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How to improve complaint resolution

WayfairData Scientist · Online Assessment
EasyData ScientistOnline AssessmentAnalytics & Experimentation

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This question evaluates a candidate's competency in data-driven analytics, causal inference, metrics design, and experimentation for customer support performance.

You are given three related tables for a Wayfair-style e-commerce customer-support dataset.

complaints

  • complaint_id STRING
  • customer_id STRING
  • order_id STRING
  • product_category STRING
  • issue_type STRING
  • complaint_channel STRING
  • complaint_created_at TIMESTAMP
  • region STRING

resolutions

  • complaint_id STRING
  • resolution_type STRING
  • first_response_at TIMESTAMP
  • resolved_at TIMESTAMP
  • agent_id STRING
  • escalated_flag BOOLEAN
  • refund_amount DECIMAL(10,2)
  • replacement_sent_flag BOOLEAN

service_ratings

  • complaint_id STRING
  • customer_service_rating INT Rating is from 1 to 10, where 10 is the highest satisfaction.
  • rating_submitted_at TIMESTAMP

Each complaint_id refers to one customer complaint and may or may not have a submitted rating. You do not have a full orders table, so be careful to distinguish complaint volume from complaint rate. The business goal is to improve customer experience while controlling support and refund costs.

Answer the following:

  1. Provide 3 data-driven insights you would look for in the data.
  2. Provide 3 recommendations linked to those insights.
  3. Choose 1 prioritized recommendation and explain why it should be implemented first.
  4. Explain how you would measure success after rollout. Specify primary metrics, guardrail metrics, and whether you would use an A/B test, phased rollout, or quasi-experimental design.
  5. Discuss confounding, selection bias in the ratings data, and key implementation risks.