Inverse Watch Docs
AppLanding
  • Overview
    • Home
    • Governance
      • Proposal 7
      • Proposal 25
      • Proposal 52
      • Proposal 107
      • Proposal 147 - S1
      • Proposal 189 - S2
  • Products
    • Inverse Alerts
      • See on Twitter
    • Inverse Chatbot
      • /doc
      • /imagine
      • /data
      • /graph
    • Inverse Subgraphs
      • See inverse-subgraph on Mainnet
      • See inverse-governance-subgraph on Mainnet
    • Inverse Watch
      • Go to App
  • User Guide
    • Quickstart
    • Alerts
      • Setting Up an Alert
      • Adding New Alert Destinations
      • Customize Alert Template
      • Multiple Column Alert
    • Queries
      • Creating and Editing Queries
      • Querying Existing Query Results
      • Query Parameters
      • How to Schedule a Query
      • Favorites & Tagging
      • Query Filters
      • How To Download / Export Query Results
      • Query Snippets
    • Visualizations
      • Cohort Visualizations
      • Visualizations How-To
      • Chart Visualizations
      • Formatting Numbers in Visualizations
      • How to Make a Pivot Table
      • Funnel Visualizations
      • Table Visualization Options
      • Visualizations Types
    • Dashboards
      • Creating and Editing Dashboards
      • Favorites & Tagging
      • Sharing and Embedding Dashboards
    • Data Sources
      • CSV & Excel Files
      • Google Sheets
      • JSON (API)
      • Python
      • EVM Chain Logs
      • EVM Chain State
      • GraphQL
      • Dune API
    • Machine Learning
      • Data Engineering
      • Regressors
        • Linear Regression
        • Random Forest
        • Ada Boosting
        • Gradient Boosting
        • Neural Network (LSTM)
      • Training and Predicting
      • Metrics & Overfitting
      • Examples
        • Price Prediction
          • Data Preprocessing
          • Model Creation & Training
          • Metrics Evaluation
          • Back Testing
          • Visualizing
        • Liquidation Risk
  • Admin & Dev Guide
    • Setup
    • Redash
    • Integrations & API
    • Query Runners
    • Users
      • Adding a Profile Picture
      • Authentication Options
      • Group Management
      • Inviting Users to Use Redash
      • Permissions & Groups
    • Visualizations
  • Cheat Sheets
    • Snippets
    • Contracts
  • More
    • Deprecated Apps
    • Github : inverse-flaskbot
    • Github : inverse-subgraph
    • Github : inverse-watch
Powered by GitBook
On this page
  • Cohorts Introduction

Was this helpful?

  1. User Guide
  2. Visualizations

Cohort Visualizations

Cohorts Introduction

A cohort analysis examines the outcomes of predetermined groups, called cohorts, as they progress through a set of stages. The signature characteristic of a cohort chart is its comparison of the change in a variable across two different time series. For example, a common cohort definition is users by sign-up period and their usage pattern by day. Other examples include:

  • Monthly hard drive failure statistics by month

  • Weekly supplier delivery performance by week

  • Monthly average class GPA’s by month

While there are many ways to define the stages of a Cohort analysis, the application supports Cohorts visualizations with daily, weekly, or monthly stages. Also, the application's cohort charts compare a cohort’s measurements in a given period against that group’s initial population size.

i. Data Format

The application expects your input samples to take the following format:

  • Cohort Date is the date that uniquely identifies a cohort. Suppose you’re visualizing monthly user activity by sign-up date, your cohort date for all users that signed-up in January 2018 would be January 1st, 2018. The cohort date for any user that signed-up in February would be February 1st, 2018.

  • Period is a count of how many periods transpired since the cohort date as of this sample. If you are grouping users by sign-up month, then your period will be the count of months since these users signed up. In the above example, a measurement of activity in July for users that signed up in January would yield a period value of 7 because seven periods have transpired between January and July.

  • Count Satisfying Target is your actual measurement of this cohort’s performance in the given period. In the above example, if thirty users who signed up in January showed activity in July then the Count Satisfying Target would be 30.

  • Total Cohort Size is the denominator that the application will use to calculate the percentage of a cohort’s target satisfaction for a given period. Continuing the example above, if seventy-two users signed up in January then the Total Cohort Size would be 72. When the visualization is rendered, the application would display the value as 41.67% (32 ÷ 72).

PreviousVisualizationsNextVisualizations How-To

Last updated 1 year ago

Was this helpful?