Product analytics

Product analytics

This feature is for Early Access Program customers only

Product analytics is only available to members of LaunchDarkly’s Early Access Program (EAP). If you want access to this feature, join the EAP.

Overview

LaunchDarkly offers a warehouse-native product analytics solution that allows you to compose your own dashboards out of different events and user data. Product analytics lets you analyze your product data and get insights.

Warehouse-native product analytics

Warehouse-native product analytics solutions are analytics platforms built to work directly with your existing data warehouse. Unlike traditional analytics platforms that come with their own database and require ETL (Extract, Transform, Load) processes to move data into that database, warehouse-native solutions access the data where it resides. You don’t need to move or copy data from your warehouse to another database. This saves time and resources.

Product analytics supports evolving data sources and scales with the event volumes associated with large amounts of data. You can slice and visualize your product data precisely the way you want. LaunchDarkly product analytics offer powerful capabilities that can help you go deep into your data and know more about customer behavior.

Prerequisites

In order to use product analytics, you must have the following prerequisites:

  • Inclusion in LaunchDarkly’s Early Access Program (EAP)
  • Events populating LaunchDarkly’s event stream
  • An active Snowflake account with the SECURITYADMIN and SYSADMIN privileges
  • Access to a Snowflake Native data warehouse
  • The Snowflake Data Export destination enabled

Product analytics components

There are several key concepts behind LaunchDarkly product analytics. Each of these components is described in its own tab in the product analytics UI.

  • Events: Unique actions on your product. For example, a user clicking a checkout button or a user signing up.
  • Cohorts: Specific groups of users segmented by properties or behaviors. For example, all users who signed up in the past 30 days.
  • Attributes: Identifying traits that describe events. For example, the country a user was located in when they signed up.
  • User activity: User activity over time. For example, the number of users who signed up each day.

Ready to get started? Read Setting up product analytics.

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