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combine_mta_mmm

Python

#Load library and data

import pandas as pd

from ChannelAttributionPro import *

import urllib.request

Data = pd.read_csv("https://channelattribution.io/csv/Data.csv",sep=",")

password="yourpassword"

#perform multi-touch attribution

res=markov_model(Data, var_path="path", var_conv="total_conversions",
var_null="total_null", flg_write_paths=1,password=password)
mta_path_attribution=res['attribution']
mta_path_attribution=mta_path_attribution[["path_id","path","channel","total_conversions"]]
mta_path_attribution.columns=["path_id","path","channel","attribution"]
print(mta_path_attribution)

#load media mix model attribution

mmm_attribution=pd.DataFrame(
{"channel":["alpha","eta","iota","beta","theta","lambda","epsilon","omega"],
"attribution":[4826.18,2757.64,2574.60,1454.74,913.53,418.50,407.34,800.00]})
print(mmm_attribution)

#combine MTA and MMM without prior weights

final_path_attribution=combine_mta_mmm(mta_path_attribution, mmm_attribution, password=password)
print(final_path_attribution)

#combine MTA and MMM with a prior weight on channel alpha

prior_weights_mta=pd.DataFrame({"channel":["alpha"],"mta_weight":[0.2]})
#prior_weights_mta is a data.frame of subjective prior weights.
#Each weight is a real number between 0 and 1 which indicates, for the channel considered,
#how much weight will receive MTA in the final attribution calculation.
#Hence (1-MTA weight) is the weight of MMM for the channel considered.
#Since we know that channel alpha had some tracking problems than we decided
#to give it an MTA weight equal to $0.2$
#which implicates that MMM will receive a weight of 0.8 and thus MMM will be more
#important for that channel.
#The channels with no prior weight specified will receive a default of $0.5$ which means that
#MTA and MMM will be equally weighted in the attribution process.

final_path_attribution=combine_mta_mmm(mta_path_attribution, mmm_attribution,
prior_weights_mta=prior_weights_mta, password=password)
print(final_path_attribution)

R

#Load library and data

library(ChannelAttributionPro)

data(PathData)

password="yourpassword"

#perform multi-touch attribution

res=markov_model(Data, var_path="path", var_conv="total_conversions",var_null="total_null",
flg_write_paths=1,password=password)
mta_path_attribution=res$attribution
mta_path_attribution=mta_path_attribution[,c("path_id","path","channel","total_conversions")]
colnames(mta_path_attribution)=c("path_id","path","channel","attribution")
print(mta_path_attribution,max=100)

#load media mix model attribution

mmm_attribution=data.frame(channel=c("alpha","eta","iota","beta","theta","lambda","epsilon","omega"),
attribution=c(4826.18,2757.64,2574.60,1454.74,913.53,418.50,407.34,800.00))
print(mmm_attribution)

#combine MTA and MMM without prior weights

final_path_attribution=combine_mta_mmm(mta_path_attribution, mmm_attribution, password=password)
print(final_path_attribution,max=100)

#combine MTA and MMM with a prior weight on channel alpha

prior_weights_mta=data.frame(channel=c("alpha"),mta_weight=c(0.2))
#prior_weights_mta is a data.frame of subjective prior weights.
#Each weight is a real number between 0 and 1 which indicates,
#for the channel considered, how much weight will receive MTA in the final attribution calculation.
#Hence (1-MTA weight) is the weight of MMM for the channel considered. Since we know that
#channel alpha had some tracking problems than we decided to give it an MTA weight equal to $0.2$
#which implicates that MMM will receive a weight of 0.8 and thus MMM
#will be more important for that channel.
#The channels with no prior weight specified will receive a default of $0.5$ which means that
#MTA and MMM will be equally weighted in the attribution process.

final_path_attribution=combine_mta_mmm(mta_path_attribution, mmm_attribution,
prior_weights_mta=prior_weights_mta, password=password)
print(final_path_attribution,max=100)