Operational Analytics Interview Questions (2024)

Operational analytics case study questions are asked during on-site interviews for business operations (BizOps) and analytics roles. These operational analytics interview questions require in-depth answers, and they are designed to see how you develop a data-driven solution to a potential BizOps problem.

Most case interviews provide around 30-45 minutes to walk through and solve the presented problem. For candidates, the goal is to:

A. Walk the interviewer through their problem-solving process.
B. Use data to back up their claims.

For example, you could be asked how you would measure the customer service quality of chatbot experiences. To develop a response, you could rely on data – such as average chat session length, number of sessions, etc. – to inform your response. You could also incorporate survey data (if it exists) or user metrics from the chatbot post-session.

Operational analytics (OA) refers to the practice of integrating data into business operations to improve customer experiences, speed decision-making, and improve efficiency in everyday operations.

Operational analytics is used in human resources, supply chain management, product development, and marketing, to name a few. OA can be scaled to inform nearly every department in a business. Some common use cases for operational analytics include:

• HR decision making - Analytics data can streamline hiring decisions and optimize team structures.
• Banking product development - An operational analytics platform provides customer insights, which fosters better segmentation of product offers and development.
• Supply chain optimization - Operational analytics platforms uncover inefficiencies in supply chain management.
• Preventative maintenance prediction - Operational analytics is widely used to forecast and schedule preventative maintenance.
• Marketing personalization - Marketing departments use OA tools to segment customers, make strategy decisions around sales, and gain insights into customers.

In operations analyst interviews, case studies are the most challenging question you will face. However, other questions are present earlier in the interview process, including behavioral, basic operations questions, mini-case questions, and simple mathematical calculations. Some less complex analyst interview questions are:

1. Describe a project you worked on that resulted in significant operational improvements.

In interviews for operations analyst roles, you will often face a variation of this question. Interviewers want to see that you can have an impact on the workplace. When discussing a project, start with a brief overview, describe your actions, then cover the results. You could say:

“In a previous role, I found the sales and marketing departments were struggling to share data. As a result, both departments were using different customer datasets, which resulted in a disjointed customer experience.”

“I met with both teams to learn about their needs. I then aggregated data, and developed a reporting system that both teams shared. As a result, they were able to provide a more seamless experience to customers, and we saw a 10% lift in conversion rates across the company.”

2. Tell us about your previous experience analyzing business operations.

This is another operations analyst behavioral question. To answer you should:

• Describe the tools that you are most familiar using.
• Describe your process for analyzing operations.
• Talk about previous work experiences in BizOps analysis.

3. How would you decide which changes a company should make to respond to a growth in demand?

Scenario-based questions are high-level case questions that assess your industry knowledge, problem-solving approach, and leadership abilities. You could say:

“First, I would gather data and stakeholder insights to help us understand the timeline for growth. One question I would want to answer is: How much time do we have before this growth occurs?”

“Scaling operations requires thoughtful planning to avoid significant cost increases, so I would want to work with leadership to determine how to allocate resources like employee training, new equipment or expanded distribution.”

4. How would you calculate the employee turnover rate for a business?

More context: You are asked to calculate turnover for FY2020. At the start of the year, there were 1,000 employees. During the year, 100 employees left, and you replaced 50 of them. This leaves you with 950 employees. What was your turnover rate for 2020?

If you are interviewing for an HR analytics role, you will likely face a question similar to this. Interviewers ask it to see if you understand basic HR metrics and can perform simple calculations based on your knowledge. One equation you can use is:

Turnover rate = # of employees who left during period / average # of employees during period

Therefore, the turnover rate for 2020 is 10.25% (100 Employees Left / 950 Average Workforce).

5. What time of the year do you think is especially hard for Uber to maintain driver supply?

This is a basic Uber operations analyst question that is asked to quickly assess your product intuition. The most obvious answer is during the holidays. It is like that during four-day holiday weeks, Christmas, New Years, etc. Uber experiences a decrease in driver supply, with drivers deciding to spend time with family or travel themselves. Another hypothesis could be that drivers stay off the roads in the height of summer as gas prices peak, following a seasonal cycle.

6. What data would you analyze to determine how a company could reduce employee attrition?

This question assesses your data sense and comfort in working with various types of data. It also explores how you gather data for operations analysis. Some types of data you would want to consider would be:

• Historical employee data.
• Employee / manager survey data.
• Third-party employer review data.

Do a sample case study: Access the data in the HR analytics case study on Kaggle to make recommendations.

7. You suggested changes to make a process more efficient. However, employees are not embracing the change. What do you do?

Questions related to change management are common in BizOps behavioral interviews. In your response, be sure to show empathy, that you can communicate with various stakeholders, and how you approach conflict resolution.

You might say:

“I’ve experienced this before, and I’ve developed a process to make technology and process changes easier to implement.”

“My first goal: Show how the new way will make their lives easier and more efficient. Then the second step is crucial: proper training. I’ve developed training and documentation processes to help colleagues more quickly grasp a new solution.”

“And finally, I’ve found that it’s important to lead by example, and show colleagues that I’m embracing the change.”

Operational analytics case studies vary by company and role. For example, if you were interviewing with an e-commerce company, you might expect cases related to pricing, product metrics, or marketing, while a hiring company might focus on HR- and talent analytics-related questions.

8. How would you choose a new partner card for a credit card company?

More context: You have access to customer spending data (transactions) and are tasked with finding partnership opportunities (such as the Starbucks Chase card) for the company.

Start by thinking about the ways you might make a recommendation based on the transactions dataset, as well as nailing down why Chase creates partnerships with merchants. Some assumptions we might make include:

1. Partnerships help increase acquisition.
2. Partnerships increase customer retention.

Based on these assumptions, we can start to analyze the data and pull metrics that would influence these two objectives.

One simple place to start: sum transactions grouped by merchants and identify which merchants their customers spend the most at. These could be good merchants for partnerships, as a high level of spending would help retain our existing users.

However, the biggest problem with this level of analysis is that some companies, like Tesla, sell high-ticket, single-purchase items (in this case an electric vehicle). We could counteract this undesired quality by looking at the average price per transaction and average number of transactions per year. What other methods could we use?

9. How would you determine the customer service quality achieved through the chatbox for those conversations that involve small businesses selling items to consumers?

For this example you work at Facebook. Your team focuses on helping small businesses increase sales through the Marketplace app on Facebook.

This question assesses your ability to pull KPIs for operations related tasks. With an automated chatbot, what might some KPIs you would like to capture be?

• Activity - Are the number of chats increasing or decreasing?
• Retention rates - How many users return to use the chatbot?
• Bounce rate - What is the volume of sessions that did not result in the intended outcome?

With this question we also have the seller metrics. You could look at sales rates before and after consulting the chatbot, which would help you understand if the chatbot is helping small businesses sell more items.

10. DoorDash is launching delivery services in NYC and Charlotte, NC. How would you decide which drivers should be assigned these deliveries?

First, it is clear the criteria would be different for each city. New York City is much more densely populated and couriers might ride bikes, whereas in Charlotte it would more likely be a car.

Hint: For deciding which drivers to choose, we should view this algorithmically. How do we optimize the delivery times? Is there a maximum elasticity the system should support on both the delivery time and quality? For example, should we allow two-hour deliveries during peak times?

11. What metrics would you look at to determine the demand for rides on a ride-sharing platform at any point?

Follow-up question: What metrics would indicate if there is high demand and low supply? How can we determine the threshold for when there is too much demand?

See a step-by-step solution to this case study question on YouTube:

12. You are tasked with determining which products to promote during the Black Friday mega-shopping day. How would you determine what products should go on sale to maximize profit?

More context: You have access to historical pricing data for products that have been on sale before.

You might want to consider: Price elasticity of demand, e.g., a decrease in price translates into an increase in demand. This would include consumer tech products, luxury items, etc. How would you go about identifying products with a high price elasticity?

Note: The question also states the goal of maximizing profit. So not only should these products have high price elasticity, but they should also have a high-profit margin.

13. Let’s say you work for a financial company. You notice that the credit card payment amount per transaction has decreased. How would you investigate what happened?

More context: You work for a financial company and observe a decline in the credit card payment amount per transaction.

You might want to consider: Transaction patterns and changes in consumer behavior. For example, a decrease in the average payment amount could indicate a shift towards smaller purchases or a reduction in high-value transactions. Investigate transaction data to identify any trends or anomalies, such as changes in transaction frequency or variations in customer spending habits. Additionally, assess whether there have been any recent changes in payment processing fees or promotional offers that might influence transaction sizes.

Note: The question also implies the need to address the decline in a way that supports overall financial stability. Therefore, while exploring these trends, consider strategies that could potentially counteract the decrease, such as adjusting transaction limits, optimizing payment processing fees, or implementing targeted customer engagement initiatives to encourage higher-value transactions.

14. Say you are given a dataset of perfectly linearly separable data. What would happen when you run logistic regression?

More context: You are provided with a dataset containing perfectly linearly separable data.

You might want to consider: The nature of logistic regression, which models the probability that a given input point belongs to a particular class. In the case of perfectly separable data, logistic regression will be able to find a decision boundary that perfectly classifies the data points without error. This situation often leads to an overfitting scenario, where the model may capture the noise of the data instead of just the underlying pattern. Evaluate how the model’s coefficients reflect the strong correlation between the features and the classes.

Note: The question also emphasizes the implications of model generalization. Therefore, while the logistic regression may perform exceptionally well on the training data, consider potential issues with overfitting when the model is applied to unseen data, which could impact its predictive performance in real-world applications.

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