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1 Type Package Package ChannelAttribution February 5, 2018 Title Markov Model for the Online Multi-Channel Attribution Problem Version 1.12 Date Author Davide Altomare, David Loris Maintainer Davide Altomare 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++. License GPL (>= 2) URL markov-model-for-the-multichannel-attribution-problem marketing-channel-attribution-markov-models-r/ Imports Rcpp (>= ) LinkingTo Rcpp, RcppArmadillo NeedsCompilation yes Repository CRAN Date/Publication :06:33 UTC R topics documented: ChannelAttribution-package Data heuristic_models markov_model
2 2 ChannelAttribution-package Index 6 ChannelAttribution-package Markov Model for the Online Multi-Channel Attribution Problem Details 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 multichannel 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: 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. Author(s) Davide Altomare, David Loris Maintainer Davide Altomare <davide.altomare@gmail.com> References 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.
3 Data 3 Data Customer journeys data Usage Format Customer path data with conversions and conversion value. data(pathdata) Data is a data.frame with rows and 4 columns: "path" containing customer paths, "total_conversions" containing total number of conversions, "total_conversion_value" containing total conversion value and "total_null" containing total number of paths that do not lead to conversion. heuristic_models Heuristic models for the online attribution problem Estimate theree heuristic models (first-touch, last-touch, linear) from customer journey data. Usage heuristic_models(data, var_path, var_conv, var_value=null, sep=">") Arguments Data var_path var_conv var_value sep data.frame containing paths and conversions. name of the the column containing paths. name of the column containing total conversions. name of the column containing total conversion value. separator between the channels. Value An object of class data.frame with the estimated number of conversions and the estimated conversion value attributed to each channel for each model. Author(s) Davide Altomare (<davide.altomare@gmail.com>).
4 4 markov_model Examples data(pathdata) #uncomment the following lines to run the examples heuristic_models(data,"path","total_conversions") #heuristic_models(data,"path","total_conversions",var_value="total_conversion_value") markov_model Markov model for the online attribution problem Estimate a k-order Markov model from customer journey data. Usage markov_model(data, var_path, var_conv, var_value=null, var_null=null, order=1, nsim=null, max_step=null, out_more=false, sep=">", seed=null) Arguments Data var_path var_conv var_value var_null order nsim max_step out_more sep seed data.frame containing paths and conversions. name of the the column containing paths. name of the column containing total conversions. name of the column containing total conversion value. name of the column containing total paths that do not lead to conversions. order of Markov Model. total simulations from transition matrix. maximum number of steps for a single simulated path. if TRUE returns the transition probabilities between channels and removal effects. separator between the channels. random seed. giving to this parameter the same value over different runs guarantee that results will not vary. Value An object of class data.frame with the estimated number of conversions and the estimated conversion value attributed to each channel.
5 markov_model 5 Author(s) Davide Altomare (<davide.altomare@gmail.com>). Examples data(pathdata) #uncomment the following lines to run the examples markov_model(data, "path", "total_conversions") #markov_model(data, "path", "total_conversions", # var_value="total_conversion_value") #markov_model(data,"path","total_conversions", # var_value="total_conversion_value", var_null="total_null") #markov_model(data, "path", "total_conversions", # var_value="total_conversion_value", var_null="total_null", out_more=true)
6 Index Topic channel attribution Topic channel marketing Topic customer journey dataset Topic customer journey Topic customer path data Topic dataset Topic first touch Topic last touch Topic linear touch Topic marketing attribution Topic markov graph markov_model, 4 Topic markov model markov_model, 4 Topic multi channel funnel Topic multi channel marketing Topic online attribution Topic web marketing Topic web statistics markov_model, 4 ChannelAttribution (ChannelAttribution-package), 2 6
The Multi-touch marketing channel attribution problem - Can R help solve it? Dr Abigail Lebrecht
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