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When importing historical data, it is crucial to ensure the consistency and accuracy of user and company data in Userpilot. Follow these guidelines to avoid potential issues in reporting or data analysis:
  1. Sequential Property Building: Begin the import process with a subset of properties and gradually build up the profile until all properties are included. This ensures a smooth transition and avoids overwriting or omitting critical data.
  2. Consistent Property Lists: Avoid submitting inconsistent sets of properties for the same user or company in sequential identify_user or identify_company events. Always include the full list of properties for each identify event. Populate known attributes and leave the remaining attributes as null or empty values.
  3. Avoid Overwriting with Partial Data: Submitting partial data in subsequent identify calls can cause critical attributes to be overwritten with null values, leading to inaccurate reporting and inconsistent profiles.
Example Approach
AttributeFirst identifySecond IdentifyThird Identify
name”John Doe""John Doe""John Doe”
email”john@example.com""john@example.com""john@example.com”
locationnull”Dublin""Dublin”
job_titlenullnull”Architect”
  • Best Practice: Gradually build up the user profile by adding properties in each step until the full profile is complete. This ensures the merge process maintains data consistency.
  • Incorrect Practice: Reducing the set of properties in subsequent calls. This may result in missing or overwritten data.
Why This Matters Userpilot’s data pipeline relies on the accuracy and completeness of historical imports. The merge tree logic aggregates data based on the most recent updates. Inconsistent or partial imports can disrupt this process, leading to incorrect data in reporting tools. By adhering to these best practices, you ensure reliable and comprehensive user and company profiles in Userpilot, maintaining the integrity of your analytics and insights.