attribution-model-comparison

--- name: attribution-model-comparison description: Runs your conversion data through different attribution models side by side — last click, first click, linear, time decay, position based, and data-driven. Shows you how credit shifts between campaigns depending on the model so you can make better budget decisions instead of over-investing in last-touch campaigns. Platform: Google and Meta. metadata: platform: Google and Meta --- # 26/ Attribution Model Comparison — Google + Meta ## What it does Runs your conversion data through different attribution models side by side — last click, first click, linear, time decay, position based, and data-driven. Shows you how credit shifts between campaigns depending on the model so you can make better budget decisions instead of over-investing in last-touch campaigns. ## How it works Claude takes your multi-touch conversion path data and applies each attribution model to the same dataset. It then compares how each campaign's attributed conversions and ROAS change under different models, highlighting campaigns that look great under last-click but contribute nothing at first-touch (and vice versa). ## Practical example Under last-click attribution, your Google Brand campaign gets credit for 420 conversions at $18 CPA, making it your "best" campaign. But when Claude runs first-click attribution, Brand drops to 31 conversions — most of those users actually discovered you through Meta prospecting (which jumps from 89 to 340 attributed conversions). Linear attribution puts Meta prospecting at 215 and Brand at 190, giving a more balanced picture. Claude recommends shifting 20% of Brand budget to Meta prospecting, which is actually originating most of your pipeline. ## What you get back - Side-by-side conversion and ROAS comparison across all models for every campaign - Campaigns most affected by model choice (high variance = their role is misunderstood) - Upper-funnel campaigns being undervalued under last-click - Lower-funnel campaigns being over-credited under last-click - Budget reallocation recommendations based on a blended attribution view - Recommended "working model" for your specific account based on funnel length and touchpoint patterns ## When to use it - When making budget allocation decisions to avoid last-click bias - During QBRs to show clients the full picture of campaign value - When upper-funnel campaigns are on the chopping block due to "poor" last-click ROAS - Before cutting any campaign that might be silently feeding conversions elsewhere ## Data access (Ryze MCP) 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: - claude.ai / Claude Desktop: Settings → Connectors → Add custom connector → `https://connector.get-ryze.ai/mcp` - Claude Code: `claude mcp add ryze --transport http https://connector.get-ryze.ai/mcp` - Cursor: Settings → MCP → add the same URL Setup guide: https://www.get-ryze.ai/how-to-connect-claude-to-google-meta-ads-mcp

["Gather your conversion data from Google Ads and Meta Ads for the [PERIOD] you want to analyze. Export reports for each campaign, including touchpoints, clicks, and conversions.","Use your analytics platform (e.g., Google Analytics 4, Meta Ads Manager, or a BI tool like Looker Studio) to apply each attribution model to your data. If using Google Ads, enable the data-driven model in your account settings.","Input the exported data into the AI tool and run the comparison using the prompt template. Specify the campaigns you want to analyze (e.g., [CAMPAIGN_1], [CAMPAIGN_2]) and the time period (e.g., [PERIOD]).","Review the AI’s output to identify shifts in credit allocation. Pay attention to campaigns that are over- or under-valued by the last-click model, as these are likely misallocated in your current budget.","Use the recommendations to adjust your budget allocation. For example, if the data-driven model shows ‘Brand Awareness’ drives more initial interest, increase its budget and reduce spend on last-click-heavy campaigns like ‘Summer Sale’ by 10-20%.","Tip: Run this analysis monthly to track shifts in attribution over time. Compare results across quarters to identify trends (e.g., seasonal changes in customer behavior)."]