The short answer?
All types of criteria are available on Meta! ✅
What about those criteria?
Meta uses different types of criteria to collect and classify user data.
Here are some examples of criteria:
Interests criteria: These criteria are based on users' interactions with Meta products, such as liked posts, joined groups, followed pages, clicked ads, etc. These interests are usually used to create targeted advertising campaigns to reach users who are likely to be interested in a particular product, service, or content.
Brands: Users who have shown interest in specific brands, such as Adidas, Sony, or Starbucks.
People: Users who follow or interact with public figures, celebrities, or influencers, such as Taylor Swift, Jeff Bezos, or Oprah Winfrey.
Interests: Users who have expressed interest in certain hobbies, activities, or topics, such as gardening, meditation, or cryptocurrency.
Job Titles: Users who hold specific job titles or are affiliated with particular industries, such as data scientists, graphic designers, or healthcare professionals.
Behaviors criteria: These criteria are based on specific user actions, such as purchases made, comments left, messages sent, videos watched, etc. Behaviors can be used to personalize the user experience by recommending relevant products and services.
Purchase Habits: Users who frequently make purchases related to outdoor gear, luxury fashion, or home electronics.
Travel Patterns: Users who frequently travel internationally, book last-minute trips, or participate in adventure tourism.
Fitness Activities: Users who regularly engage in activities like running, yoga, or weightlifting.
Content Consumption: Users who consume a lot of content related to entrepreneurship, self-improvement, or parenting.
Preferences criteria: These are based on users' stated choices and preferences, such as language preferences, privacy preferences, etc. Preferences can be used to personalize the user's experience by providing customization options tailored to their preferences.
Communication Preferences: Users who prefer to receive communication via email, SMS, or push notifications.
Privacy Settings: Users who have opted for increased privacy controls, limiting data sharing or ad tracking.
Content Preferences: Users who prefer specific types of content, such as long-form articles, videos, or podcasts.
Browsing Patterns criteria: These are based on users' browsing behavior on Meta's partner websites and applications. Browsing patterns can be used to personalize the user experience by delivering content and advertisements that are relevant to the user's interests and browsing behavior.
Website Visits: Users who frequently visit websites related to financial news, fashion trends, or political commentary.
App Usage: Users who regularly use apps for food delivery, language learning, or meditation.
Engagement Levels: Users who spend significant time engaging with content on specific topics like climate change, space exploration, or gourmet cooking.
Déclarative or Deductive Insights? 📣
From the list provided, the following criteria can be considered declarative (based on users' stated choices or preferences) or deductive (involve drawing conclusions based on available information). Here are some examples:
Declarative Criteria:
Job Titles: Users provide their job titles or affiliations with specific industries either during account creation or by updating their profile information. Meta collects this information as part of the user's profile data.
Communication Preferences: Users explicitly select their communication preferences within Meta's settings or during account setup. They may choose to receive notifications via email, SMS, or push notifications. Meta respects these preferences and utilizes them to tailor communication methods accordingly.
Privacy Settings: Users have the option to set their privacy preferences within Meta's settings. They can specify the level of data sharing they are comfortable with, including opting out of certain data collection practices or ad tracking. Meta adheres to these preferences to ensure user privacy.
Content Preferences: Users indicate their content preferences through various actions such as liking specific types of posts, joining groups related to certain topics, or interacting with content in specific categories. Meta analyzes these interactions to infer users' content preferences and tailor their feed accordingly.
Deductive Criteria:
Brands: Meta analyzes users' interactions such as likes, follows, comments, and shares related to specific brands. For example, if a user frequently engages with posts or pages associated with Nike, Meta deduces that the user is interested in the Nike brand.
People: Meta tracks users' interactions with public figures, celebrities, or influencers on the platform. This includes actions like following, commenting, sharing, or reacting to posts from these individuals. Based on these interactions, Meta infers users' interest in these public figures.
Purchase Habits: Meta gathers data on users' purchasing behavior, including the types of products they buy, the frequency of purchases, and the brands they prefer. By analyzing transaction histories and interactions with marketplace features, Meta deduces users' purchase habits and preferences.
Travel Patterns: Meta collects data on users' travel-related activities, such as check-ins, posts about travel destinations, or interactions with travel-related content. By analyzing these actions, Meta deduces users' travel patterns, preferences, and interests.
Fitness Activities: Meta tracks users' engagement with fitness-related content, such as joining fitness groups, reacting to fitness posts, or using fitness tracking features. Based on these interactions, Meta deduces users' interest and involvement in fitness activities.
Content Consumption: Meta analyzes users' browsing history, interactions with content, and engagement metrics to infer their content preferences. This includes tracking the types of articles they read, videos they watch, or topics they frequently engage with.
Website Visits: Meta collects data on users' browsing activities across its network of partner websites and applications. By analyzing the websites users visit and their interactions on those sites, Meta deduces their interests and preferences.
App Usage: Meta tracks users' interactions with apps within its ecosystem, including time spent on specific apps, frequency of usage, and actions performed within the apps. By analyzing this data, Meta deduces users' interests and preferences related to app usage.
Engagement Levels: Meta assesses users' engagement with content on specific topics by analyzing their interactions, such as likes, comments, shares, or time spent on relevant posts. Based on this engagement data, Meta deduces users' interests and engagement levels in different subject areas.
All in all ✅
The Meta ecosystem possesses a comprehensive understanding of its users' interests, preferences, behaviors, and habits. Through a combination of declarative criteria obtained from users' stated choices and preferences, as well as deductive criteria inferred from users' actions and interactions on the platform, Meta has amassed a wealth of data about its user base.
This knowledge allows Meta to personalize user experiences, deliver targeted advertising campaigns, recommend relevant content, and tailor its services to better meet the needs and interests of its users. And it is exactly all that knowledge that you can enjoy using SOPRISM, either while creating a new profiling project or understanding new insights about your target audience.
☝️ One more thing, in the SOPRISM platform, the criteria available mirror those accessible on Meta when targeting an audience. It's important to note that criteria represent raw data imported directly from Meta into SOPRISM, contrasting with topics and mindsets, which are constructed by our SOPRISM experts in data and insights.