Вивчайте North Star Metric and Supporting Metrics

A North Star Metric is a single, critical metric that best captures the core value your product delivers to customers. This metric acts as a guiding light for your business, aligning cross-functional teams toward a shared goal. By focusing on the North Star Metric, you ensure that everyone—from product development to marketing and support—is working to drive sustainable growth and long-term value. Supporting metrics break down the North Star Metric into actionable components, helping teams understand the levers they can pull to influence the main goal.


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import pandas as pd

# Imagine a SaaS platform that provides project management tools
# Sample user activity data
data = {
    "user_id": [101, 102, 103, 104, 105, 106],
    "active_days": [20, 15, 28, 10, 25, 18],
    "projects_created": [5, 3, 7, 1, 6, 4],
    "tasks_completed": [50, 30, 80, 10, 75, 40],
    "invited_team_members": [3, 1, 4, 0, 2, 2]
}
df = pd.DataFrame(data)

# Define the North Star Metric: "Weekly Active Users" (WAU)
north_star_metric = df[df["active_days"] >= 7].shape[0]

# Supporting metrics:
# 1. Average projects created per active user
avg_projects_per_user = df["projects_created"].mean()

# 2. Average tasks completed per user
avg_tasks_completed = df["tasks_completed"].mean()

# 3. Average team members invited per user
avg_team_invites = df["invited_team_members"].mean()

print("North Star Metric (Weekly Active Users):", north_star_metric)
print("Supporting Metric - Avg Projects per User:", round(avg_projects_per_user, 2))
print("Supporting Metric - Avg Tasks Completed per User:", round(avg_tasks_completed, 2))
print("Supporting Metric - Avg Team Members Invited per User:", round(avg_team_invites, 2))

When choosing a North Star Metric and supporting metrics, you should focus on those that reflect genuine, recurring value for your customers and the business. The North Star Metric should be a leading indicator of growth, not just a lagging outcome like revenue. Supporting metrics must be actionable and tightly linked to the North Star Metric, so improvements in these areas reliably drive the main metric forward. To validate your metrics, analyze historical data to confirm that changes in supporting metrics consistently lead to positive movement in the North Star Metric. This approach ensures your teams stay focused on activities that truly matter for long-term business success.

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              123456789101112131415161718192021222324252627282930
            
import pandas as pd

# Imagine a SaaS platform that provides project management tools
# Sample user activity data
data = {
    "user_id": [101, 102, 103, 104, 105, 106],
    "active_days": [20, 15, 28, 10, 25, 18],
    "projects_created": [5, 3, 7, 1, 6, 4],
    "tasks_completed": [50, 30, 80, 10, 75, 40],
    "invited_team_members": [3, 1, 4, 0, 2, 2]
}
df = pd.DataFrame(data)

# Define the North Star Metric: "Weekly Active Users" (WAU)
north_star_metric = df[df["active_days"] >= 7].shape[0]

# Supporting metrics:
# 1. Average projects created per active user
avg_projects_per_user = df["projects_created"].mean()

# 2. Average tasks completed per user
avg_tasks_completed = df["tasks_completed"].mean()

# 3. Average team members invited per user
avg_team_invites = df["invited_team_members"].mean()

print("North Star Metric (Weekly Active Users):", north_star_metric)
print("Supporting Metric - Avg Projects per User:", round(avg_projects_per_user, 2))
print("Supporting Metric - Avg Tasks Completed per User:", round(avg_tasks_completed, 2))
print("Supporting Metric - Avg Team Members Invited per User:", round(avg_team_invites, 2))

Все було зрозуміло?

Дякуємо за ваш відгук!

Секція 6. Розділ 1