Opportunity Sizing Case: Airbnb
How many of you came across Airbnb case studies during an interview? It is quite common for a PM interview, but the type of answers expected from the panel varies with the role and position offered. The answer presented by an entry-level or an experienced PM, or by a Group product manager (GPM) lies majorly between two parameters - execution & strategy.
Interviewers generally follow sets of rubrics to assess the answers on these two parameters. For instance, you have appeared for an interview for a mid to senior-level PM role in a high growth product company.
The classic Airbnb case is explained to you by the interviewer as stated below-
Interviewer: You have been hired as a Sr. Product manager of Airbnb, and leadership wants you to ponder on the idea to allow guests to book cars through the Airbnb Platform. It can be an Airbnb add-on to stay Homes and experiences (curated travel experience). How would you ensure building this new idea would be surely aligned with the Airbnb scalability vision?
Category: Data-Driven skills & strategic thinking
For this role, the expected response from the candidate is finding the value associated with this idea. The method is called “opportunity sizing”.
What is opportunity sizing?
It is a method showcasing the impact (value) associated with an idea over others ideas, and a great tool that helps PMs to prioritize the capabilities and new features. Additionally, it brings stakeholders alignment and is an excellent way to communicate to leadership about the impact associated with the ideas.
Expected Response of A Candidate:
The candidate should ask for historical market data e.g Airbnb user base, revenue, Y-O-Y growth, average paid amount per night, curated experience market size on Online travel agents (OTA) etc that helps you to make your model for opportunity sizing.
Next, an important point is to explore the “North start metrics” or “input metrics”. For instance, in the case of Airbnb, the north start metric is typical “number of nights. And let’s consider an input metrics as CLTV (Customer Lifetime)”.
Typically the opportunity sizing backlog template is described as-
By [explain the idea], [what outcomes are expected], leading to [improve/increase /effect on the North Star metric].
The final opportunity sizing definition for the Airbnb case can be written as-
The curated experience of renting or booking a car through the Airbnb platform will increase customer satisfaction multifold, leading to an increase x% increase in the customer Lifetime value. [Define a baseline for x% based that a company sees as a success]
To begin with, calculate the opportunity sizing. Let’s collect the historical data facts first.
Build your model and forecast the CLTV (Customer Lifetime Value) from the rental care curated experience for the next 3 years.
As a product manager, opportunity sizing is the perfect way to showcase the projected impact of Y-O-Y or Q-O-Q, and it helps in keeping away any negating opinions that can directly impact your roadmap. Instead of focusing only on revenue, the opportunity sizing is aligned with input metrics (CLTV) that feeds the Northstar metrics of Airbnb.
Most importantly, the expected answer from the candidate is not only about exhibiting the number-crunching skill, but it is also about demonstrating the product common sense, customer empathy, stellar communication in the form of storytelling.
Pro tips 💡: Ensure to create the model of opportunities sizing focusing forecast in terms of north star metrics and not the revenue solely.
Further Interviewer may ask: The opportunity sizing for curated experience is good based on the number we arrived at. Now quickly demonstrate the discovery and test hypothesis for potential use cases aligned with the Airbnb mission, vision and strategy? Also, define some success measures too.
Category: Discovery, problem, validation, experiment, user research
Expected Response of A Candidate:
Personas:
Let’s define the persona first before moving to discovery and testing the hypothesis. The “host” & “guests” are the important personas for Airbnb now. Further, the car rental will bring two more personas for consideration - car owner or rental car company.
Discovery Hypothesis:
Discovery Hypothesis 1: As a guest, I want to book a car along with a homestay so that I can easily commute from homestay to any destination.
Test Hypothesis H1: X% Frequent travellers who commute from homestay to office will book the car
Discovery Hypothesis 2: As a host, I want to place my car along with homestay options so that I can earn more.
Test Hypothesis H2: The host will place the car on a rental if the listing car can be done easily.
Discovery Hypothesis 3: As a rental car company supervisor, I should be able to list the cars on the Airbnb site so that guests can rent them.
Test Hypothesis H3: x% guest would like to rent the car of their choice as listed by rental car companies.
These are some samples of Discovery and test hypotheses that can be mentioned and based on the time and context many more can be defined.
Pro tips 💡: During the interview or in practice too, we should keep the discovery hypothesis as limited as possible to get the MVP quickly. The thumb rule creates more value for the customer than the number of features.
Success Measures:
There as a few examples of lead measures to assess the MVP at the beginning
Tips 💡: Ask clarifying questions and check with the interviewer periodically that what part of Airbnb product strength (Payment, filter, search, listing) he wants you to explore during the discovery hypothesis.
Interviewer: Suggest a few high-level features
I will not cover the sample answer of this part since the feature/solution is pulled off easily by the majority of the candidates. What I suggest is to avoid randomly guessing the features and use cases. The features should be extracted from the discovery hypothesis created at the beginning of the case. It brings a great impression as someone who knows how to build the right product.
I would reiterate that one-size-fits-all doesn’t work for solving cases. Although it matters how effectively a PM showcases a data-driven mindset through any data-driven techniques such as “Opportunity sizing”. It is one of the best ways to prioritize the initiatives or any high business value ideas or communicate with the stakeholders. Additionally, it protects your product from any random views that might take over your roadmap.
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