Add customer LTV calculator code

.gitignore

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+.vscode/launch.json

app.py

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+from datetime import datetime
+
+import pandas as pd
+import streamlit as st
+
+
+def months_between_dates(start_date, end_date):
+    return (end_date.year - start_date.year) * 12 + (end_date.month - start_date.month)
+
+def calculate_lifespan(row):
+    if pd.notna(row["Churned"]):
+        return (row["Churned"] - row["Date"]).days
+    else:
+        return (datetime.now() - row["Date"]).days
+    
+def date_filtered_df(df, start_date, end_date):
+    return df[(df['Date'] >= start_date) & (df['Date'] <= end_date)]
+
+def average_customer_lifespan_calculation(
+        df,
+        start_date,
+        end_date,
+    ) -> float:
+    df.sort_values(by=['Customer', 'Date'], inplace=True)
+    mask = (df['Date'] >= start_date) & (df['Date'] <= end_date)
+    df = df.loc[mask]
+    df["Lifespan"] = df.apply(calculate_lifespan, axis=1)
+    df = df.dropna(subset=["Value"])
+    # Calculate average customer lifespan
+    return round(df["Lifespan"].mean(), 0)
+
+
+def icon_select(value):
+    if value >= 7:
+        return '🚀'
+    elif value >= 5:
+        return '🔥'
+    elif value > 3.5:
+        return '💤'
+    else:
+        return '💀'
+
+@st.cache_data(ttl="5m")
+def get_data(file_link):
+    
+    if 'dl=0' in file_link:
+        file_link = file_link.replace('dl=0', 'dl=1')
+    all_data_df = pd.read_excel(file_link)
+    return all_data_df
+
+
+st.title('Customer LTV Calculator')
+
+file_link = st.text_input(
+    'Link to data file',
+)
+
+if not file_link:
+    st.stop()
+
+all_data_df = get_data(file_link)
+
+col1, col2, col3 = st.columns(3)
+with col1:
+    start_date = st.date_input(
+        'Start Date:',
+        value=pd.to_datetime('2022-09-01'),
+        max_value=pd.to_datetime(datetime.now().date()),
+        format='DD-MM-YYYY',
+    )
+with col2:
+    end_date = st.date_input(
+        'End Date:',
+        value=pd.to_datetime(datetime.now().date()),
+        max_value=pd.to_datetime(datetime.now().date()),
+        format='DD-MM-YYYY',
+    )
+with col3:
+    start_datetime = pd.to_datetime(start_date)
+    end_datetime = pd.to_datetime(end_date)
+    number_of_months = months_between_dates(start_datetime, end_datetime)
+    st.write(str(number_of_months), 'months')
+
+calculated_acl = average_customer_lifespan_calculation(
+    all_data_df,
+    start_datetime,
+    end_datetime,
+)
+
+if start_date < end_date:
+    # Filter the dataframe based on the selected date range
+    mask = (all_data_df['Date'] >= start_datetime) & (all_data_df['Date'] <= end_datetime)
+    all_data_df = all_data_df.loc[mask]
+else:
+    st.error('Error: End date must be after the start date.')
+
+all_data_date_filtered = date_filtered_df(all_data_df, start_datetime, end_datetime)
+average_order_size = all_data_date_filtered['Value'].mean()
+formatted_num = "£{:,.2f}".format(average_order_size)
+st.write('Average order size (AOS):', str(formatted_num))
+
+purchase_frequency = all_data_date_filtered.groupby('Customer')['Date'].nunique()
+average_purchase_frequency_rate = purchase_frequency.mean()/number_of_months
+st.write('Average purchase frequency rate (APFR) per customer per month:', str(round(average_purchase_frequency_rate, 2)))
+customer_value = average_order_size * average_purchase_frequency_rate
+customer_value_formatted = "£{:,.2f}".format(customer_value)
+st.write('Customer Value (AOS x APFR):', customer_value_formatted)
+
+average_customer_lifespan = 12
+average_customer_lifespan = st.slider(
+    f'Average Customer Lifespan (months) - calculated value {calculated_acl} days',
+    min_value=1,
+    max_value=50,
+    step=1,
+    value=12,
+)
+customer_lifetime_vale = average_customer_lifespan * customer_value
+customer_lifetime_vale_formatted = "£{:,.2f}".format(customer_lifetime_vale)
+st.write('Customer Lifetime Value (CLV):', customer_lifetime_vale_formatted)
+
+acquisition_cost = 50
+acquisition_cost = st.slider('Cost of acquisition', min_value=0, max_value=1000, step=10, value=50)
+clv_cac_ratio = customer_lifetime_vale/acquisition_cost
+
+all_data_df['year_month'] = all_data_df['Date'].dt.to_period('M')
+all_data_df = all_data_df.sort_values(by='Date')
+
+st.write(
+    'CLV to CAC ratio:',
+    "{:,.2f}".format(clv_cac_ratio),
+    ': 1',
+    icon_select(clv_cac_ratio),
+)

requirements.txt

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+pandas
+openpyxl