Why has Mambu's loan origination module seen a 30% drop in new applications over the past month?

Introduction

Mambu's loan origination module experiencing a 30% drop in new applications over the past month is a critical issue that demands immediate attention. This significant decline could have far-reaching implications for the company's revenue, market position, and customer satisfaction. To address this problem, I'll employ a systematic approach to identify, validate, and resolve the root cause while considering both short-term fixes and long-term strategic implications.

Step 1

Clarifying Questions (3 minutes)

• Given the sudden drop, I'm wondering about recent changes. Have there been any significant updates to the loan origination module in the past 1-2 months?

Why it matters: Recent changes could directly impact user experience and application rates. Expected answer: Yes, there was a UI update or No, no major changes. Impact on approach: If yes, we'd focus on the update's impact; if no, we'd look at external factors or gradual issues.

• Considering user segments, has this drop been consistent across all customer types, or are certain segments more affected?

Why it matters: Helps identify if the issue is universal or specific to certain user groups. Expected answer: It's affecting all segments equally or There's a more significant drop in [specific segment]. Impact on approach: Uniform impact suggests a system-wide issue, while segment-specific problems narrow our focus.

• Thinking about market conditions, have there been any notable changes in the competitive landscape or economic factors in the past month?

Why it matters: External factors could be driving the decline in applications. Expected answer: Yes, a new competitor launched or No, the market has been stable. Impact on approach: If yes, we'd consider competitive positioning; if no, we'd focus more on internal factors.

• Regarding data integrity, can we confirm that the 30% drop is accurate and not due to any changes in tracking or reporting methods?

Why it matters: Ensures we're addressing a real issue and not a data anomaly. Expected answer: Yes, the data has been verified or We need to double-check our tracking. Impact on approach: If verified, we proceed with analysis; if uncertain, we prioritize data validation.