استراتژی های قیمت گذاری پویا : مدارک و شواهد از هتل های اروپایی

How much do hoteliers actually make use of dynamic pricing strategies? We collected data on the price of a single room booked in advance (from three months to a single day), from almost 1000 hotels in eight European capital cities. Pricing strategies were analyzed by means of descriptive statistics, box plots and econometric panel data techniques. The empirical results show that the inter-temporal pricing structure primarily depends on the type of customer, the star rating and the number of suppliers with available rooms.

The nature of hotel rooms as a perishable asset is prompting hoteliers to maximize their revenue by trying to achieve optimal dynamic prices using different strategies. Conversely, customers can strategically change their purchase plans in order to pay as little as possible. In this context, heterogeneity among hotels and customers plays a key role. The best form of inter-temporal pricing strategies depends on the composition of the customer population alongside factors such as customer valuations and patience, as highlighted by Su (2007). In particular, if high-valuation customers have a proportionally lower degree of patience while low-valuation customers are often sufficiently patient to wait for sales, setting last-minute promotional low prices is preferable. Otherwise, strategic waiting by high-value customers would need to be discouraged by setting increasing price dynamics. The rapid growth of the Internet has had a massive impact on the hotel industry; however, hospitality literature lacks published articles that examine the trend and the variability of prices in online markets (Tso and Law, 2005). Unlike those methods in decline (such as agency, fax and phone), the Internet encourages greater price scrutiny since the relevant information is both easier to obtain and transparent, given that any party can compare the prices of several alternatives with very little effort. This is bound to have an impact on how hotel operators set room prices since they too can easily obtain this information and rapidly respond accordingly. This work aims to provide some evidence on the actual behavior of operators in the hotel industry. Do hoteliers really make use of dynamic pricing strategies? If yes, do we observe increasing or decreasing price trends when approaching the check-in date? From the customer perspective, how should they react to the seller's pricing strategies? What are the main drivers behind the price trend structure? The empirical analysis is carried out on a sample of almost 1000 hotels distributed around different European capital cities. The idea was to observe the evolution of prices on a predefined booking day in order to verify the extent of price variability and the significance of any trend. Moreover, we investigate the presence of alternative pricing policies in relation to different hotel characteristics, dynamic competition in the overall booking period and potential customers. With respect to the latter, we collected data for different types of booking days, in particular, an intraweek day – Tuesday night – which is usually characterized by business travelers, as confirmed by the manager of an important international chain, and a weekend day – Saturday night – more in line with a leisure trip. The remainder of this paper is organized as follows. Section 2 provides the literature review. Section 3 describes the conceptual framework, defining the hypothesis that will be tested in the empirical analysis. Section 4 starts by clarifying the sources of data (Section 4.1), thereafter presenting both descriptive data analysis (Section 4.2) and a more structured empirical model based on panel data econometric techniques (Section 4.3). Results of the panel data analysis are shown in Section 4.4. Section 5 provides a comprehensive discussion of the conceptual hypotheses in light of the results obtained. Finally, Section 6 offers concluding remarks and directions for future research.

Through a better understanding of online pricing practices, hotel management room distributors can effectively manage the online market, leading to the capture of high-value customers. This research tries to bridge a gap in literature, investigating not only the customer perspective but also the relationship between online pricing strategies and the interest of hotel room suppliers. According to the empirical results, we demonstrated that over 90% of prices changed in the period considered, with an inter-temporal structure of the trend primarily depending on the type of customer (leisure or business) and on star rating. Considering weekdays, when customers are prevalently business people, the lowest prices seem to appear in the period immediately preceding the hotel stay. Instead, on a weekend, when the number of leisure customers is predominant, prices tend to increase when approaching the check-in date. The presence of significant trends that are heterogeneous according to the booking period and hotel characteristics, such as the star rating, is confirmed by means of panel data techniques. In particular, both in the mid-week and in the weekend date, last minute booking is characterized by a higher price differential between high star and low star hotels. Furthermore, a variable approximating demand shocks at the city level was created and proved to significantly affect hotelier pricing dynamics. As expected, the price tends to increase when there is a scarcity of hotels available to book in a certain area. This suggests strategic behavior, with hotels adapting optimal prices according to competitor room availability. The results in this paper can be extended in two broad directions. The first is to investigate the relation between price dynamics and occupancy rates. In this paper, the latter information is not available. It would be interesting to consider price decisions, with occupancy rates from three months before the date of query. Mannix (2008) suggested an historical database, owned by Hotelligence and Smith Travel Research, which contains historical information on some occupancy rates. The second is to obtain data referring to more booking dates. On this occasion, Piga and Bachis, 2006a and Piga and Bachis, 2006b used an electronic spider directly connected to the website of the data source, saving collection time and granting the opportunity to analyze a longer trend. Our analysis, in fact, has an impressive number of hotels but it could be beneficial to increase the number of booking dates to strengthen the results. It could also be interesting to test if in high season the gap in the price trend between mid-week and weekend days becomes smaller.