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. In many cases, advertisers approach this problem through some simple heuristics methods that do not take into account any customer interactions and often tend to underestimate the importance of small channels in marketing contribution. This package provides a function that approaches the attribution problem in a probabilistic way. It uses a k-order Markov representation to identify structural correlations in the customer journey data. This would allow advertisers to give a more reliable assessment of the marketing contribution of each channel. The approach basically follows the one presented in Eva Anderl, Ingo Becker, Florian v. Wangenheim, Jan H. Schumann (2014). Differently for them, we solved the estimation process using stochastic simulations. In this way it is also possible to take into account conversion values and their variability in the computation of the channel importance. The package also contains a function that estimates three heuristic models (first-touch, last-touch and linear-touch approach) for the same problem.
| Package: | ChannelAttribution |
| Type: | Package |
| Version: | 1.11 |
| Date: | 2018-02-01 |
| License: | GPL (>= 2) |
Package contains two functions: markov_model which estimates a k-order Markov model and heuristic_model which estimates three heuristic models (first-touch, last-touch and linear-touch) from customer journey data.
Davide Altomare, David Loris (2015). Markov Model for the Online Multi-Channel Attribution Problem.
Eva Anderl, Ingo Becker, Florian v. Wangenheim, Jan H. Schumann. Mapping the Customer Journey (2014). A Graph-Based Framework for Online Attribution Modeling.