Multi-touch attribution model & software
Run Markov, Shapley and other MTA models in-house with ChannelAttribution Pro. Built for agencies and analytics teams that want transparent attribution software in Python or R instead of another black-box dashboard.
No paid production commitment is required during evaluation.
ChannelAttribution Pro combines the model and the software layer: choose an attribution methodology, run it on customer journeys, compare outputs and integrate the result into your own measurement workflow.
Use customer journeys across multiple touchpoints to estimate contribution beyond last-click and first-click rules.
Run MTA models in Python or R, inspect the logic and keep attribution inside your own analytics stack.
Connect channel and journey data to a repeatable modeling workflow for campaign measurement, reporting and budget decisions.
ChannelAttribution Pro is an in-house attribution engine for Python and R. Run Markov attribution models, Shapley-based attribution and other approaches on journey data, compare results and keep control over the assumptions, inputs and outputs.
You can test the modeling value before deciding to use it in production for client work.
Use the software for free while you evaluate it. You only need a paid production agreement when you decide to put ChannelAttribution Pro into production.
Send us a sample of your client data. We will return a free report explaining what can be measured, which models are suitable, and where the data may need improvement.
ChannelAttribution was first released more than 10 years ago. We use that experience to help agencies move from raw data to realistic attribution use cases.
Use one MTA software workflow to test different attribution approaches on the same customer journeys and understand how model choice changes channel contribution.
Estimate channel contribution from transition paths and removal effects. Use Markov attribution when journey sequence and channel interactions matter.
Apply cooperative-game attribution logic to distribute conversion value across combinations of marketing touchpoints.
Compare Markov, Shapley and heuristic attribution outputs on the same dataset instead of treating one model as automatically correct.
Where data allows it, enrich attribution with campaign attributes, time effects or other client-specific explanatory variables.
Use the attribution engine in notebooks, scripts and pipelines so measurement can live inside your existing analytics workflow.
Inspect assumptions and outputs, explain limitations and use reproducible results in internal or client-facing analysis.
Send a representative sample. We review it and return a practical assessment of what can be done.
Share anonymized or representative customer journeys, channels, conversions and any relevant marketing fields.
We check whether the data supports a multi-touch attribution model and which Markov, Shapley or alternative approaches are realistic.
Receive a practical summary of what can be measured, which attribution models fit and what should be improved before production.
Quick answers for teams comparing MTA models, Markov attribution and attribution software.
A multi-touch attribution model estimates how multiple marketing interactions contribute to a conversion instead of assigning all credit to one touchpoint. Markov and Shapley approaches are examples of data-driven MTA models.
Multi-touch attribution software provides the tooling to prepare journey data, run attribution models, compare channel contribution and integrate the results into analytics or reporting workflows. ChannelAttribution Pro runs in Python and R.
MTA stands for multi-touch attribution. The MTA model is the attribution methodology; MTA software is the implementation used to run that methodology on real customer journeys and produce repeatable outputs.
Yes. ChannelAttribution Pro supports Markov-chain attribution based on customer journeys and removal effects, alongside other attribution approaches that can be compared on the same data.
Yes. A key use case is comparing Markov, Shapley and simpler attribution approaches to understand how model assumptions affect channel contribution before choosing a production method.
No. It is designed for agencies and in-house analytics or marketing measurement teams that have journey-level data and want transparent attribution logic inside their own technical workflow.
Install ChannelAttribution Pro, run the MTA models on your data and validate whether the outputs are useful. Evaluation is free until you decide to use the software operationally in production.
Try ChannelAttribution Pro