Factors Influencing Hotels’ Online Prices

ABSTRACT

Digital corporations are creating new paths of business driven by consumers empowered by social media. Understanding the role that each feature drawn from online platforms has on price fluctuation is vital for leveraging decision-making. In this study, 5,603 simulations of online reservations from 23 Portuguese cities were gathered, including characterizing features from social media, web visibility, and hotel amenities, from four renowned online sources: Booking.com, TripAdvisor, Google, and Facebook. After data preparation, including removal of irrelevant features in terms of modeling and outlier cleaning, a tuned dataset of 3,137 simulations and 30 features (including the price charged per day) was used first for evaluating the modeling performance of an ensemble of multilayer perceptrons, and then for extracting valuable knowledge through the data-based sensitivity analysis. Findings show that all features from the encompassed factors (social media, online reservation, hotel characteristics, web visibility, and city) play a significant role in price.

数字公司正在创造由社会媒体授权的消费者推动的新的商业道路。了解在线平台上每个特性对价格波动的作用对于利用决策至关重要。 在这项研究中，来自葡萄牙的城市23 5603模拟在线预订，包括特征的社会媒体，网站的知名度，和酒店的设施，从四个知名在线来源：Booking.com、TripAdvisor、谷歌、脸谱网。在数据准备，包括在和异常清洁建模方面的无关特征去除，调谐数据集3137模拟和30特征（包括每天收取的价格）先用一个多层感知集成的建模性能评估，然后通过基于数据的敏感性分析，提取有价值的知识。 调查结果显示，包含的因素（社会媒体，网上预订，酒店的特点，网络能见度和城市）的所有功能发挥了重要作用的价格。

KEYWORDS:

• Online booking
• pricing
• hotel reservation
• social media
• data mining