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 the online multi-channel attribution problem. This package contains a probabilistic algorithm for the attribution problem. The model uses a k-order Markov representation to identify structural correlations in the customer journey data. The package also contains three heuristic algorithms (first-touch, last-touch and linear-touch approach) for the same problem. The algorithms are implemented in C++.
| Version: | 1.16 |
| Imports: | Rcpp |
| LinkingTo: | Rcpp, RcppArmadillo |
| Published: | 2019-04-10 |
| Author: | Davide Altomare, David Loris |
| Maintainer: | Davide Altomare <davide.altomare at gmail.com> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| URL: | http://www.slideshare.net/adavide1982/markov-model-for-the-multichannel-attribution-problem http://www.lunametrics.com/blog/2016/06/30/marketing-channel-attribution-markov-models-r/ http://analyzecore.com/2016/08/03/attribution-model-r-part-1/ |
| NeedsCompilation: | yes |
| CRAN checks: | ChannelAttribution results |
| Reference manual: | ChannelAttribution.pdf |
| Package source: | ChannelAttribution_1.16.tar.gz |
| Windows binaries: | r-devel: ChannelAttribution_1.16.zip, r-devel-gcc8: ChannelAttribution_1.16.zip, r-release: ChannelAttribution_1.16.zip, r-oldrel: ChannelAttribution_1.16.zip |
| OS X binaries: | r-release: ChannelAttribution_1.16.tgz, r-oldrel: ChannelAttribution_1.16.tgz |
| Old sources: | ChannelAttribution archive |
| Reverse imports: | ChannelAttributionApp |
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