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https://github.com/eeghor/mta
Multi-Touch Attribution
https://github.com/eeghor/mta
marketing markov-model probabilistic-models shapley
Last synced: 3 months ago
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Multi-Touch Attribution
- Host: GitHub
- URL: https://github.com/eeghor/mta
- Owner: eeghor
- Created: 2018-10-31T07:08:10.000Z (about 6 years ago)
- Default Branch: master
- Last Pushed: 2022-02-26T11:33:50.000Z (over 2 years ago)
- Last Synced: 2024-05-14T00:11:30.831Z (6 months ago)
- Topics: marketing, markov-model, probabilistic-models, shapley
- Language: Python
- Homepage:
- Size: 344 KB
- Stars: 97
- Watchers: 12
- Forks: 32
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
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README
# 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](https://cran.r-project.org/web/packages/ChannelAttribution/ChannelAttribution.pdf) - 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](img/data_snippet.png)
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](https://eprints.soton.ac.uk/380534/1/GHLEFMG_FGMJHM_VJ1QM9QF.pdf)
* Shao and Li (2011) - Data-driven Multi-touch Attribution Models [pdf](http://www0.cs.ucl.ac.uk/staff/w.zhang/rtb-papers/data-conv-att.pdf)
* Dalessandro et al (2012) - Causally Motivated Attribution for online Advertising [pdf](https://dstillery.com/wp-content/uploads/2016/07/CAUSALLY-MOTIVATED-ATTRIBUTION.pdf)
* Cano-Berlanga et al (2017) - Attribution models and the Cooperative Game Theory [pdf](https://www.recercat.cat/bitstream/handle/2072/290758/201702.pdf?sequence=1)
* Ren et al (2018) - Learning Multi-touch Conversion Attribution
with Dual-attention Mechanisms for Online Advertising [pdf](https://arxiv.org/pdf/1808.03737.pdf)
* Zhang et al (2014) - Multi-Touch Attribution in Online Advertising with Survival Theory [pdf](http://www0.cs.ucl.ac.uk/staff/w.zhang/rtb-papers/attr-survival.pdf)
* Geyik et al (2014) - Multi-Touch Attribution Based Budget Allocation in Online Advertising [pdf](https://arxiv.org/pdf/1502.06657.pdf)