Analyze Google and Meta conversion journeys from the first ad click to a purchase or inquiry. Identify common paths, drop-offs, campaign contributions, and conversion delays to guide budget and targeting adjustments.
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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.
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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.
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.
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.
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:
https://connector.get-ryze.ai/mcpclaude mcp add ryze --transport http https://connector.get-ryze.ai/mcpSetup guide: https://www.get-ryze.ai/how-to-connect-claude-to-google-meta-ads-mcp
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Analyze A/B test results to assess whether observed differences are likely due to chance. Check test duration and sample size, compare audience segments, distinguish statistical findings from practical value, and generate hypotheses for follow-up tests.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Plan and analyze A/B tests for Google and Meta advertising. Estimate required traffic and duration, track results, and assess whether observed differences are statistically significant, meaning unlikely to be explained by chance alone.
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Review Google and Meta campaign structures against goals, budgets, and conversion data. Identify excessive segmentation, overlapping targeting, and fragmented budgets, then get consolidation recommendations, an explained account structure, and a migration plan.
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Analyze successful Google and Meta ads to find useful patterns in headlines, body text, and calls to action. Generate new copy variants that preserve those patterns, with platform formatting, test priorities, and suggested pairings for comparison.
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Review Google Ads extensions, the extra links and details shown with ads, across campaigns. Find missing coverage, weak performance, and outdated information, then get replacement copy based on successful ads and landing pages, plus rollout priorities.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Analyze spending, revenue, and conversions across advertising channels to recommend budget shifts. Compare overall efficiency with the revenue gained from additional spending, identify saturation, and produce a gradual reallocation plan with monitoring priorities.
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