MMM is only as good as the data. Attribution has the best data.
Every media mix model needs the same thing: clean daily spend by channel and accurate conversion data. Most tools give you campaign-level aggregates with double-counted conversions. Attribution gives you user-level, de-duplicated, API-pulled spend data. Feed it into an LLM, an open-source framework, or Keen. No separate MMM platform required.
The Problem
MMM doesn't have to be expensive and inaccessible anymore
Media mix modeling tells you how your total marketing spend drives outcomes — accounting for diminishing returns, saturation, and cross-channel effects that multi-touch attribution can't capture. It answers questions like "if I shift $50K from Meta to LinkedIn next quarter, what happens to pipeline?"
But historically, MMM required hiring a consulting firm or licensing a dedicated platform costing $50K–$200K per year. The barrier was never the math. It was getting clean, structured, daily spend and conversion data by channel in one place.
Attribution's Data Export Tool removes that barrier. Every Attribution customer already has the dataset MMM models require.
What MMM Models Need
Attribution already exports the exact inputs
Daily spend by channel
Attribution's amounts table contains actual ad spend pulled from platform APIs, by filter and date. Not estimated. Not self-reported. With currency conversion for international campaigns.
Daily conversions
Attribution's events table contains every conversion with timestamps and revenue. De-duplicated across platforms — not inflated numbers from summing platform-reported conversions.
Channel taxonomy
Attribution's filters table contains your complete channel hierarchy with parent/child relationships. No manually building a channel mapping spreadsheet.
Prompt Your LLM
Connect your data. Prompt. Get a full analysis in minutes.
Export your Attribution data from your warehouse as a CSV. Upload it to Claude, ChatGPT, or any LLM with code execution. The LLM writes the code, fits the model, and gives you a complete MMM dashboard with actionable recommendations.
Key finding: The model recommends shifting budget from LinkedIn Ads (-14.9%) to Google Ads (+6.7%) and Meta Ads (+5.4%). LinkedIn's high CPA ($83.14) and low saturation suggest the channel converts at a low rate regardless of spend level. This reallocation is estimated to improve daily conversions by 3.9% with the same total budget.
This analysis took less than two minutes. No data scientist. No $50K MMM platform. Just a CSV from Attribution's data export and one prompt.
Other approaches
If your team prefers a specific framework or managed solution, Attribution's export works with all of them.
Open-source frameworks
For data scientists who want full control. Attribution's export maps directly to the input format these frameworks expect.
Keen (managed MMM platform)
For teams that want a managed solution with a visual interface for scenario planning and budget optimization. Keen integrates with Attribution's data export so you skip the manual data wrangling that most MMM platforms require.
Keen is a separate subscription and is one of several options for running MMM on Attribution's data.
Learn more about Keen →Data Quality Matters
Garbage in, garbage out. Attribution gives you the cleanest input.
MMM models are regression-based. The cleaner your data, the more reliable your results. Here's why Attribution's export produces better models than ad platform data pulled through Fivetran or similar tools.
Attribution + your model vs. standalone MMM platforms
Dedicated MMM tools solve the data problem by collecting it themselves. Attribution already solved it.
Frequently asked questions
Everything you need to know about the product and billing.
Yes. Attribution's Data Export Tool provides the exact data that media mix models need: daily spend by channel from the amounts table and daily conversions from the events table. You can feed this data into Meta's open-source Robyn framework, Google's open-source Meridian framework, an LLM like Claude or ChatGPT, or the managed MMM platform Keen. No additional Attribution license or module is required — the data export add-on is all you need.
No. Attribution's Data Export Tool is available as an add-on for any plan. Once you have the export, you can run any analysis on the data, including media mix modeling, incrementality testing, and custom attribution models. There is no separate MMM module, no per-analysis fee, and no vendor lock-in. Standalone MMM platforms like Measured and Marketing Evolution typically cost $50,000 to $200,000 or more per year.
Yes. Export your daily spend-by-channel and daily conversions as a CSV from your warehouse, upload it to Claude, ChatGPT, or any LLM with code execution, and ask it to run a media mix model. The LLM writes and executes Python code to fit a Bayesian regression model, produces saturation curves and channel ROI estimates, and recommends budget reallocation. This is the fastest path to MMM insights for teams without a dedicated data scientist.
Any framework that accepts daily spend by channel and daily conversions as input. The most widely used are Meta's Robyn (R-based, automated hyperparameter tuning and saturation modeling), Google's Meridian (Python, Bayesian with geo-level modeling), PyMC-Marketing (Python, flexible custom Bayesian models), and LightweightMMM (Google, JAX-based, optimized for speed). All are free, open-source, and well-documented.
Fivetran replicates what ad platform APIs report: campaign-level aggregates with platform-reported conversions. Each platform claims full credit for every conversion it touched, meaning conversions are double-counted across platforms. If you feed this into an MMM model, the model works with inflated conversion numbers and produces unreliable channel-level ROI estimates. Attribution tracks conversions at the user level with first-party tracking and de-duplicates across platforms, giving MMM models accurate outcome data.
Not anymore. The LLM path makes MMM accessible to any marketing analyst who can export a CSV and write a prompt. For teams that want more control, the open-source frameworks require Python or R skills. For teams that want a fully managed solution, Keen provides a visual interface. Attribution gives you the data — you choose the tool that matches your team's capabilities.
