Diagnose a dip in approval/conversion rate

You work as a Staff Data Scientist examining a time series metric for a payments/fintech product.

Data: A daily (or weekly) time series covering January 2013 through January 2015.

• The metric is a rate, such as a credit card approval rate or conversion rate.
• There is a clear dip near February 2013, a small surge near November 2013, and sustained growth beginning around January 2014.

Task:

1. Identify the most likely explanations for the February 2013 dip, the November 2013 surge, and the January 2014 growth, covering both product/business causes and data or measurement issues.

2. Since the metric is a ratio, describe how you would break down and diagnose changes by examining these separately:

   • Numerator (for example, approved applications or conversions)
   • Denominator (for example, total applications or eligible sessions)
   • Any shifts in the mix of traffic, users, or applications

3. Outline a concrete investigation plan:

   • Which slices or segments would you use to break down the data (for example, channel, geography, device, risk tier, issuer/bank, new versus returning)?
   • Which supporting metrics would you collect?
   • How would you separate seasonality, one-off shocks, and true causal impact from a launch or policy change?

State any assumptions you need to make, such as timezone, aggregation cadence, the definition of approval, or logging stability.

Overview:

This Analytics & Experimentation prompt tests time-series diagnostics, ratio decomposition (numerator/denominator and traffic mix), segmentation and data-quality triage, and causal inference at a staff-level applied analytics scope.