Introduction
The doubling of average resolution time for Slack's enterprise customer support tickets is a critical issue that demands immediate attention. This problem not only impacts customer satisfaction but also has potential long-term consequences for Slack's reputation and market position in the enterprise segment. I'll approach this analysis 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: Product changes often lead to unexpected issues or user confusion. Expected answer: Yes, a major update was released. Impact on approach: If true, we'd focus on the new features and their potential impact on support ticket volume and complexity.
Why it matters: Changes in ticket types could indicate new problems or user pain points. Expected answer: There's been an increase in complex integration issues. Impact on approach: This would lead us to investigate integration partnerships and documentation.
Why it matters: Team changes can affect support efficiency and knowledge transfer. Expected answer: Some senior support staff left recently. Impact on approach: We'd need to look into knowledge management and training processes.
Why it matters: A surge in tickets could overwhelm the support system. Expected answer: Ticket volume has increased by 30%. Impact on approach: We'd need to investigate both the cause of increased tickets and support team capacity.
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