Airbnb has a 150+ million guest user base. (Ref- https://www.igms.com/airbnb-statistics/)
So, how has Airbnb managed to build a guest user data base of over 150+ million?
Airbnb uses data analytics to apprehend the trend in supply on its platform, addressing the changing demographics on both the supply and the demand-side. This enables Airbnb to rightfully balance the marketplace and ensure sales. In decoding various diverse data points, the data on hosts show a mix of behaviours, availability of real estate inventory, and active markets.
Here are some of the ways that Airbnb uses data to track and understand supply trends:
Segmentation Analysis
Airbnb does a lot of segmentation analysis of its large host community. Segmentation analysis is where hosts are broken down into groups based on their characteristics such as location, types of properties and how frequently they are hosting. This way, Airbnb can direct its services and support to various categories of hosts. For example, urban hosts have different challenges from those faced by rural hosts and this approach allows the offerings of targeted resources and guidance.
Market Research and Predictive Analytics
Airbnb must do market research to stay ahead of shifts in supply and demand so that the housing stock is always adequate as much as possible. Analysis of booking patterns, seasonal trends and field specific demand moves Airbnb to anticipate future market conditions. Such predictive capacity permits the platform to help hosts in finding the most appropriate pricing as well as rental availability, thereby mitigating the supply-demand discrepancy.
User Behaviour Analysis
Holding information about guest preferences is significant for a balanced supply. Airbnb reviews search trends among users, booking patterns, as well as guest feedback to understand what guests are looking for in a property. With this insight, Airbnb can offer tips to hosts about how to improve their listings in order to meet guests’ expectations, thereby giving them the best chance of getting bookings and in turn, retain a desirable supply of good properties.
Machine Learning Models
By using machine learning models, Airbnb tries to foresee listing rates and patterns of the market. The location of the property, flight attributes and historical data on transactions leave a structure for these models to choose what would be the best value for property and identify new requested functionalities. This way, it retains host competitiveness and responds immediately to any certain supply shortfalls or excessive listings.
Data-Driven Policy Decisions
The scope of Airbnb’s analysis embraces studying policy decisions that have an impact on supply. Airbnb advises on how to change strategies that would comply with local regulations and still maintain an ample amount of listings by examining how restrictive short-term rental laws may affect host participation and availability of real estate properties. For example, in cities with strict short-term rental laws, Airbnb correlates data on how the law is affecting the supply of hosts and available properties.
Conclusion
Airbnb works across various datasets to monitor and follow supply trends of its platform. Airbnb balances modern and responsive markets through means of segmentation analysis, user behaviour analysis, market research with predictive analytics, machine learning models, and data-driven policy considerations that fulfil control over movement in late-age guest and host demands.
CRG Solutions is a Business Performance Improvement company helping organizations traverse their Data to Insights journey and beyond. We leverage curated Analytics Technology such as Tableau & Alteryx. We are one of Tableau’s longest standing Gold Partners with vast experience in Visual Analytics best practices. Through Alteryx, we cover all aspects of creating data-marts, data cleansing, as well as Advanced Analytics such as predictive/prescriptive analytics.
Reach out to us – https://crgsolutions.co/contact-us/
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