User Management & Profiles

In Metrix, users are at the heart of automation. Understanding who your users are and how they interact with your app or website is crucial for creating personalized experiences and running successful marketing campaigns

1. The Concept of a User in Metrix: From Anonymous to Identified

Metrix enables tracking of user behavior across time and different devices by identifying users.

• Anonymous User: As soon as a user installs and launches your app or visits your website, Metrix automatically creates a unique anonymous identifier for them (typically based on device ID). Even at this stage, the user can have specific attributes (such as city or first name), and their Attribution data (like install source and time) is also recorded if available. These anonymous users can still receive messages through channels such as SMS (if a phone number is available), Web Push (if subscribed), or App Push (if permissions are granted).

• Identified User: Converting an anonymous user into an “identified” user is a pivotal milestone in user management. This occurs when you register a custom User ID for the user in Metrix. This identifier serves as the primary key for linking all data of a single user within the Metrix system.

  • Importance of Custom User ID:

    • Unified Tracking: Enables tracking of a single user’s behavior across multiple devices or platforms (e.g., mobile and web).
    • Data Synchronization: Connects data collected in Metrix with user information in your internal systems (such as CRM or databases).
    • Comprehensive Profile: Builds a complete and unified profile for each user, encompassing all their events, attributes, and interactions.

  • Best Practices for Custom User ID:

    • Unique and Stable: Use an identifier that is unique and permanent within your system and does not change over time (e.g., an internal database User ID or the user’s Username).
    • Timing of Setting: Set the Custom User ID immediately after the user logs in or registers.
    • Avoid Sensitive/Volatile Data: It is advisable not to use information that may change (like email) or is sensitive (like phone number) as the Custom User ID.
    • How to Clear: In a logout scenario, you can clear the Custom User ID in the SDK.

2. User Attributes: Enriching Profiles

User Attributes are additional descriptive information linked to a user’s profile. These attributes are essential for precise user segmentation, message personalization, and in-depth behavioral analysis.

• Definition and Importance: These attributes can include information such as “name”, “email”, “gender”, “city”, “registration date”, or “subscription level”. Recording this information helps you better understand your users and interact with them more effectively.

• Types of User Attributes:

  • Standard Attributes: Metrix provides pre-defined attributes for common user information (e.g., name, email, mobile, gender, birth_date, country, city, language). Using these attributes is recommended where possible.
  • Custom Attributes: You can define and add attributes specific to your business to user profiles (e.g., membership_level, last_purchase_date, is_premium_user).

• Data Types: Metrix supports various data types for attributes (e.g., String, Number, Boolean, Date).

• Best Practices for User Attributes:

  • Consistent Naming: Use clear and consistent naming (preferably in snake_case) for custom attributes.
  • Timely Updates: Update attributes when changes occur (e.g., when a user edits their profile information).
  • Relevant Information Only: Record only the information critical for analysis or engagement.

3. User Live Attributes: Dynamic and Powerful Insights

User Live Attributes are dynamic properties that Metrix automatically calculates based on user events and interactions. These attributes require no additional coding on your part and are defined through the Metrix panel. Once defined, they update automatically as users perform new activities.

• Applications and Examples:
  • Event Counting: You can automatically calculate the number of times a specific event occurs for each user, such as number_of_purchases or number_of_logins.
  • Aggregate Functions: This feature allows you to perform mathematical operations on numeric fields of an event and save the result as a live attribute for the user. Examples include:
    • Maximum: The highest purchase amount the user has ever made.
    • Minimum: The lowest purchase amount the user has ever made.
    • Average: The average amount of all purchases made by the user.
    • Sum: The total sum of all purchases made by the user.
  • These attributes are highly effective for dynamic user segmentation, such as identifying “users who have made at least 3 purchases in the last 7 days” or “users with an average purchase value above X.”