Designs statistically valid split tests for ads, audiences, landing pages, or bid strategies. Calculates required sample sizes before you start, monitors results during the test, and calls winners when statistical significance is actually reached — not when you feel like one is winning. Platform: Google and Meta.
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Designs statistically valid split tests for ads, audiences, landing pages, or bid strategies. Calculates required sample sizes before you start, monitors results during the test, and calls winners when statistical significance is actually reached — not when you feel like one is winning.
Claude takes your test hypothesis, current baseline metrics, and the minimum detectable effect you care about, then calculates how much traffic and time the test needs to produce a reliable result. During the test, it tracks results and tells you whether differences are statistically significant or just noise. It prevents premature test calls that waste the effort.
You want to test a new headline variant against your current best performer. Your current ad gets a 2.8% CTR with 1,200 daily impressions. Claude calculates that to detect a 15% improvement (3.22% CTR) with 95% confidence, you need approximately 12,400 impressions per variant — about 10 days at current traffic levels. After 7 days, the new variant shows 3.1% CTR vs 2.7% for control. Claude tells you it's trending positive but not yet significant (p=0.14, need p<0.05) — keep running for 4 more days before making a call.
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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概要と使いどころ
Statistical significance calculator for A/B test results with sample size requirements, segment breakdowns, and hypothesis generation. Use when feeding test results, checking statistical significance, calculating sample sizes, analyzing experiment outcomes, or generating next test ideas based on results. Platform: Google and Meta.
日本語の概要は準備中です。原文の説明を表示しています。
Evaluates your campaign and ad set structure against your actual goals and budget. Flags over-segmentation that fragments your data, under-segmentation that hides performance differences, budget allocation issues, and consolidation opportunities that would improve algorithmic delivery and your ability to optimize. Platform: Google and Meta.
日本語の概要は準備中です。原文の説明を表示しています。
Analyzes your top performing ads, identifies what's working in the hooks, CTAs, messaging angles, and formats, then generates new variants that follow the same winning patterns while introducing enough variation to test meaningfully. Platform: Google and Meta.
日本語の概要は準備中です。原文の説明を表示しています。
Reviews all your Google Ads extensions — sitelinks, callouts, structured snippets, call extensions, image extensions, price extensions — across every campaign. Flags what's missing, what's underperforming, what's outdated, and writes replacements based on your best performing ads and landing pages. Platform: Google.
日本語の概要は準備中です。原文の説明を表示しています。
Multi-channel budget optimization using MER, marginal ROAS, and diminishing returns analysis. Use when pasting multi-channel spend and results data, requesting reallocation recommendations, analyzing budget shift priorities, or optimizing marketing efficiency across Google, Meta, TikTok, and other channels. Platform: Google and Meta.
日本語の概要は準備中です。原文の説明を表示しています。
Audit how visible your brand is inside AI answers (ChatGPT, Claude, Gemini, Perplexity, AI Overviews). Claude builds a prompt panel for your category, scores where you show up vs competitors, and turns the gaps into a prioritized fix list. Platform: AI visibility.
日本語の概要は準備中です。原文の説明を表示しています。