What caused the sudden 30% decrease in new user signups for Kiavi's rental loan pre-qualification tool last month?

Introduction

The sudden 30% decrease in new user signups for Kiavi's rental loan pre-qualification tool last month is a critical issue that demands immediate attention. This analysis will systematically identify, validate, and address the root cause while considering both short-term and long-term implications for the product and business.

I'll approach this problem by first clarifying key details, ruling out external factors, and then diving deep into the product, user journey, and metrics. From there, I'll form data-driven hypotheses, conduct root cause analysis, and propose validation methods and solutions.

Step 1

Clarifying Questions (3 minutes)

• Looking at the timing, I'm thinking there might be a seasonal component. Has this 30% decrease been compared to the same month last year?

Why it matters: Seasonal trends could explain fluctuations in loan pre-qualifications. Expected answer: Yes, it's been compared and the decrease is still significant. Impact on approach: If seasonal, we'd focus on year-over-year comparisons rather than month-over-month.

• Considering user segments, I'm curious if this decrease is uniform across all user types. Have we seen any differences in the drop-off rate between first-time investors and experienced landlords?

Why it matters: Different user segments may be affected by different factors. Expected answer: The decrease is more pronounced among first-time investors. Impact on approach: We'd investigate factors specifically affecting new investors, such as changes in lending criteria or market conditions.

• Thinking about recent changes, were there any updates to the pre-qualification criteria or user interface in the weeks leading up to this decrease?

Why it matters: Recent changes could directly impact user behavior and success rates. Expected answer: A minor UI update was implemented two weeks before the decrease. Impact on approach: We'd scrutinize the UI changes and their potential impact on user experience and conversion.

• Considering data integrity, has there been any change in how we're tracking or defining new user signups?

Why it matters: Ensures we're comparing apples to apples and not facing a data anomaly. Expected answer: No changes in tracking or definitions. Impact on approach: If there were changes, we'd need to recalibrate our analysis based on the new definitions.