Lernen Challenge: Segment Users by Activity | Product Metrics and Data Exploration

Segmenting users by their activity levels is a powerful way to interpret product engagement and tailor strategies for different user groups. By grouping users into categories such as "high", "medium", and "low" activity, you can quickly assess which segments are most engaged, spot opportunities for growth, and present clear, actionable insights on a product dashboard. This approach helps you prioritize feature development, retention efforts, and marketing strategies by focusing on the needs of each segment.


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# Example: Categorizing users by session activity

user_activity = [
    {"user_id": 1, "sessions": 22},
    {"user_id": 2, "sessions": 5},
    {"user_id": 3, "sessions": 12},
    {"user_id": 4, "sessions": 30},
    {"user_id": 5, "sessions": 8},
]

def categorize_user(sessions):
    if sessions >= 20:
        return "high"
    elif sessions >= 10:
        return "medium"
    else:
        return "low"

for user in user_activity:
    segment = categorize_user(user["sessions"])
    print(f"User {user['user_id']} is in the '{segment}' activity group.")

Aufgabe

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Segment users into "high", "medium", and "low" activity groups based on their session counts. This helps visualize user engagement for a product dashboard.

Assign users with 20 or more sessions to the "high" group.
Assign users with 10 to 19 sessions to the "medium" group.
Assign users with fewer than 10 sessions to the "low" group.
Return a dictionary with the counts of users in each group.

Lösung

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Abschnitt 1. Kapitel 5

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Suggested prompts:

Can you explain how to adjust the thresholds for each activity group?

What other metrics can I use to segment users besides session count?

How can I visualize these user segments on a dashboard?

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# Example: Categorizing users by session activity

user_activity = [
    {"user_id": 1, "sessions": 22},
    {"user_id": 2, "sessions": 5},
    {"user_id": 3, "sessions": 12},
    {"user_id": 4, "sessions": 30},
    {"user_id": 5, "sessions": 8},
]

def categorize_user(sessions):
    if sessions >= 20:
        return "high"
    elif sessions >= 10:
        return "medium"
    else:
        return "low"

for user in user_activity:
    segment = categorize_user(user["sessions"])
    print(f"User {user['user_id']} is in the '{segment}' activity group.")