Mastering Product Case Studies as a Data Scientist

A Data Scientist must have a clear understanding of the product and must be able to think like an owner: you should be able to understand the mission of the company, pain points of the users, and how the features address the users needs. A thorough understanding of the product and context will enable you to clearly communicate your findings, and recommend clear strategies

The heart of the case studies is to evaluate your ability to showcase the depth and breadth of thinking, ability to provide critical reasoning at every step, ability to identify and evaluate the data required for the analysis, ability to generate data driven insights, and ability to collate all the pieces and communicate clearly. The way you solve case studies provide insights as to whether you will be able to handle complex questions, manage the expectations of key stakeholders and drive key product decisions.

To start with you should engage the interviewer and not convert the discussion into a monologue. Secondly, you should be able to apply the following key pillars in your answers to showcase that you can solve the problems in a structured manner:

1. Understanding the case study context
2. Structuring your response
3. The feasible and scalable response is appreciated more than novel ideas
4. Metric knowledge: choosing the right metric

Understanding the case study context

‘Understanding the context’ is a very loaded phrase. The real world question provided will be open-ended. So, let’s deep dive into what ‘context’ refers to.
Focus on trying to find answers to the following paradigms.
And how do you do that?

Ask intelligent questions one after the other such that the depth and breadth of the problem is unraveled. Trust me, this requires practice.

Key focus paradigms:
- What is the mission of the company?
- What is the goal of the product?
- Do the aforementioned mission and goal tie with each other?
- Who are the end users? Do we have details of their preference, usage, behavior, etc.?
- Are there segments of the users that we plan to target initially?
- Are we implementing this for the first time or are we improving/modifying an existing feature?

Structure your response

At this juncture, you must have a clear picture of the task and its context. If not, practice until you are satisfied. It’s easier if you work closely with a mentor who can provide you with constructive feedback and guide you.

The next step is to provide your response in a clear, logical, and well-reasoned manner. Along with your response, you need to focus on key issues, challenges/caveats identified in the case study along with their mitigation.

If you are able to provide strategic recommendation(s) with a coherent plan of action it is an icing on the cake.

There are multiple frameworks you can incorporate to design a well aligned response, like the CIRCLE and BUS frameworks, etc. But they all have similar foundations. It includes:

1. Identifying the key problem or challenge presented in the case study. This should be the main focus of your response, and should be stated clearly and concisely at the beginning of your answer.

2. Analyze the information provided in the case study to gather relevant data and insights. This may involve asking clarifying questions, processing the data provided or using data analysis techniques to identify trends and patterns in the data.

3. Develop a plan of action to address the problem or challenge identified in the first step. This should be a detailed and well-reasoned plan that outlines the specific steps you would take to address the problem, and should take into account any constraints or limitations identified in the case study.

4. Communicate your plan to the interviewer, explaining the key points and rationale behind your approach. This should be done in a clear and concise manner, and should be tailored to the specific audience and context of the case study.

The feasible and scalable response is appreciated more than novel ideas

To develop feasible and scalable solutions in a product management interview, it is important to consider both the specific constraints and requirements of the problem or challenge being presented, as well as the broader context and goals of the product or business.

This can help you identify solutions that are both technically and practically feasible, and that can be easily implemented and scaled as needed.

The following factors must be taken into account in order to develop scalable and workable business solutions for a product case study:
- The market size of the product
- The cost of production
- The quality of the product
- The price of the product
- The marketing strategy behind the product

Metric knowledge - choosing the right metric.

In a product case study, "metric knowledge" refers to the understanding and use of metrics or key performance indicators (KPIs) to evaluate the performance and success of a product. These metrics may include measures of customer satisfaction, product usage, revenue, and profit, among others. By analyzing these metrics, businesses can gain insights into how well the product is meeting customer needs and identify areas for improvement. This information can then be used to inform product development and marketing strategies.

When choosing the right metrics to measure success during a product case study interview, it is important to consider the goals and objectives of the product. Different metrics may be appropriate for different products and different stages of the product's lifecycle. 

For example, awareness, acquisition and activation metrics are more important for products that are in the early stage. Retention and monetization are more relevant for products on the growth and mature stages.

In an actual scenario, it is helpful to consult with stakeholders and subject matter experts, such as product managers and market researchers, to gather their insights and perspectives on which metrics are most important and relevant for the product. Additionally, it may be useful to conduct market research and gather data on the performance of similar products in order to benchmark the product's performance and identify areas for improvement.

Let's take a look at an example:

You are the Product Manager at Uber. How would you design a feature so that the users can get groceries from the desired grocery stores from their locality etc.

Before you begin designing the product, we need to understand the context by asking clarifying questions that can provide key insights:
1. With this product are we trying to establish a new market or are we trying to increase user engagement by embedding in the Uber application?
For the sake of this discussion, let us assume that this is a standalone application.
2. Is there a certain audience that we are creating this product?
We are targeting individuals belonging to all ages and genders.
3. How does this product align with the company’s mission?
Uber’s mission is to connect physical and digital worlds to help make movement happen at the tap of a button. Uber is committed to bring innovative solutions to its customers, local communities, and cities.
4. Can I assume that the goal is to acquire and activate users in Uber's Grocery Delivery platform? Are there any other needs?
Awareness, acquisition and activation are the immediate focus
5. Are we targeting the companies who have employees who can provide household services. If not, can we assume that the users and the skill providers (individuals) as the stakeholders?
We are not targeting companies who can provide services. We are only targeting only individual service providers.

Now that we have an idea of the vision and goal, let’s drill down to provide our ideas in a structured manner by clearly identifying key pillars such as the ones stated below:

Key problem: Uber wants to enter into a home management solution with a separate application. The key goal is to increase awareness and acquire new users to the platform.

The Market Size of the product: The total addressable market is the US population. To start with, we can focus on acquiring users from the Uber and Uber Eats application.
User awareness and acquisition strategies: We can provide promotions in the existing Uber applications directing users to the Grocery Delivery application.

Minimum viable product (MVP): Enumerate the features that can be included in the launch.

Cost of creating application: You can mention that we can leverage the time taken to create the application and the associated cost from our experience of launching the applications

Data: Internal data from Uber and Uber Eats can be leveraged. External data such as demographics can also be incorporated

Metric: Focus on key metrics on awareness, acquisition and activation.

Here are a few examples: Awareness → number of users downloading the application, Activation → number of users making the first transaction, etc.

Conclusion

Now that you have a fair idea of solving a product case study, what is the next step? Focus on developing ‘Product Thinking’ by practicing this framework on everyday products that you use. 

Product knowledge can help data scientists in a number of ways. First, having a deep understanding of the product and its features can help data scientists better understand the data that they are working with. This can enable them to identify patterns and trends in the data that may not be immediately obvious and to develop more accurate and effective models and algorithms.

Second, product knowledge can help data scientists better understand the business context and the goals and objectives of the product. This can enable them to develop data-driven solutions that are aligned with the product's strategic objectives, and to communicate the value and potential impact of their work to stakeholders.

Third, product knowledge can help data scientists identify potential areas for improvement and innovation within the product. By understanding the product's features and capabilities, as well as the needs and preferences of its users, data scientists can identify opportunities to enhance the product's performance and user experience. This can lead to the development of new features and functionality, and help drive product growth and success.

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