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 the Facebook composer, the posting tool, dropped from 3% posts per user last month to 2.5% post per user today. How would we investigate what happened?](https://www.interviewquery.com/questions/post-composer-drop) 

Let’s say the drop is in photo posts. What would we investigate now?

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




The metric we’re given is the average number of posts per user. What are the two ways that this number could decrease?

For the average number of posts per user to decrease, either the number of users must increase, or the number of posts must decrease. 

If the number of posts is decreasing, how could we segment the posts on Facebook to get a better sense of what's happening?

We could segment our posts according to post type (photos, events, statuses, etc.) and posting area (group posts, newsfeed posts, marketplace posts, etc.) to understand better where our decrease in posts is centralized. 

Now, let’s consider the other side of the equation. If the number of users increases, how can we segment our user base to understand the decreasing trend in average posts per user?


We can segment our user base into active or inactive users or bucket them according to the date they joined to understand our decreasing trend in average posts per user.

How can we apply our analysis of these different segments to the problem of a decrease in the average number of photo posts per user on the platform?

# Solution

The first thing we have to do is clarify the problem’s context before jumping to conclusions about metrics. The question states the drop is from 3% a month ago to 2.5% today. Is today a weekday and one month from today a weekend, so users are posting less? Is there a special event or seasonality? Is this an ongoing downward trend or a one-time  downward spike?

The second part is understanding the metric itself. What exactly drove the decrease: was it the number of *users that increased* or the number of *posts that decreased*? Likely, the interviewer will ask you to jump into one or both metrics to dive into what could have caused the decrease.

Let’s start by focusing on the cause of the number of posts decreasing. How can we split this into different cohorts of users and segments to understand where there is a drop in the posting rate?

Let’s frame the activity of posting holistically. Think about yourself as a Facebook user from a non-biased point of view and how many different segments posting can be divided into.

There are two immediate ways we can segment the act of posting: type and product location.






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


Sample Question: Post Composer Drop

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: Post Composer Drop

Problem

Solution