Frameworks for answering business case questions during analytics and data science interviews

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A common step during interviews for analytics and data science roles is business case study questions. These are usually relevant to the industry or business of the company. The goal is to understand how you solve problems as well as gauge your business sense.

So how do you answer these questions during interviews? It’s helpful to have industry knowledge and business sense as well as a framework for your answer.

To brush up on industry knowledge, read the content put out by the company you’re interviewing with (and/or their competitors). Many companies put out blogs — here is a list of tech company blogs where they outline problems they’ve faced and how they’ve solved them. You can also look for white papers on their website or company LinkedIn page.

When it comes to frameworks, there are a few different approaches you can take based on the problem that you need to solve.

If you’re interviewing for product analytics roles, below are some common approaches to solving problems around things like root cause analysis, defining new metrics, measuring feature adoption, and running an A/B (hypothesis) test.

Additionally, these frameworks should be used once you’re in the role and faced with these types of questions.

Diagnosing a change in KPI or other metric

One common task is figuring out why there was a change in a KPI (key performance indicator) or other metric. Maybe it was a sharp increase or decrease or a steady change over time. Either way, getting to the root cause can be very insightful for product managers and other business partners.

1. Clarify the scope of the change

Before you dig into the data, you need to understand what exactly you are evaluating. Asking clarifying questions can help:

• What is the definition of the metric?
• Why is this metric important?
• What’s the magnitude of the change?
• Clarify the time frame that the change was observed and if it was it sudden or gradual.
• Additionally, what time period are they comparing it to?

2. Hypothesize contributing factors

• Was it accidental — is there a problem with the data pipeline?
• Was it natural — is the data seasonal? Do you see the same change at the same time of week/month/year?
• Was it internal —was there a recent product launch, change, or bug fix? Was there a marketing campaign that started — or ended?
• Was it external — what’s going on with the competition? Was there a significant world event? Was there an external technical change to the browser or operating system?

3. Validate each contributing factor

• Check demographic segments to see if you can isolate the issue to categories such as age, gender, device type, operating system, browser type, location, language, length of use, new vs returning users, other user type, etc.
• Look at upstream metrics — has behavior shifted elsewhere or is there a bigger issue with the product?

4. Classify each factor (What category does it fall in?)

• Root cause
• Contributing
• Correlated
• Unrelated

Defining Product Metrics

An important part of working in product analytics is helping the product team define product metrics and KPIs. What should you take into consideration?

1. Clarify the product scope & purpose

• What is the business purpose?
• What is the product’s goal?
• Who is the intended user?
• What is the user flow?

2. Explain product & business goals

• How does it support the company mission?

3. Define specific success metrics

• Follow User Funnel (AARRR): Acquisition, Activation, Retention, Referral, Revenue — what stage are you at?
• Think about volume-based versus rate-based metrics
• Note: Mention any tradeoffs of selecting one metric over another

Measure Success of a New Feature

When launching a new feature, the product team generally wants to know if it’s successful or useful — did it help to improve the user experience? Did it support business success?

What should you look at to determine if a new feature was successful or not?

1. Measure basic usage of the new feature — how many users or visitors adopt the new feature? What percent does this represent?
2. Dig deeper into data to look for patterns — how and when do users typically use this feature?
3. Understand what users are doing right before using the feature — where does the feature fall in the user journey? Is this expected?
4. Build a behavioral cohort of users who have adopted the new feature to analyze how they compare to the overall user population.
5. Analyze the impact of the new feature on retention — do users who adopted the new feature come back more often?
6. Measure the impact of the new feature on your key conversion funnels — do they convert at a higher rate? Is their revenue higher?
7. Measure the impact of your new feature on engagement.

A/B Test Design

A/B testing (also called hypothesis testing or experimentation) is very common for product analytics roles. Product teams want to scientifically measure the impact of a new feature or change, and doing a controlled experiment allows them to do so. However, it is important to make sure tests are implemented correctly.

Before doing any tests:

• Is the right infrastructure in place? Can engineering teams easily launch tests?
• Is there enough traffic to reach significance within an acceptable timeframe?
• Can different experiences be isolated?

Before launching a test, consider:

• What kind of impact is anticipated— does this change matter to users? Can you actually measure the business impact?
• Can you avoid network effects — can you avoid control and treatment groups from interacting?
• Will there be novelty effects — will a change or new feature attract attention? For example, you can’t really test changing a company logo for just some users.
• Do you have a proper hypothesis statement? If ___ then ___ because ___.

Once you’re ready to run a test:

1. Pick a success metric to test — make sure it is something that will be impacted by the test and relevant to success of the business. Otherwise, what’s the point of trying to change it?
2. Decide on threshold:

• Minimum detectable effect — how much of a change do we want to see for the test to be worth it? If you can tie the change to revenue, you can determine a threshold for what is “worth it.”
• Alpha —this is the chance we reject null when true
• Power (1-beta) — this is the probability of rejecting null when alt is true (Type II error — incorrectly failing to reject null)

3. Sample size and experiment length

• Calcuate sample size based on the above (alpha, power, minimum detectable effect)
• Compare sample size to normal traffic volumes to estimate how long the test will need to run
• Also consider the minimum amount of time to observe normal variations in your data. (For example, at least 2 weeks for day-of-week variations.)

4. Assign groups randomly so there are no confounding variables

5. Run the test and measure the results

• z-test or t-test (compare the means of the two populations)
• non-normal: bootstrapping (take random samples and average)
• decide what cutoff to use for the p-value

6. Ship it — before launching, determine who will decide how to act on the results. Often this is the product manager.

CIRCLES Framework for Product Case Study Questions

Another popular framework for case study questions for product-adjacent roles is CIRCLES.

C — Comprehend the Situation
Make sure you understand the question being asked, the goal or outcome desired, why this is important, and clarify any assumptions.

I — Identify the Customer
Think about who the product is for.

R — Report Customer Needs
What are the customer's needs being addressed by the product?

C — Cut (via prioritization)
Prioritize where you will start.

L — List Solutions
What other features or solutions can you think of?

E — Evaluate Trade-offs
What are the pros and cons of each proposed solution?

S — Summarize Recommendations
How do you recommend moving forward?

Read more about each step in the CIRCLES framework.

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