Forecast advertising return for Google and Meta over the next 30, 60, and 90 days. Uses performance trends, seasonal patterns, and planned changes to provide scenario ranges, underlying assumptions, risks, and revenue estimates tied to planned spending.
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Projects your ROAS for the next 30, 60, and 90 days based on current performance trends, seasonality patterns from your historical data, and planned budget or campaign changes. Gives you a range with confidence intervals, not a single number pretending to be precise. Platform: Google and Meta.
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Projects your ROAS for the next 30, 60, and 90 days based on current performance trends, seasonality patterns from your historical data, and planned budget or campaign changes. Gives you a range with confidence intervals, not a single number pretending to be precise.
Claude takes your trailing performance data, identifies the trend line (improving, declining, or stable), overlays seasonal patterns from the same period in prior years if available, and factors in any known upcoming changes (budget shifts, new campaigns, creative launches). It produces a forecast range showing best case, expected case, and conservative case.
Your ecommerce account is averaging 4.2x ROAS in March. Claude's analysis shows ROAS typically dips 15-20% in April based on last year's data (post-spring-sale slowdown), your top creative set is showing early fatigue signals that will reduce efficiency by ~10% over the next 30 days, and you're planning a 20% budget increase which historically reduces marginal ROAS by 8-12%. 30-day forecast: 3.1x (conservative) to 3.6x (expected) to 4.0x (best case, if new creatives outperform).
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.
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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。