Apprendre Automating Customer Segmentation Reports | Automating Customer Success Workflows

Python for Customer Success Managers

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Customer segmentation is the process of dividing your customer base into groups based on shared characteristics, such as engagement level, product usage, or risk of churn. For Customer Success Managers, segmentation enables more targeted communication, efficient prioritization, and tailored engagement strategies. Automating segmentation reports saves time, reduces manual errors, and ensures you always have up-to-date insights to drive proactive outreach.


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# Hardcoded list of customers with engagement scores
customers = [
    {"name": "Alice", "engagement_score": 92},
    {"name": "Bob", "engagement_score": 65},
    {"name": "Charlie", "engagement_score": 40},
    {"name": "Diana", "engagement_score": 78},
    {"name": "Eve", "engagement_score": 30},
    {"name": "Frank", "engagement_score": 55}
]

# Segment customers by engagement level
segments = {
    "High Engagement": [],
    "Medium Engagement": [],
    "Low Engagement": []
}

for customer in customers:
    score = customer["engagement_score"]
    if score >= 80:
        segments["High Engagement"].append(customer["name"])
    elif score >= 50:
        segments["Medium Engagement"].append(customer["name"])
    else:
        segments["Low Engagement"].append(customer["name"])

# Generate summary report
print("Customer Segmentation Report")
for segment, names in segments.items():
    print(f"\n{segment} ({len(names)}):")
    for name in names:
        print(f" - {name}")

This code defines a simple segmentation logic based on customer engagement scores. Customers are grouped into "High Engagement," "Medium Engagement," and "Low Engagement" segments using score thresholds. The code then generates a summary report showing each segment and the customers within it. Automating this process ensures you can quickly identify which customers need more attention and which are already highly engaged, supporting data-driven decisions.


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# Add recommendations for each segment in the report
recommendations = {
    "High Engagement": "Send appreciation and explore upsell opportunities.",
    "Medium Engagement": "Offer targeted tips to increase product usage.",
    "Low Engagement": "Schedule a check-in to address potential issues."
}

print("Customer Segmentation Report with Recommendations")
for segment, names in segments.items():
    print(f"\n{segment} ({len(names)}):")
    print(f" Recommendation: {recommendations[segment]}")
    for name in names:
        print(f" - {name}")

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Section 3. Chapitre 4

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Section 3. Chapitre 4

Automating Customer Segmentation Reports

1. Why is automated segmentation reporting valuable for Customer Success?

2. How can you use Python to generate actionable recommendations for each segment?

3. What is a key consideration when designing automated reports?