Introduction
The recent 15% drop in Affirm's Split Pay approval rate is a critical issue that demands immediate attention. This unexpected decline could significantly impact user experience, revenue, and overall business performance. I'll approach this problem systematically, focusing on identifying the root cause, validating hypotheses, and developing both short-term and long-term solutions.
Framework overview
This analysis follows a structured approach covering issue identification, hypothesis generation, validation, and solution development.
Step 1
Clarifying Questions (3 minutes)
Why it matters: Changes in risk assessment directly impact approval rates. Expected answer: Yes, there was a minor update to the algorithm. Impact on approach: If confirmed, we'd focus on analyzing the algorithm change and its effects.
Why it matters: Helps identify if the issue is systemic or specific to certain user groups. Expected answer: The drop is more pronounced in new customers and those with lower credit scores. Impact on approach: We'd investigate factors specifically affecting these segments.
Why it matters: Data quality issues could lead to incorrect risk assessments. Expected answer: No known issues with data providers. Impact on approach: If confirmed, we'd shift focus to internal factors rather than external data issues.
Why it matters: Technical issues could lead to incomplete assessments or false rejections. Expected answer: Some intermittent latency issues were observed. Impact on approach: We'd prioritize investigating the technical infrastructure and its impact on approvals.
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