How do I approach Analytics & Experimentation interview questions?

Analytics & Experimentation questions require understanding of core concepts and practice. PracHub provides solutions with explanations to help you master analytics & experimentation interviews.

What difficulty level is this interview question?

This is a easy difficulty Analytics & Experimentation question, commonly asked during Take-home Project rounds at Wayfair.

What role is this question designed for?

This question is commonly asked for Data Scientist candidates at Wayfair during technical interviews.

Derive insights and improve complaint resolutions

Q: Derive insights and improve complaint resolutions

This question evaluates a data scientist's competency in customer experience analytics, causal inference, experimentation design, and multi-table data analysis by requiring synthesis of complaints, resolutions, and service ratings to generate insights and prioritized actions.

You work on customer experience analytics for an e-commerce furniture retailer. You have 3 data tables related to customer complaints and how they were resolved.

Table 1: `complaints`

Each row is a complaint/contact.

complaint_id (string, PK)
customer_id (string)
order_id (string)
product_category (string)
issue_type (string; e.g., damaged item, late delivery, missing parts)
channel (string; phone/chat/email)
created_at (timestamp)
region (string)

Table 2: `resolutions`

One row per complaint resolution attempt (may be multiple per complaint).

resolution_id (string, PK)
complaint_id (string, FK -> complaints)
resolution_type (string; e.g., refund, replacement, coupon, escalate-to-carrier)
resolved_at (timestamp)
sla_met (boolean)
refund_amount (numeric)

Table 3: `service_ratings`

Customer satisfaction after the resolution.

complaint_id (string, FK -> complaints)
rating (int 1–10; 10 = most satisfied)
rated_at (timestamp)

Questions

Provide 3 insights you would try to extract from these data. (Be specific about cuts/segments and what “insight” means.)
Provide 3 actionable recommendations based on those insights.
Choose one prioritized recommendation and explain why it should be prioritized (impact vs effort, customer value, cost, risk).
Describe how you would measure success of that recommendation:
- define a primary metric and supporting diagnostic/guardrail metrics,
- propose an evaluation method (e.g., A/B test, phased rollout, difference-in-differences),
- discuss key confounders/biases (seasonality, selection into channels, case-mix differences, delayed ratings).
What risks are associated with implementing it, and how would you mitigate them?

You may assume standard data quality issues (missing ratings, multiple resolutions per complaint, backfilled timestamps) and should call out how you’d handle them.

You work on customer experience analytics for an e-commerce furniture retailer. You have 3 data tables related to customer complaints and how they were resolved.

Table 1: `complaints`

Each row is a complaint/contact.

complaint_id (string, PK)
customer_id (string)
order_id (string)
product_category (string)
issue_type (string; e.g., damaged item, late delivery, missing parts)
channel (string; phone/chat/email)
created_at (timestamp)
region (string)

Table 2: `resolutions`

One row per complaint resolution attempt (may be multiple per complaint).

resolution_id (string, PK)
complaint_id (string, FK -> complaints)
resolution_type (string; e.g., refund, replacement, coupon, escalate-to-carrier)
resolved_at (timestamp)
sla_met (boolean)
refund_amount (numeric)

Table 3: `service_ratings`

Customer satisfaction after the resolution.

complaint_id (string, FK -> complaints)
rating (int 1–10; 10 = most satisfied)
rated_at (timestamp)

Questions

Provide 3 insights you would try to extract from these data. (Be specific about cuts/segments and what “insight” means.)
Provide 3 actionable recommendations based on those insights.
Choose one prioritized recommendation and explain why it should be prioritized (impact vs effort, customer value, cost, risk).
Describe how you would measure success of that recommendation:
- define a primary metric and supporting diagnostic/guardrail metrics,
- propose an evaluation method (e.g., A/B test, phased rollout, difference-in-differences),
- discuss key confounders/biases (seasonality, selection into channels, case-mix differences, delayed ratings).
What risks are associated with implementing it, and how would you mitigate them?

You may assume standard data quality issues (missing ratings, multiple resolutions per complaint, backfilled timestamps) and should call out how you’d handle them.

Derive insights and improve complaint resolutions

Quick Overview

Derive insights and improve complaint resolutions

Table 1: `complaints`

Table 2: `resolutions`

Table 3: `service_ratings`

Questions

Write your answer

Derive insights and improve complaint resolutions

Quick Overview

Derive insights and improve complaint resolutions

Table 1: `complaints`

Table 2: `resolutions`

Table 3: `service_ratings`

Questions

Write your answer

Derive insights and improve complaint resolutions

Quick Overview