Introduction
The recent 30% drop in Truist's mobile check deposit feature usage over the past month is a significant concern that requires immediate attention and a thorough root cause analysis. As we delve into this issue, we'll follow a structured approach to identify, validate, and address the underlying causes while considering both short-term fixes and long-term strategic implications.
This analysis follows a structured approach covering issue identification, hypothesis generation, validation, and solution development.
Step 1
Clarifying Questions (3 minutes)
Why it matters: Seasonal patterns could explain temporary fluctuations. Expected answer: No significant seasonal correlation. Impact on approach: If seasonal, we'd focus on cyclical patterns; if not, we'd investigate other factors.
Why it matters: Identifies if the issue is widespread or localized to specific users. Expected answer: The drop is relatively uniform across segments. Impact on approach: Uniform drop suggests a system-wide issue; segmented drop would focus our investigation on specific user groups.
Why it matters: Recent changes could directly impact feature usage. Expected answer: Minor UI updates, no significant feature changes. Impact on approach: Significant changes would prompt us to investigate those specific updates; minor or no changes would shift focus to other factors.
Why it matters: External market forces could influence user behavior. Expected answer: No major competitive shifts noted. Impact on approach: Significant competitive changes would require market analysis; absence of changes focuses us on internal factors.
Why it matters: Ensures we're working with accurate, consistent data. Expected answer: No changes in metric definition or tracking systems. Impact on approach: Any discrepancies would necessitate a data audit before proceeding with further analysis.
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