Channel Attribution

Go Beyond First and Last Touch Attribution

The Leading Solution for Attribution Modeling with Over 300,000 Downloads
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Channel Attribution for Python and R

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Total Downloads

Over 250k R package downloads

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Attribution Marketers

Estimated number of advanced attribution marketers

Multi-Touch Attribution Modeling

Advertisers seek to determine the contribution of different online marketing channels to their overall success. This is known as the online multi-channel attribution problem. Many advertisers use heuristic methods that overlook customer interactions and underestimate smaller channels’ contributions.

To provide a more accurate assessment of each channel’s impact, Channel Attribution utilizes a probabilistic approach that utilizes a k-order Markov representation to identify correlations in customer journey data. Additionally, the package includes a function that estimates three heuristic models.

Better Marketing Campaigns

Markov-based multi-touch attribution models help businesses optimize their marketing campaigns. By analyzing how different combinations of touchpoints impact the likelihood of conversion, businesses can adjust their campaigns to maximize their effectiveness.

Full Customer Journey Analysis

Multi-touch attribution models help businesses understand their customer’s journey and identify areas where improvements can be made. By analyzing the touchpoints that lead to conversions, businesses can develop a deeper understanding of their customers and tailor their marketing and sales strategies accordingly.

Budget Allocation and Pacing

Multi-touch attribution models can help businesses allocate marketing budgets more effectively by identifying which touchpoints are most effective at driving conversions. By understanding the impact of each touchpoint, businesses can optimize their spending and focus their resources on the channels that deliver the best results.

Better Sales Forecasting

Attribution models can be used to forecast sales based on past marketing activities. By understanding the impact of different touchpoints on sales, businesses can develop more accurate sales forecasts and make better decisions about inventory, staffing, and other operational needs.

Getting Started

					pip install --upgrade setuptools
pip install Cython
pip install ChannelAttribution

Python library ChannelAttribution

Only Python3 is supported! Note! Only Python3 is supported! Installation on Windows requires Microsoft Visual C++ 14.0 or greater


R package ChannelAttribution

Source and binary packages are available. Check CRAN package results for more details: 

Channel Attribution Pro

For enterprises we created ChannelAttribution Pro, a streamlined suite of scalable and customizable models.

Half the money I spend on advertising is wasted; the trouble is I don't know which half.

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Thousands of Data Scientists Worldwide

Features for Pros

Advanced Models

Channel Attribution Pro offers advanced models such as transaction-level attribution and Markov-Models with odds

Add Media Mix Modeling

Combine attribution form multi-touch models and media-mix models at transaction level

Find the Best Model

Leverage out-of-sample choice of the best Markov model order

Realtime Attribution

Real-Time Attribution with Markov Model

Scalable Processing

Run enterprise-level loads and leverage distributed computing environments

Container Support

Preconfigured Docker Containers with RStudio or Jupyter and ChannelAttributionPro installed

Get Channel Attribution Pro serves North American customers exclusively. If you are outside Canada or the United States, please get in touch with direclty.
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Client Testimonials

“ChannelAttribution Pro is a cornerstone of our marketing measurement strategy. The methodology came up from an intense collaboration with the ChannelAttribution team. As far as we know, it is an industry breakthrough.”
Baptiste Amar, Senior Data Analyst, Get Your Guide
“We are using ChannelAttribution Pro to create automated reports on campaign level marketing performance. This already gives us a much better understanding of the performance of certain channels (for example Display and App Acquisition channels) than what we had with more simplistic (heuristic) attribution models.”
Hampus Hansson, Marketing Technology Lead, Albelli
“By leveraging a data-driven attribution model we have eliminated the biases associated with traditional attribution mechanisms. We have been able to understand how various messages influence our potential customers and the variances by geography and revenue type.”
James Kinley, Principal Data Scientist, Cloudera

“Markov chains can be a pain to implement (especially at scale), but luckily for us, the “ChannelAttribution” R package makes this a lot easier.”

-Trevor Paulsen Group Product Manager, Adobe Analytics

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Get Services and Development for R and Python

Installation and Setup

On-Prem or in the Cloud. We install Channel Attribution Pro and set up your workflows and pipelines.

Data Cleaning and Exploration

We help with Data Cleaning and preparation. builds scalable pipelines with open-source workflow orchestrators like Airflow, Flyte and others.

Custom Visualisation

Want to productize your model? We are hear to help. We build custom shiny apps.

Model Deployment

Need help with modeling? can do the work for you.

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