Leer Tracking Feature Adoption Automatically | Automating Product Management Workflows

Tracking feature adoption is a crucial process for any product manager striving to understand how new features are received by users. By monitoring which users are engaging with specific features and how frequently, you gain valuable insights into the effectiveness of product launches and the real-world value your team delivers. Consistent tracking enables you to identify adoption trends, spot potential issues early, and measure the success of your product decisions.


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# Hardcoded user data representing feature usage
user_feature_usage = [
    {"user_id": 1, "features_used": ["chat", "search"]},
    {"user_id": 2, "features_used": ["chat"]},
    {"user_id": 3, "features_used": ["search", "analytics"]},
    {"user_id": 4, "features_used": ["chat", "analytics"]},
    {"user_id": 5, "features_used": ["chat", "search", "analytics"]},
]

# Track feature usage counts
feature_counts = {}

for user in user_feature_usage:
    for feature in user["features_used"]:
        if feature not in feature_counts:
            feature_counts[feature] = 0
        feature_counts[feature] += 1

print("Feature usage counts:")
for feature, count in feature_counts.items():
    print(f"{feature}: {count}")

Understanding how users adopt new features allows you to iterate your product more effectively. When you know which features are being embraced and which are ignored, you can prioritize improvements, refine onboarding, or even sunset underused capabilities. Tracking adoption also helps you set realistic goals for rollouts and provides concrete evidence to support your product strategy.


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# Calculate adoption rates for each feature
total_users = len(user_feature_usage)
feature_adoption_rates = {}

for feature, count in feature_counts.items():
    adoption_rate = count / total_users
    feature_adoption_rates[feature] = adoption_rate

print("\nFeature adoption summary:")
for feature, rate in feature_adoption_rates.items():
    print(f"{feature}: {rate:.2%} adoption rate")

1. What is feature adoption and why does it matter?

2. How can Python help track feature usage?

3. Which metric best indicates successful feature adoption?

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Suggested prompts:

Can you explain how the adoption rate is calculated in this example?

What insights can I gain from these adoption rates?

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