What is cohort analysis?
Cohort analysis is a method for tracking a group of users over time. A cohort is a user group that shares a common characteristic such as an acquisition date, product type, or behavior. By analyzing a cohort—say, paying users—teams can adjust features of their website or app to cater to those individuals and increase their engagement. Learn more about cohort analysis here.
Why is cohort analysis useful?
Customer cohort analysis makes user tracking simpler by grouping users by their behaviors and characteristics. Cohorts are, simply, user segmentations that are saved and named for quick reference. Rather than setting new reporting filters each time they log into an analytics suite, teams can name cohorts things like “Power Users,” “Busy Brenda,” or “Week 7,” and check back repeatedly to see how that cohort is performing over time. Cohorts allow teams to understand their users in a more meaningful way than if they simply looked at data for their entire user base. Broad averages often conceal the specific preferences of smaller groups. If teams don’t understand and cater to those specific preferences, they can lose those users. Take, for example, a video streaming service that notices that a slim majority of its users—51 percent—love horror films. They’d make a big mistake by recommending horror flicks to all of their users, some of whom are not interested in horror films and are more likely to churn from the platform if they feel that the service doesn’t cater to them. With insights into each cohort’s preferences, on the other hand, the video platform could send more relevant recommendations and increase retention for all users.
How to conduct cohort analysis
To analyze cohorts, teams will need a cohort analytics tool that tracks cohorts with enough informational granularity to be useful. It isn’t enough to view vanity metrics such as page views or likes, which don’t offer much insight into why users do what they do. Teams need to correlate all user behaviors and characteristics across devices back to concrete metrics such as subscriptions or revenue. A cohort analytics tool should feature:
- Data ingestion and integrations with common systems like CRM
- Data storage and reporting
- Graphical interface
- Tracking and measurement
Four steps to analyze cohorts
1. Select a question to answer
As with any journey, teams must know the destination before they begin. For instance, is the team most interested to know what factors drive retention, which group of users is most likely to upgrade, or what group of users is mostly likely to commit fraud? A streaming music app, as an example, could be curious about the users that continue to use its service after 30 days. The team there could create a cohort to track those users to understand how to attract and retain others like them.
2. Define the metrics
Which metrics help answer the question the team has selected? In the case of the music app, the cohort would include any user that exhibits positive retention after thirty days. However, cohorts can be measured by all sorts of metrics, including:
- Timing: When users signed up
- Behaviors: Whether users purchased once or repeatedly
- Characteristics: Based on region, age, marketing source
Learn more about the most important user engagement metrics.
3. Define the cohorts
Within the cohort analytics tool, create a new cohort and filter for the behaviors and characteristics that define the cohort in question. Save the cohort, and verify that the users gathered within the cohort report match the cohort’s parameters. Adjust if needed.
4. Analyze the results
Teams can run cohort reports as they need them, or periodically, and track the performance of cohorts against a range of factors such as:
- Average order size
Teams can compare cohorts against one another, or compare cohorts to the broader user population to understand any differences. For example, cohorts that signed up for a marketing software during a particular week may exhibit far higher usage and retention than normal, and the team can investigate that cohort’s behaviors to generate ideas for improving the service. If the team finds that those users came from a particular marketing campaign, they know to repeat that campaign. If they note that an unusual percentage of the cohort completed onboarding sequence, the team knows to promote the onboarding sequence more aggressively.
Cohort analysis examples
Here are three real-life examples of how to use cohort analysis to improve a product:
The children’s education software CodeSpark improved its retention by dividing users into cohorts based on acquisition sources. The team A/B tested new features with each group separately and found that new users that joined from its Hour of Code program exhibited different behaviors than those who joined during a school program. Knowing which groups liked which features allowed the team to retain students longer. Read the case study.
The entertainment ticket sales leader Ticketmaster used Mixpanel’s user analytics to segment its B2B user base and create separate cohorts for venues, artists, and promoters. By sending personalized messaging and A/B testing marketing campaigns to each different cohort, the marketing team significantly improved its return on marketing spend. Read the case study.
Subscription-based media and entertainment giant STARZ-PLAY used cohorts to reduce fraud within its platform 1000x and saved 8x on its marketing spend. The company created a cohort for users acquired through free trial promotions and found that a substantial number of users were circumventing the paywall and signing up for multiple free trials. Tracking users helped the team close the loophole and deactivate fraudulent accounts. Read the case study.
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