What caused the sudden 30% decrease in new auto loan applications on Upstart's platform this week?

Introduction

The sudden 30% decrease in new auto loan applications on Upstart's platform this week is a critical issue that demands immediate attention. As we delve into this product execution problem, I'll employ a systematic approach to identify, validate, and address the root cause while considering both short-term and long-term implications.

My analysis will follow a structured framework, beginning with clarifying questions to establish context, ruling out basic external factors, understanding the product and user journey, breaking down the metric, gathering and prioritizing data, forming hypotheses, conducting root cause analysis, and finally proposing validation methods and next steps.

Step 1

Clarifying Questions (3 minutes)

• Looking at the timing, I'm thinking this could be a recent change. Has there been any significant product update or policy change in the past week?

Why it matters: Recent changes often correlate with sudden metric shifts. Expected answer: Yes, there was a UI update to the application form. Impact on approach: If true, we'd focus on the UI changes and user experience.

• Considering the magnitude of the decrease, I'm wondering about system stability. Have there been any reported technical issues or downtime in the past week?

Why it matters: Technical issues can dramatically impact user engagement. Expected answer: No major reported issues. Impact on approach: If false, we'd prioritize technical investigation.

• Given the specificity to auto loans, I'm curious about market conditions. Has there been any notable change in the auto industry or interest rates recently?

Why it matters: External market factors can significantly influence loan demand. Expected answer: Interest rates have remained stable, no major industry news. Impact on approach: If true, we'd need to look more closely at internal factors.

• Thinking about user segments, I'm wondering if this decrease is uniform across all user groups. Do we see any differences in the drop-off rate among various demographics or credit score ranges?

Why it matters: Segmented analysis can reveal targeted issues or changes in user behavior. Expected answer: The decrease is relatively uniform across segments. Impact on approach: If false, we'd focus on specific user groups or potential targeting issues.