How to improve complaint resolution

Q: How to improve complaint resolution

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

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Question

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:

Provide 3 data-driven insights you would look for in the data.
Provide 3 recommendations linked to those insights.
Choose 1 prioritized recommendation and explain why it should be implemented first.
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.
Discuss confounding, selection bias in the ratings data, and key implementation risks.

How to improve complaint resolution

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