Customer Feedback Trend Analysis Questions

Interpret customer feedback patterns to identify systemic issues. Example: 'Our feedback shows 35% of complaints are about billing issues. How do you address this?' Break down the analysis: Is this a product issue, support training gap, process problem, or communication issue? What data would you gather? Who would you collaborate with? How would you implement a fix? Show you can translate customer voice into actionable operational changes.

MediumSystem Design

34 practiced

Design a canonical taxonomy and tagging system for customer feedback that supports automated tagging, manual overrides, and evolution over time. Include hierarchy (broad categories → subcategories), naming conventions, versioning strategy, guidelines for labelers, and governance to ensure consistency and utility for analytics and routing.

MediumTechnical

29 practiced

You're aggregating signals across channels: CS tickets, in-app NPS comments, app-store reviews, and social media. Propose an approach to weight these channels when computing an overall trend score. Explain how you'd validate and adjust the weights over time and how you'd surface uncertainty to stakeholders.

EasyTechnical

34 practiced

Describe common customer feedback channels (in-app NPS, CS tickets, support calls, app-store reviews, social media, interview notes). For each channel explain the typical bias or coverage limitations and one concrete adjustment you would apply when aggregating across channels to produce a fair trend analysis.

EasyTechnical

26 practiced

You have 10 recurring complaint trends extracted from feedback. Describe how you would prioritize which trends to address in the next quarter. Compare at least two prioritization frameworks (e.g., RICE, ICE, cost-of-delay, MoSCoW), list the data you would need to score each trend, and explain how you'd present trade-offs to stakeholders.

EasyTechnical

32 practiced

You have a PostgreSQL table of feedback items:

sql

feedback(
  feedback_id bigint PRIMARY KEY,
  user_id bigint,
  category varchar, -- e.g., 'billing', 'usability'
  created_at timestamptz,
  channel varchar, -- 'email','in-app','phone'
  severity int -- 1 low .. 5 critical
)

Write a SQL query (Postgres) to compute: (a) the percent of feedback items classified as 'billing' in the last 90 days, and (b) an approximate 95% confidence interval for that proportion. Explain any assumptions about nulls or sampling.

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