How to Improve Cohort Retention | Startup School

30 Aug 2024 (4 months ago)

Understanding Cohort Retention

Cohort retention involves monitoring groups of new users, known as cohorts, to see how many continue using a product over time. (41s)
To track cohort retention, define cohorts (e.g., users acquired each week or month), identify an action that signifies active usage (e.g., viewing multiple posts on Instagram), and choose a relevant time period for measurement (e.g., daily for a social app). (3m12s)
Selecting an action closely tied to users deriving value from the product, such as viewing photos in Google Photos, is crucial for accurate cohort retention analysis. (5m16s)
When choosing a time period to measure cohort retention, it is important to select a period that aligns with the intended usage frequency of your product. For example, daily for a product intended for daily use, weekly for a utility product, and quarterly, semi-annually, or annually for a travel app. (6m37s)

A triangle chart can be used to visualize cohort retention, with rows representing months and columns tracking the retention of each cohort over subsequent months. Each user is counted only once per time period, regardless of their usage frequency within that period. (6m54s)
Cohort retention curves, typically visualized as line graphs, provide insights into the long-term performance of each cohort and can help determine if retention is improving or worsening over time. Each line on the graph represents a different cohort, with the longest line representing the oldest cohort. (10m4s)
The shape of a cohort retention curve is more important than the absolute number of users retained. The goal is for the curve to flatten, indicating long-term user retention. (11m59s)

Measuring retention over longer periods (quarters or years) can mask poor retention, as it increases the likelihood of users returning within the timeframe. It's crucial to select a time period relevant to the product's intended use frequency. (14m20s)
Choosing user actions that are too easy, such as simply opening the app, can lead to misleading retention metrics. These actions might not reflect meaningful engagement or value derived from the product. (15m47s)
Google+ once had high active user numbers because a notification bell in Google products directed users to the platform, but these users were not truly engaged, highlighting the importance of accurate cohort retention analysis. (17m16s)
While payment is a significant metric, users often stop using a product before canceling subscriptions, making it crucial to consider both payment and active product usage for a comprehensive understanding of retention. (18m9s)
Relying solely on analytics tools for cohort retention can be misleading, as their measurements might not align with specific definitions or separate cohorts accurately, making it essential to develop independent understanding and compare results. (20m47s)

Products that have undergone improvements may show improved cohort retention in newer cohorts compared to older cohorts. (22m24s)
Acquiring users more aligned with a product's purpose can improve cohort retention, as demonstrated by Google Photos' experience targeting younger demographics. (22m45s)
Analyzing cohort curves sliced by attributes like country or customer type can reveal specific areas for product improvement or targeted user acquisition. (24m10s)

Instead of using relative months, data is aligned in absolute time to show from which cohort users originated in a given calendar month. (27m16s)
A layer cake chart visually represents the total active users per month (top line) with each layer indicating the cohort from which they originated. (27m42s)

Founders should gather qualitative feedback from users to gain product insights, while cohort retention curves primarily indicate if the product is on the right track or needs adjustments based on whether the curves flatten. (28m30s)