Aprende Customer Lifetime Value (CLV) Estimation | Advanced Analytics for Marketers

Customer Lifetime Value (CLV) is a critical metric for marketers because it estimates the total revenue a business can expect from a single customer account throughout their relationship with the company. Understanding CLV allows you to identify your most valuable customers, allocate marketing resources more efficiently, and tailor retention strategies to maximize long-term profitability. By focusing on customers with higher CLV, you can prioritize efforts that drive sustainable growth and justify marketing investments with greater confidence.


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import pandas as pd

# Create a DataFrame with customer purchase history
data = {
    "customer_id": [1, 2, 3, 1, 2, 3, 1, 2],
    "purchase_value": [100, 200, 150, 120, 180, 130, 110, 220],
    "purchase_date": [
        "2024-01-05", "2024-01-07", "2024-01-09", 
        "2024-02-10", "2024-02-12", "2024-02-14",
        "2024-03-15", "2024-03-17"
    ]
}

df = pd.DataFrame(data)

# Function to estimate CLV using average purchase value and frequency
def estimate_clv(df):
    clv = (
        df.groupby("customer_id")
        .agg(
            avg_purchase_value=("purchase_value", "mean"),
            purchase_frequency=("purchase_value", "count")
        )
    )
    clv["estimated_clv"] = clv["avg_purchase_value"] * clv["purchase_frequency"]
    return clv

clv_df = estimate_clv(df)
print(clv_df)

The formula for estimating customer lifetime value in its simplest form is:

CLV = average purchase value × purchase frequency

Marketers use this calculation to identify which customer segments are most valuable over time. By ranking customers based on their estimated CLV, you can focus retention campaigns, personalized offers, and premium services on those who are likely to generate the most profit. This approach helps you make data-driven decisions about where to invest your marketing budget for the greatest impact.


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# Ranking customers by estimated CLV
clv_df_sorted = clv_df.sort_values("estimated_clv", ascending=False)
print(clv_df_sorted[["estimated_clv"]])

1. Why is CLV important for marketers?

2. Which two factors are multiplied to estimate CLV in a simple model?

3. Fill in the blank: Customers with higher CLV are often targeted for ______ marketing efforts.

¿Todo estuvo claro?

¡Gracias por tus comentarios!

Sección 3. Capítulo 2

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Suggested prompts:

Can you explain how the estimated CLV values are calculated in the code?

What are some ways to improve the accuracy of CLV estimation?

How can I use this CLV ranking to inform my marketing strategies?

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import pandas as pd

# Create a DataFrame with customer purchase history
data = {
    "customer_id": [1, 2, 3, 1, 2, 3, 1, 2],
    "purchase_value": [100, 200, 150, 120, 180, 130, 110, 220],
    "purchase_date": [
        "2024-01-05", "2024-01-07", "2024-01-09", 
        "2024-02-10", "2024-02-12", "2024-02-14",
        "2024-03-15", "2024-03-17"
    ]
}

df = pd.DataFrame(data)

# Function to estimate CLV using average purchase value and frequency
def estimate_clv(df):
    clv = (
        df.groupby("customer_id")
        .agg(
            avg_purchase_value=("purchase_value", "mean"),
            purchase_frequency=("purchase_value", "count")
        )
    )
    clv["estimated_clv"] = clv["avg_purchase_value"] * clv["purchase_frequency"]
    return clv

clv_df = estimate_clv(df)
print(clv_df)