Analyze how often people see Meta ads and where repeated exposure starts reducing performance. Get campaign-specific frequency cap recommendations, estimated spending savings, and projections of additional reach from redirecting spending toward people who have not seen the ads.
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Analyzes frequency data across your Meta campaigns, identifies where you're overserving ads to the same people, and recommends frequency caps by campaign objective. Tells you the point where additional impressions stop driving conversions and start burning money. Platform: Meta.
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Analyzes frequency data across your Meta campaigns, identifies where you're overserving ads to the same people, and recommends frequency caps by campaign objective. Tells you the point where additional impressions stop driving conversions and start burning money.
Claude correlates frequency levels with conversion rates, CTR, and CPA across your campaigns. It finds the inflection point for each campaign type — the frequency at which performance starts declining — and recommends caps that maximize reach and conversions while cutting waste from overexposure.
Your Meta prospecting campaigns show that conversion rate peaks at frequency 2.3 and drops 40% by frequency 4. But your current average frequency is 5.1, meaning a significant portion of impressions are hitting people who've already seen the ad too many times. Claude recommends a frequency cap of 3 per 7 days for prospecting. For retargeting, the data shows conversions continue up to frequency 6 before dropping — so a cap of 5 per 7 days is appropriate. Estimated savings from capping: $3,400/month redirected to fresh reach.
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。