This section refers to a deprecated version of the product. The new version is FE&R. To access FE&R, contact your CSM.
📘 To learn more, read the FE&R documentation.
LogoLogo
PlatformsPricingRessources
  • User documentation
  • Onboarding
  • Help Center
  • Release Notes
  • Flagship - Deprecated
  • Feature Experimentation & Rollout (ex-Flagship) is evolving!
  • GETTING STARTED
    • 👉Managing multiple environments
    • Using the trial version of Flagship
  • First steps with Flagship
    • Quick start guide
    • Glossary
  • Implementation
    • Sending transactions from the AB Tasty Shopify app directly to Feature Experimentation & Rollouts
  • Integrations
    • Heap Analytics integration (Push)
    • Tealium AudienceStream (receive audiences)
    • FullStory integration (segment exports)
    • Heap Analytics integration (Pull)
    • Google Analytics integration (pull audiences)
    • Segment Integration (receive traits)
    • Mixpanel integration (cohort exports)
    • 👉Retrieving your third-party tools’ audiences in AB Tasty - Feature Experimentation & Rollouts
    • Zapier integration
    • Segment integration
  • Steps configuration
    • 👉Configuring Sequential Testing Alerts
    • 👉Configuring your Flags
    • 👉Using the scheduler
    • 🛠️[Troubleshooting] How to target a large number of users at the same time?
    • 👉Configuring KPIs
    • 👉Using the automatic rollback option
    • 👉Targeting configuration
    • 👉Dynamic allocation
    • 👉Traffic allocation
  • Team
    • Access Rights, Teams & User Management
    • 👉Defining rights per project
  • DEMO
    • AB Tasty - Feature Experimentation & Rollouts Demo - How to use it
  • Navigating the interface
    • 👉Archiving use cases from the dashboard
    • 👉Flags page
    • 👉Running a search on the dashboard
    • Navigating the Flagship interface
  • REPORTING
    • 👉Verifying your hit setup
    • 👉Exporting reporting data
    • Understanding the "Chances to win" indicator
    • 🛠️[Troubleshooting] How can I know my test is reliable and my data significant enough to be analyzed?
    • Reporting - A/B Test
    • 👉Using the reporting filters
  • API keys & Settings
    • 👉Acting on your account remotely
    • 👉Using Experience Continuity
    • visitor experiment option
  • FEATURES SETUP
    • 👉Bucket allocation
  • SDKs integration
    • 👉Managing visitor consent
    • 👉Understanding the use of SDKs
  • FAQ
    • Can I make a comparison for each reporting?
    • Can I use Flagship even if my SDK stack is not available?
  • Platform integration
    • 👉Webhooks page
  • Decision API
    • Decision API for non-techie users
  • Account & Profile
    • 👉Configuring account restrictions with MFA
    • 👉Configuring a FA on your profile
  • RELEASE NOTES
    • October - Flagship becomes Feature Experimentation & Rollouts
    • February - Release Notes
    • 📅January - Release Notes
    • 🎉December - Release Notes 🎉
    • 🦃November - Release Notes
    • September Release Notes 🎨
    • June Release Notes 🐞
    • 🍸May Release Notes ☀️
    • Flagship Release Notes April 🐇
    • Flagship February release notes 🏂
    • Flagship January release notes 🎉
    • Flagship November release notes 🦃
    • Flagship October Release Notes 🎃
    • Flagship September Release note 🎒
    • Flagship August Release Notes 🐬
    • Flagship Release Notes July ☀️
    • Flagship Release notes June 🌻
    • Flagship Spring Release May 🌸
    • Flagship Release Notes: Fall
  • Use cases
    • 👉Duplicating a winning variation
    • 👉Configuring a Feature Toggle/Flag
    • 👉Configuring an A/B Test
    • 👉Configuring a Progressive rollout
    • 👉Configuring a Personalization
  • VIDEO TUTORIALS
    • [Video Tutorial] AB Test
    • [Video Tutorial] Feature Flag
    • [Video Tutorial] Progressive Deployment
Powered by GitBook
LogoLogo

AB Tasty Website

  • Home page AB Tasty
  • Blog
  • Sample size calculator
  • Release note

AB Tasty Plateform

  • Login

© Copyright 2025 AB Tasty, Inc, All rights reserved

On this page
  • 📖 Definition
  • ⚙️ Configuration
  • 💡 Use case

Was this helpful?

Edit on GitLab
Export as PDF
  1. Steps configuration

Targeting configuration

PreviousUsing the automatic rollback optionNextDynamic allocation

Last updated 3 days ago

Was this helpful?

📖 Definition

The targeting step enables you to make your feature visible to one or several groups of users with shared characteristics. To define your targeting, you can either configure feature targeting for:

  • All users: target all the visitors landing on your feature.

  • Users by ID: target the users belonging to a specific ID.

  • Targeting by key: target the users matching a specific key.

Using the Decision API directly, or one of our SDK (in API or Bucketing mode), you can target your users via different criteria depending on the package you have subscribed to. These criteria are also called User Context keys and are useful to target users, but also to filter reporting of your feature when analyzing its results.

⚙️ Configuration

For targeting by key, you need to define a User Context key. To do so, you can either use an existing key, by selecting Key from the first dropdown list and its matching value in the Select a value field; or create a new key and define its value. To configure a new User Context key, click ‘Add a criterion’ and select Key in the first dropdown list and 'Add new' in the Select a value field.

There are two types of User Context keys:

  • Technical keys (for example device, system, geolocation, version).

  • Behavioral keys (for example VIP, Early Adopter, Buyer, Viewer, DefaultUser, age, name).

⭐ Good to know

You can also create targeting keys in the Persona screen or when calling the Decision API with new context keys.

To send these criteria through one of our SDKs or our Decision API, refer to the Developer Portal.

🚩 Heads up

Once you have retrieved data from your various experiments or feature management use cases, you can use the reporting to filter according to the user context keys that you have configured in the step targeting (see screenshot).

💡 Use case

For example, you may need to deploy your feature progressively to different groups of users. Let’s say you want to test your feature internally first, then make it visible only to your early adopters and finally to all your users. To do so, you can first push a user context key that you would call “userType”:“internal”, then change it to “userType”:“earlyAdopter” and finally to “All users”.

Need additional information?

Submit your request at product.feedback@abtasty.com

Always happy to help!

reporting.png
👉