System for Preparing Behaviour Summaries from Product Usage Events

The problem

In day-to-day operations, anonymised product events and the feature dictionary arrive through different systems and files, preventing the team from reviewing the same case in one view.

As a result, the link to source evidence can be lost while a usage-pattern summary is prepared, leading to repeated checks and uncertainty about the basis for the decision.

Incomplete automation may overlook a change in the measurement schema, while incorrect automation risks treating correlation as user intent.

What we set out to improve

• Time taken for the usage-pattern summary draft to be ready for review
• Correct routing of records affected by a measurement-schema change to the appropriate specialist queue
• Record of suggestions corrected or rejected by the product analyst

How the system works

1. 01 Retrieve anonymised product events and the feature dictionary from authorised sources
2. 02 Verify the source system, record identifier and recency information
3. 03 Map the fields to the target process schema
4. 04 Prepare the usage-pattern summary draft
5. 05 Compare the draft with business rules and source records
6. 06 Check for an exception: the measurement schema has changed
7. 07 Have the product analyst approve, revise or reject the suggestion
8. 08 Write the input, suggestion, changes and final decision to the audit log

Methods we used

• Source-traceable data extraction for anonymised product events and the feature dictionary
• Structured output that limits the usage-pattern summary fields through business rules
• An exception gate that separates cases affected by a measurement-schema change
• A queue that shows the product analyst the source evidence and suggestion together
• Duplicate-processing controls and a reversal record to reduce the risk of treating correlation as user intent

Where people stay involved

Data and security

Only fields required for the task are processed from anonymised product events and the feature dictionary. Source-system permissions are preserved, and personal or commercially sensitive data is minimised before being passed to the model. Every read, suggestion, human change and write to the target system is logged.

Who this suits

Frequently asked questions