auto_markov_model {ChannelAttribution}R Documentation

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=Inf, nfold=10, seed=0, conv_par=0.05, rate_step_sim=1.5, 
             verbose=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 is displayed.

nsim_start

minimum number of simulations used in computation.

max_step

maximum number of steps for a single simulated path.

out_more

if TRUE returns the transition probabilities between channels and removal effects.

sep

separator between the channels.

ncore

number of threads used in computation. Default is number of CPUs available.

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 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

show additional information about process convergence.

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 (davide.altomare@gmail.com).

Examples


## Not run: 

library(ChannelAttribution)

data(PathData) 

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


## End(Not run)


[Package ChannelAttribution version 1.16 Index]