Compare Google and Meta campaign results across mobile, desktop, and tablet. Identify device performance gaps, investigate whether ads, landing pages, or audiences explain them, and receive recommendations for bids, campaign splits, and page changes.
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Analyzes how your campaigns perform across mobile, desktop, and tablet. Identifies where device performance diverges significantly and recommends bid adjustments, campaign splits, or creative/landing page changes to capture the gap. Platform: Google and Meta.
安装前可以查看交给 AI 代理的指令内容。
Analyzes how your campaigns perform across mobile, desktop, and tablet. Identifies where device performance diverges significantly and recommends bid adjustments, campaign splits, or creative/landing page changes to capture the gap.
Claude segments all your campaign data by device type and compares CPA, CVR, CTR, bounce rate, and average order value. It identifies campaigns where one device significantly outperforms or underperforms others, then determines whether the issue is ad-side (creative not working on mobile), landing-page-side (poor mobile experience), or audience-side (different intent by device).
Your B2B lead gen campaigns show desktop CPA at $41 with 3.8% CVR and mobile CPA at $78 with 1.6% CVR. But mobile accounts for 58% of clicks because that's where the impressions are. Claude digs deeper and finds the landing page form has 9 fields that are painful on mobile, and the page load time on mobile is 4.2 seconds vs 1.8 on desktop. Recommendation: create a simplified mobile form (3 fields), fix mobile page speed, and reduce mobile bid adjustments by 25% until the fixes are live to stop the bleeding.
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.
中文简介正在准备中。当前显示英语简介。
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.
中文简介正在准备中。当前显示英语简介。
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.
中文简介正在准备中。当前显示英语简介。
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.
中文简介正在准备中。当前显示英语简介。
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.
中文简介正在准备中。当前显示英语简介。
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.
中文简介正在准备中。当前显示英语简介。