Beyond the bid amount : an empirical analysis of online upselling of hotel rooms

Abstract

Hotel companies implement online upselling with the motive of increasing revenue per customer. However, several unknowns exist regarding selecting the right customers for online upselling and quantifying the impact of these decisions on incremental revenues. This study applied a two-step analytical approach involving logit regression and propensity score matching to analyze longitudinal online upselling bidding data. The purpose was to uncover factors influencing a hotel’s online upselling decisions and their impact on the multiple revenue-generating points: rooms, food and beverage, and other facilities and services. The results show that upselling bids from repeated and long-haul guests are more likely to be accepted, while upselling bids with Saturday-night stay(s) and longer stay lengths are less likely to be accepted. The bidding time, the number of bids and the average amount of bids also increase the odds of acceptance. Upsell decisions based on these factors positively impacted revenue per customer from rooms, other amenities and services, and total revenue by an average of 48%, 84%, and 42%, respectively. These insights can be leveraged to target valuable customers for upselling. 酒店企业实施在线追加销售的动机是增加每位客户的收入。然而,在选择适合进行在线追加销售的客户以及量化这些决策对增量收入的影响方面,仍存在一些未知因素。本研究采用包含逻辑回归和倾向得分匹配的两步分析方法,对纵向在线追加销售竞价数据进行分析,旨在揭示影响酒店在线追加销售决策的因素及其对客房、餐饮和其他设施服务等多个收入增长点的影响。研究结果表明,回头客和长途旅客的追加销售报价更可能被接受,而包含周六晚入住和入住时间较长的追加销售报价被接受的可能性较低。出价时间、出价次数和平均出价金额也会增加报价被接受的概率。根据这些因素做出的追加销售决策,对每位客户的客房收入、其他设施服务收入和总收入的正向影响,分别平均达到48%、84% 和 42%。这些见解可用于锁定有价值的客户进行追加销售。