Impara Automating Customer Segmentation | Automating Marketing Tasks with Python

Automating customer segmentation can transform how you target and engage your audience. By using Python to segment customers, you save time and reduce errors compared to manual approaches. Automated segmentation lets you quickly sort customers into meaningful groups based on objective, data-driven criteria. Some of the most common criteria used for segmentation include purchase frequency (how often a customer buys), recency (how recently a customer made a purchase), and value (how much a customer spends). These criteria help you identify your most loyal, most valuable, or at-risk customers, making it easier to tailor marketing efforts and maximize return on investment.


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import pandas as pd

# Create a DataFrame with customer purchase data
data = {
    "CustomerID": [101, 102, 103, 104, 105, 106],
    "Purchases": [2, 10, 5, 1, 8, 3],
    "Recency": [30, 5, 15, 45, 7, 25],  # days since last purchase
    "Value": [150, 1200, 500, 80, 950, 200]
}
df = pd.DataFrame(data)

# Function to segment customers by purchase frequency
def segment_by_frequency(purchases):
    if purchases >= 8:
        return "High"
    elif purchases >= 4:
        return "Medium"
    else:
        return "Low"

# Apply segmentation function to each customer
df["FrequencySegment"] = df["Purchases"].apply(segment_by_frequency)
print(df)

Automated segmentation like this enables you to deliver personalized marketing at scale. Instead of sending the same message to your entire customer base, you can tailor offers, content, and communication to each segment. High-frequency buyers might receive loyalty rewards, while low-frequency buyers could get re-engagement offers. This approach increases relevance for the customer and improves campaign effectiveness, all while saving valuable time for your marketing team.


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# Update the DataFrame with a new 'Segment' column based on purchase frequency thresholds

def assign_segment(purchases):
    if purchases >= 8:
        return "Gold"
    elif purchases >= 4:
        return "Silver"
    else:
        return "Bronze"

# Add the new 'Segment' column
df["Segment"] = df["Purchases"].apply(assign_segment)
print(df)

1. What is one benefit of automating customer segmentation?

2. Which DataFrame method can be used to add a new column for customer segments?

3. Fill in the blank: Customers who purchase most frequently are often labeled as _ _ _ value.

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Sezione 2. Capitolo 4

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Suggested prompts:

Can you explain how to segment customers based on recency or value instead of frequency?

What are some best practices for choosing segmentation thresholds?

How can I use these segments to personalize marketing campaigns?

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