Analyzing Retention Cohorts
Understanding how users engage with your product over time is crucial for sustainable growth. One powerful method to measure long-term engagement is cohort analysis. In this technique, you group users based on a shared characteristic—often their signup date—and track how their behavior changes over time. By analyzing these groups, called retention cohorts, you can identify patterns, spot drop-off points, and assess the effectiveness of product updates or marketing campaigns. Cohort analysis helps you answer questions like: Are users acquired in January more loyal than those in March? Did a new feature improve retention for recent signups? This approach is a cornerstone of retention-focused growth hacking because it reveals how well you keep users coming back, not just how many you acquire.
1234567891011121314151617181920212223242526import pandas as pd # Hardcoded user data with signup and activity dates data = { "user_id": [1, 2, 3, 4, 5, 6, 7, 8], "signup_date": [ "2024-01-15", "2024-01-22", "2024-02-10", "2024-02-18", "2024-03-02", "2024-03-28", "2024-03-30", "2024-04-05" ], "last_active_date": [ "2024-02-15", "2024-01-30", "2024-03-10", "2024-02-25", "2024-04-01", "2024-03-29", "2024-04-10", "2024-04-12" ] } df = pd.DataFrame(data) # Convert dates to datetime df["signup_date"] = pd.to_datetime(df["signup_date"]) df["last_active_date"] = pd.to_datetime(df["last_active_date"]) # Assign cohort based on signup month df["signup_month"] = df["signup_date"].dt.to_period("M") # Display users grouped by cohort cohorts = df.groupby("signup_month")["user_id"].apply(list) print(cohorts)
Once you have grouped users into cohorts by signup month, you can measure how many users from each cohort remain active over time. Retention rate is typically calculated as the percentage of users in a cohort who are still active after a certain period (such as one month, two months, etc.). In the example above, you could compare the number of users from each signup month who have a last_active_date at least one month after their signup_date. This calculation reveals which cohorts are more engaged and helps you spot trends or improvements after product changes. By tracking retention rates for each cohort, you gain actionable insights into user loyalty and the long-term impact of your growth strategies.
123456789101112131415161718import matplotlib.pyplot as plt # Example retention rates for three cohorts cohort_labels = ["2024-01", "2024-02", "2024-03"] month_0 = [1.0, 1.0, 1.0] # 100% at signup month_1 = [0.5, 0.75, 0.66] # Retention after 1 month month_2 = [0.3, 0.5, 0.4] # Retention after 2 months plt.plot(cohort_labels, month_0, marker="o", label="Signup Month") plt.plot(cohort_labels, month_1, marker="o", label="Month 1 Retention") plt.plot(cohort_labels, month_2, marker="o", label="Month 2 Retention") plt.title("Cohort Retention Rates") plt.xlabel("Cohort (Signup Month)") plt.ylabel("Retention Rate") plt.ylim(0, 1.05) plt.legend() plt.grid(True) plt.show()
1. What is a retention cohort?
2. How can cohort analysis inform product decisions?
3. Which pandas method helps group users by signup month?
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Understanding how users engage with your product over time is crucial for sustainable growth. One powerful method to measure long-term engagement is cohort analysis. In this technique, you group users based on a shared characteristic—often their signup date—and track how their behavior changes over time. By analyzing these groups, called retention cohorts, you can identify patterns, spot drop-off points, and assess the effectiveness of product updates or marketing campaigns. Cohort analysis helps you answer questions like: Are users acquired in January more loyal than those in March? Did a new feature improve retention for recent signups? This approach is a cornerstone of retention-focused growth hacking because it reveals how well you keep users coming back, not just how many you acquire.
1234567891011121314151617181920212223242526import pandas as pd # Hardcoded user data with signup and activity dates data = { "user_id": [1, 2, 3, 4, 5, 6, 7, 8], "signup_date": [ "2024-01-15", "2024-01-22", "2024-02-10", "2024-02-18", "2024-03-02", "2024-03-28", "2024-03-30", "2024-04-05" ], "last_active_date": [ "2024-02-15", "2024-01-30", "2024-03-10", "2024-02-25", "2024-04-01", "2024-03-29", "2024-04-10", "2024-04-12" ] } df = pd.DataFrame(data) # Convert dates to datetime df["signup_date"] = pd.to_datetime(df["signup_date"]) df["last_active_date"] = pd.to_datetime(df["last_active_date"]) # Assign cohort based on signup month df["signup_month"] = df["signup_date"].dt.to_period("M") # Display users grouped by cohort cohorts = df.groupby("signup_month")["user_id"].apply(list) print(cohorts)
Once you have grouped users into cohorts by signup month, you can measure how many users from each cohort remain active over time. Retention rate is typically calculated as the percentage of users in a cohort who are still active after a certain period (such as one month, two months, etc.). In the example above, you could compare the number of users from each signup month who have a last_active_date at least one month after their signup_date. This calculation reveals which cohorts are more engaged and helps you spot trends or improvements after product changes. By tracking retention rates for each cohort, you gain actionable insights into user loyalty and the long-term impact of your growth strategies.
123456789101112131415161718import matplotlib.pyplot as plt # Example retention rates for three cohorts cohort_labels = ["2024-01", "2024-02", "2024-03"] month_0 = [1.0, 1.0, 1.0] # 100% at signup month_1 = [0.5, 0.75, 0.66] # Retention after 1 month month_2 = [0.3, 0.5, 0.4] # Retention after 2 months plt.plot(cohort_labels, month_0, marker="o", label="Signup Month") plt.plot(cohort_labels, month_1, marker="o", label="Month 1 Retention") plt.plot(cohort_labels, month_2, marker="o", label="Month 2 Retention") plt.title("Cohort Retention Rates") plt.xlabel("Cohort (Signup Month)") plt.ylabel("Retention Rate") plt.ylim(0, 1.05) plt.legend() plt.grid(True) plt.show()
1. What is a retention cohort?
2. How can cohort analysis inform product decisions?
3. Which pandas method helps group users by signup month?
¡Gracias por tus comentarios!
