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 *
import pandas as pd

### 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 aggregated data with conversion value

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

# Perform attribution with UAM

target = "conversions"

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

measure_map = {
    "A": "clicks",
    "B": "impressions",
    "C": "impressions",
    "D": "clicks",
    "E": "clicks",
    "F": "clicks",
}

D_variables = pd.DataFrame({
    "variable": channels,
    "channel": channels,
    "measure": [measure_map.get(ch, "clicks") for ch in channels],
})

uam_res = uam(
    df_aggr=df_aggr[["timestamp_from", "timestamp_to", target] + channels],
    D_variables=D_variables,
    target=target,
    df_paths=None,
    baseline_model="reward_model",  # default; use "auto_mmm" to use the MMM baseline
    max_p=12,
    nsim=1000,
    seed=1234567,
    verbose=1,
    order=1,
    sep=">",
    ncore=1,
    password=token,
    return_diagnostics=False,
)

# Some ChannelAttributionPro versions return the attribution DataFrame directly,
# while others return a dict/list containing the attribution under "attribution".
res_attr = uam_res["attribution"] if isinstance(uam_res, dict) else uam_res

res_attr = pd.melt(
    res_attr,
    id_vars=["timestamp_from", "timestamp_to", target],
    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[target]
)
res_attr = res_attr.rename(columns={"attribution": "total_conversions"})
res_attr = res_attr[
    [
        "timestamp_from",
        "timestamp_to",
        target,
        "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)