Introduction
The recent 30% decrease in new Dropbox Business team creations over the past month is a significant concern that requires immediate attention. This analysis will systematically identify, validate, and address the root cause while considering both short-term and long-term implications for our product strategy.
I'll approach this issue by first clarifying key details, ruling out external factors, and then diving deep into our product, user journey, and metrics. From there, I'll form data-driven hypotheses, conduct root cause analysis, and propose a comprehensive plan for validation and resolution.
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: Seasonal fluctuations could explain the decrease without indicating a deeper problem. Expected answer: Yes, it has been compared, and this decrease is unusual for this time of year. Impact on approach: If seasonal, we'd focus on why this year is different; if not, we'd look at recent changes or market shifts.
Why it matters: Different segments may be affected by different factors, helping us narrow down potential causes. Expected answer: The decrease is more pronounced in the SMB segment, with enterprise relatively stable. Impact on approach: We'd focus our investigation on factors specifically affecting SMBs.
Why it matters: Recent changes could directly impact new team creations and provide a clear starting point for investigation. Expected answer: A new onboarding flow was implemented 6 weeks ago, and a pricing tier was adjusted. Impact on approach: We'd prioritize analyzing the impact of these specific changes on team creation rates.
Why it matters: Ensures we're not chasing a phantom problem caused by measurement changes. Expected answer: No changes to tracking or definitions have been made. Impact on approach: If changes were made, we'd need to recalibrate our analysis; if not, we can proceed with confidence in our data.
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