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 medium difficulty Analytics & Experimentation question, commonly asked during Technical Screen rounds at Lyft.

What role is this question designed for?

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

Investigate Metric Drops and Coupon Retention

Q: Investigate Metric Drops and Coupon Retention

This question evaluates a data scientist's skills in product analytics, metric instrumentation and validation, causal inference, cohort selection, experimental design and ROI measurement within the analytics & experimentation domain.

You are a Data Scientist for a ride-sharing marketplace in Toronto. Answer the following related product analytics questions.

A key dashboard metric suddenly changes: monthly active users decreased by 7% and average rider wait time increased by 20%. How would you investigate whether this is a real business issue or a data issue, and how would you identify the root cause?
Leadership believes the user decline may be caused by churn and proposes sending coupons to retain riders. How would you decide which users should receive coupons? How would you design an experiment to measure whether coupons reduce churn? Include the experiment unit, randomization strategy, primary metrics, guardrail metrics, and how you would measure cost versus benefit.
Suppose a coupon test produced the following before-and-after numbers. Before the coupon, revenue per ride was $10 and variable cost per ride was$ 4. After the coupon, total rides increased by 10%, while total revenue increased by only 3%. Assume variable cost per ride remains $4, excluding any explicit coupon or marketing cost unless otherwise stated. Should the company launch the coupon program? What additional information would you need?

You are a Data Scientist for a ride-sharing marketplace in Toronto. Answer the following related product analytics questions.

A key dashboard metric suddenly changes: monthly active users decreased by 7% and average rider wait time increased by 20%. How would you investigate whether this is a real business issue or a data issue, and how would you identify the root cause?
Leadership believes the user decline may be caused by churn and proposes sending coupons to retain riders. How would you decide which users should receive coupons? How would you design an experiment to measure whether coupons reduce churn? Include the experiment unit, randomization strategy, primary metrics, guardrail metrics, and how you would measure cost versus benefit.
Suppose a coupon test produced the following before-and-after numbers. Before the coupon, revenue per ride was $10 and variable cost per ride was$ 4. After the coupon, total rides increased by 10%, while total revenue increased by only 3%. Assume variable cost per ride remains $4, excluding any explicit coupon or marketing cost unless otherwise stated. Should the company launch the coupon program? What additional information would you need?

Investigate Metric Drops and Coupon Retention

Quick Overview

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Investigate Metric Drops and Coupon Retention

Quick Overview

Solution

Comments (0)