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 we work for a social media company that has launched in a new city. Looking at weekly metrics, we see a slight decrease in the average number of comments per user from January to March in this city.](https://www.interviewquery.com/questions/decreasing-comments)

The company has consistently acquired new users in this city from January to March.

What are some reasons that the average number of comments per user would decrease, and which metrics would you look into?

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







How would you calculate the metric for average comments per user?


Let's model out an example scenario to help us see the data. 

| **Month** | **Users** | **Comments** | **Comments per User**
|-|-|-|-|
|January| 10000 | 30000 | 3 |
|February| 20000 | 50000 | 2.5 |
|March| 30000 | 60000 | 2 |

We're given information that the total user count is increasing linearly, which means that the **decreasing comments/user** is not an effect of a declining user base creating a loss of network effects on the platform.

In that case, what else can we hypothesize?

We know that even as the company is increasing users, it’s likely users will fall off and churn off the platform. Let’s say we model the user churn by month.

|**Month**|**Churn Rate**|
|-|-|
| January | 25% |
| February | 20% |
| March | 15% |

What would that tell us about what is causing our metric of average comments per user to decline?

What if we looked at the number of comments posted per active user? That way, if we see a decrease or no change, we can go in several different directions.

If there is a decrease, we could attribute it to active users on the platform commenting less. If there isn't, then maybe the decrease is due to a flat-lining of active users as old users churn out and use the platform less. 

# Solution

Let’s investigate a couple of different metrics.

Average comments per user are calculated by taking the total number of comments divided by the total number of users. Let’s model an example scenario.

| **Month** | **Users** | **Comments** | **Comments per User**
|-|-|-|-|
|January| 10000 | 30000 | 3 |
|February| 20000 | 50000 | 2.5 |
|March| 30000 | 60000 | 2 |

We’re given information that the total user count is increasing linearly, which means that the decreasing comments/user is not an effect of a declining user base.

With this, we can hypothesize the following answers:

1. **Could more users cause less individual engagement?** 

   Otherwise known as the crowding effect, we can model this by looking at a cohort of users that started in the first week of January and another cohort of users that started the first week of March. If we see no difference between the number of comments after the first, second, and third week, then there likely is no crowding effect happening.

2. **What about active user engagement?**

   We know that even as the company is increasing users, it’s likely that users will fall off and churn off the platform. Let’s say we model the user churn by month.

|**Month**|**Churn Rate**|
|-|-|
| January | 25% |
| February | 20% |
| March | 15% |

This means that for the cohort of users that starts in January, there are now only 7500 (10000 * 75%) active users by February. In March, there are 6000 active users from a 20% churn.

This can explain a likely effect of why we see a decrease in comments per user even though the total user count is increasing linearly. Churn decreases the active user counts, which is assumed to be directly correlated to how many comments will exist on a platform. Because we have fewer active users on the platform, the denominator is, in this case, a fake proxy for actual platform engagement.

If we want to measure if active users will still comment, we can look at comments per active user.


3. **What does retention look like?**

   Users that join during the launch could be the ones with the most engagement, given their bias towards being first adopters. One way to measure if this hypothesis is true towards having higher comments/users in earlier users is to look at other cities in their first few months of launch. Let’s suppose the retention curves look similar in terms of the number of comments per user when segmented by city. In that case, it will provide insight into whether or not the initial rate was inflated due to a concentration of highly active users.




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


Sample Question: Decreasing Comments

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: Decreasing Comments

Problem