Google Ads campaigns for a B2B client. The client wants to understand why last month's ROAS dropped from 4.2 to 2.8.
Solution
Input all campaign metrics into the analyzer. The AI identifies that CTR remained strong (3.2%) but conversion rate dropped from 4.5% to 2.1%. Analysis reveals the landing page was changed mid-campaign. Recommendations include reverting landing page, A/B testing new variants, and improving post-click experience. Client presentation includes benchmark comparisons showing their CTR is excellent but conversion rate is now below industry average.
Scenario
An e-commerce brand ran a Black Friday Facebook Ads campaign that generated $45,000 in revenue on $12,000 spend. They want to know if this was good performance and what to improve for next year.
Solution
Analyzer shows 3.75 ROAS is above e-commerce benchmark of 3.2 for Facebook. However, CPA of $26 is slightly high for their AOV of $98. AI identifies that mobile performed significantly better than desktop (4.2 vs 2.8 ROAS). Recommendations include mobile-first creative strategy, carousel ads for product showcases, and higher mobile bid adjustments for future campaigns.
Scenario
A SaaS company spent $8,500 on LinkedIn Ads generating 127 leads. Sales team says lead quality was poor - only 8 converted to opportunities. Need to understand if it's a campaign problem or sales problem.
Solution
Analysis shows $67 CPA is within LinkedIn B2B benchmarks. However, 6.3% lead-to-opportunity rate is below typical 15-20%. AI identifies that broad job title targeting likely contributed to low quality. Recommendations include tighter targeting (specific job titles, seniority levels), lead qualification questions in forms, and retargeting engaged prospects who didn't convert.
Scenario
A product launch campaign ran across Google Ads, Facebook Ads, and TikTok Ads simultaneously. Need to compare performance and allocate future budget efficiently.
Solution
Analyze each platform separately, then compare results. Google delivered best ROAS (5.2) but lowest volume. TikTok had highest volume but worst ROAS (1.8). Facebook was middle on both. AI recommends increasing Google budget (impression share opportunity), optimizing TikTok creative for better conversion, and maintaining Facebook as testing ground for new audiences.
Best practices
Analyze campaigns 7-14 days after end to capture all conversion data
Include conversion value, not just conversion count, for true ROAS calculation
Compare similar time periods (avoid comparing holiday vs non-holiday campaigns)
Look beyond ROAS - sometimes volume growth with lower ROAS is the right strategy
Document qualitative factors like creative themes, targeting changes, competitive actions
Analyze top-performing ad groups/keywords separately for deeper insights
Track metrics over time to identify trends beyond single campaign performance
Consider external factors - market changes, seasonality, economic conditions
Use insights to create hypothesis for next campaign, then test systematically
Share learnings across team so everyone benefits from campaign insights
More capabilities
FAQ
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