## Part A — Diagnose a conversion/approval-rate time series You are given a monthly time series of **credit card approval rate** from **Jan 2013 to Jan 2015**. You observe: - **Feb 2013:** a noticeable dip - **Nov 2013:** a small surge - **From Jan 2014 onward:** sustained growth Assume approval rate is a conversion-like metric: \[ \text{Approval Rate} = \frac{\#\text{approved applications}}{\#\text{applications submitted}} \] **Questions** 1. What are plausible hypotheses for the Feb dip, Nov surge, and the growth starting Jan 2014? 2. What **assumptions** would you want to validate before drawing conclusions? 3. How would you “go deeper” by analyzing the **numerator vs. denominator** and explaining why the rate moved? 4. What slices/segments and additional data would you request to validate causality vs. correlation? --- ## Part B — Evaluate swapping two CTAs on a landing page On an Airwallex webpage there are two primary CTAs (buttons): - **“See a demo”** - **“Get started”** A proposal is to **swap their positions** (e.g., left/right or top/bottom, whichever is more prominent). **Questions** 1. What user/business problem could this change be trying to solve, and why might swapping help? 2. How would you design an experiment (or evaluation plan) to test it? 3. Define: - a **primary success metric** - **diagnostic metrics** to explain movement - **guardrail metrics** to prevent harm 4. What confounders/pitfalls (traffic mix, selection bias, novelty effects, downstream lead quality, etc.) would you watch for, and how would you mitigate them?