markov_model

0th

Percentile

Markov model for the online attribution problem

Estimate a k-order Markov model from customer journey data.

Keywords
Markov model, markov graph
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

data.frame containing paths and conversions.

var_path

name of the the column containing paths.

var_conv

name of the column containing total conversions.

var_value

name of the column containing total conversion value.

var_null

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

order

order of Markov Model.

nsim

total simulations from transition matrix.

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.

seed

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.

Aliases
  • markov_model
Examples
# NOT RUN {
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)



# }
Documentation reproduced from package ChannelAttribution, version 1.13, License: GPL (>= 2)

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