Go Beyond First and Last Touch Attribution
The Leading Solution for Attribution Modeling with Over 300,000 Downloads
Channel Attribution for Python and R
Channel Attribution mentioned online
Over 250k R package downloads
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.
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.
Thousands of Data Scientists Worldwide
Features for Pros
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
Real-Time Attribution with Markov Model
Run enterprise-level loads and leverage distributed computing environments
Preconfigured Docker Containers with RStudio or Jupyter and ChannelAttributionPro installed
Get Channel Attribution Pro
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Bend.ai and ChannelAttribution.io are partnering to bring the best Channel Attribution Solution to more advanced Marketers in North America.
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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. Bend.ai builds scalable pipelines with open-source workflow orchestrators like Airflow, Flyte and others.
Want to productize your model? We are hear to help. We build custom shiny apps.
Need help with modeling? Bend.ai can do the work for you.