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Here's your Q1 channel performance. Google drove the most revenue at $1.24M on $156K spend (7.9x ROAS). LinkedIn had the lowest cost per conversion at $589 despite lower volume.
| Channel | Spend | Conv | Revenue | ROAS | Cost/Conv |
|---|---|---|---|---|---|
| $156,177 | 1,253 | $1,241,500 | 7.9x | $125 | |
| Meta | $60,959 | 425 | $487,200 | 8.0x | $143 |
| $60,927 | 103 | $310,107 | 5.1x | $589 | |
| Organic | $0 | 312 | $518,300 | -- | -- |
| Direct | $0 | 574 | $892,100 | -- | -- |
| X | $107,435 | 26 | $74,850 | 0.7x | $4,061 |
| Total | $385,499 | 2,992 | $3,694,910 | 9.6x | $129 |
Here's your Q1 first-time purchaser breakdown from Shopify. Meta drove the most new customers (812) but Google's first-time buyers had 34% higher AOV and a 28% repeat rate within 60 days. Also: 340 first-time purchasers from the last 30 days aren't in any Klaviyo welcome flow yet.
| Channel | New Cust | Revenue | AOV | 60d Repeat | CPA |
|---|---|---|---|---|---|
| Meta | 812 | $73K | $90 | 18% | $28 |
| 524 | $63K | $121 | 28% | $41 | |
| Organic | 287 | $30K | $105 | 24% | -- |
| 134 | $13K | $96 | 31% | $2 | |
| TikTok | 90 | $7K | $75 | 11% | $52 |
Found Acme Corp - 4 visitors, $48K closed-won deal, 22.9x ROAS on $2.1K in attributed ad spend. Here's the full journey with HubSpot deal stage progression.
ROAS shifts significantly by model. Meta looks strongest under first touch (10.2x) because it often initiates journeys, but drops to 8.0x under last touch. Google gains from 5.7x to 8.1x because it closes deals that other channels started. LinkedIn drops from 6.7x to 3.6x, suggesting it drives awareness rather than conversions.
| Channel | FT ROAS | LT ROAS | Linear ROAS | FT→LT Shift |
|---|---|---|---|---|
| 5.7x | 8.1x | 6.9x | +2.4x | |
| Meta | 10.2x | 8.0x | 9.1x | -2.2x |
| 6.7x | 3.6x | 5.2x | -3.1x | |
| X | 0.3x | 0.7x | 0.5x | +0.4x |
| Organic | -- | -- | -- | -- |
| Direct | -- | -- | -- | -- |
Based on your Q1 data, here are three high-impact moves. The biggest opportunity is reallocating X (Twitter) spend - it's returning $0.70 for every $1 spent. Shifting that budget to Meta and Google, which aren't showing saturation yet, should lift blended ROAS from 9.6x to an estimated 12.1x.
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For years, getting real answers from marketing data meant one of two things: staring at Google Analytics dashboards that never match your actual database, or hiring a data science team to pull the data, clean it, build a model, and argue about whether the results are right. Most marketing teams are stuck with the first option. They make decisions on gut feel, or on platform-reported numbers they know are inflated, because the alternative costs six figures and takes six months.
Agentic marketing analytics changes this completely. You ask a business question — “where should I spend my next dollar?” or “which channels are actually working?” or “what happens if I cut this budget in half?” — and the AI chooses the right model, the right parameters, and the right data to answer it. You don't pick a methodology. You don't configure a report. You don't need a data scientist. You just ask.
And it works because of what's underneath. Attribution's data pipeline gives the AI something most tools can't: user-level cost allocation, de-duplicated conversions, fully auditable touchpoints, and real-time data collected from every source through its CDP — ad platforms, CRMs, email systems, and more. When the AI recommends reallocating $67K from one channel to another, that recommendation is grounded in data you can trace back to individual visitors and clicks. Not estimates. Not modeled aggregates. Actual data.
The right answer to the right question, backed by the best data. No data science team required. That's agentic marketing analytics.
Questions that used to require a data science team. Answered in seconds.
The AI analyzes hundreds of closed-won journeys and surfaces the patterns no human would find manually. Then tells you how to replicate them.
Not a guess. The AI models scenarios based on your actual channel performance, saturation signals, and conversion data, then gives you a specific reallocation plan.
The AI identifies underperforming spend across every channel and gives you a same-day action plan with projected savings.
The AI diagnoses what changed — which channels got more expensive, where conversion rates dropped, where you're overspending — and tells you exactly what to adjust.
The AI compares journey patterns across deal sizes and tells you which channels, content, and sequences drive enterprise revenue.
Connects what's in your CRM right now with historical marketing performance to project revenue, with the data to back it up.
The AI is only as good as the data.
And we have the best data. 60+ endpoints give the AI access to every visitor, every cost, every touchpoint, and every dollar of revenue — at the individual level. Here's what that means.
Real cost per visitor, not campaign averages
Attribution binds actual ad spend to each individual visitor's journey. The AI doesn't divide total campaign spend by platform-reported conversions. It knows what you actually paid to acquire each customer, across every channel they touched. That's how it calculates true ROAS, CAC, and LTV — numbers that match your bank account.
Every visitor. Every touchpoint. Full context.
The AI can look up any individual visitor and see every page they viewed, every ad they clicked, every event they triggered, every dollar of revenue they generated — with timestamps, source, referrer, and UTM parameters. It can reconstruct any customer journey from raw events, not pre-computed summaries.
Cross-system identity resolution
The same person is tracked across devices, sessions, and systems. An anonymous first visit connects to a named signup, connects to a CRM deal, connects to a payment. The AI sees one unified journey where other tools see disconnected fragments in Shopify, HubSpot, Klaviyo, and Google Ads.
Revenue from your source of truth
Not ad-platform-reported revenue. Actual revenue from Shopify orders, Stripe payments, Salesforce Hubspot or Pipedrive deals. When the AI tells you a channel's ROAS, it's calculated from money that actually hit your account — de-duplicated across every platform that wants to claim credit.
Complete channel taxonomy at every level
The AI understands your full channel hierarchy — from 'Google' down to 'Google > Search - Brand > US > Enterprise document storage.' It can analyze performance at the channel, campaign, ad group, or keyword level and zoom in or out as the question demands. Spend, conversions, and revenue roll up cleanly at every level.
Fully auditable — every number drills down
When the AI says Google drove 340 conversions, you can ask it to show you the raw events. Then pick one visitor and trace their entire journey click by click. No ML layer between you and the data. No proprietary model you can't inspect. Every recommendation the AI makes can be verified in the underlying data.
Not a chatbot with canned answers. Full access to your data.
The same data that powers every dashboard and report, queried in real time.
Built on the Model Context Protocol.
Open standard. No lock-in.
MCP is an open standard created by Anthropic that lets AI assistants connect to external data sources. Attribution's MCP server exposes 60+ tools covering reports, visitors, companies, paths, conversions, revenue, filters, spend, and configuration.
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Frequently asked questions
Agentic marketing analytics is a new approach to marketing measurement where an AI agent has direct access to your attribution data and can pull reports, analyze performance, compare models, and make optimization recommendations through natural language conversation. Instead of navigating dashboards and selecting from dropdowns, marketers ask questions like 'what's my ROAS by channel this quarter' or 'what should I change to improve performance next quarter' and receive complete reports and actionable recommendations in seconds. Attribution is the first multi-touch attribution platform to offer agentic marketing analytics through its MCP (Model Context Protocol) integration.
Attribution is the first multi-touch attribution platform with a native AI integration through the Model Context Protocol (MCP). The integration connects Attribution's full dataset - including dashboard reports, channel performance, conversion paths, visitor and company histories, raw conversions, revenue data, and spend data - directly to Claude and other MCP-compatible AI assistants. Unlike chatbot features that return canned summaries, Attribution's MCP exposes over 60 live data endpoints that the AI queries in real time. Other attribution tools like Rockerbox, Triple Whale, Northbeam, HockeyStack, and Dreamdata do not currently offer MCP integrations.
You don't pick a methodology or configure a model. You ask a business question - 'where should I spend my next dollar' or 'which channels are actually driving revenue' or 'what happens if I cut this budget in half' - and the AI selects the right model, parameters, and data to answer it. It might use attribution modeling for channel performance, statistical modeling for budget scenario planning, or a combination of approaches for optimization recommendations. The AI has access to Attribution's full dataset including user-level cost data, de-duplicated conversions, conversion paths, and real-time data from ad platforms, CRMs, email systems, and every other connected source, so it can draw from whatever data and approach best answers your specific question. No data science team required.
Attribution's MCP integration uses the Model Context Protocol, an open standard created by Anthropic, to connect Attribution's data directly to AI assistants. Users enable the Attribution MCP server in Claude (claude.ai or Claude Desktop), authenticate with their Attribution account via OAuth, and then query their data in natural language. The AI can pull dashboard reports with any attribution model and date range, analyze conversion paths, look up individual visitors and companies, compare attribution models side by side, access HubSpot deal stage data alongside marketing touchpoints, and generate optimization recommendations. All queries run against live data with the same security and access controls as the Attribution dashboard.
Yes. Attribution's MCP integration queries your data in real time from your Attribution account. No attribution data is stored by the AI assistant. The integration uses OAuth authentication, and each team member connects their own AI account with access limited to the same data they can see in the Attribution dashboard. The Model Context Protocol is an open standard with built-in security controls, and your data stays within your Attribution account at all times.
Attribution's MCP integration exposes over 60 data endpoints covering the platform's complete dataset. This includes dashboard reports and channel performance with any attribution model (first touch, last touch, linear, time decay, position-based), cohort analysis and time-period breakdowns, first-time purchaser reports, raw individual conversion and revenue events, aggregated and individual conversion paths, visitor lookup and full visit history, company lookup with account-level buying journeys, HubSpot deal stage progression alongside marketing touchpoints, channel taxonomy and filter configuration, spend data by filter and filter group with original currency, and CSV export from any report.
Attribution's MCP integration is live now. Join the waitlist at attributionapp.com/mcp and we'll notify you when your account is enabled. Both existing Attribution customers and new signups are eligible.
Yes. Each team member connects their own Claude account to Attribution through OAuth. They have access to the same data they can see in the Attribution dashboard, subject to their existing Attribution permissions. There is no limit on the number of team members who can connect.
