-
Notifications
You must be signed in to change notification settings - Fork 0
/
dashboard.py
139 lines (118 loc) · 5.75 KB
/
dashboard.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
import streamlit as st
from pydantic import BaseModel
from typing import List
from AdvertisingModel import OptimizationInput, optimize_budget_func
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import plotly.express as px
products = ["Clothing","Beauty", "Home Decor"]
num_channels = 3
num_products = 3
# Helper function
def str_to_2darray(s):
rows = s.strip().split('\n')
return [list(map(float, row[1:-1].split(', '))) for row in rows]
# Streamlit UI
st.title('Marketing Budget Allocation Optimization')
# Create input form in the sidebar using Pydantic model
with st.sidebar:
st.header('Input Parameters')
conversion_rates = st.text_area('Conversion Rates', value="[0.04, 0.01, 0.015]\n[0, 0.03, 0.015]\n[0.01, 0, 0.015]")
avg_ticket_size = st.text_area('Average Ticket Size', value="[25, 55, 55]\n[0, 60, 70]\n[40, 0, 80]")
cost_per_click = st.text_area('Cost per Click', value="[1.1, 1.6, 1.9]")
total_budget = st.slider('Total Budget', 5000, 20000, 10000)
min_budget_percent = st.slider('Minimum Budget Percentage per Channel', 5, 30, 15) / 100
min_transactions_per_product = [st.number_input(f"Minimum Transactions for {prod} Product:",
min_value = 0,
max_value = 100,
step= 1,
key = "min_trx_for_"+str(prod))
for prod in products]
min_clicks = st.slider('Minimum Clicks', 5000, 15000, 7000)
max_cost_percent = st.slider('Maximum Cost Percentage', 50, 90, 80) / 100
# Parse input
if st.sidebar.button('Optimize'):
# Parse input data
conversion_rates = str_to_2darray(conversion_rates)
avg_ticket_size = str_to_2darray(avg_ticket_size)
cost_per_click = eval(cost_per_click)
input_data = OptimizationInput(
conversion_rates=conversion_rates,
avg_ticket_size=avg_ticket_size,
cost_per_click=cost_per_click,
total_budget=total_budget,
min_budget_percent=min_budget_percent,
min_transactions_per_product=min_transactions_per_product,
min_clicks=min_clicks,
max_cost_percent=max_cost_percent
)
# Run optimization
allocations, revenue = optimize_budget_func(input_data)
# Process outcome
if allocations is not None:
results = {
'Channel': [],
'ROAS': [],
'Budget': [],
'Clicks': [],
'Transactions': [],
'Revenue': [],
'Budget %': [],
'Clicks %': [],
'Transactions %': [],
'Revenue %': []}
total_budget_allocated = sum(allocations)
total_clicks = sum(allocations[i] / cost_per_click[i] for i in range(num_channels))
total_transactions = sum(conversion_rates[p][i] * allocations[i] / cost_per_click[i] for p in range(num_products) for i in range(num_channels))
total_revenue = sum(avg_ticket_size[p][i] * conversion_rates[p][i] * allocations[i] / cost_per_click[i] for p in range(num_products) for i in range(num_channels))
for i in range(num_channels):
channel_budget = allocations[i]
clicks = channel_budget / cost_per_click[i]
transactions = sum(conversion_rates[p][i] * clicks for p in range(num_products))
revenue = sum(avg_ticket_size[p][i] * conversion_rates[p][i] * channel_budget / cost_per_click[i] for p in range(num_products))
roas = revenue/channel_budget
budget_percent = (channel_budget / total_budget_allocated) * 100
clicks_percent = (clicks / total_clicks) * 100
transactions_percent = (transactions / total_transactions) * 100
revenue_percent = (revenue / total_revenue) * 100
results['Channel'].append(f'Channel {i + 1}')
results['ROAS'].append(roas)
results['Budget'].append(channel_budget)
results['Clicks'].append(clicks)
results['Transactions'].append(transactions)
results['Revenue'].append(revenue)
results['Budget %'].append(budget_percent)
results['Clicks %'].append(clicks_percent)
results['Transactions %'].append(transactions_percent)
results['Revenue %'].append(revenue_percent)
summary_df = pd.DataFrame(results)
formatted_df = pd.DataFrame(summary_df[['Channel','Budget','Clicks','Transactions','Revenue']]).style.format(precision=2, thousands=",", decimal=".")
# Show key metrics
metric_val = [total_revenue,total_budget_allocated,total_revenue/total_budget_allocated]
metric_name = ["Total Revenue","Total Ad Spend","ROAS"]
metric_col = st.columns(3)
for c in range(2):
metric_col[c].metric(metric_name[c], "$ {:,.2f}".format(metric_val[c]))
metric_col[2].metric(metric_name[2], "{:,.2f}".format(metric_val[2]))
# Show budget by channel
budget_col = st.columns(3)
for c in range(3):
budget_col[c].metric(f'Channel {c + 1} Budget', "$ {:,.2f}".format(allocations[c]))
st.divider()
# Show performance by channel
st.subheader("ROAS by Channel")
roas_col = st.columns(3)
for c in range(3):
roas_col[c].metric(f'Channel {c + 1}', "{:,.2f}".format(summary_df['ROAS'][c]))
st.subheader("Channel Performance")
df_plot = pd.melt(summary_df[['Channel','Budget %','Clicks %','Transactions %','Revenue %']],id_vars='Channel',var_name='Metrics', value_name='Value')
fig = px.bar(df_plot, x="Channel", color="Metrics",
y='Value',
barmode='group',
height=600
)
st.plotly_chart(fig)
st.table(formatted_df)
else:
st.write("No solution found.")