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 3) 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).