What caused the sudden 30% increase in failed payments for Esusu's credit-building program last week?

Introduction

The sudden 30% increase in failed payments for Esusu's credit-building program last week 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 its users.

I'll approach this problem by first clarifying key details, ruling out external factors, and then diving deep into the product's 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 this could be related to a recent product update. Has there been any significant change to the payment process or credit-building program in the past month?

Why it matters: Recent changes often correlate with sudden metric shifts. Expected answer: Yes, a new payment gateway was implemented two weeks ago. Impact on approach: If confirmed, I'd focus on technical integration issues.

• Considering user segments, I'm curious about the distribution of failed payments. Are we seeing this increase across all user groups or is it concentrated in specific segments?

Why it matters: Helps determine if it's a systemic issue or limited to certain user types. Expected answer: The increase is primarily seen in new users who joined in the last month. Impact on approach: If true, I'd investigate onboarding processes and new user experiences.

• Given the nature of credit-building programs, I'm wondering about any changes in credit reporting or scoring models. Have there been any updates to how credit scores are calculated or reported recently?

Why it matters: External changes in credit systems could impact payment success rates. Expected answer: No significant changes in credit reporting systems. Impact on approach: If confirmed, I'd shift focus to internal factors and user behavior.

• Thinking about system health, I'm concerned about potential data discrepancies. Can we confirm that the payment failure tracking system is functioning correctly and hasn't undergone any recent changes?

Why it matters: Ensures we're dealing with a real issue and not a data anomaly. Expected answer: The tracking system is functioning normally with no recent changes. Impact on approach: If confirmed, I'd proceed with confidence in the data's accuracy.