Impara Challenge: Track Feature Adoption | Automating Product Management Workflows

As you continue to automate your product management workflows, understanding and communicating feature adoption metrics becomes increasingly important. Feature adoption refers to how many users are actually using a specific feature within your product. This metric helps you identify which features are gaining traction and which may need further promotion or improvement. When reporting these metrics, especially in a product update email, it's crucial to present the data clearly and concisely so that stakeholders can quickly grasp the impact and make informed decisions.


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# Sample user data: each user has a list of features they've used
user_data = [
    {"user_id": 1, "features": ["Search", "Export", "Dashboard"]},
    {"user_id": 2, "features": ["Search", "Dashboard"]},
    {"user_id": 3, "features": ["Export"]},
    {"user_id": 4, "features": ["Search", "Export"]},
    {"user_id": 5, "features": ["Dashboard"]},
]

# Count the number of users for each feature
feature_counts = {}
for user in user_data:
    for feature in user["features"]:
        feature_counts[feature] = feature_counts.get(feature, 0) + 1

total_users = len(user_data)

# Calculate adoption rate for each feature
feature_adoption = {}
for feature, count in feature_counts.items():
    adoption_rate = count / total_users
    feature_adoption[feature] = adoption_rate

# Format results for a product update email
for feature, rate in feature_adoption.items():
    percent = round(rate * 100, 1)
    print(f"Feature '{feature}': {feature_counts[feature]} users ({percent}% adoption)")

Compito

Swipe to start coding

Write a script that tracks and reports feature adoption rates using the provided user data.

Count the number of users for each feature.
Calculate the adoption rate for each feature as a proportion of total users.
Print each feature's name, the number of users who used it, and its adoption rate as a percentage in the format:
Feature 'FeatureName': X users (Y% adoption).

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# Sample user data: each user has a list of features they've used
user_data = [
    {"user_id": 1, "features": ["Search", "Export", "Dashboard"]},
    {"user_id": 2, "features": ["Search", "Dashboard"]},
    {"user_id": 3, "features": ["Export"]},
    {"user_id": 4, "features": ["Search", "Export"]},
    {"user_id": 5, "features": ["Dashboard"]},
]

# Count the number of users for each feature
feature_counts = {}
for user in user_data:
    for feature in user["features"]:
        feature_counts[feature] = feature_counts.get(feature, 0) + 1

total_users = len(user_data)

# Calculate adoption rate for each feature
feature_adoption = {}
for feature, count in feature_counts.items():
    adoption_rate = count / total_users
    feature_adoption[feature] = adoption_rate

# Format results for a product update email
for feature, rate in feature_adoption.items():
    percent = round(rate * 100, 1)
    print(f"Feature '{feature}': {feature_counts[feature]} users ({percent}% adoption)")