Oppiskele Summarizing Customer Engagement Metrics | Customer Data Analysis Essentials

Python for Customer Success Managers

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Understanding how customers interact with your product is essential for driving retention and growth. Customer engagement metrics are quantitative measures that help you track how often and how deeply customers use your product. Common engagement metrics include login frequency (how many times a customer logs in over a period) and feature usage (which product features are used most often). These metrics are crucial for Customer Success Managers because they help you identify healthy accounts, spot potential churn risks early, and tailor your outreach to maximize customer value.


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# Sample customer activity records
customer_activities = [
    {"customer_id": "A001", "logins": 12, "features_used": ["dashboard", "reports", "alerts", "dashboard"]},
    {"customer_id": "A002", "logins": 5, "features_used": ["dashboard", "dashboard", "alerts"]},
    {"customer_id": "A003", "logins": 8, "features_used": ["reports", "dashboard", "settings"]},
    {"customer_id": "A004", "logins": 15, "features_used": ["dashboard", "reports", "dashboard", "alerts", "dashboard"]},
]

# Calculate average login frequency
total_logins = sum(activity["logins"] for activity in customer_activities)
average_logins = total_logins / len(customer_activities)

# Find the most-used feature across all customers
from collections import Counter

all_features = []
for activity in customer_activities:
    all_features.extend(activity["features_used"])

feature_counts = Counter(all_features)
most_used_feature = feature_counts.most_common(1)[0][0]

print("Average login frequency:", average_logins)
print("Most-used feature:", most_used_feature)

To compute the average login frequency, the code first sums the logins value for each customer in the list, then divides that sum by the number of customers. This gives you a single number representing how often, on average, customers are logging in—a key indicator of overall engagement. For feature usage, the code collects all features used by all customers into a single list, then uses the Counter class to count how many times each feature appears. The most-used feature is identified as the one with the highest count. Interpreting these results helps you understand both the general engagement level (via average logins) and which features drive the most activity, allowing you to focus on what matters most to your customers.


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# Identify customers with below-average engagement using list comprehensions

# Assume average_logins was calculated earlier
below_average_customers = [
    activity["customer_id"]
    for activity in customer_activities
    if activity["logins"] < average_logins
]

print("Customers with below-average engagement:", below_average_customers)

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Summarizing Customer Engagement Metrics

1. Why is it important to identify customers with low engagement?

2. Which Python technique allows you to filter customers based on a condition?

3. What metric could indicate a customer is at risk of churning?