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mta

Multi-Touch Attribution

https://github.com/eeghor/mta

Science Score: 10.0%

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    Scientific vocabulary similarity
    Low similarity (7.7%) to scientific vocabulary

Keywords

marketing markov-model probabilistic-models shapley
Last synced: 6 months ago · JSON representation

Repository

Multi-Touch Attribution

Basic Info
  • Host: GitHub
  • Owner: eeghor
  • Language: Python
  • Default Branch: master
  • Homepage:
  • Size: 344 KB
Statistics
  • Stars: 117
  • Watchers: 11
  • Forks: 36
  • Open Issues: 1
  • Releases: 0
Topics
marketing markov-model probabilistic-models shapley
Created over 7 years ago · Last pushed almost 4 years ago
Metadata Files
Readme

README.md

mta

Multi-Touch Attribution. Find out which channels contribute most to user conversion.

Models

This package contains implementations the following Multi-Touch Attribution models:

  • Shapley
  • Markov
  • So-called Simple Probabilistic Model by Shao and Li
  • Bagged Logistic Regression by Shao and Li
  • Additive Hazard (Survival)

In addition, some popular heuristic “models” are included, specifically

  • First Touch
  • Linear
  • Last Touch
  • Time Decay
  • Position Based

Included Data

The package comes with the same test data set as an R package called ChannelAttribution - there are 10,000 rows containing customer journeys across 12 channels: alpha, beta, delta, epsilon, eta, gamma, iota, kappa, lambda, mi, theta and zeta.

data_snippet

These are conversion aggregations by path. Suppose there’s a path (customer journey) a > b > c with total_conversions equal to 2 and total_null equal to 5. This means that we recorded 2 consumer journeys a > b > c > (conversion) and 5 customer journeys a > b > c > (null)

There’s an option to generate timestamp data if you want to use the Additive Hazard model (the only model that explicitly incorporates exposure times).

References

  • Nisar and Yeung (2015) - Purchase Conversions and Attribution Modeling in Online Advertising: An Empirical Investigation pdf
  • Shao and Li (2011) - Data-driven Multi-touch Attribution Models pdf
  • Dalessandro et al (2012) - Causally Motivated Attribution for online Advertising pdf
  • Cano-Berlanga et al (2017) - Attribution models and the Cooperative Game Theory pdf
  • Ren et al (2018) - Learning Multi-touch Conversion Attribution with Dual-attention Mechanisms for Online Advertising pdf
  • Zhang et al (2014) - Multi-Touch Attribution in Online Advertising with Survival Theory pdf
  • Geyik et al (2014) - Multi-Touch Attribution Based Budget Allocation in Online Advertising pdf

Owner

  • Name: IK
  • Login: eeghor
  • Kind: user

GitHub Events

Total
  • Watch event: 10
  • Fork event: 2
Last Year
  • Watch event: 10
  • Fork event: 2

Committers

Last synced: over 2 years ago

All Time
  • Total Commits: 81
  • Total Committers: 2
  • Avg Commits per committer: 40.5
  • Development Distribution Score (DDS): 0.062
Past Year
  • Commits: 0
  • Committers: 0
  • Avg Commits per committer: 0.0
  • Development Distribution Score (DDS): 0.0
Top Committers
Name Email Commits
Unknown e****r@g****m 76
IK 2****r 5

Issues and Pull Requests

Last synced: 6 months ago

All Time
  • Total issues: 8
  • Total pull requests: 1
  • Average time to close issues: 12 months
  • Average time to close pull requests: 4 minutes
  • Total issue authors: 7
  • Total pull request authors: 1
  • Average comments per issue: 0.75
  • Average comments per pull request: 0.0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 0
  • Pull requests: 0
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Issue authors: 0
  • Pull request authors: 0
  • Average comments per issue: 0
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
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Top Authors
Issue Authors
  • xiawang (2)
  • svbueno (1)
  • peterbayerle (1)
  • divyesh-mangroliya-inferenz (1)
  • thibaultM-jellyfish (1)
  • hrithwikk (1)
  • JulianZeissler (1)
Pull Request Authors
  • dovstern (1)
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Packages

  • Total packages: 1
  • Total downloads:
    • pypi 61 last-month
  • Total dependent packages: 0
  • Total dependent repositories: 1
  • Total versions: 7
  • Total maintainers: 1
pypi.org: mta

Multi-Touch Attribution

  • Versions: 7
  • Dependent Packages: 0
  • Dependent Repositories: 1
  • Downloads: 61 Last month
Rankings
Forks count: 7.1%
Stargazers count: 7.5%
Dependent packages count: 10.0%
Average: 14.1%
Dependent repos count: 21.7%
Downloads: 24.2%
Maintainers (1)
igk
Last synced: 6 months ago