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Version: 3.15

budget-allocation

R

### You will learn how to:

### 1. Allocate budget with last-touch model
### 2. Allocate budget with Markov model
### 3. Allocate budget with UAM

### Import libraries

library(ChannelAttributionPro)
options(max.print=100)

### Set your token

token="yourtoken"

### Load data

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

### Set the total budget you want to allocate

total_budget_new=100000

### Set the percentage of the budget you want to reallocate from your previous allocation
#The parameter can be set in the range (0,1]. Setting it to 0.1 means that you will reallocate the 10% of your previous allocation. Drastic changes in your previous allocation are not suggested because the allocation algorithm is based on ROI and thus it is a local optimization algorithm. We suggest to reallocate a small percentage each time.


perc_reall=0.1

## 1. Allocate budget with last-touch model

#Perform last touch attributiuon

res_attr=heuristic_models(Data=Data, var_path="path", var_conv="total_conversions", var_value="total_conversion_value")

res_attr=res_attr[,c('path_id','channel','channel_position','last_touch_conversions','last_touch_value')]
colnames(res_attr)=c('path_id','channel','channel_position','total_conversions','total_conversion_value')

#Allocate budget when Costs are available

tab_costs=data.frame('channel'=c('alpha','iota','eta','beta','theta','lambda','epsilon','zeta','kappa','gamma','mi','delta'),
'value'=c(41111.43,10387.21,23816.66,6743.46,1650.4,523.52,709.94,288.77,269.46,153.57,0.61,0.43))

res=budget_allocation(tab_attribution=res_attr,tab_costs=tab_costs,tab_index=NULL,total_budget_new=total_budget_new,perc_reall=perc_reall,min_perc_budget=0.01)
print("Budget Allocation when Costs are available")
print(res)

#Allocate budget when Cost are not available

res=budget_allocation(tab_attribution=res_attr,tab_costs=NULL,tab_index=NULL,total_budget_new=total_budget_new,perc_reall=perc_reall,min_perc_budget=0.01)
print("Budget Allocation when Costs are not available")
print(res)

#Allocate budget when Conversion Value is not available


res_attr=subset(res_attr, select = -total_conversion_value)

res=budget_allocation(tab_attribution=res_attr,tab_costs=NULL,tab_index=NULL,total_budget_new=total_budget_new,perc_reall=perc_reall,min_perc_budget=0.01)
print("Budget Allocation when Conversion Value is not available")
print(res)

## 2. Allocate budget with Markov model

#Perform attribution with Markov Model

res_attr=markov_model(Data=Data, var_path="path", var_conv="total_conversions", var_value="total_conversion_value", var_null="total_null")

tab_attribution=res_attr$attribution

tab_index=res_attr$parameters
colnames(tab_index)=c('channel','value')

#Allocate budget when Costs are available

tab_costs=data.frame('channel'=c('alpha','iota','eta','beta','theta','lambda','epsilon','zeta','kappa','gamma','mi','delta'),
'value'=c(41111.43,10387.21,23816.66,6743.46,1650.4,523.52,709.94,288.77,269.46,153.57,0.61,0.43))

res=budget_allocation(tab_attribution=tab_attribution,tab_costs=tab_costs,tab_index=tab_index,total_budget_new=total_budget_new,perc_reall=perc_reall,min_perc_budget=0.01)

print("Budget Allocation when Costs are available")
print(res)

#Allocate budget when Costs are not available

res=budget_allocation(tab_attribution=tab_attribution,tab_costs=NULL,tab_index=NULL,total_budget_new=total_budget_new,perc_reall=perc_reall,min_perc_budget=0.01)

print("Budget Allocation when Costs are not available")
print(res)

#Allocate budget when Conversion Value is not available

tab_attribution=subset(tab_attribution, select = -total_conversion_value)

res=budget_allocation(tab_attribution=tab_attribution,tab_costs=NULL,tab_index=NULL,total_budget_new=total_budget_new,perc_reall=perc_reall,min_perc_budget=0.01)

print("Allocate budget when Conversion Value is not available")
print(res)

## 3. Allocate budget with UAM

#Load data

df_paths = read.csv("https://app.channelattribution.io/data/data_paths.csv",sep=";")
df_aggr = read.csv("https://app.channelattribution.io/data/data_aggregated_w_value.csv",sep=";")
df_ctr = read.csv("https://app.channelattribution.io/data/data_ctr.csv",sep=";")

#Perform attribution with UAM

channels = setdiff(names(df_aggr), c('timestamp_from','timestamp_to','conversions','value'))

res_attr=uam(df_aggr=df_aggr[,c(c('timestamp_from','timestamp_to','conversions'),channels)],df_ctr=df_ctr,df_paths=NULL,channel_conv_name="((CONV))",order=1,nsim_start=1e5,max_step=NULL,ncore=1,nfold=10,seed=1234567,conv_par=0.05,rate_step_sim=1.5,verbose=1)

res_attr = reshape(res_attr$attribution,
varying = list(names(res_attr$attribution)[!(names(res_attr$attribution) %in% c("timestamp_from", "timestamp_to", "conversions"))]),
v.names = "attribution",
timevar = "channel",
times = names(res_attr$attribution)[!(names(res_attr$attribution) %in% c("timestamp_from", "timestamp_to", "conversions"))],
idvar = c("timestamp_from", "timestamp_to", "conversions"),
direction = "long")

res_attr = merge(res_attr, df_aggr[c("timestamp_from", "timestamp_to", "value")],
by = c("timestamp_from", "timestamp_to"),
all = FALSE)

res_attr$total_conversion_value = res_attr$value * res_attr$attribution / res_attr$conversions

names(res_attr)[names(res_attr) == "attribution"] = "total_conversions"
res_attr = res_attr[c("timestamp_from", "timestamp_to", "conversions", "value", "channel", "total_conversions", "total_conversion_value")]

#Allocate budget when Costs are available

tab_costs = data.frame(channel = c('A', 'B', 'C', 'D', 'E', 'F'),
value = c(1345, 1456, 987, 1121, 879, 124))

res=budget_allocation(tab_attribution=res_attr,tab_costs=tab_costs,tab_index=NULL,total_budget_new=total_budget_new,perc_reall=perc_reall,min_perc_budget=0.01)

print("Budget Allocation when Costs are available")
print(res)

#Allocate budget when Costs are not available

res=budget_allocation(tab_attribution=res_attr,tab_costs=NULL,tab_index=NULL,total_budget_new=total_budget_new,perc_reall=perc_reall,min_perc_budget=0.01)

print("Budget Allocation when Costs are not available")
print(res)