Skip to main content

Hidden Touch Attribution model

Import libraries​

library(ChannelAttributionPro)

Set the password​

password="mypassword"

Load data​

Data = read.csv("https://app.channelattribution.net/data/Data_ata.csv",sep=",")
print(Data)

Define list of channels you are interested in performing attribution​

channels=c("facebook_impressions","tv_impressions","google_adv_clicks",
           "direct_searches","youtube_impressions","linkedin_impressions")

Define the global conversion rate for the use case or set it to None if not known​

glob_conv_rate=NULL

Define the conversion rates, one for each channel if known, or set it to None otherwise​

L_conv_rate=NULL

Define list of weights (between 0 and 1), one for each channel (suggestion: use weight=1 for clicks)​

L_weight=list()
L_weight["facebook_impressions"]=0.01
L_weight["tv_impressions"]=0.01
L_weight["google_adv_clicks"]=1
L_weight["direct_searches"]=1
L_weight["youtube_impressions"]=0.01
L_weight["linkedin_impressions"]=0.01

Define the how many units of time are long the customer journeys on average for the use case considered​

mean_cj_length=1

Define the adstock rates (between 0 and 1), one for each channels​

L_ads_rate=list()
L_ads_rate["facebook_impressions"]=0
L_ads_rate["tv_impressions"]=0.40
L_ads_rate["google_adv_clicks"]=0
L_ads_rate["direct_searches"]=0
L_ads_rate["youtube_impressions"]=0
L_ads_rate["linkedin_impressions"]=0

Run the model​

we set nsim=1 for speed reasons but at least nsim=10 is suggested​

res=hta_model(Data, var_time="week", var_conv="conversions", channels=channels, var_value=NULL, 
              glob_conv_rate=glob_conv_rate, L_conv_rate=L_conv_rate, L_weight=L_weight, 
              mean_cj_length=mean_cj_length, L_ads_rate=L_ads_rate, verbose=TRUE, nsim=1, 
              password=password)
print(res)