Oppiskele Calculating Unit Economics in Python

To effectively analyze your business, you need to calculate and interpret key unit economics metrics: Customer Lifetime Value (LTV), Customer Acquisition Cost (CAC), and Margin. These metrics help you understand how much revenue each customer brings over their relationship with your business, how much it costs to acquire them, and how much profit you retain after costs. Recall the typical formulas:

\text{LTV} = \text{Average Revenue per User (ARPU)} \times \text{Gross Margin (\%)} \times \text{Average Customer Lifespan} \\ \ \\ \text{CAC} = \frac{\text{Total Marketing and Sales Costs}}{\text{Number of New Customers Acquired}} \\ \ \\ \text{Margin} = \frac{\text{Revenue} - \text{Cost of Goods Sold}}{\text{Revenue}}

Assume your data is structured in a pandas DataFrame with columns such as customer_id, acquisition_channel, segment, revenue, cost_of_goods_sold, marketing_cost, acquisition_date, and churn_date. This setup allows you to aggregate and segment unit economics by customer characteristics.


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import pandas as pd

# Example customer data
data = {
    'customer_id': [1, 2, 3, 4, 5],
    'revenue': [500, 300, 400, 600, 200],
    'cost_of_goods_sold': [300, 180, 220, 350, 120],
    'marketing_cost': [50, 40, 60, 70, 30],
    'acquisition_date': pd.to_datetime(['2021-01-01', '2021-02-01', '2021-01-15', '2021-03-01', '2021-02-20']),
    'churn_date': pd.to_datetime(['2022-01-01', '2021-08-01', '2022-01-15', '2022-03-01', '2021-12-20'])
}
df = pd.DataFrame(data)

# Calculate average lifespan in years
df['lifespan_years'] = (df['churn_date'] - df['acquisition_date']).dt.days / 365

# Calculate ARPU (average revenue per user)
arpu = df['revenue'].mean()

# Calculate gross margin percentage
df['gross_margin_pct'] = (df['revenue'] - df['cost_of_goods_sold']) / df['revenue']
avg_gross_margin_pct = df['gross_margin_pct'].mean()

# Calculate average customer lifespan
avg_lifespan = df['lifespan_years'].mean()

# Calculate LTV
ltv = arpu * avg_gross_margin_pct * avg_lifespan

# Calculate CAC
total_marketing_cost = df['marketing_cost'].sum()
num_customers = df['customer_id'].nunique()
cac = total_marketing_cost / num_customers

# Calculate average margin
df['margin'] = (df['revenue'] - df['cost_of_goods_sold']) / df['revenue']
avg_margin = df['margin'].mean()

print(f"Average LTV: {ltv:.2f}")
print(f"Average CAC: {cac:.2f}")
print(f"Average Margin: {avg_margin:.2%}")


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# Segmenting unit economics by acquisition channel or customer segment
# Suppose you add 'acquisition_channel' and 'segment' columns
df['acquisition_channel'] = ['Organic', 'Paid', 'Organic', 'Referral', 'Paid']
df['segment'] = ['SMB', 'Enterprise', 'SMB', 'Enterprise', 'SMB']

# Group by acquisition channel and calculate metrics
channel_group = df.groupby('acquisition_channel').agg(
    arpu=('revenue', 'mean'),
    gross_margin_pct=('gross_margin_pct', 'mean'),
    avg_lifespan=('lifespan_years', 'mean'),
    cac=('marketing_cost', 'mean'),
    avg_margin=('margin', 'mean')
)

channel_group['ltv'] = channel_group['arpu'] * channel_group['gross_margin_pct'] * channel_group['avg_lifespan']

print(channel_group[['arpu', 'gross_margin_pct', 'avg_lifespan', 'ltv', 'cac', 'avg_margin']])

When you segment unit economics by acquisition channel or customer segment, you can spot significant differences in profitability and efficiency. For instance, if customers acquired through organic channels have higher LTV and lower CAC compared to paid channels, this suggests organic growth is more cost-effective. Conversely, if a particular segment has a lower margin, it might indicate higher costs or less pricing power in that group. These insights allow you to prioritize channels and segments that drive sustainable growth, optimize marketing spend, and refine your targeting strategies. Always interpret unit economics in context—variations may stem from different customer behaviors, product mixes, or marketing approaches.

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Osio 3. Luku 2

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Suggested prompts:

Can you explain how to interpret the output of the segmented unit economics?

How can I use these metrics to make business decisions?

What should I do if I notice large differences between channels or segments?

Pyyhkäise näyttääksesi valikon

\text{LTV} = \text{Average Revenue per User (ARPU)} \times \text{Gross Margin (\%)} \times \text{Average Customer Lifespan} \\ \ \\ \text{CAC} = \frac{\text{Total Marketing and Sales Costs}}{\text{Number of New Customers Acquired}} \\ \ \\ \text{Margin} = \frac{\text{Revenue} - \text{Cost of Goods Sold}}{\text{Revenue}}


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import pandas as pd

# Example customer data
data = {
    'customer_id': [1, 2, 3, 4, 5],
    'revenue': [500, 300, 400, 600, 200],
    'cost_of_goods_sold': [300, 180, 220, 350, 120],
    'marketing_cost': [50, 40, 60, 70, 30],
    'acquisition_date': pd.to_datetime(['2021-01-01', '2021-02-01', '2021-01-15', '2021-03-01', '2021-02-20']),
    'churn_date': pd.to_datetime(['2022-01-01', '2021-08-01', '2022-01-15', '2022-03-01', '2021-12-20'])
}
df = pd.DataFrame(data)

# Calculate average lifespan in years
df['lifespan_years'] = (df['churn_date'] - df['acquisition_date']).dt.days / 365

# Calculate ARPU (average revenue per user)
arpu = df['revenue'].mean()

# Calculate gross margin percentage
df['gross_margin_pct'] = (df['revenue'] - df['cost_of_goods_sold']) / df['revenue']
avg_gross_margin_pct = df['gross_margin_pct'].mean()

# Calculate average customer lifespan
avg_lifespan = df['lifespan_years'].mean()

# Calculate LTV
ltv = arpu * avg_gross_margin_pct * avg_lifespan

# Calculate CAC
total_marketing_cost = df['marketing_cost'].sum()
num_customers = df['customer_id'].nunique()
cac = total_marketing_cost / num_customers

# Calculate average margin
df['margin'] = (df['revenue'] - df['cost_of_goods_sold']) / df['revenue']
avg_margin = df['margin'].mean()

print(f"Average LTV: {ltv:.2f}")
print(f"Average CAC: {cac:.2f}")
print(f"Average Margin: {avg_margin:.2%}")


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# Segmenting unit economics by acquisition channel or customer segment
# Suppose you add 'acquisition_channel' and 'segment' columns
df['acquisition_channel'] = ['Organic', 'Paid', 'Organic', 'Referral', 'Paid']
df['segment'] = ['SMB', 'Enterprise', 'SMB', 'Enterprise', 'SMB']

# Group by acquisition channel and calculate metrics
channel_group = df.groupby('acquisition_channel').agg(
    arpu=('revenue', 'mean'),
    gross_margin_pct=('gross_margin_pct', 'mean'),
    avg_lifespan=('lifespan_years', 'mean'),
    cac=('marketing_cost', 'mean'),
    avg_margin=('margin', 'mean')
)

channel_group['ltv'] = channel_group['arpu'] * channel_group['gross_margin_pct'] * channel_group['avg_lifespan']

print(channel_group[['arpu', 'gross_margin_pct', 'avg_lifespan', 'ltv', 'cac', 'avg_margin']])

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Osio 3. Luku 2