The ultimate guide to product analytics interviews. Strategies developed by FAANG engineers to craft structured, impactful answers and increase your chances of landing a data science job.

Product analytics interviews evaluate your skills and creativity as an aspiring data analyst. Interview questions evaluate if you can use your technical knowledge to guide the further development of a product. Learning how to answer these questions is crucial to ace most job interviews for data scientists and analysts.

In this course, you’ll first gain an overview of what product analytics interviews look like and what kinds of questions usually come up. Next, you’ll learn to use a framework for answering product analytics questions, showcasing your analytical skills. Finally, you’ll learn how to apply said framework to different kinds of product analytics interview questions by looking at several examples.

With this course, you’ll increase your chances of landing a job in data science by learning how to effectively answer product analytics interview questions. You’ll be able to provide well-structured and well-communicated answers, and show you can creatively tackle any business problem you may face.

Ace the Product Analytics Job Interview

# Problem

[Let’s say that at Netflix, we offer a subscription where customers can enroll for a 30-day free trial. After 30 days, customers will be automatically charged based on the package selected.](https://www.interviewquery.com/questions/netflix-retention)

For this scenario, we want to measure the success of acquiring new users through the free trial.

How can we measure acquisition success, and what metrics can we use to measure the success of the free trial?

Try answering this on your own before continuing. Use the hints below if you are stuck.




Try to solve this question to test your understanding of the material.


Sample Question: Netflix Retention

Try to solve this question to test your understanding of the material.

Introduction

Investigating Metrics

Fractional Metrics

Measuring Success

Feature Change

Metric Trade-Offs

Growth

Conclusion

Sample Question: Netflix Retention

Problem