Cohort analysis is a behavioral analytics technique that groups users with shared characteristics and analyzes their behavior over time. It's also known as "static pool analysis".?
Cohort analysis can help businesses understand how users engage with their brand, and make decisions to improve product development and customer retention. Here are some examples of how businesses can use cohort analysis.
- Understand marketing response: See how users respond to short-term marketing efforts, like email campaigns?
- Identify trends: Spot changes in trends and respond accordingly?
- Reduce churn: Analyze churn rates to identify the cause and find ways to improve?
- Increase revenue: Increase average revenue per user (ARPU) by identifying which channels are most effective?
- Launch campaigns: Design campaigns to encourage desired actions?
- Shift marketing budget: Adjust marketing budgets at the right time in the customer lifecycle?
- End trials and offers: Determine when to end trials and offers to maximize value?
- A/B test: Generate ideas for A/B testing, such as pricing and upgrade paths.
- Cohort analysis works by grouping users based on shared characteristics, such as acquisition date. Once formed, cohorts are unchanging groups, with no new customers joining or existing customers moving between cohorts.
- The steps typically involved in the analysis process include:
- extracting raw data: raw data is pulled from a database using MySQL and exported into spreadsheet software, where user attributes can be joined and further segmented.?
- creating cohort identifiers: group user data into different buckets, such as date joined, date of first purchase, graduation year, all mobile devices at a particular place and time, etc.?
- calculating lifecycle stages: once users have been divided into cohorts, the amount of time between events attributed to each customer is measured in order to calculate lifecycle stages.
- creating tables and graphs: pivot tables and graphs create visual representations of user data comparisons, and help calculate the aggregation of multiple dimensions of user data.
