Cohort analysis

Cohort Analysis 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. ‍ Cohort Analysis example on spreadsheet. Image from Optimize Smart ‍ FAQs 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