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Diagnose a dip in approval/conversion rate

AirwallexData Scientist · Onsite
MediumData ScientistOnsiteAnalytics & Experimentation

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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.

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:

  1. 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.
  2. 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
  1. 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).