Suppose that you are working as a data analyst at Airbnb. For the past few months, Airbnb has seen a major decline in revenue. Now that the restrictions have started lifting and people have started to travel more, Airbnb wants to make sure that it is fully prepared for this change.
The different leaders at Airbnb want to understand some important insights based on various attributes in the dataset so as to increase the revenue such as:
- Which type of hosts to acquire more and where?
- The categorisation of customers based on their preferences.
- What are the neighbourhoods they need to target?
- What is the pricing ranges preferred by customers?
- The various kinds of properties that exist w.r.t. customer preferences.
- Adjustments in the existing properties to make it more customer-oriented.
- What are the most popular localities and properties in New York currently?
- How to get unpopular properties more traction? and so on...
These points are just to give you an initial understanding of how to proceed with the analysis and the kind of questions you need to ask. In addition to this, you would need to further understand the data (attached below in this page) and analyse important insights that you feel are required for the audiences mentioned for each of the two presentations.
Data Analysis Managers: These people manage the data analysts directly for processes and their technical expertise is basic. Lead Data Analyst: The lead data analyst looks after the entire team of data and business analysts and is technically sound.
Head of Acquisitions and Operations, NYC: This head looks after all the property and host acquisitions and operations. Acquisition of the best properties, price negotiation, and negotiating the services the properties offer falls under the purview of this role. Head of User Experience, NYC: The head of user experience looks after the customer preferences and also handles the properties listed on the website and the Airbnb app. Basically, the head of user experience tries to optimise the order of property listing in certain neighbourhoods and cities in order to get every property the optimal amount of traction.