Measure the success of Netflix Recommendation Engine - Netflix PM
Product Metrics - Netflix PM Interview: How would you measure the success of the Netflix recommendation engine?
Let’s get started with the solution -
Interview Tip: Always remember to follow the framework. Jumping right into defining metrics is not the best way to answer this type of questions.
How to Answer a Product Metrics Question?
The key to correctly answering a Product Metric Question is following a structured framework.
Here is a step by step framework, you can follow while answering Product Metrics questions in Product Management Interviews :
Describe the Product.
Ask Clarifying Questions
Define the feature Goals you want to achieve
Describe the User Journey of the feature
Define the Metrics for each step of the User Journey
Evaluate and Prioritize the Metrics
Summarize your answer
Now, let’s go through each of the above points and understand them in details,
Step 1: Describe the Product & it’s Features
To start off in the right direction, you need to explain your understanding of the product – what does it do, who uses it, and how.
“Netflix is a leading over-the-top (OTT) streaming service offering a vast library of movies, TV shows, documentaries, and original content. A key feature that distinguishes Netflix is its robust recommendation engine, designed to offer personalized content suggestions to users. The recommendation engine plays a pivotal role in enhancing user experience and engagement by presenting tailored content choices based on user preferences, viewing history, and behavior.”
Recommendation Engine: Netflix's recommendation system analyzes user data to offer tailored suggestions, enhancing content discovery.
Original Content: Netflix invests heavily in producing exclusive, high-quality content, creating a unique value proposition.
Offline Viewing: Subscribers can download content for offline viewing, catering to diverse viewing preferences.
Multi-Device Access: Netflix is accessible on various devices, including smartphones, smart TVs, tablets, and computers.
Target Audience: Netflix caters to a broad audience, encompassing different age groups, interests, and demographics. Its target audience includes:
Young Adults and Professionals: Offering a mix of entertainment and original content.
Families: Providing diverse content suitable for all age groups.
Movie Enthusiasts: Extensive library catering to diverse cinematic tastes.
Amazon Prime Video: A major competitor, offering a similar range of content and original productions.
Disney+: Competing with a focus on family-friendly content and a vast catalog of Disney, Marvel, and Star Wars titles.
Hulu: Providing a mix of current TV shows, movies, and exclusive content.
HBO Max: Known for premium content, including HBO's original programming.
Local and Regional Services: Depending on the region, local platforms may pose competition.
Step 2: Ask Clarifying Questions
When faced with a product metrics question or any question related to assessing the success or performance of a specific feature, it's crucial to seek clarification from the interviewer.
Here is a list of clarifying questions that can help you better understand the context and requirements:
Are we focusing on any particular platform like desktop web or mobile app?
A) No, overall.
Are we focusing on any particular OS like, IOS, Android?
A) No, overall.
Measuring the success of Netflix Recommendation in a particular region or overall?
A) No, overall.
When referring to the measurement of success, are there specific key performance indicators (KPIs) in mind, or should I determine what to track and measure based on the goals of the recommendation engine?
A) You can decide.
Are there specific user personas we are focusing on (New, Casual, Power users)?
A) No, we can consider overall users.
What are the primary business goals associated with the Netflix recommendation engine?
(Are we aiming to increase revenue, enhance user engagement, capture more market share, or achieve other specific objectives?)
A) You can decide.
Are there any technical limitations or constraints that might impact the measurement of metrics for the Netflix recommendation engine?
Are there any specific timeframes for which the success of the Netflix recommendation engine should be evaluated, or should the assessment be ongoing without specific time constraints?
A) No time constraints.
Are there upcoming features or changes in the Netflix product roadmap that could influence the metrics we choose to measure for the recommendation engine?
Step 3: Define the Goal you want to achieve
The goal of Netflix, particularly concerning its recommendation engine, is to optimize user engagement and retention by providing personalized and relevant content suggestions. Netflix aims to enhance the overall user experience by leveraging its recommendation engine to offer a curated selection of movies and TV shows tailored to each user's preferences, viewing history, and behavior.
The primary objective is to keep users actively engaged on the platform, leading to increased watch time and a higher likelihood of subscription renewal. In the highly competitive online streaming industry, where content discovery plays a pivotal role, Netflix aims to stand out by delivering a seamless and enjoyable content consumption experience.
Netflix's success is not solely defined by revenue but is closely tied to user satisfaction and loyalty. The recommendation engine plays a crucial role in achieving this goal by efficiently connecting users with content they are likely to enjoy, thereby reducing the time spent searching for something to watch.
Step 4 : Describe User Journey
At this step you are expected to give a walkthrough of the user journey from the start to the end of the user interaction with the feature. This would demonstrate your ability to think and understand the different key experiences a user goes through while interacting with the feature or product that might effect the success of the feature.
You can ask the interviewer if they believe you have covered all of the important user journey steps at the end of this step.
Following is the user journey for Netflix Recommendations,
Opening Netflix Platform:
The user opens the Netflix application on their chosen device, whether it's a smart TV, computer, smartphone, or tablet.
The home screen is the first point of interaction, and it provides a snapshot of personalized content recommendations based on the user's viewing history, preferences, and behavior.
Choosing User Profile:
If there are multiple user profiles associated with the account, the user selects their specific profile to ensure that the recommendations are tailored to their individual preferences.
Content Recommendations Display:
Upon entering the platform, the user is presented with a grid or carousel of content recommendations. These recommendations are generated by Netflix's recommendation engine, taking into account various factors such as watch history, genre preferences, and trending content.
Scrolling and Exploration:
The user can scroll horizontally or vertically through the recommended content to explore different options.
Each content tile includes key information such as title, brief description, and possibly a trailer or preview.
Engaging with Recommendations:
The user has several options for engaging with the recommended content:
Clicking on a content tile to view more details.
Watching a trailer to get a preview of the content.
Liking or disliking a recommendation to provide feedback to the recommendation engine.
Adding to Watchlist:
The user has the option to add a recommended movie or TV show to their watchlist for future viewing.
Decision to Watch:
After exploring recommendations and engaging with the content details, the user makes a decision to watch a specific movie or TV show.
The user initiates playback of the chosen content, and the recommendation engine continues to refine its understanding based on this selection.
After watching a piece of content, the user may provide additional feedback by rating it or providing a thumbs-up or thumbs-down.
Netflix may prompt the user with additional recommendations based on their viewing history and the content they've just watched.
Return to Home Screen:
The user may return to the home screen for future content discovery or exploration.
The recommendation engine continuously updates and refines suggestions based on the user's evolving preferences and behavior.
Step 5: Metrics for each step of the User Journey
Once you are done describing the User Journey of the feature / product, its time to define the metrics for each phase in the customer journey. Below is a list of all the customer journey phases a user can go through: (3AE3R )