Cohort Analysis Definition
Cohort analysis is an analytical technique that categorizes and divides data into groups with common characteristics prior to analysis. This technique is typically used to make it easier for organizations to isolate, analyze, and detect patterns in the lifecycle of a user, to optimize customer retention, and to better understand user behavior in a particular cohort.
What is Cohort Analysis?
A cohort is a group of people who share a common characteristic over a certain period of time, such as users that have become customers at approximately the same time, a graduating class of students, or contact tracing individuals during a pandemic.
Cohort analysis is a study that concentrates on the activities of a specific cohort type. A cohort analysis table is used to visually display cohort data in order to help analysts compare different groups of users at the same stage in their lifecycle, and to see the long-term relationship between the characteristics of a given user group.
How to Use Cohort Analysis
Cohort analysis tools for marketing professionals, provided by services such as Google Analytics, generate a cohort analysis report in the form of a cohort table with the website’s acquisition date range by user retention rate. Examples of popular metrics by which to analyze a cohort include: conversion rates, goal completions per user, page views per user, revenue per user, sessions per user, session duration per user, and transactions per user.
Advanced cohort analysis tools also provide further segmenting options, such as acquisition cohorts vs behavioral cohorts, or mobile users vs desktop users, to further narrow down data groups. The date range and cohort size are fully customizable and can be adjusted depending on the scope of the project.
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.
When to Use Cohort Analysis
Customer cohort analysis is particularly useful in business use cases and marketing efforts. Analyzing trends in cohort spending from various periods in time can help analysts gauge whether or not the quality of the average customer is improving throughout the customer lifecycle. This process is known as lifetime value cohort analysis.
Cohort analysis for retention can also provide in-depth insights into user behavior as well as website performance. In comparing different user groups, analysts can identify trends and patterns, and identify which behavioral changes cause different results.
Cohort retention analysis and acquisition cohort analysis are useful practices for reducing early customer churn. Cohort analysis charts will indicate the period of time after which customers churn. If churn occurs within the first few weeks, the typical culprits may be: product is not meeting customers’ expectations; poor onboarding processes; and/or a poor user acquisition model.
Another use case is contact tracing. A combination of data provided by trusted data partners, such as SafeGraph and X-Mode Social, and spatiotemporal cohort analysis capabilities, can help authorities or health professionals track certain individuals via their mobile devices during a disaster or pandemic. Cohorts may be segmented into buckets denoted by particular locations, times, and devices within certain time frames.
Does HEAVY.AI Offer a Cohort Analysis Solution?
HEAVY.AI Immerse offers spatiotemporal cohort analysis capabilities that enable analysts and data scientists to easily visualize, segment, aggregate, and instantly interact with massive cohort datasets. With Immerse, analysts can aggregate multiple levels of geographic and temporal data, and observe and monitor granular patterns of life for different cohorts of users in specific spatiotemporal bins. Immerse can also display dozens of distinct data visualizations in the same dashboard without having to join underlying tables, which saves data preparation time and reveals otherwise undetected multi-factor relationships.