markov_model: Markov model for the online attribution problem.

In ChannelAttribution: Markov Model for Online Multi-Channel Attribution

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 ([e-mail]).

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)

ChannelAttribution documentation built on May 31, 2023, 9:26 p.m.