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-2.1.3.tar.gz (738.2 kB view details)

Uploaded Source

File details

Details for the file ChannelAttribution-2.1.3.tar.gz.

File metadata

  • Download URL: ChannelAttribution-2.1.3.tar.gz
  • Upload date:
  • Size: 738.2 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.1.1 pkginfo/1.4.2 requests/2.22.0 setuptools/59.5.0 requests-toolbelt/0.8.0 tqdm/4.62.2 CPython/3.8.10

File hashes

Hashes for ChannelAttribution-2.1.3.tar.gz
Algorithm Hash digest
SHA256 be6ce4f59631415660f8fb18c14cffd612e5c13595535d57c55321216ef2a3cd
MD5 11a4864ac48db9bc056ea117fc460161
BLAKE2b-256 6d000ff384b01cd195966f82727c3e4c087887609e9524a3d17b78e6b62b1a78

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