Debug a spike in complaints from students on Playlists at global scale

Problem Statement Description

Product context: Spotify is an audio streaming company; its products include music, podcasts, audiobooks, playlists, recommendations, creator tools, subscriptions, and ads.

Spotify has seen a sudden spike in complaints from student users related to Playlists across multiple markets. The issue is occurring at global scale, but the exact nature, severity, affected workflows, and root cause are unclear. You are the product manager responsible for leading the investigation and coordinating the response.

Students rely on playlists for study sessions, commuting, social listening, parties, discovery, and mood-based listening. Complaints may involve playlist creation, editing, sharing, playback, recommendations, downloads, collaborative playlists, ads, account eligibility, or cross-device behavior. Your task is to frame how you would diagnose the anomaly, separate signal from noise, and determine what action Spotify should take.

This is an RCA-style product interview. Focus on how you would structure the investigation, validate whether the spike is real, identify impacted cohorts and workflows, generate hypotheses, use evidence to narrow causes, and define mitigations and prevention mechanisms without jumping directly to a solution.

The experience should consider:

- How to define the complaint spike, including baseline, time window, denominator, complaint channels, severity, and affected playlist actions.

- How to segment the issue by geography, platform, app version, student plan status, device type, network conditions, playlist type, and listening context.

- How to verify instrumentation and data quality across support tickets, in-app feedback, social signals, telemetry, crash logs, playback events, and playlist mutation events.

- How to distinguish product defects, recommendation quality issues, rights/catalog availability, ads or subscription eligibility, pricing/student verification, latency, and recent experiment or release effects.

- How to prioritize hypotheses based on user impact, business risk, reproducibility, and alignment with Spotify’s personalization, retention, discovery, and monetization goals.

- How to coordinate with engineering, data science, customer support, localization, trust and safety, payments, content operations, and regional teams.

- What immediate mitigations, user communications, monitoring, rollback options, and escalation paths should be considered while the root cause is being confirmed.

- How to prevent recurrence through alerting, release guardrails, cohort monitoring, experiment checks, and clearer ownership of playlist-related reliability.

The goal is to demonstrate a rigorous, structured approach to diagnosing a large-scale product complaint spike for a high-usage Spotify surface, with particular attention to student behavior, global variability, evidence quality, and practical mitigation under uncertainty.