How to approach identity stitching with Snowplow data

Identity stitching, or user stitching, is a process used by companies to connect various user identifiers to form a comprehensive view of user behavior across multiple devices and platforms. This technique, which results in a 360-degree customer view, allows businesses to personalize user experiences more effectively. The process typically involves event properties to identify users, understanding how these identifiers map onto each other, and a method to utilize the mapping despite incomplete data. The choice between batch and real-time stitching depends on the specific use case, with batch processing being economical for non-time-sensitive tasks and real-time approaches being essential for applications like recommendation engines. Tools like Snowplow offer the capability to handle both batch and real-time data, providing identifiers such as domain_userid, network_userid, and custom user_id. Effective identity stitching requires mapping these identifiers appropriately and considering the relationships between them, such as multiple identifiers per user or vice versa. The process must comply with data privacy regulations, and Snowplow enables businesses to maintain control over their data while providing the flexibility to implement identity stitching in a manner that suits their needs.