RTO jumped to 16%

A root cause drill question from a product manager interview, set at Bazaario, a fictional value e-commerce company.

The question

What you're told upfront

• RTO rate (orders shipped but returned to the seller undelivered) rose 12% → 16% over the last two weeks; step change starting on a Monday.
• Placed orders flat; delivered orders down ~4.5%.
• Not a sale; data verified; post-delivery customer returns are separate and flat.

Skills this case tests

A starting metric tree

Delivered orders = placed orders × (1 − cancellations before dispatch) × (1 − RTO rate)

How to approach this case

You're told placed orders are flat and customer returns are counted separately, so go straight to the RTO tree. Split RTO by payment mode first, since cash-on-delivery and prepaid orders fail for different reasons. Then split by RTO reason code: a buyer refusing at the door and a buyer who couldn't be reached are different problems with different owners. A step change that starts on a specific day points to an operational change, so ask where the rise is concentrated, who handles those deliveries and how they work. Then find a comparison group that didn't get the change. A strong close weighs the cost of the extra RTOs (two-way shipping, handling, lost sales, unhappy buyers) against any savings from recent decisions, and gives a fix with a cost-per-delivered-order guardrail.

The full method for quick structuring drills · A worked example answer

After the mock interview you get scores on five skills, what to fix with quotes from your answers, and the ideal path for this case.