New Insights into Hotel Customers’ Revisiting Intentions, Based on Big Data

Purpose: This research employs big data analysis and sheds light on key hotel features that play a role in the revisit intention of customers. In addition, we endeavor to highlight hotel features for different customer segments. Design/Methodology/Approach: We employ a machine learning method and analyze around 100,000 reviews of customers of one hundred selected hotels around the world where they had indicated on Trip Advisor their intention to return to a particular hotel. The important features of the hotels are then extracted in terms of the 7Ps of the marketing mix. We have then segmented customers intending to revisit hotels, based on the similarities in their reviews. Findings: 71 important hotel features are extracted using text analysis of comments. The most important features are the room, staff, food, and accessibility. Also, customers are segmented into fifteen groups, and key hotel features important for each segment are highlighted. Originality/Value: By employing text mining analysis, we identify and classify important hotel features that are crucial for the revisit intention of customers based on the 7Ps. Methodologically, we suggest a comprehensive method to describe the revisit intention of hotel customers based on customer reviews. Practical implications: This study highlights key hotel features that are crucial for customers’ revisit intention and identifies related market segments that can support managers in better designing their strategies and allocating their resources. Limitations: In this research, the number of repetitions of words was employed to identify key hotel features, while sentence-based analysis or group analysis of adjacent words can be employed.