I started by trying to map out where users actually get stuck, like finding the right help article, troubleshooting playback issues, or figuring out billing stuff.
Start by framing the problem around Netflix's core support goals: reducing contact volume, improving resolution speed, and maintaining high satisfaction. Then propose AI solutions across the support journey—self-service, agent assist, and proactive outreach—prioritizing based on impact and feasibility. Finally, define success metrics and a test-and-learn plan to validate improvements.
Pro tip: Anchor your answer in Netflix's culture of experimentation and member obsession: propose A/B tests for AI features and emphasize that AI should augment, not replace, human agents for complex or emotional issues.
Clarify Netflix's support challenges (e.g., high contact volume, resolution time, CSAT) and set objectives like reducing contacts per member or improving first-contact resolution.
Identify key touchpoints—self-service help center, chat/phone with agents, and proactive notifications—to pinpoint where AI can add value.
Propose specific AI applications: conversational AI for self-service, agent-assist tools (e.g., real-time suggestions, summarization), and predictive models for proactive outreach.
Evaluate ideas by impact (e.g., contact deflection, CSAT lift) and effort (data, tech, risk), then outline a phased rollout with A/B tests.
Establish success metrics (e.g., containment rate, CSAT, AHT) and a feedback loop to continuously improve AI models and support experience.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.