Repeatability
High
The pipeline is structurally identical every time: ingest JSON, apply cleaning rules, standardize categories, extract numerics, aggregate. This is a deterministic ETL pattern that runs the same way on every export.
Ambiguity Tolerance
Medium
The output metrics (median NPS, feature counts, pricing buckets) are crisply defined, but the rules for 'incomplete response' and the mapping of raw company-size strings to Small/Mid/Enterprise require upfront decisions the user hasn't fully specified. These are resolvable with a short clarification or sensible defaults, not a blocker.
Data & Tool Availability
High
The user has the full dataset as an exported JSON file and just needs a code-capable agent with Python/pandas or a spreadsheet tool. No external APIs, live credentials, or third-party access required.
Error Cost
Low
The source data is preserved; any transformation errors are visible in the output spreadsheet and easily corrected. A wrong median or miscategorized bucket is annoying but not irreversible or high-stakes.
Human Judgment Required
Low
The task is mechanical aggregation, not interpretation. The one judgment call — how to bucket ambiguous company-size free text — can be handled with a simple mapping table or a quick human review of edge cases in the output.