budget-allocation

Python

### 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

from ChannelAttributionPro import *

### Set your token

token="yourtoken"

### Load data

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

### 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[['path_id','channel','channel_position','last_touch_conversions','last_touch_value']]
res_attr.columns=['path_id','channel','channel_position','total_conversions','total_conversion_value']

#Allocate budget when Costs are available

tab_costs=pd.DataFrame({'channel':['alpha','iota','eta','beta','theta','lambda','epsilon','zeta','kappa','gamma','mi','delta'],
                        'value':[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=None,total_budget_new=total_budget_new,perc_reall=perc_reall)
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=None,tab_index=None,total_budget_new=total_budget_new,perc_reall=perc_reall)
print("Budget Allocation when Costs are not available")
print(res)

#Allocate budget when Conversion Value is not available

del res_attr['total_conversion_value']

res=budget_allocation(tab_attribution=res_attr,tab_costs=None,tab_index=None,total_budget_new=total_budget_new,perc_reall=perc_reall)
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']
tab_index.columns=['channel','value']

#Allocate budget when Costs are available

tab_costs=pd.DataFrame({'channel':['alpha','iota','eta','beta','theta','lambda','epsilon','zeta','kappa','gamma','mi','delta'],
                        'value':[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)

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=None,tab_index=None,total_budget_new=total_budget_new,perc_reall=perc_reall)

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

#Allocate budget when Conversion Value is not available

del tab_attribution['total_conversion_value']

res=budget_allocation(tab_attribution=tab_attribution,tab_costs=None,tab_index=None,total_budget_new=total_budget_new,perc_reall=perc_reall)

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

## 3. Allocate Budget with UAM

#Load data

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

#Perform attribution with UAM

channels=[x for x in df_aggr.columns if x not in ['timestamp_from','timestamp_to','conversions','value']]

res_attr=uam(df_aggr=df_aggr[['timestamp_from','timestamp_to','conversions']+channels],df_ctr=df_ctr,df_paths=None,channel_conv_name="((CONV))",order=1,nsim_start=1e5,max_step=None,ncore=1,nfold=10,seed=1234567,conv_par=0.05,rate_step_sim=1.5,verbose=1)
res_attr=res_attr['attribution']

res_attr=pd.melt(res_attr, id_vars=['timestamp_from','timestamp_to','conversions'], var_name='channel', value_name='attribution')
res_attr=pd.merge(res_attr,df_aggr[['timestamp_from','timestamp_to','value']],how='inner',on=['timestamp_from','timestamp_to'])
res_attr['total_conversion_value']=res_attr['value']*res_attr['attribution']/res_attr['conversions']
res_attr=res_attr.rename(columns={'attribution':'total_conversions'})
res_attr=res_attr[['timestamp_from','timestamp_to','conversions','value','channel','total_conversions','total_conversion_value']]

tab_costs=pd.DataFrame({'channel':['A','B','C','D','E','F'],
                        'value':[1345,1456,987,1121,879,124]})

#Allocate budget when Costs are available

res=budget_allocation(tab_attribution=res_attr,tab_costs=tab_costs,tab_index=None,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=None,tab_index=None,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)