Package 'ChannelAttribution'

Title: 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. 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++.
Authors: Davide Altomare [cre, aut], David Loris [aut]
Maintainer: Davide Altomare <[email protected]>
License: GPL-3 | file LICENSE
Version: 2.0.7
Built: 2024-11-20 06:23:26 UTC
Source: CRAN

Help Index


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: ChannelAttribution
Type: Package
Version: 2.0.7
Date: 2023-05-17
License: GPL (>= 2)

Package contains functions for channel attribution in web marketing.

Author(s)

Davide Altomare, David Loris

Maintainer Davide Altomare <[email protected]>

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


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

Data

data.frame containing customer journeys data.

var_path

column name containing paths.

var_conv

column name containing total conversions.

var_null

column name containing total paths that do not lead to conversions.

var_value

column name containing total conversion value.

max_order

maximum Markov Model order considered.

roc_npt

number of points used for approximating roc and auc.

plot

if TRUE, a plot with penalized auc with respect to order will be displayed.

nsim_start

minimum number of simulations used in computation.

max_step

maximum number of steps for a single simulated path. if NULL, it is the maximum number of steps found into Data.

out_more

if TRUE, transition probabilities between channels and removal effects will be shown.

sep

separator between the channels.

ncore

number of threads used in computation.

nfold

how many repetitions are used to verify if convergence is reached at each iteration.

seed

random seed. Giving this parameter the same value over different runs guarantees that results will not vary.

conv_par

convergence parameter for the algorithm. The estimation process ends when the percentage of variation of the results over different repetitions is less than convergence parameter.

rate_step_sim

number of simulations used at each iteration is equal to the number of simulations used at previous iteration multiplied by rate_step_sim.

verbose

if TRUE, additional information about process convergence will be shown.

flg_adv

if TRUE, ChannelAttribution Pro banner is printed.

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 ([email protected]).

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

Data

data.frame containing customer journeys.

var_path

column name of Data containing paths.

var_conv

column name of Data containing total conversions.

var_null

column name of Data containing total paths that do not lead to conversion.

max_order

maximum Markov Model order considered.

sep

separator between channels.

ncore

number of threads used in computation.

roc_npt

number of points used for approximating roc and auc.

plot

if TRUE, a plot with penalized auc with respect to order will be displayed.

flg_adv

if TRUE, ChannelAttribution Pro banner is printed.

Value

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

Author(s)

Davide Altomare ([email protected]).

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)

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.


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

Data

data.frame containing paths and conversions.

var_path

column name containing paths.

var_conv

column name containing total conversions.

var_value

column name containing total conversion value.

sep

separator between the channels.

flg_adv

if TRUE, ChannelAttribution Pro banner is printed.

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 ([email protected]).

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

Data

data.frame containing customer journeys data.

var_path

column name containing paths.

var_conv

column name containing total conversions.

var_value

column name containing total conversion value.

var_null

column name containing total paths that do not lead to conversions.

order

Markov Model order.

nsim_start

minimum number of simulations used in computation.

max_step

maximum number of steps for a single simulated path. if NULL, it is the maximum number of steps found into Data.

out_more

if TRUE, transition probabilities between channels and removal effects will be returned.

sep

separator between the channels.

ncore

number of threads used in computation.

nfold

how many repetitions are used to verify if convergence has been reached at each iteration.

seed

random seed. Giving this parameter the same value over different runs guarantees that results will not vary.

conv_par

convergence parameter for the algorithm. The estimation process ends when the percentage of variation of the results over different repetions is less than convergence parameter.

rate_step_sim

number of simulations used at each iteration is equal to the number of simulations used at previous iteration multiplied by rate_step_sim.

verbose

if TRUE, additional information about process convergence will be shown.

flg_adv

if TRUE, ChannelAttribution Pro banner is printed.

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 ([email protected]).

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

Data

data.frame containing customer journeys data.

var_path

column name containing paths.

var_conv

column name containing total conversions.

var_null

column name containing paths that do not lead to conversions.

order

Markov Model order.

sep

separator between the channels.

flg_equal

if TRUE, transitions from a channel to itself will be considered.

flg_adv

if TRUE, ChannelAttribution Pro banner is printed.

Value

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

Author(s)

Davide Altomare ([email protected]).

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)