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.
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)data.frame containing customer journeys data.
column name containing paths.
column name containing total conversions.
column name containing total paths that do not lead to conversions.
column name containing total conversion value.
maximum Markov Model order considered.
number of points used for approximating roc and auc.
if TRUE, a plot with penalized auc with respect to order will be displayed.
minimum number of simulations used in computation.
maximum number of steps for a single simulated path. if NULL, it is the maximum number of steps found into Data.
if TRUE, transition probabilities between channels and removal effects will be shown.
separator between the channels.
number of threads used in computation.
how many repetitions are used to verify if convergence is reached at each iteration.
random seed. Giving this parameter the same value over different runs guarantees that results will not vary.
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.
number of simulations used at each iteration is equal to the number of simulations used at previous iteration multiplied by rate_step_sim.
if TRUE, additional information about process convergence will be shown.
An object of class data.frame with the estimated number of conversions and the estimated conversion value attributed to each channel.
# NOT RUN {
# }
# NOT RUN {
library(ChannelAttribution)
data(PathData)
auto_markov_model(Data, "path", "total_conversions", "total_null")
# }
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# }
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