UV7221
Dec. 6, 2016
Have Text, Will Travel:
Can Airbnb Use Review Text Data to Optimize Profits?
Teaching Note
Overview
Airbnb, founded in 2008 and based in San Francisco, California, is an online platform connecting owners
of homes, condos, apartments, villas, and even castles to prospective renters. The platform overturned the
hospitality industry in the half decade after its founding and has continued to gain traction in the market since.
The goal of Airbnb hosts is to use the company’s website to attract guests willing to pay to stay in their
homes. The questions for students engaging with this case is: What, if anything, can Airbnb do with data drawn
from its website to assist its customers in increasing rental volumes and optimizing revenues at their locations?
The case investigates the value of the unstructured review text data to Airbnb in particular.
The data used in the case refers to renters’ preferences and behaviors and includes text data drawn from
online reviews. The goal when using this case in a classroom setting is to explore the marketing-analytics process
(see Board 1), which includes determining the business challenge at hand, pinpointing the necessary metrics
for overcoming the challenge, using available data to describe and find the drivers of the metrics, and making
businesses changes and recommendations based on the findings.
Teaching Objectives
Understand a company’s basic business challenge and apply data and analytics to address it.
Determine the critical metrics used to measure and overcome a business challenge.
Understand the differences between structured and unstructured data and evaluate data’s business
value.
Demonstrate ways to optimize revenue potential and make recommendations based on available data.
Gather feedback and measure the effectiveness of business changes.
This Teaching Note is authorized for use only by ELENA PIKULINA, University of British Columbia until Mar 2026. Copying or posting is an infringement of copyright.
or 617.783.7860.
, Page 2 UV7221
Topical Area and Course Placement
This public-sourced case has been successfully taught in a second-year marketing elective entitled “Strategic
Marketing Analytics: Leveraging Big Data,” and an executive MBA marketing course called “Marketing
Analytics” in a module focused on research techniques, as well as in a text analytics class. The material could
be used in most courses designed to develop marketing strategies and make resource-allocation decisions based
on quantitative analysis. It is suitable for MBA, executive MBA, and executive education programs.
Student Assignments
Reading
“Have Text, Will Travel: Can Airbnb Use Review Text Data to Optimize Profits?,” UVA-OM-0897.1
Data sheets accompanying UVA-OM-0897 are available by e-mailing the author at
.
Study questions
1. What value proposition does Airbnb offer its customers?
2. What is the primary innovation Airbnb brought to its industry?
3. How does Airbnb make money?
4. What is Airbnb’s core asset?
5. What is Airbnb good at and what are the company’s capabilities?
6. How does data influence the different aspects of Airbnb’s business (e.g., customer value proposition,
processes, capabilities)?
7. How could you calculate the potential revenue of a property on Airbnb using the available data?
8. How does review sentiment affect a property’s potential to be rented on Airbnb?
9. What are the drivers of Airbnb property revenues in Miami, Florida, and Paris, France?
10. How would you optimize Airbnb property revenues in Miami and Paris? Are your strategies different
or the same in these cities?
Suggested Time-Allocation Plan for an 85-Minute Class
5 min. Introduction and bridge from last class
15 min. Who has used Airbnb? Why did you choose Airbnb? What were some of the property characteristics that were
important to you? Were reviews helpful? Do your preferences and behaviors change according to location? What are
some of Airbnb’s business challenges, and how could it use the marketing-analytics process (Board 1) to address
them?
1 Shea Gibbs and Rajkumar Venkatesan, “Have Text, Will Travel: Can Airbnb Use Review Text Data to Optimize Profits?,” UVA-OM-0897
(Charlottesville, VA: Darden Business Publishing, 2015).
This Teaching Note is authorized for use only by ELENA PIKULINA, University of British Columbia until Mar 2026. Copying or posting is an infringement of copyright.
or 617.783.7860.
Dec. 6, 2016
Have Text, Will Travel:
Can Airbnb Use Review Text Data to Optimize Profits?
Teaching Note
Overview
Airbnb, founded in 2008 and based in San Francisco, California, is an online platform connecting owners
of homes, condos, apartments, villas, and even castles to prospective renters. The platform overturned the
hospitality industry in the half decade after its founding and has continued to gain traction in the market since.
The goal of Airbnb hosts is to use the company’s website to attract guests willing to pay to stay in their
homes. The questions for students engaging with this case is: What, if anything, can Airbnb do with data drawn
from its website to assist its customers in increasing rental volumes and optimizing revenues at their locations?
The case investigates the value of the unstructured review text data to Airbnb in particular.
The data used in the case refers to renters’ preferences and behaviors and includes text data drawn from
online reviews. The goal when using this case in a classroom setting is to explore the marketing-analytics process
(see Board 1), which includes determining the business challenge at hand, pinpointing the necessary metrics
for overcoming the challenge, using available data to describe and find the drivers of the metrics, and making
businesses changes and recommendations based on the findings.
Teaching Objectives
Understand a company’s basic business challenge and apply data and analytics to address it.
Determine the critical metrics used to measure and overcome a business challenge.
Understand the differences between structured and unstructured data and evaluate data’s business
value.
Demonstrate ways to optimize revenue potential and make recommendations based on available data.
Gather feedback and measure the effectiveness of business changes.
This Teaching Note is authorized for use only by ELENA PIKULINA, University of British Columbia until Mar 2026. Copying or posting is an infringement of copyright.
or 617.783.7860.
, Page 2 UV7221
Topical Area and Course Placement
This public-sourced case has been successfully taught in a second-year marketing elective entitled “Strategic
Marketing Analytics: Leveraging Big Data,” and an executive MBA marketing course called “Marketing
Analytics” in a module focused on research techniques, as well as in a text analytics class. The material could
be used in most courses designed to develop marketing strategies and make resource-allocation decisions based
on quantitative analysis. It is suitable for MBA, executive MBA, and executive education programs.
Student Assignments
Reading
“Have Text, Will Travel: Can Airbnb Use Review Text Data to Optimize Profits?,” UVA-OM-0897.1
Data sheets accompanying UVA-OM-0897 are available by e-mailing the author at
.
Study questions
1. What value proposition does Airbnb offer its customers?
2. What is the primary innovation Airbnb brought to its industry?
3. How does Airbnb make money?
4. What is Airbnb’s core asset?
5. What is Airbnb good at and what are the company’s capabilities?
6. How does data influence the different aspects of Airbnb’s business (e.g., customer value proposition,
processes, capabilities)?
7. How could you calculate the potential revenue of a property on Airbnb using the available data?
8. How does review sentiment affect a property’s potential to be rented on Airbnb?
9. What are the drivers of Airbnb property revenues in Miami, Florida, and Paris, France?
10. How would you optimize Airbnb property revenues in Miami and Paris? Are your strategies different
or the same in these cities?
Suggested Time-Allocation Plan for an 85-Minute Class
5 min. Introduction and bridge from last class
15 min. Who has used Airbnb? Why did you choose Airbnb? What were some of the property characteristics that were
important to you? Were reviews helpful? Do your preferences and behaviors change according to location? What are
some of Airbnb’s business challenges, and how could it use the marketing-analytics process (Board 1) to address
them?
1 Shea Gibbs and Rajkumar Venkatesan, “Have Text, Will Travel: Can Airbnb Use Review Text Data to Optimize Profits?,” UVA-OM-0897
(Charlottesville, VA: Darden Business Publishing, 2015).
This Teaching Note is authorized for use only by ELENA PIKULINA, University of British Columbia until Mar 2026. Copying or posting is an infringement of copyright.
or 617.783.7860.
