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35 Product Analyst Interview Questions (New for 2022)

Overview

A product analyst is a hybrid role between a data analyst and a business analyst. At its core, product analysts work cross-functionally with other teams to bring new products to the market by using data to drive decision making.

Product analyst interviews assess the unique skill set needed to pull this balancing act off. You can expect plenty of data analytics questions - namely SQL writing and statistics - as well as business/product sense questions. These interviews are designed to test your ability to use data in making sound product decisions.

Product Analyst Interviews: What Questions Get Asked?

Product analyst interviews vary by company. However, after analyzing more than 15,000 product analyst interview experiences, we know the most commonly asked topics are product metrics case studies, SQL questions, and data analytics:

A/B TestingAlgorithmsAnalyticsMachine LearningPresentationProbabilityProduct MetricsPythonSQLStatisticsTakehomeWhiteboard
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What Is the Interview Process for Product Analysts?

Product analyst interviews typically include 3-5 rounds that assess your technical skills, product intuition, and ability to communicate. Google Product Analyst interviews, for example, include three rounds:

Step 1: Recruiter Screen

The majority of tech companies use recruiter screens as a first step. This provides the recruiter the chance to get to know the candidate and understand if the candidate is right for the role, assess communication skills and see if the candidate has a genuine interest in the role.

Step 2: Technical Screen

A technical round assesses your technical skills. Specifically, SQL and product intuition are tested during this round. You will be asked to write SQL queries and functions to solve problems.

You can also expect product case study and metrics questions. These questions ask you to analyze data and use that analysis to answer questions about a product.

Step 3: On-Site Round

During the on-site round, you can expect more technical and product rounds. Many companies also do a one-on-one with the product manager (PM). This interview assesses your knowledge of the product, and to see if your product intuition is a strong match.

Product Case Study Questions

Product case study questions assess your ability to use data to influence product decisions. Typically, these questions ask about feature changes, metrics anomalies, measuring product success and/or product improvements.

1. How would you determine why the number of comments per user is decreasing at a social media company for the last three months?

To add context on why the question is being posed, even though comments per user is decreasing, overall the company has been consistently growing users month-over-month for three months.

With a question like this, start by modeling the scenario. Your model might look like this:

  • Jan: 10000 users, 30000 comments, 3 comments/user
  • Feb: 20000 users, 50000 comments, 2.5 comments/user
  • Mar: 30000 users, 60000 comments, 2 comments/user

Using this model, you might also model churn as Month 1 - 25%, Month 2 - 20% and Month 3 - 15%. Knowing that some users are churning off the platform each month, what can you infer about the decrease in comments per user?

2. What’s the first change you would make or new feature that you would add to product X?

Interviewers ask this question to see that you have done your research and have knowledge of the company’s products. In particular, they want to know:

  • If you understand what the product is and its features.
  • If you understand who the target audience is.
  • If you recognize the problem the product solves for the audience.

Prior to the interview, create some answers for a question like this. In particular, you should propose changes or new features that will enhance the product, address a problem and align with the company’s overarching objectives.

3. How can you use the Facebook app to promote Instagram?

This product question is more focused on growth and is actively in Facebook’s growth marketing analyst technical screen. With growth questions, we have to come up with solutions in the form of growth ideas and provide data points for how they might support our hypothesis.

One hypothesis we could propose is that implementing notifications to Facebook users of friends that have joined Instagram would help to promote Instagram. So if a user’s friend on Facebook decides to join Instagram, we could send a notification to the user that their friend joined Instagram. We can test this hypothesis by implementing an A/B test. We can randomly bucket users into a control and test group where the test group gets notifications on Facebook each time their friend joins Instagram, while the control group does not. At the end of the test, we can observe the sign-up rate on Instagram between the two groups.

4. You have ten experiment ideas for improving conversion rates on an ecommerce website. How do you choose which ideas to test?

One of the most effective ways is to conduct quantitative analysis. You can measure the opportunity size of each idea using historical data.

For example, if one of the ideas was to introduce cart upsells, you could analyze the number of multi-item orders historically. If only a small percentage of customers purchase multiple items, introducing upsells would be a sizable opportunity. You might then choose an A/B test related to cart upsells.

5. Which variables might Uber use to estimate pick-up ETA, besides the ETA from the GPS system?

With metrics questions, start by listing broad variables that could affect ETA. In this case, that would include things like:

  • Driver speed - Some drivers may be faster than others.
  • Finding the passenger - Locations that experience large crowds would make it harder to locate a passenger.
  • Weather conditions - Poor weather conditions could slow pick-up times.
  • Wrong turn rates - An area where drivers are prone to wrong turns could slow pick-up times.
  • Construction Seasonality - Construction and street/housing improvement projects tend to occur at higher rates during certain times of year, and could impact road access in high density areas.

Once you’ve created a list of broad variables, you can then start to go deeper and choose which ones might have the greatest effect. Weather, how crowded a location is, and wrong turn rate could all help to improve the accuracy of the ETA model.

6. How would you decide whether updating the permanent deletion rate of Dropbox’s trash feature is a good idea?

Let’s say that Dropbox wants to change the logic of the trash folder from never permanently deleting items to automatically deleting items after 30 days. How would you validate this idea? See a step-by-step solution to this question on YouTube:

Dropbox product analyst mock interview

7. How can we measure Netflix’s success in acquiring new users through a 30-day free trial?

More context. Let’s say 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, unless they opt out. What metrics would you look at?

First step, think about Netflix’s business model. They want to focus on:

  1. Acquiring new users to their subscription plan.
  2. Decreasing churn and increasing retention.

How would this free-trial plan affect how Netflix might acquire new users or manage their customer churn?

SQL Product Analyst Questions

The types of SQL questions in product analyst interviews range from definition-based discussions, e.g. “When would you use DELETE vs TRUNCATE?”, to writing queries based on provided data. Multi-step SQL case studies are also common. These questions ask you to propose metrics, and then write SQL to pull those metrics.

8. What is the difference between the WHERE and HAVING clause?

Both WHERE and HAVING are used to filter a table to meet the conditions that you set. The difference between the two is shown when they are used in conjunction with the GROUP BY clause. The WHERE clause is used to filter rows before grouping (before the GROUP BY clause) and HAVING is used to filter rows after grouping.

9. What are the different types of joins? Explain them.

There are four different types of joins:

  • Inner join: Returns records that have matching values in both tables
  • Left (outer) join: Returns all records from the left table and the matched records from the right table
  • Right (outer) join: Returns all records from the right table and the matched records from the left table
  • Full (outer) join: Returns all records when there is a match in either left or right table

10. How would you isolate the date from a timestamp in SQL?

EXTRACT allows us to pull temporal data types like date, time, timestamp and interval from date and time values.

If you wanted to find the year from 2022-03-22, you would write EXTRACT ( FROM ):

SELECT EXTRACT(YEAR FROM DATE '2022-03-22') AS year;

11. Write a SQL query to get the last transaction for each day.

More context. Given a table of bank transactions with columns id, transaction_value, and created_at (date and time for each transaction), write a query to get the last transaction for each day. The output should include the id of the transaction, datetime of the transaction, and the transaction amount. Order the transactions by datetime.

Because the created_at column is in DATETIME format, we can have multiple entries that were created at different times on the same date. For example, transaction 1 could happen on ‘2020-01-01 02:21:47’, and transaction 2 could happen on ‘2020-01-01 14:24:37’.

To make partitions, we should remove information about the time that the transaction was created. But, we would still need that information to sort the transactions

Is there a way you could do both these tasks at once?

12. How do you create a histogram using SQL?

Let’s say you wanted to create a histogram to model the number of comments per user in the month of January 2020. Assume the bin buckets have intervals of one.

A histogram with bin buckets of size one means that we can avoid the logical overhead of grouping frequencies into specific intervals.

For example, if we wanted a histogram of size five, we would have to run a SELECT statement like so:

SELECT

CASE WHEN frequency BETWEEN 0 AND 5 THEN 5

WHEN frequency BETWEEN 5 AND 10 THEN 10 etc..

13. How will you write a query to get the post success rate?

More context. A table contains information about the phases of writing a new social media post. The action column can have values post_enter, post_submit, or post_canceled for when a user starts to write a post (post_enter), successfully posts (post_submit), or ends up canceling their post (post_cancel).

Write a query to get the post success rate for each day in the month of January 2020. You can assume that a single user may only make one post per day.

Let’s see if we can clearly define the metrics we want to calculate before just jumping into the problem. We want the post success rate for each day over the past week. To get that metric let’s assume post success rate can be defined as:

(total posts submitted) / (total posts entered)

Additionally, since the success rate must be broken down by day, we must make sure that a post that is entered must be completed on the same day. What comes next?

14. We have a table that represents the total number of messages sent between two users by date on messenger. What are some insights that could be derived from this table?

In addition to thinking through possible insights, what do you think the distribution of the number of conversations created by each user per day looks like? Write a query to get the distribution of the number of conversations created by each user by day in the year 2020. This visualization can also help you hone the insights to be gleaned.

See a step-by-step solution to this problem on YouTube:

Amazon SQL product analyst mock interview video

Data Analytics Questions

Analytics questions are a subset of SQL questions, and often require SQL code writing. These questions assess your ability to pull actionable insights from data. In these questions, you might be asked to pull metrics or perform a multi-step data analytics case study.

15. We have a hypothesis that the Clickthrough Rate (CTR) is dependent on the search result rating. Write a query to return data to support or disprove this hypothesis.

This is a classic data analytics case study type question, in that you are being asked to:

  1. Create a metric to analyze a problem.
  2. Pull the metric you created with SQL.

With this question, start by thinking about how we could prove or disprove the hypothesis. For example, if CTR is high when search ratings are high, and low when search ratings are low, then the hypothesis is supported. With that in mind, you can solve this problem by looking at results split into different search ratings buckets.

16. Given three tables representing customer transactions and customer attributes, write a query to get the average customer order value by gender.

Quick solution. For this problem, note that we are going to assume that the question states the average order value for all users that have ordered at least once. Therefore, we can apply an INNER JOIN between users and transactions.

SELECT
    u.sex
    , ROUND(AVG(quantity  *price), 2) AS aov
FROM users AS u
INNER JOIN transactions AS t
   ON u.id = t.user_id
INNER JOIN products AS p
    ON t.product_id = p.id
GROUP BY 1

17. Given a table with customer purchase data, write a query to output a table that includes every product name a user has ever purchased.

More context. The products table includes id, name and category_id information for customers. In addition, your output should include a boolean column with 1 if the customer has previously purchased that product category, or 0 if they have not.

Additionally, the table should have a boolean column with a value of 1 if the user has previously purchased that product category and 0 if it is their first time buying a product from that category.

Your output should look like this:

product_name category_previously_purchased
toy car 0
toy plane 1
basketball 0
football 1
baseball 1

A/B Testing and Experimentation Questions

Product analysts are tested on their ability to design, conduct and evaluate A/B tests. These questions explore A/B testing and statistics, and include definitions-based questions and A/B testing case studies.

18. What is the difference between a t-test and a z-test?

The biggest difference comes down to sample size. Z-tests are best performed when the experiment has a large sample size, while t-tests are best for small sample sizes.

Further, a z-test is a statistical test that is used to determine whether the means of two samples are different, a calculation which requires variance to be known as well as a large sample size. A t-test is a type of statistical test that is used to determine if the means of two samples are different, and the datasets that you have used must follow a normal distribution while potentially having unknown variance.

19. How do you structure a hypothesis for an A/B test?

A question like this assesses your foundational knowledge of A/B testing. A sample response might include that there are three components you need:

  1. The variable.
  2. The result.
  3. A rationale for why the variable produced the given result.

Then, provide an example. If you wanted to know what effect an upsell offer on a cart had on users (more personalized vs. best-sellers), you might say: If we personalize the upsell offer (variable), then customers will convert at higher rates (result), because the personalized upsells are more relevant for the audience (rationale).

20. What types of questions should you ask before designing an A/B test?

This question assesses your ability to design an A/B test. First, ask about the problem the A/B test is trying to solve. This will help you tailor the questions you would ask. Some examples you might use are:

  • How big is the sample size?
  • Is this a multivariate test?
  • Are the control and test groups truly randomized?

21. You are A/B testing a feature to increase conversion rates. The results show a .04 p-value. How would you assess the validity of the result?

Let’s start out by asking some clarifying questions here:

  • What details is the interviewer leaving out of the question?
  • Are there more assumptions that we can make about the context of how the A/B test was set up and measured that will lead us to discovering invalidity?
  • What rephrasing of the question would help us understand more about the problem at hand?

Basically, this type of question is asking: Was the A/B test set up and measured correctly? If it was set up and measured correctly, what could we say about the p-value?

22. How would you measure the impact that financial incentives have on user response rates?

More context. The results of an A/B test show that the treatment group ($10 reward) has a 30% response rate, while the control group without rewards has a 50% response rate. Can you explain why that happened? How would you improve the experimental design?

See a step-by-step solution for this question on YouTube:

Product analyst mock interview video

Python Product Analyst Questions

Product analyst interviews may include basic Python questions, especially for coding-intensive roles. In particular, Python questions cover definitions or ask you to perform a basic-to-intermediate coding exercise.

23. What is a split in Python? Why is it used?

A split() is used to separate strings in Python. For example, if the string was “basic python,” the split function would break that into ‘basic’, ‘python’. Here’s an example:

string='basic python'

print(string.split())

Output:

['basic', 'python']

24. Write a Python function to sort a numerical dataset.

list = ['1', '4', '0', '6', '9']

list = [int(i) for i in list]

list.sort()

print (list)

Output:

[0, 1, 4, 6, 9]

25. Write a function that can take a string and return a list of bigrams.

At its core, bi-grams are two words that are placed next to each other. Two words versus one word feature in engineering for a NLP model that gives an interaction effect. To actually parse them out of a string, we need to first split the input string. We would use the python function .split() to create a list with each individual word as an input. Create another empty list that will eventually be filled with tuples.

Then, once we have identified each individual word, we need to loop through the list k-1 times (if

k is the amount of words in a sentence) and append the current word and subsequent word to make a tuple. This tuple gets added to a list that we eventually return.


def find_bigrams(sentence):

input_list = sentence.split()

bigram_list = []

# Now we have to loop through each word

for i in range(len(input_list)-1):

#strip the whitespace and lower the word to ensure consistency

bigram_list.append((input_list[i].strip().lower(), input_list[i+1].strip().lower()))

return bigram_list

26. Write a function to generate N samples from a normal distribution and plot the histogram. You may omit the plot to test your code.

This is a relatively simple problem because we have to set up our distribution and then generate n samples from it, which are then plotted. In this question, we make use of the scipy library which is a library made for scientific computing.

First, we will declare a standard normal distribution. A standard normal distribution, for those of you who may have forgotten, is the normal distribution with mean = 0 and standard deviation = 1. To declare a normal distribution, we use the scipy stats.norm(mean, variance) function and specify the parameters as mentioned above

Statistics and Probability Questions

Statistics and probability questions are asked to test your data sense, as well as your ability to analyze large datasets. These questions can include basic definitions alongside short statistical problems that require you to make a calculation.

27. How would you explain p-value to a non technical person?

In the simplest terms, p-value is used to measure the statistical significance of a test. The higher the p-value, the more likely you are to accept the null hypothesis (typically that the two variables can be explained by random interaction). A smaller p-value would indicate that there was a statistically significant interaction between the variables, and that you are able to reject the null, which is to say, something more than randomness explains how the variables interact.

28. Determine the cause of drop in capital approval rates.

More Context: Capital approval rates have gone down for our overall approval rate. Let’s say last week it was 85%, but fell this week to 82%, a statistically significant reduction.

The first analysis shows that all approval rates stayed flat or increased over time when looking at the individual products.

  • Product 1: 84% to 85% week over week
  • Product 2: 77% to 77% week over week
  • Product 3: 81% to 82% week over week
  • Product 4: 88% to 88% week over week

This would be an example of Simpson’s Paradox, which is a phenomenon in statistics and probability. Simpson’s Paradox occurs when a trend shows in several groups but either disappears or is reversed when combining the data. This is often because the subgroups are offset from each other on the Y-axis, and when aggregated show only the movement between the groups, and not the trends within. For the original example, there could have been quite a few more sales of Product 2, which pulled the overall approval rate down, even though no drop actually occured for the product.

29. Given two fair dice, what is the probability of getting scores that sum to 4? to 8?

This is a simple calculation problem:

  • There are 4 combinations of rolling a 4 (1+3, 3+1, 2+2): P(rolling a 4) = 336 = 112

  • There are 5 combinations of rolling an 8 (2+6, 6+2, 3+5, 5+3, 4+4):

Solution: P(rolling an 8) = 536

Behavioral Interview Questions

product analyst behavioral questions

Behavioral questions are discussion-based, and they are designed to understand if you are the right culture fit for a position. These questions also dig into your past experiences and soft skills. Your responses should reference your work and impact.

30. What are your favorite data visualization techniques?

Before you jump into an answer, work backwards. The best techniques really depend on the data being conveyed. For example, you might choose a donut chart if you’re conveying percentages. Then, provide some examples of your favorite visualizations you have created, what was unique about them, and what techniques you used.

31. What are some of your favorite products and why?

For any product-related role, expect a question like this. First, you want to provide an overview of the product: what it is, key features, etc. Then, explain the problems the product solves for the user (which is you in this case). Finally, explain why the product solves the problem better than competitors.

32. The product manager provides you with unclear directions for a project. What do you do?

If you are given unclear directions, it may be because the PM is not sure how to proceed. First, you might ask some clarifying questions like:

  • What is the end goal?
  • Are examples available?
  • Can you provide some more details?
  • What overarching goal is this tied to?

Once you have more information, you can create a plan and run it by the PM, asking for feedback, suggestions or the final green light. A good PM might use your guiding questions to clarify with other stakeholders, so being comprehensive in your probing can create a better result for internal and external partners.

33. How do you stay updated on market trends?

Interviewers ask this question to gauge your passion for analytics and product. To answer, you might talk about:

  • Product-related podcasts.
  • Blogs or news sources.
  • People you follow on Twitter.
  • How you gain new skills.
  • Interesting case studies you have read.

34. How would you assess the quality of a dataset?

With this question you might talk about performing a data quality assessment and analyzing particular features of the dataset. Some metrics you might be interested in include:

  • Completeness
  • Validity
  • Timeliness
  • Consistency

35. What would your first 90 days on the job look like?

Expect a variation of this question. Interviewers ask it to assess your drive and ambition, as well as how fast you can adapt to new situations.

Start with an overview of what you would need. What information would you gather in the first 30 days? How would you familiarize yourself with the product? Then, think about where you might be able to add the most value. If you have deep experience in churn analysis, you could describe jumping into details like analyzing churn, developing predictive models for churn, and identifying opportunities to reduce it.

More Resources for Product Analyst Interviews

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