Metrics question

You've noticed a significant increase in customer complaints about inaccurate billing in the Zoomcar app. Customers report discrepancies between the charges quoted during booking and the final amount billed to their accounts. As a product manager, how would you identify the root cause(s) of this issue?

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What this question tests

Structured root cause analysis: can you methodically narrow down a billing discrepancy problem instead of guessing at a single cause.

Segment the complaints first: by vehicle type, city, booking channel, and time period, to see if the issue is isolated or systemic.
Form hypotheses tied to the billing flow: incorrect fuel or distance calculation, late fees applied incorrectly, surge or add on charges not disclosed at booking, or a recent pricing engine change.
Check for a recent change, since a spike in complaints often correlates with a specific release, pricing update, or new partner integration.
Pull a sample of flagged bookings and manually trace each charge line against what was quoted, to isolate exactly which charge category is wrong.
Once isolated, quantify impact, how many bookings and how much revenue or customer trust is affected, to prioritize the fix.
Define resolution success as complaint volume returning to baseline and no new discrepancies in a post fix audit sample.

Starts with segmentation and hypothesis generation before jumping to a fix, showing real diagnostic discipline.
Explicitly checks for a recent change as the most likely root cause of a sudden spike.
Proposes manually tracing a sample of bookings, a concrete, executable diagnostic step.

Jumping straight to a fix without first isolating which specific charge or booking segment is the source.
Assuming a single obvious cause instead of methodically testing hypotheses.

How would you communicate this issue to affected customers while you investigate?
How would you prevent a similar billing error after a future pricing update?