Diagnose a dip in approval/conversion rate
Quick Overview
This Analytics & Experimentation prompt evaluates time-series diagnostics, ratio decomposition (numerator/denominator and traffic mix), segmentation and data-quality triage, and causal inference skills at a staff-level applied analytics abstraction.
Airwallex
Oct 14, 2025, 12:00 AM
Data Scientist
Onsite
Analytics & Experimentation
0
0
You are a Staff Data Scientist analyzing a time series metric for a payments/fintech product.
Data: Daily (or weekly) time series from Jan 2013 to Jan 2015.
- Metric shown is a rate (e.g., credit card approval rate or conversion rate ).
- There is a noticeable dip around Feb 2013 , a small surge around Nov 2013 , and then sustained growth starting around Jan 2014 .
Task:
- List the most plausible explanations for the Feb 2013 dip , Nov 2013 surge , and Jan 2014 growth , including both product/business causes and data/measurement issues .
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Because the metric is a
ratio
, explain how you would decompose and diagnose changes by separately analyzing:
- Numerator (e.g., approved applications / conversions)
- Denominator (e.g., total applications / eligible sessions)
- Any changes in the mix of traffic/users/applications
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Propose a concrete investigation plan:
- What slices/segments would you break down by (examples: channel, geo, device, risk tier, issuer/bank, new vs returning)?
- What supporting metrics would you pull?
- How would you distinguish seasonality , one-off shocks , and true causal impact from a launch or policy change?
State any assumptions you need to make (e.g., timezone, aggregation cadence, definition of approval, logging stability).
Solution
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