Compare Google and Meta campaign results across countries, states, cities, or postal codes. Identify weak and strong regions, then get recommendations for bid changes, separate campaigns, and budget redistribution based on meaningful performance differences.
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Breaks down campaign performance by geographic location at whatever level matters — country, state, city, DMA, zip code. Flags underperforming geos that are quietly eating budget and high-performing ones that deserve more spend. Recommends geo bid adjustments or campaign splits. Platform: Google and Meta.
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查看安装方法安装前可以查看交给 AI 代理的指令内容。
Breaks down campaign performance by geographic location at whatever level matters — country, state, city, DMA, zip code. Flags underperforming geos that are quietly eating budget and high-performing ones that deserve more spend. Recommends geo bid adjustments or campaign splits.
Claude analyzes your performance data segmented by location, identifies statistically significant performance differences (not just noise from low-volume areas), and calculates the cost of running campaigns in underperforming regions vs the conversions you'd lose by excluding or reducing them.
Your national ecommerce campaigns spend evenly across the US. Claude finds that 8 states produce 65% of your conversions at a $24 CPA, while 12 states spend $8,400/month combined with a $71 CPA and only 118 conversions. Three metro areas — Dallas, Phoenix, and Atlanta — outperform their state averages by 40%+ and could absorb more budget. Recommendation: reduce bids 40% in the 12 underperforming states, increase 25% in the top 8, and create separate campaigns for the 3 outperforming metros to give them dedicated budgets.
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。