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 you’re a data scientist on the engagement team. A product manager comes up to you and says that the weekly active users metric is up by 5%, but email notification open rates are down 2%. What would you investigate to diagnose what’s happening?](https://www.interviewquery.com/questions/wau-vs-open-rates)

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



### Hints

What is the expected relationship between weekly active users and email open rates?

How does this affect our analysis of why we see one increase while the other decreases?


Given that we assume weekly active users and email open rates to be directly correlated (especially when an email is used to bring users back onto the site), our observed trade-off is noteworthy. What are the two ways in which the open rate can decrease? 

**Note:** An **open rate** is a fraction with a numerator and denominator.



The open rate can decrease when its numerator decreases (fewer people open emails) or its denominator increases (more emails are sent). Considering these two factors, what are some hypotheses we can make about our decrease in open rate compared to our increase in weekly active users?

Initially, we should assume that it is a debugging question and possibly a dive into trade-offs.

WAU (weekly active users) and email open rates are often directly correlated, especially if emails are used as a call to action to bring users back onto the website. An email that is opened and clicked would lead to an active user, but it doesn't necessarily mean they have correlations or are the only reason causing changes.


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


Sample Question: WAU vs. Open Rates

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: WAU vs. Open Rates

Problem