Learn Designing Business Rule Segments | Rule-Based Customer Segmentation

Rule-based segmentation is a practical approach to grouping customers by applying explicit business logic rather than relying solely on statistical clustering or machine learning. With rule-based segmentation, you use clear, interpretable conditions—such as purchase frequency, recency, or spending—to assign customers to segments like "VIP", "at-risk", "new", or "loyal". This method has several advantages: it is transparent, easy to communicate to stakeholders, and directly aligns with business objectives. Unlike purely statistical methods, rule-based segmentation allows you to define segments that are immediately actionable, ensuring that marketing and operational strategies can target the right groups with precision.


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import pandas as pd

# Sample customer data
data = {
    "customer_id": [101, 102, 103, 104, 105],
    "total_spent": [1200, 200, 50, 800, 0],
    "orders_count": [15, 3, 1, 10, 0],
    "last_purchase_date": ["2024-06-10", "2024-03-20", "2024-06-25", "2023-12-15", None],
    "region": ["North", "South", "North", "East", "West"]
}

customers = pd.DataFrame(data)
customers["last_purchase_date"] = pd.to_datetime(customers["last_purchase_date"])
print(customers)


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import numpy as np
from datetime import datetime, timedelta

today = pd.Timestamp("2024-06-30")

def segment_customer(row):
    if row["total_spent"] > 1000 and row["orders_count"] >= 10:
        return "VIP"
    elif pd.isnull(row["last_purchase_date"]):
        return "Inactive"
    elif (today - row["last_purchase_date"]).days > 180:
        return "At-Risk"
    elif row["orders_count"] >= 5:
        return "Loyal"
    elif (today - row["last_purchase_date"]).days <= 30 and row["orders_count"] == 1:
        return "New"
    else:
        return "Regular"

customers["segment"] = customers.apply(segment_customer, axis=1)
print(customers[["customer_id", "total_spent", "orders_count", "last_purchase_date", "segment"]])

To ensure your segments drive real business value, you must regularly validate and refine your rules. Start by reviewing segment sizes and characteristics—are the VIP and At-Risk groups meaningful and actionable? Analyze how these segments align with actual customer behavior and business outcomes. Gather feedback from stakeholders and experiment with threshold adjustments to better reflect strategic goals. Continuous refinement ensures that your segmentation remains relevant as customer patterns and business priorities evolve, making your rule-based approach a powerful ongoing tool for targeted decision making.

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Section 5. Chapter 1

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              1234567891011121314
            
import pandas as pd

# Sample customer data
data = {
    "customer_id": [101, 102, 103, 104, 105],
    "total_spent": [1200, 200, 50, 800, 0],
    "orders_count": [15, 3, 1, 10, 0],
    "last_purchase_date": ["2024-06-10", "2024-03-20", "2024-06-25", "2023-12-15", None],
    "region": ["North", "South", "North", "East", "West"]
}

customers = pd.DataFrame(data)
customers["last_purchase_date"] = pd.to_datetime(customers["last_purchase_date"])
print(customers)


              123456789101112131415161718192021
            
import numpy as np
from datetime import datetime, timedelta

today = pd.Timestamp("2024-06-30")

def segment_customer(row):
    if row["total_spent"] > 1000 and row["orders_count"] >= 10:
        return "VIP"
    elif pd.isnull(row["last_purchase_date"]):
        return "Inactive"
    elif (today - row["last_purchase_date"]).days > 180:
        return "At-Risk"
    elif row["orders_count"] >= 5:
        return "Loyal"
    elif (today - row["last_purchase_date"]).days <= 30 and row["orders_count"] == 1:
        return "New"
    else:
        return "Regular"

customers["segment"] = customers.apply(segment_customer, axis=1)
print(customers[["customer_id", "total_spent", "orders_count", "last_purchase_date", "segment"]])

Everything was clear?

Thanks for your feedback!

Section 5. Chapter 1