Have Text, Will Travel: Can Airbnb Use Review Text Data to Optimize Profits? Page 2 UV7220 Airbnb's specific model involved charging both its hosts and guests a fee for using its online connection Hundreds of thousands of would-be hoteliers have been popping up all around you. One might have been service. Anywhere from 6% to 12% of the reservation subtotal went to Airbab, and this reservation foe your unassuming (0-year-old neighbor, looking to pad her pension-others might include the young couple decreased as the price of the accommodation increased. Hosts paid a 3%% service fee. To grow revenues, the down the hall from your Chicago high-rise apartment. company had, since its inception, looked for ways to help its hosts market their homes. For example, the company encouraged homeowners to use professional photography services, ' and in winter 2014, it launched The one thing all these individuals had in common was that they wanted to list their properties so they Pineapple, a magazine in which users in several key markets told their personal stories.? could invite complete strangers into their homes and charge them a premium to treat the space as their own. More often than not, these entrepreneurs had zero experience in the hospitality industry and no idea how to It was a strategy that made Airbnb the world's leading peer-driven home rental website, and as of 2015, the company offered properties in more than 34,000 cities in 190 countries. Hosts who rented their homes through run a successful guesthouse. Welcome to the Airbnb cra. Airbab-more than 800,000 people worldwide-had housed more than 20 million guests. A 2012 study by neal The goal of Airbob's aspiring hosts was to use the Airbab website (www.airbnb.com) to attract guests who estate consulting firm HR&A Advisors indicated that Airbnb provided a significant economic boost to both its users and the locations they visited.' In an examination of the city of San Francisco, the firm found that people were willing to pay the highest rates to stay in their homes for a short time. For Airbab, the goal was to improve who rented their homes on Airbnb used the income they camed to stay afloat in difficult coonomic times, and customer review performance so it could, in turn, increase profits. How could the company achieve its goal? that travelers who used Airbnb spent more money in the cities they visited and brought income to less-visited Enter text mining, a technique that allowed businesses to scour Internet pages, decipher the meaning of groups neighborhoods than travelers who used traditional accommodations. of words, and assign the words a sentiment proxy through the use of a software package. "Airbnb represents a new form of travel," said Brian Chesky, Airbnb's CEO and cofounder, at the time of In order for text mining to be useful for Airbnb, its marketing professionals first had to gain access to the study. "This study shows that Airbnb is having a huge positive impact-not just on the lives of our guests customer review data on the vacation rental firm's own website. The team then had to analyze the data to find and hosts, but also on the local neighborhoods they visit and live in."'s ways to improve property performance. Was the text data in the reviews adequate to the team's purposes? Was the team going to be able to leverage this large amount of data to determine a strategy (c.g., a location-specific The HR&A study showed that, from April 2011 to May 2012, guests and hosts utilizing Airbnb contributed approach) going forward? $56 million in total spending to San Francisco's economy. Further examinations of Airbab's economic impact have shown that the service generated $61 million in one year in Portland, Oregon; $175 million in Barcelona, If the marketing team was successful, hosts would be more likely to continue to list their properties on the Spain;7 $240 million in Paris, France,* and $632 million in New York." Airbnb site, rather than being attracted away by the growing number of competitors in the market. Airbnb was not alone in its dominion over the user-generated vacation rentals market. HomeAway, which operated a number of rental platforms such as VRBO and VacationRentals.com, was a large and growing Taking Flight: The Rise of Airbnb competitor that had shifted its business model to more closely match that of Airbnb. HomeAway had previously charged its hosts on a per-listing basis, but it changed a portion of its services to charge users only when they Founded in 2008 and based in San Francisco, California, Airbab was an online platform that connected successfully rented their properties." owners of homes, condos, apartments, villas, and even castles to prospective renters. This platform overturned the hospitality industry in the half decade after its founding and continued to gain traction in the market. Indeed, Airbnb was unique, however, in that all its listings were located in one place its website. This made it an nb's rise came at a time when shared economies-wherein the creation, production, distribution, trade, ideal candidate for collecting large amounts of text data using a web-scraping tool and represented a way for and consumption of goods and services were performed by a disparate group of individuals-were increasingly the company's marketing team to gain an advantage on its fast-growing competition. popular. The strategy had become entrenched in retail settings (c.g., cBay and Craigslist) and was gaining a foothold in other areas such as transportation (c.g., Zapcar and Uber).Gathering Text Data Alternatively, consider the following sentence: "Nicolas is a bad host everything was horrible and the flat The idea of gathering and using text data from Internet-based sources was not a new one. It had been used is dirty and the location is great in a quiet area close to the subway I will not come back here." Balancing the with some success to examine other large web marketplaces such as Yelp, and it proved useful in automotive two predefined positive words-"great" and "quiet"-with the three negative ones-"bad," horrible," and market segmentation." "dirty"-qdap in R returned a polarity score of -0.036. Recently, several commercially available tools made text mining far more practical for businesses such as Once a numerical value was assigned to the sentiment present in each review, Airbnb's marketing managers Airbnb, as well as smaller businesses the world over that were looking to turn their web text into actionable could use it as a variable in a regression analysis designed to optimize revenues just as it would any other information. Import io was a fully web-based tool that was simple to use and could be customized to individual variable. The Airbnb marketing team might consider price, reviews, saved wish, and min_stay, for example. The data-mining projects." By deploying the tool on a set number of sample pages, the software understood the team might then consider drivers of these metrics such as price itself, whether people rent out entire homes, type of information available and could use that model to extract data from a large number of pages in a short private rooms, or shared rooms; and number of bedrooms. amount of time. The data output was in a simple CSV export that could be used in the subsequent analysis. Although text mining tools still had their limitations as of 2015, they were becoming smarter day by day, Optimizing Price and Beyond and the latest packages were capable of sophisticated sentiment analysis that could turn written words into quantifiable consumer preferences. The goal of Airbnb's marketing team in this exercise was to improve its users' performance so it could reap the benefits of ongoing host and renter foes. If the company's hosts were not happy, they were not likely to Analyzing Sentiment and Developing a Revenue Model continue listing their properties through Airbob, and in a competitive and burgeoning marketplace, such attrition could be devastating. Imagine that Airbnb marketing professionals have received text data from the company's IT department What could the Airbnb marketing team offer to improve its users' experience? Should it rank properties it and asked that the data be cleaned (inc., have all text changed to lowercase, and have punctuation, numbers, and suggested to users based on some metric such as review sentiment? How would review sentiment compare to special characters removed) and whittled down to only two sample cities: Paris, France, and Miami, Florida. (A sample of the raw data is shown in Exhibit 1) summary-rating value in terms of its ability to predict revenues? Given what we know about the performance of properties in Miami and Paris, did Airbnb need a region-specific strategy? Could the company suggest Text itself was not usable in a regression model, so in order to use consumer reviews in a model designed optimal pricing for hosts, or suggest other ways hosts could improve overall earnings? to optimize property performance, Airbnb's marketing team needed to apply a numerical value to the sentiment implied in a group of related words. Several forms of sentiment-analysis tools were available. The software chosen for this particular analysis was called qdap in R. Once the raw data was imported into this sentiment- analysis tool, all the data could be mapped to the variables shown in Exhibit 2. Using qdap in R, the Airbnb marketing team had two options when determining how to represent sentiment in its model: granular (e.g., at the level of each review) and high level (c.g., using multiple reviews). The team in this case selected the high-level analysis and elected to create a polarity metric to represent sentiment. The qlap in R polarity algorithm used a prespecified dictionary of positive and negative words, as well as context shifters-words around the positive and negative words. The context shifters could be neutral, amplifiers, or deamplifiers. Neutral words did not add to the polarity score, and the weight of the positive and negative words was the net sum of the number of amplifiers and deamplifiers. The polarity score computed the weighted average of positive and negative words in a sentence, and the weights were dependent on the combination of the words and the context shifters. For example, if a reviewer stated, "Nicolas is a great host everything was perfect and the flat is amazing and the location is great in a quiet area close to the subway I would definitely come back here," the polarity algorithm, after preprocessing, recognized "great," "perfect," "amazing," "great" again, and "quiet" as positive words. It identified no negative words and produced a polarity score of 1.22Exhibit 1 Have Text, Will Travel: Can Airbnb Use Review Text Data to Optimize Profits? Sample Data price TCTICO'S accommodates man_SET aciniment salep clanfee weekfos monthfee bedroom bathroom bads 45 3.704471 $100 171 3.35 5278 2.9621 61 $125 20 7 2.139501 10 3.6285-48 $12 22 5 3.134242 72 45 1464 0_72571 $1,300 NA NA 80--80-0 Variable Description rating Average user rating on a scale of 1 to 5, with 5 being the top rating reviews Number of user reviews iTICE Daily rental price of the property saved wish Number of times property was saved to the wish list review text Raw dump of all the user reviews sentiment Numeric value assigned to review text describing its positivity or negativity iccommodates Number of people the property can accommodate bedroom Number of bedrooms bathroom Number of bathrooms eds Number of beds min_stay Minimum number of days required for rental secdep Security deposit necessary? cleanfee Fees for cleaning services weekfee Discount for weekly stay monthfee Discount for monthly stay extpoop Fees for extra people Source: Created by case writer, variables are from Airbnb public website, accessed April 2114