Picking a Good North Star

# Picking a Good North Star A north-star metric is a single number that captures how much value the product is delivering. ## What a good north star looks like 1. **Tied to value delivered to the user**, not just the company. 2. **Leading, not lagging.** It should move when you do good work. 3. **Controllable.** Product changes should be able to move it. ## Testing North-Star correlation in Python ```python import numpy as np import pandas as pd # 200 customer accounts tracking candidate leading metric vs lagging outcome np.random.seed(42) df = pd.DataFrame({ "account_id": range(1, 201), "signup_page_views": np.random.poisson(lam=15, size=200), # Vanity metric "weekly_active_collaborators": np.random.poisson(lam=4, size=200), # Leading candidate }) # True revenue at Month 6 depends heavily on active collaborators, not page views df["m6_arr_usd"] = ( df["weekly_active_collaborators"] * 1200 + df["signup_page_views"] * 10 + np.random.normal(0, 500, size=200) ) corr_matrix = df[["signup_page_views", "weekly_active_collaborators", "m6_arr_usd"]].corr() print("--- Correlation Matrix: Candidates vs 6-Month ARR ---") print(corr_matrix["m6_arr_usd"].round(3)) ``` ## Takeaway Validate that your candidate north star actually predicts downstream retention and revenue before committing your team's OKRs to it.