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Note: The following content is summarized from Ace Product/Business Case Interview Questions: A Data-driven Approach for Data Scientists from the Emma Ding channel.

Example Interview Questions

  1. What are the pros and cons of using Daily Active Users as a success metric?
  2. How would you investigate a negative metric shift in time spend on the app?
  3. How would you design an experiment to test a new feature?
  4. How would you go about making a launch decision?

Note: At least 1 round of onsite interviews focused on case questions.

Data-Driven Approach

What is a Data-driven Approach?

The idea behind a data-driven approach involves:

  • Examining real interview questions to understand the distribution of interview questions and to anticipate what to expect when discussing product case questions.
  • Efficiently covering common types of problems in minimal time.

7 Common Categories of Product Case Interview Questions

1. Measure Success (23%)

Example Questions:

  • How would you measure the success of YouTube’s Story feature?

    • Sample Answer1: Success for YouTube’s Story feature would be evident in robust engagement metrics, notably view counts and likes. Equally pivotal is tracking how many users adopt this feature and remain consistent users over time. Direct feedback, through surveys or comments, provides invaluable insights, complementing hard data. Of course, ensuring the feature operates without glitches is fundamental..

    • Sample Answer2: Evaluating the success of YouTube’s Story feature requires a multi-faceted approach. High engagement levels, reflected in views and interactions, serve as immediate indicators. Monitoring user growth and retention provides insights into its longer-term appeal. Gathering direct user feedback can highlight areas for refinement, and flawless technical performance is a given.

  • What metrics would you look at to see if it’s doing well?

    • Sample Answer1: Primarily, I’d gauge success by looking at user engagement. Key metrics such as daily active users, session duration, and interaction rates give a clear picture. When users are deeply engaged, it’s a strong indicator of the feature’s effectiveness. Additionally, tracking user retention and referrals is essential, as returning users and recommendations signal long-term value and satisfaction.

    • Sample Answer2: I’d prioritize both engagement and the overall user experience. While metrics like views and interactions provide immediate insights, the bigger picture emerges when considering user retention and recommendations. Additionally, examining the user journey helps identify potential bottlenecks or areas for enhancement, ensuring not just current success but also paving the way for future improvements. Alternate Version:

  • If Uber is planning to launch a referral program for riders, what metrics would you use to measure its success?

    • Sample Answer1: To gauge the success of Uber’s rider referral program, I’d focus on the number of new riders acquired through referrals and the retention rate of these referred riders. Additionally, tracking the average number of referrals made by each existing rider can shed light on the program’s appeal and effectiveness.

    • Sample Answer2: Evaluating the success of such a program would require a mix of acquisition and engagement metrics. Key indicators would include the count of successful referrals, the lifetime value of referred riders, and the frequency with which existing riders utilize the referral feature. It’s not just about getting new riders, but also ensuring they become loyal users.

2. A/B Testing (22%)

Aspects:

  1. You might be given an idea and asked to design an experiment to test it.
  2. You might be asked to come up with an idea first before designing an experiment to test it.

Example Questions:

  • How would you set up an experiment to test a new feature in Quora?

    • Sample Answer: First, I’d define our success criteria, perhaps it’s increased engagement. I’d then segment users into two groups: one that experiences the new feature and another that doesn’t. We’d compare the behavior between the groups over a set period and see if the new feature delivers on our success criteria.

  • How would you decide whether or not to launch a feature change?

    • Sample Answer: After the A/B test, I’d evaluate if the feature meets our defined success criteria. If there’s a positive impact and user feedback aligns, it would make a strong case for launch. It’s key to combine both data insights and user sentiment.

  • What changes would you make to the TikTok app?

  • How would you test if the proposed change is effective or not?

    • Sample Answer: I’d conduct an A/B test. One user group would have the new feature while the other wouldn’t. By comparing engagement metrics, especially participation in these feature, we can gauge the feature’s effectiveness.

3. Diagnose a Problem (18%)

How to tackle it?

  • There could be multiple reasons for a problem, so having a structured approach that prioritizes investigation steps is key.

What you need to know:

  • What to look at first and what to look at later.

Scenario Examples:

  1. The ETA of Lyft or Uber drivers has increased by 3 minutes, and you are asked to explain why.

    • Internal factors (e.g., changes to the app’s algorithm)

    • External factors (e.g., changes in traffic patterns or increased demand for rides)

    • Sample Answer: I’d start by checking if there were any recent changes to the app’s algorithm that might affect the ETA. If that’s not the case, I’d then look into external factors. It’s possible that there’s been a significant change in traffic patterns, or there might be an increased demand for rides causing drivers to be busier than usual.

  2. Investigating a 1% drop in daily active users of Slack.

    • Sample Answer: My initial step would be to check if there were any recent updates or changes to Slack that might have caused user dissatisfaction. If nothing stands out, I’d then consider external factors, such as competitor activity or market changes. Additionally, it would be crucial to gather user feedback to directly understand any pain points they might be facing.

  3. A referral program in DoorDash isn’t generating the expected response rate.

    • Sample Answer: First, I’d want to examine the user experience of the referral process itself. Is it intuitive and easy? If the process is fine, then I’d look at the incentives being offered – are they compelling enough for users to refer? Beyond that, I’d analyze external factors like market saturation or if competitors are offering more attractive referral schemes.

4. Product Specific (13%)

Sample Questions:

  • How do you evaluate the impact of fake news on Facebook?

    • Sample Answer: To assess the impact, I’d first measure the spread and reach of flagged fake news. By understanding how many users are exposed to it and how frequently, we’d get an initial sense of the scale. Then, it’s crucial to identify any correlation between exposure to fake news and user churn rates or reduced activity. Lastly, surveying users on their perception of Facebook’s content credibility could give insights into the platform’s trustworthiness.

  • How do you determine the optimal ratio between company posts and individual posts for LinkedIn feeds?

    • Sample Answer: To find the optimal ratio, I’d start with a hypothesis-based on current user behavior. For instance, if professionals are more active during certain times (like weekdays) for job searching or networking, company posts might be more relevant. Next, I’d conduct A/B tests, showing different user groups varied ratios. Monitoring engagement – such as likes, shares, and comments – would provide insight into which ratio fosters the best user interaction. Additionally, retention metrics could show if a particular mix keeps users returning and staying active on the platform. Over time, as the platform and user behavior evolve, this ratio might need periodic re-evaluation.

5. Improve a Product (10%)

Sample Questions:

  • How would you improve user engagement on LinkedIn?
  • How would you improve TikTok and what new features would you add to it?
  • How would you improve “What’s on your mind” posting on Facebook?

6. Strategic Thinking (6%)

Sample Questions:

  • How do you decide whether to launch this feature or not?
  • What should the hourly rate for Instacart shoppers be?

These questions are designed to test the ability to Identify obstacles, Analyze data, and Create solutions.

7. Estimation (6%)

Example:

  • You might be asked to calculate the profit for a credit card partnership considering existing users, revenue, cost, and potential joint marketing campaigns.

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