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ChannelAttribution Pro is a machine learning library for data-driven marketing attribution and budget allocation from customer-journey data. It is an R package and a Python library available for the main Operative Systems (Linux, Windows and Mac). We can also provide a preconfigured Docker container with RStudio or Jupyter and ChannelAttribution Pro installed.

ChannelAttribution Pro is installed locally and all the elaborations are made on the local system where it is installed. It means that no data are transferred outside your organization.

ChannelAttribution Pro is the professional version of ChannelAttribution which improves ChannelAttribution by offering the following extra features:

Next best action with Markov model
  • Guide customers along journeys to maximize the conversion probability
≥ 3.4
Buget allocation with Markov model
  • Improve your budget allocation when customer journeys are available maximizing your ROI
≥ 3.3
Transaction-level attribution with heuristic models, Markov model and Shapley value
  • Monitor ROI for each channel at path-level and for aggregation of paths at time intervals
≥ 3.0
Real-time attribution with Markov model and Shapley value
  • Save computational time. Train the model on huge amount of customer journeys, store the model parameters and then use it for performing attribution on new customer-journeys
≥ 3.0
Markov model and Shapley value with odds
  • More accurate attribution at path-level
≥ 3.0
Combine results from Media-mix Model and Multi-touch attribution models at path-level
  • If you have previously estimated a Media-mix model on your data, you can combine its results, at path level, with those of one of our multi-touch model
≥ 3.0
Out-of-sample validation algorithm for choosing the best Markov model order
  • More accurate attribution with Markov models
  • Choose the best order also for highly inbalanced data using precision-recall curve instead of roc curve
≥ 3.0
Simplified Shapley value formula
  • Classical Shapley value formula limit the use of Shapley value to problems with less than 10 channels, while simplified Shapley value can be used also with thousands of channels
≥ 3.0
  • Faster execution of Markov model when huge amounts of customer journeys are elaborated
≥ 3.0
Read customer journeys directly from CSV files
  • Process huge amount of customer journeys avoiding out-of-memory issues
≥ 3.0


Student / Researcher
  • Free