Skip to main content

Markov Model for Online Multi-Channel Attribution

Project description

Python library ChannelAttribution

Advertisers use a variety of online marketing channels to reach consumers and they want to know the degree each channel contributes to their marketing success. This is called online multi-channel attribution problem. ChannelAttribution implements a probabilistic algorithm for the attribution problem. The model uses a k-order Markov representation to identify structural correlations in the customer journey data.

PyPi installation

Note! Only Python3 is supported! Note! Only Python3 is supported! Installation on Windows requires Microsoft Visual C++ 14.0 or greater. (https://visualstudio.microsoft.com/it/downloads/)

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

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

ChannelAttribution-2.0.8.tar.gz (94.7 kB view details)

Uploaded Source

File details

Details for the file ChannelAttribution-2.0.8.tar.gz.

File metadata

  • Download URL: ChannelAttribution-2.0.8.tar.gz
  • Upload date:
  • Size: 94.7 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.1.1 pkginfo/1.4.2 requests/2.22.0 setuptools/45.2.0 requests-toolbelt/0.8.0 tqdm/4.30.0 CPython/3.8.5

File hashes

Hashes for ChannelAttribution-2.0.8.tar.gz
Algorithm Hash digest
SHA256 0263b10f62ee35ffd506d5e25fb4e7d158dd098aaa75048f07837f9babfb43ad
MD5 1a52283d53b27c71a1455bccdf817184
BLAKE2b-256 eb607c6f2562d65c0a216c903bb5f5eedce3d2d5874d118f9cc9f3ee6b4e35a4

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page