Factor Analytics Project on Airbnb Marketing Strategy

For marketing management course in summer term, our team decided to pick Airbnb as our research target. We designed a Qualtrics questionnaire and spread it across the internet. We retrieved about 50 samples. Although the sample size was not very big and the analytics process might not be very robust (owing to time constraint), I still like to share some of findings and insight here.

Goal of the project:

1. By analyzing the quantitative data, we tried to identify the desired persona for the survey this time.
2. Based on the persona we build, what kind factors related to Airbnb does this persona care most and least? And what Airbnb can propose to enhance the marketing campaign toward this kind of persona?
3. What kind of factors do non-Airbnb users care most and least? What drives them away from Airbnb? And what Airbnb can do to attract and transfer them from other booking channel?

Intro of Airbnb:

Before starting the analytic, let me give the reader a brief intro of Airbnb. With simple number, we can see the overall picture of Airbnb. Airbnb is a double-sided platform, which connected hosts and guests, so the metrics showcase the overview of two sides.

Presented by marketing team I belong to. (data source: Airbnb website)

Another benefit of these secondary data released from Airbnb is we can then try to compare their result with ours. It can then used to justify the exploratory power of our research.

Structure of Analyses:

Our questionnaire contains three parts of question: personal info, Likert scale for Airbnb users on different factors and amenities, and Likert scale for non-Airbnb users on different factors.

Therefore, the structure for the analyses is quite simple. I will present:

1. Persona build based on the majority of data from personal info section
2. Factor analyses on Airbnb users, and recommendation based on the analyses
3. Factor analyses on non-Airbnb users, and recommendation based on the analyses

PART 1: Creation of Persona

Based on the personal info the users are require to answer from the first part of questionnaire, we create the overall persona from the samples we collected. We called here Janet.

She is student, who is around 26 years old. She is single, and she has a bachelor’s degree in French. Language is quite her passion. She loves to travel, but she is usually searching for some economical choice for staying.

More facts about her will be completed after the factor analyses. We are able to know what customers like Janet care about.

PART 2: Factor Analyses on Airbnb User (like Janet)

We analyzed the factors the Airbnb users care most. The result is presented below.

Findings:

1. Airbnb user like Janet are really price sensitive and rating caring, including the score of rating and numbers of rating. Furthermore, they probably can’t afford pricey shuttle service or Uber, so they care much about how close the listing to the public transportation.
2. For the amenities aspect, wifi is the thing they care most, and also some bedding essentials and AC, most of which are the basic demand for a basic type of trip. They don’t care about some additional and luxurious amenities, including parking space and TV.

Recommendation - To user like Janet, Airbnb should:

1. Emphasize some economical listings with convenient transportation. Also some personalized targeting might be effective too.
2. Promote the availability of basic amenities such as Wifi, bedding essentials and AC. And Aribnb shouldn’t promote listings with extra amenities to them.

PART 3: Factor Analyses on non-Airbnb User

From the beginning of the non-Airbnb User analyses, the concerns for them not booking via Airbnb are investigated. The result shows for male, quality is the biggest concern, while for female, safety is the biggest concern.

After having a short look at the reason why non-Airbnb users not using Airbnb, we further investigated what kinds of factors they care about most, and we can find some breakthrough points for Airbnb to attract these non-users and convert them to the Airbnb platform.

Findings:

1. For the non-Airbnb users in our samples, we can find they are quite price sensitive and they care about review, ratings and cancellation policy.

Findings:

1. For Non-Airbnb users, they really care about proximity of public transportation and airport. I think it is because they are somehow thrifty, and tend to commute in a more economical method.
2. They took the closeness to attraction spots and shuttle service lightly, which indirectly indicates the non-users in the sample are not tourist-alike. They might have other purpose of travelling.
3. Non-Airbnb users care basic condition of room and services. They don’t really see some extra facilities (i.e. business center, fitness center, and restaurants as a must).

Recommendations - To non-Airbnb users, Airbnb should:

1. Boost and raise the consumer awareness that Airbnb is actually quite safe and with fine quality. Through social media, traveler forums, and word-of-month, Airbnb should link the listing to safety and good quality in the mind of the users who are unwilling to use Airbnb.
2. What non-Airbnb users care about is actually quite similar to Janet, our persona. With the emphasis on price, ratings, cancellation policy, and some basic services, Airbnb can promote the qualified listing to this group of users and convert them into the user funnel.

PART 4: What we might be wrong?

1. The sample might be too small to be representative. And we didn’t confirm which group of segmented user Airbnb are targeting now, so it might cause some bias in the analyses process.
2. The factors we list in the questionnaires might not be inclusive. Therefore, some missing and incompleteness might happen.

PART 5: Further Research

We used primary data here in the project. Owing to time constraint, we gave up the exploration in some of secondary datasets, which might grant more fruitful results. It is the further direction we might go to in the future.

Team members: Kevin, Phoebe, Sushanth