Want to know how AirBnb Improved Marketing using Data Science


By iNeuBytes Content Writer


AirBnB is one of the firms disrupting the startup market that is expanding at the quickest rate. AirBnB is undeniably a force to be reckoned with in the field of data science technology. The company has expanding market for data science skills coming from all areas of the organisation, including product, finance, and operations. Building up guests' levels of trust has been AirBnB's most successful strategy for expanding their business. Since its founding in 2008, AirBnB has seen phenomenal expansion. According to AirBnB's quick facts in 2022 [1]:

There are over than 6+ million listings IN 2022.

There are currently over 4+ million AirBnB hosts worldwide and 6+ million listings on the platform IN 2022.

 There are over 100,000 cities worldwide that have AirBnB listings in them.

AirBnB has more than 150 million worldwide users that have booked over 1 billion stays.

AirBnB’s current valuation is about $113 billion.

Design thought-work AirBnB Logo First Impression

Figure shows Design thought-work AirBnB Logo First Impression

There are millions of listings on AirBnB, so you might be wondering how the company selects the listing that would provide its guests with the best possible match and provide its customers a personalised experience. Well, in the scenario of AirBnB, Data Science is indeed an essential component of their platform, and as a result, they are in a position to efficiently provide their consumers with a variety of offers and services.


Wow! Wait, what is Data Science?

“Data science is an interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract or extrapolate knowledge and insights from noisy, structured and unstructured data, and apply knowledge from data across a broad range of application domains,” according to Wikipedia's definition of the subject [2]. The information that may be cleaned through data science is put to use by businesses either to solve an issue or to improve an existing solution.


How does AirBnB successfully leveraged the Product & Marketing with data science?


Maintaining up with the rapid expansion of the firm was one of the most significant challenges faced by the data science team at AirBnB. At the beginning of 2011, the organization consisted of only three data scientists; nevertheless, because the firm was still relatively small at the time, the three were still able to practically meet with each and every individual worker and fulfil the data demands that each person had. Because AirBnB had 10 worldwide offices by the end of the fiscal year and substantially enlarged staff, the analytics team can no longer aspire to cooperate with everyone else throughout the company.

According to Newman, "We wanted to discover a means to democratise our job, stretching beyond individual encounters to engaging teams, the firm, and even our community." Those are Newman's exact words. To accomplish this goal, investments were made in technology that were both quicker and more dependable in order to manage the increasing volume of data. With the assistance of monitoring systems as well as the Airpal query tool, they shifted basic data investigation and enquires away from the data scientists and onto the teams located throughout the company. This enabled the teams to become more self-sufficient while also releasing the data scientists from the burden of responding to ad hoc requests so that they could concentrate on work that had a greater impact.

The tools of data science are at the centre of the process of finding the determinants of trust in order to engage more consumers and discover creative approaches to ease trust issues. The application of data science technology has been the primary differentiating factor in AirBnB's explosive development and in the company's ability to improve its suggestions by bringing the appropriate individuals together. The data scientists of AirBnB have been at the forefront of inventing one-of-a-kind data products and tweaking current open-source tools to make them more suitable for their organization's requirements.

Data science techniques applied by AirBnB

Figure shows the Data science techniques applied by AirBnB


Data Science at AirBnB helps prioritize product decisions and is the secret behind tremendous growth of this startup. AirBnB data scientists are the loudhailers for amplifying the voice of the customers by predicting their desires from customer interaction logs and interpreting them to incorporate actionable decision for the product, customer support and the marketing team. There are several data science techniques being used by AirBnB to learn more about its users such as A/B Testing, Natural Language processing, image recognition, predictive modelling, collaborative filtering, and regression analysis. These techniques helped to overcome challenges for AirBnB and generate great conversions from hosts, some of the uses were:


Implementation of split testing for User Impression

The business strategy utilised by AirBnB is far more intricate and involved than those utilised by typical companies. When developing AirBnB, the firm must always keep both hosts and visitors in mind at each and every stage of the process. In order to overcome this obstacle, AirBnB developed employing a split tests methodology as well as its A/B testing framework. Particular attention was paid to the ways in which modifications impacted different user groups.


Use the information as an indicator of the wants of the clients.

Riley Newman, who had previously held the position of head of data at AirBnB, stated that the company views "data as that of the voice of the consumer & data science as that of the analysis of that voice." At AirBnB, the company's in-house data scientists & analysts aggressively seek out and interact with a variety of different stakeholders, including marketers, product managers, and designers, amongst others. This helps to generate an educated view of their user information and enables the team in correctly comprehending and interpreting what is meant by the phrase "voice of the consumer."


Make improvements to their search function

The search function on AirBnB is the website's most important component since it is intended to deliver the best possible user experience. In the beginning of its existence, before it underwent a transformation that was driven by data science, AirBnB was uncertain about how to provide location-specific suggestions to its consumers.

Back in those days, they simply complied with a model that provided the listings of the greatest quality that were found within a given distance of the user's location and in accordance with the radius.

A few years later, AirBnB completely redesigned its search function and introduced a new one that was more data-driven and intelligent.


Personalization choices with data analytics.

Because AirBnB wanted to pair people who needed lodgings with those who wanted to rent out their spaces, the company needed to figure out a method to balance the preferences of potential hosts with the requirements of potential guests. AirBnB took use of machine learning algorithm and develop an application that personalises search results based on the host's and guest's preferences. This was done in an effort to find a solution that would satisfy all parties involved in the conflict. Because of this, more accurate matches were made between the visitors and the hosts, and the percentage of successful matches was increased.


User experience Measurement.

In addition to other metrics for measuring customer success, AirBnB uses its Net Promoter Score (NPS) [3] to assess the quality of the user experience. This enables inferences and predictions to be made on how and when customers are most likely to suggest products or services.



[1] Airbnb Statistics [2022]: User & Market Growth Data, Matthew Woodward. online available at: https://www.matthewwoodward.co.uk/work/airbnb-statistics/, accessed on December 2022

[2] Data science, Wikipedia. online available at : https://en.wikipedia.org/wiki/Data_science , accessed on December 2022

[3] Net promoter score, Wikipedia. online available at: https://en.wikipedia.org/wiki/Net_promoter_score  , accessed on December 2022