Maps out how users move through your funnel from first ad click to conversion. Identifies where the biggest drop-offs happen, which campaigns contribute most at each stage, and how long the typical conversion path takes across different audience segments. Platform: Google and Meta.
git clone https://github.com/irinabuht12-oss/marketing-skills.git--- name: conversion-path-analysis description: Maps out how users move through your funnel from first ad click to conversion. Identifies where the biggest drop-offs happen, which campaigns contribute most at each stage, and how long the typical conversion path takes across different audience segments. Platform: Google and Meta. metadata: platform: Google and Meta --- # 16/ Conversion Path Analysis — Google + Meta ## What it does Maps out how users move through your funnel from first ad click to conversion. Identifies where the biggest drop-offs happen, which campaigns contribute most at each stage, and how long the typical conversion path takes across different audience segments. ## How it works Claude analyzes your conversion data, assisted conversions, and multi-touch paths to show the full journey. It looks at first-touch vs last-touch attribution, identifies common paths (e.g., Meta prospecting → Google brand → conversion), and flags where users are falling out of the funnel at higher-than-expected rates. ## Practical example Your ecommerce account shows that 62% of conversions involve 2+ touchpoints. The most common path is Meta prospecting ad → Google branded search → purchase. But Claude also discovers that users who see a Meta retargeting ad between those two steps convert at 3.2x the rate. Meanwhile, users who click PMax ads rarely convert on the first visit and almost never return — suggesting PMax is driving low-intent traffic. Claude recommends increasing retargeting budget for Meta-sourced traffic and re-evaluating PMax targeting. ## What you get back - Most common conversion paths ranked by volume and conversion rate - Average time to conversion by path and audience segment - Drop-off points in the funnel with estimated revenue impact - Campaign-level contribution at each funnel stage (awareness, consideration, conversion) - Recommendations for budget and targeting adjustments based on path analysis ## When to use it - When attribution feels murky and you need to understand the actual user journey - Before making budget cuts to understand which "low-performing" campaigns assist conversions elsewhere - When conversion lag is long (B2B, high-ticket ecommerce) and last-click data misleads - During multi-channel strategy reviews ## Data access (Ryze MCP) This skill works best with live account data. Connect the free Ryze MCP once and Claude reads your Google Ads, Meta Ads, GA4 and Search Console directly: - claude.ai / Claude Desktop: Settings → Connectors → Add custom connector → `https://connector.get-ryze.ai/mcp` - Claude Code: `claude mcp add ryze --transport http https://connector.get-ryze.ai/mcp` - Cursor: Settings → MCP → add the same URL Setup guide: https://www.get-ryze.ai/how-to-connect-claude-to-google-meta-ads-mcp
["Gather your campaign data: Export conversion path data from Google Ads or Meta Ads for the [PRODUCT/CAMPAIGN] you want to analyze. Ensure you include metrics like clicks, impressions, conversions, and time-to-conversion for each stage of the funnel.","Define your audience segments: Identify the key segments you want to compare (e.g., high-intent vs. broad audiences, new vs. returning users, or demographic groups). Use the platform's audience insights or your CRM data to segment accurately.","Run the analysis: Paste the prompt template into your AI tool (e.g., Claude or ChatGPT) and replace the placeholders with your specific campaign, platform, and audience segments. Include any additional context, such as your conversion goals or KPIs.","Review and act on insights: Use the AI's output to identify the biggest drop-offs and underperforming campaigns. Cross-reference with your platform's data (e.g., Google Analytics or Meta Ads Manager) to validate the findings. Prioritize optimizations based on the impact on conversion rates and time-to-conversion.","Test and iterate: Implement the suggested optimizations (e.g., retargeting strategies, checkout improvements) and monitor performance over the next 2-4 weeks. Use the same analysis to track progress and refine your approach."]
No install command available. Check the GitHub repository for manual installation instructions.
git clone https://github.com/irinabuht12-oss/marketing-skills/tree/main/skills/conversion-path-analysisCopy the install command above and run it in your terminal.
Launch Claude Code, Cursor, or your preferred AI coding agent.
Use the prompt template or examples below to test the skill.
Adapt the skill to your specific use case and workflow.
Analyze the conversion paths for [PRODUCT/CAMPAIGN] in [PLATFORM: Google Ads / Meta Ads]. Identify the top 3 drop-off points in the funnel, the average time from first click to conversion for [AUDIENCE SEGMENT], and which campaigns drive the most conversions at each stage. Compare performance between [AUDIENCE SEGMENT 1] and [AUDIENCE SEGMENT 2]. Suggest 2-3 optimizations to reduce drop-offs and improve conversion rates.
For the 'Premium Headphones' campaign in Meta Ads, the conversion path analysis revealed three critical drop-off points: 68% of users abandon the funnel after viewing the product page, 42% of those who add to cart do not proceed to checkout, and 23% of checkout initiators abandon at the payment step. The average time from first click to conversion for high-intent audiences (users who watched >80% of the video ad) is 3.2 days, while for broad audiences, it stretches to 7.8 days. Campaign performance varied significantly: the 'Limited Time Offer' retargeting campaign drove 45% of conversions at the awareness stage but only 12% at the decision stage, whereas the 'Customer Reviews' prospecting campaign contributed 38% of conversions at the decision stage but only 8% at awareness. High-intent audiences converted 3.5x faster and had a 22% higher conversion rate than broad audiences. The data suggests prioritizing retargeting efforts to address the product page drop-off and testing a one-click checkout option to reduce payment abandonment. Additionally, shifting budget from broad prospecting to high-intent retargeting could improve overall efficiency.
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