Repeatability
High
The transformation logic is structurally identical every run: parse JSON, group by signup month, compute retention and churn by period, aggregate CLV. This can be scripted once and re-run monthly with new data.
Ambiguity Tolerance
High
The required outputs are explicitly named — cohort month, retention rates for months 0–24, average CLV, churn counts — leaving little room for interpretation. Minor ambiguity exists around CLV formula (revenue source not specified), but success criteria are otherwise crisp.
Data & Tool Availability
High
The user has the data in hand as JSON with all required fields (customer ID, signup date, product tier, churn date). No live API calls or external permissions are needed to execute the transformation.
Error Cost
Low
The output is an analytical pivot table for internal review, not a financial transaction or customer-facing action. Errors are visible on inspection and easily corrected before any decisions are made.
Human Judgment Required
Low
The task is deterministic data engineering with no subjective calls. The only judgment needed is defining CLV if revenue data is absent, which is a one-time clarification, not ongoing human oversight.