Markov Model for Online Multi-Channel Attribution

Description

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 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.

Details

Package contains functions for channel attribution in web marketing.

Author(s)

Davide Altomare, David Loris

Maintainer Davide Altomare <info@channelattribution.io>

References

ChannelAttribution Official Website: https://channelattribution.io

Eva Anderl, Ingo Becker, Florian v. Wangenheim, Jan H. Schumann: Mapping the Customer Journey, 2014, doi: 10.2139/ssrn.2343077

Customer journeys data.

Description

Example dataset.

Usage

data(PathData)

Format

Data is a data.frame with 10.000 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.

Automatic Markov Model.

Description

Estimate a Markov model from customer journey data after automatically choosing a suitable order. It requires paths that do not lead to conversion as input.

Usage

auto_markov_model(Data, var_path, var_conv, var_null, var_value=NULL, 
             max_order=10, roc_npt=100, plot=FALSE, nsim_start=1e5, 
             max_step=NULL, out_more=FALSE, sep=">", 
             ncore=1, nfold=10, seed=0, conv_par=0.05, rate_step_sim=1.5, 
             verbose=TRUE, flg_adv=TRUE)

Arguments

Value

An object of class data.frame with the estimated number of conversions and the estimated conversion value attributed to each channel.

Author(s)

Davide Altomare (info@channelattribution.io).

Examples

## Not run: 

library(ChannelAttribution)

data(PathData) 

auto_markov_model(Data, "path", "total_conversions", "total_null")


## End(Not run)

Choose order for Markov model.

Description

Find the minimum Markov Model order that gives a good representation of customers' behaviour for data considered. It requires paths that do not lead to conversion as input. Minimum order is found maximizing a penalized area under ROC curve.

Usage

choose_order(Data, var_path, var_conv, var_null, max_order=10, sep=">", 
             ncore=1, roc_npt=100, plot=TRUE, flg_adv=TRUE)

Arguments

Value

An object of class List with the estimated roc, auc and penalized auc.

Author(s)

Davide Altomare (info@channelattribution.io).

Examples

## Not run: 

library(ChannelAttribution)

data(PathData) 

res=choose_order(Data, var_path="path", var_conv="total_conversions",
                 var_null="total_null")

#plot auc and penalized auc	   
	   
plot(res$auc$order,res$auc$auc,type="l",xlab="order",ylab="pauc",main="AUC")
lines(res$auc$order,res$auc$pauc,col="red")
legend("right", legend=c("auc","penalized auc"), 
       col=c("black","red"),lty=1)


## End(Not run)

Heuristic models for the online attribution problem.

Description

Estimate theree heuristic models (first-touch, last-touch and linear) from customer journey data.

Usage

heuristic_models(Data, var_path, var_conv, var_value=NULL, sep=">", flg_adv=TRUE)

Arguments

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 (info@channelattribution.io).

Examples

## Not run: 

library(ChannelAttribution)

data(PathData) 

heuristic_models(Data,"path","total_conversions")
heuristic_models(Data,"path","total_conversions",var_value="total_conversion_value")


## End(Not run)

Markov model for the online attribution problem.

Description

Estimate a k-order Markov model from customer journey data. Differently from markov_model, this function iterates estimation until convergence is reached and enables multiprocessing.

Usage

markov_model(Data, var_path, var_conv, var_value=NULL, var_null=NULL, 
             order=1, nsim_start=1e5, max_step=NULL, out_more=FALSE, sep=">", 
             ncore=1, nfold=10, seed=0, conv_par=0.05, rate_step_sim=1.5, 
             verbose=TRUE, flg_adv=TRUE)

Arguments

Value

An object of class data.frame with the estimated number of conversions and the estimated conversion value attributed to each channel.

Author(s)

Davide Altomare (info@channelattribution.io).

Examples

## Not run: 

library(ChannelAttribution)

data(PathData) 

#Estimate a Makov model using total conversions
markov_model(Data, var_path="path", "total_conversions")

#Estimate a Makov model using total conversions and revenues
markov_model(Data, "path", "total_conversions",
var_value="total_conversion_value")

#Estimate a Makov model using total conversions, revenues and paths that do not lead to conversions
markov_model(Data, "path", "total_conversions",
var_value="total_conversion_value", var_null="total_null")

#Estimate a Makov model returning transition matrix and removal effects
markov_model(Data, "path", "total_conversions",
var_value="total_conversion_value", var_null="total_null", out_more=TRUE)

#Estimate a Markov model using 4 threads
markov_model(Data, "path", "total_conversions",
var_value="total_conversion_value", ncore=4)


## End(Not run)

Transition matrix.

Description

Estimate a k-order transition matrix from customer journey data.

Usage

transition_matrix(Data, var_path, var_conv, var_null, order=1, sep=">", 
                  flg_equal=TRUE, flg_adv=TRUE)

Arguments

Value

An object of class List containing a dataframe with channel names and a dataframe with the estimated transition matrix.

Author(s)

Davide Altomare (info@channelattribution.io).

Examples

## Not run: 

library(ChannelAttribution)

data(PathData) 

transition_matrix(Data, var_path="path", var_conv="total_conversions",
                  var_null="total_null", order=1, sep=">", flg_equal=TRUE)

transition_matrix(Data, var_path="path", var_conv="total_conversions",
                  var_null="total_null", order=3, sep=">", flg_equal=TRUE)


## End(Not run)