User Targeting & Rules

Flagix provides powerful targeting capabilities to deliver features to the right users at the right time. Using rules, you can target specific user segments, gradually roll out features, or run A/B tests.

Understanding the Rule System

Every flag in Flagix has an ordered list of rules that determine which users see which variations. Rules are evaluated in order from top to bottom—the first rule that matches determines the result.

Rule Types

Flagix supports two main types of rules:

1. Targeting Rules

Serve a specific variation to users who match certain conditions.

Use cases:

• Show premium features only to premium users
• Target specific users for early access
• Region-based feature delivery
• Role-based feature access

Example:

Rule: "Premium Users"
  Conditions: plan == "premium"
  Rollout: 100%
  Target Variation: "new-ui-premium"

With rollout percentage:

Rule: "Gradual Rollout to All Users"
  Conditions: (none - applies to everyone)
  Rollout: 25%
  Target Variation: "new-ui"

2. Experiment Rules (A/B Tests)

Split traffic between multiple variations to compare their performance.

Use cases:

• A/B test different UX designs
• Compare feature implementations
• Test different pricing strategies
• Optimize conversion rates

Example:

Rule: "Checkout Flow Experiment"
  Variation A (Control): 50% traffic
  Variation B (Test): 50% traffic

Rule Evaluation Order

Rules are evaluated top-to-bottom. The first rule that matches determines the result:

1. Is user a beta tester? → Show "beta-features"
2. Is user premium? → Show "premium-features"  
3. Gradual rollout (50%)? → Show "new-ui"
4. Default → Show "old-ui"

If none of the rules match, Flagix returns the default variation.

Rule Conditions

Conditions are the criteria that determine if a rule applies to a user. All conditions in a rule must be true (AND logic).

Available Operators

Condition Examples

// String conditions
{ attribute: "plan", operator: "==", value: "premium" }
{ attribute: "email", operator: "contains", value: "@company.com" }

// Numeric conditions
{ attribute: "age", operator: ">=", value: "18" }
{ attribute: "accountDays", operator: ">", value: "365" }

// List conditions
{ attribute: "role", operator: "in", value: "admin,moderator,viewer" }

Setting Up Rules

Creating a Targeting Rule

1. Open your flag in the dashboard
2. Click Add Rule
3. Enter a descriptive name (e.g., "Premium Users")
4. Select Targeting Rule as the type
5. Add conditions to target specific users:
   • plan == premium
   • country == US
6. Choose the rollout percentage (default: 100%)
7. Select the target variation
8. Click Save

Creating an Experiment Rule

1. Open your flag in the dashboard
2. Click Add Rule
3. Enter a descriptive name (e.g., "Checkout Redesign A/B Test")
4. Select Experiment Rule as the type
5. Set up variation splits:
   • Variation A: 50%
   • Variation B: 50%
6. Click Save

Real-World Targeting Examples

Example 1: Gradual Feature Rollout

Roll out a new feature to increasing percentages of users:

Rule 1: "10% Rollout"
  Conditions: (none)
  Rollout: 10%
  Target: "new-checkout"
  
[Wait for metrics and feedback]

Rule 1: "25% Rollout"  
  Conditions: (none)
  Rollout: 25%
  Target: "new-checkout"

[Continue scaling...]

Example 2: Segment-Based Targeting

Deliver different experiences based on user segment:

Rule 1: "Free Users - Simple Checkout"
  Conditions: plan == "free"
  Rollout: 100%
  Target: "simple-checkout"

Rule 2: "Premium Users - Advanced Checkout"
  Conditions: plan == "premium"
  Rollout: 100%
  Target: "advanced-checkout"

Rule 3: "Enterprise Users - Custom Checkout"
  Conditions: plan == "enterprise"
  Rollout: 100%
  Target: "custom-checkout"

Example 3: A/B Test with Early Access

Run an A/B test while giving early access to a specific group:

Rule 1: "Early Testers - New UI"
  Conditions: betaTester == true
  Rollout: 100%
  Target: "new-ui"

Rule 2: "A/B Test - 50/50 Split"
  Conditions: (none)
  Rollout: 100%
  Variation A: 50%
  Variation B: 50%

[Measure results in the Analytics dashboard]

Example 4: Geographic Targeting

Show features only in specific regions:

Rule 1: "US Users Only"
  Conditions: country in ["US", "CA", "MX"]
  Rollout: 100%
  Target: "us-features"

Rule 2: "EU Users with GDPR Compliance"
  Conditions: country in ["DE", "FR", "IT", "ES"]
  Rollout: 100%
  Target: "gdpr-features"

Rollout Percentages

Rollout percentages control what fraction of users matching a rule's conditions receive the target variation.

How it works:

• Each user is consistently assigned to a "bucket" (0-99) based on their user ID
• Users with buckets below the rollout percentage see the variation
• The same user always gets the same bucket (consistent bucketing)

Use cases:

• 100%: All matching users see the variation
• 50%: Half of matching users see the variation (useful for A/B tests)
• 10%: Only 10% of matching users see the variation (safe rollouts)

How Consistent Bucketing Works

Flagix uses a hashing algorithm to ensure the same user always gets the same variation:

1. User ID is hashed (SHA-256)
2. Hash is converted to a bucket number (0-99)
3. Bucket is compared against rollout percentage
4. Result is deterministic and stable

Benefit: Users see consistent experiences across page refreshes, devices, and time.

Check out our Analytics Guide to measure the impact of your targeting strategies and A/B tests.