As a Product Manager at Slack, I was tasked to 1st measure activation, retention, and churn and then 2nd suggest experiments to improve these metrics.

Part 1: Activation Hypothesis
You've been tasked to define a more robust activation funnel to achieve shorter time-to-value, product usage, and revenue.
Create a hypothesis of what the habit, aha, and setup moments should be for Slack. (Note: this is only for data-dump and for critical thinking. This data is not used elsewhere)

Parts 2: Habit, Aha, Setup Moments and Metrics
You need to conduct an analysis on what habit moments and metrics drive users to long-term activation.
For each Habit, Aha, and Setup Moments you will:
calculate % overlap to measure the relationship of % overlap to habit metric
create line charts to visualize the relationship of the metric
analyze which are the primary habit moments and metric that lead to long-term activation.

Part 3: Activation Funnel
In this part, you will be putting all the analysis you've done in previous 3 part together. From the 3 previous parts, you should have identified the primary Habit, Aha, and Sign-up moment that most influenced activation.

Part 4: Activation Segment Analysis
Analyze how different segments flow through the activation funnel by determining the # and % of users activated per segment.

Part 5: Engagement Type Analysis
Use the activation funnel to determine what use-case a user has that gets them to your moments. After a user has reached activation, you then have to ensure the user stayed engaged. Conducting this analysis, you can then know how to keep your users engaged and engaged, and avoid churn as much as possible early on. After conducting this analysis, you will know different types of triggers that increase engagement.

Part 6: Engagement State Analysis.
Based on the engagement types Power, Core, and Casual, place your existing users into separate buckets that represent their range of engagement.

Part 7: Retention Cohort Analysis
Create a cohort analysis. From this, you will be able to analyze retention over time by looking at how a specific cohort of users (weekly cohorts) remain active over time.

Part 8: Retention Curve
Create a retention curve chart from the previous retention cohort analysis. In the next section, you'll analyze retention over time by looking at how cohorts change over time.

Part 9: Retention Segmentation Analysis
Create a retention cohort and a retention curve chart for provided segments. Analyze retention by looking at how many users are retained over time for the different segments.

Part 10: Life Cycle Chart
Create a lifecycle analysis of your users over time by looking at active user and inactive user cohorts over time. Calculate the weekly active users, dormant users, and resurrected users.

Part 11: Churn & LTV Analysis
You are asked to conduct cohort analysis to calculate the churn rate, retention rate, and LTV.

Part 12: Experiment Brief
Create experiments and include an analysis on the impacts, rationale, and hypothesis of the experiment.



Full Details: https://docs.google.com/document/d/19qYohNza__tMAVhtpV4hFRxbsnJsXAv6epyFiejJ8Qg/edit#