Research Paper
The 7 Ps marketing mix of home-sharing services: Mining travelers’ online reviews on Airbnb

https://doi.org/10.1016/j.ijhm.2020.102616Get rights and content

Highlights

  • This study reports a big-data, supervised machine learning analysis under 7 Ps.

  • The data include 1,148,062 English reviews for 37,092 Airbnb listings in SF & NYC.

  • The most mentioned marketing mix are Service Product & Physical Evidence.

  • Multi-unit and single-unit hosts show little difference under 7 Ps.

  • Superhosts and ordinary hosts differ in Service Product, Physical Evidence, & People.

Abstract

The 7 Ps model is a very useful tool in helping service firms solve managerial issues in marketing. Guided by the 7 Ps marketing mix framework, a big-data, supervised machine learning analysis was performed with 1,148,062 English reviews of 37,092 Airbnb listings in San Francisco and New York City. The results disclose similar patterns in both markets, where travelers shared their experience about Service Product and Physical Evidence most often; Price and Promotion were the least mentioned elements. Furthermore, through a series of comparisons of Airbnb’s 7 Ps marketing mix among the listings managed by different types of hosts, multi-unit and single-unit hosts seem to offer similar services with a small observable difference; whereas superhosts and the ordinary hosts deliver different services. This study makes valuable methodological contributions and provides practical marketing insights for hoteliers and the hosts and webmasters on home-sharing websites. Policymakers should pay special attention to multi-unit hosts.

Keywords

Marketing mix
Machine learning
Big data analytics
Online reviews
Home-sharing
Airbnb

Cited by (0)

Linchi Kwok, Ph.D., is an associate professor in the Collins College of Hospitality Management at California State Polytechnic University Pomona (Cal Poly Pomona). He received an M.S. and a Ph.D. degree in Hospitality Administration at Texas Tech University, as well as an MBA at Syracuse University. His research interests include information technology and service operations.

Yingying Tang, M.S., recently graduated from the Business Analytics Program in the Whitman School of Management at Syracuse University, where she received a Master of Science degree. Her interest is in business intelligence and data mining.

Bei Yu, Ph.D., is an Associate Professor at the School of Information Studies, Syracuse University. Her research expertise is in applied natural language processing and computational social science.

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