A user has to have at least an ID and optionally additional key/value properties.

Databases can be configured with various types of item IDs, such as UUIDs or integers. When creating the database above, we selected uint32, which stands for 32-bit (4-bytes) unsigned integers.

See the Flexible Identifiers documentation to find the available types of user IDs.

User Properties

Uploading user data along with their associated properties is an integral aspect for yielding effective personalized recommendations. The properties attached to a user can span a wide range, including straightforward demographics such as ‘age', 'gender’, or 'location'.

For instance, the user's 'age' can be instrumental in tailoring age-appropriate recommendations on a content streaming platform, while 'location' can influence recommendations on a shopping platform, such as suggesting region-specific items. Importantly, Beam fully adheres to data privacy norms and is capable of working with anonymized user IDs, ensuring that personalized recommendations are generated without compromising user privacy.

Moreover, the Recommendation API offers more than just personalized recommendations — it's also a powerful tool for deriving actionable business insights. Based on the user interactions, Beam generates valuable analytics and metrics such as Conversion, Gross Merchandise Value (GMV), Average Order Value (AOV), and user engagement rates. These metrics offer profound insights into user behavior, measure business performance, and can guide strategic decisions. The API thus serves as a comprehensive solution for enhancing the user experience through personalized recommendations while simultaneously providing critical business intelligence.

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One Step Ahead

The provided user properties, along with the ones created by the API, offer a rich source of data that can be leveraged for an array of strategic applications. They form the basis for conditional business rules, which allow you to govern the operation of the recommendation engine according to your specific business needs. For instance, based on user 'gender' and 'location', you could establish rules to filter and prioritize certain kinds of recommendations.

These properties can also be employed to define condition flows. With condition flows, you can create logic that adapts to the specifics of each user. For example, you might set up a flow that presents different promotional offers to users based on their historical purchasing behavior or geographic location.

Further, user properties play a significant role in cohort creation. By grouping users based on shared characteristics or behaviors (like users of the same age group or from the same location), you can form cohorts. These cohorts can then be analyzed to glean insights into user behavior, test new features, or measure the impact of business decisions on specific subsets of your user base. In this way, user properties serve as the foundation for a range of data-driven strategies and business decisions.