Exploring online prices with an advance booking horizon on Booking.com

© 2020 The Author(s) This work is licensed under the Creative Commons Attribution 4.0 International (CC BY 4.0). To view a copy of this license, visit https://creativecommons.org/licenses/by/4.0/ RESEARCH PAPER 1 Exploring online prices with an advance booking horizon on Booking.com Gloria Sánchez-Lozano1*, Luis Nobre Pereira2 and Esther Chávez-Miranda 3 1 Facultad de Ciencias Económicas y Empresariales, Departamento de Economía Financiera y Dirección de Operaciones, Universidad de Sevilla, Campus de Ramón y Cajal, 41018 Sevilla, Spain; Email: [email protected]. 2 Research Centre for Tourism, Sustainability and Well-being (CinTurs) & Escola Superior de Gestão, Hotelaria e Turismo, Universidade do Algarve, Campus da Penha, 8005-139 Faro, Portugal; Email: [email protected] 3 Facultad de Ciencias Económicas y Empresariales, Departamento de Economía Financiera y Dirección de Operaciones, Universidad de Sevilla, Campus de Ramón y Cajal, 41018 Sevilla, Spain; Email: revenu[email protected]; Phone: +34 954551606 * Corresponding author Abstract The online market enables hotels to enhance their visibility and drive up their revenue. This study analysed both the average room price and price count (i.e. the sum of the number of prices that hotels offer) on Booking.com for a period of 300 days prior to check-in, with data classified by official hotel category. Hotels’ number of rooms, day of the week, room type, room capacity (i.e. maximum number of guests per room) and length of stay were also tracked. This research was based on a stratified sample of hotels gathered by using random sampling and proportional allocation, as well as defining the strata by hotel categories. The dataset included 1,353,751 records. The results reveal that channel management activities are an important area of hotels’ operations, which generate a considerable workload in terms of the time devoted to updating data and other related tasks. However, hotels’ participation in online channels does not always match their importance in the market as measured by their relative number of rooms. Most of the variables under study have a significant positive impact on prices, except for hotels’ number of rooms, which failed to follow any discernible pattern of influence. Keywords: pricing, online distribution channel, channel management, booking horizon, revenue management, hotel Citation: Sánchez-Lozano, G., Nobre Pereira, L. & Chávez-Miranda, E. (2020). Exploring online prices with an advance booking horizon on Booking.com. European Journal of Tourism Research 26, 2606 Exploring online prices with an advance booking horizon on Booking.com 2 Introduction Implementing revenue management in the hotel industry requires managers to determine ‘the right price for the right product for the right customer’ (Smith, Leimkuhler & Darrow, 1992) ‘at the right moment’ (Cross, 1997; Kimes, 1989; Kimes, Chase, Choi, Lee & Ngonzi, 1998; Kimes & Singh, 2008) and through the right distribution channel. When correctly formulated, this decision mix generates the maximum possible revenue and, where possible, extracts the greatest profit from hotels’ perishable assets or available resources (Baker & Collier, 2003; Donaghy, McMahon & McDowell, 1995; Guadix, Onieva, Muñuzuri & Cortés, 2011). Time is an unambiguously essential dimension of revenue management, which has been shown to be highly significant because a proper use of time allows managers to establish resource distribution and determine selling conditions in advance. However, time also increases revenue management’s complexity (Maier, 2012) and the number of decisions to be made. In the lodging sector, a resource or inventory unit (e.g. room night) and its related components (e.g. room type and length of stay) and/or conditions can be offered to potential customers up to a year in advance (Cross, Higbie & Cross, 2009; Schütze, 2008) in order to generate bookings up to the time that the service is actually provided. When hotel companies such as Marriott International (Hormby, Morrison, Dave, Meyers & Tenca, 2010) and Carlson Rezidor (Pekgün, Menich, Acharya, Finch, Deschamps, Mallery & Sistine, 2013) publish or update their prices and room availability daily, these hotel groups could benefit from an analysis of the relationship between prices and booking pace (i.e. booking on hand registered along the booking horizon prior to customers’ arrival). Researchers have found evidence of a relationship between advanced time periods and published prices (Choi & Kimes, 2002). This information is used to obtain new forecasts and update prices and availability on all hotel channels. Revenue managers – or the individuals responsible for these management activities – must define and use appropriate strategies and tactics during these periods to improve their hotels’ level of profitability. Therefore, managers have to learn more about the way that hotels’ resources and features evolve throughout the entire booking period preceding check-in dates. Price is considered to be one of the main variables in revenue management (Guillet & Mohammed, 2015; Ropero, 2011). The existing research has focused strongly on pricing applied in distribution channels, which has been widely studied to explain customer behaviour from a demand perspective (Liu & Zhang, 2014; Schamel, 2012; Soler et al., 2019) and, to a lesser extent, a supply perspective (Hung et al., 2010; Kim et al., 2016; Kreeger & Smith, 2017; Thrane, 2007). Significant changes have occurred in the overall pattern of hotel distribution that require further study (Law, Leung, Lo, Leung & Fong, 2015). In particular, channel management is an increasingly important part of hotel distribution and an emerging line of research within the field of revenue management (Domingo-Carrillo, Chávez-Miranda & Cubilesde la Vega, 2017; Ivanov & Zhechev, 2012; Kimes, 2011). The online booking market is currently a source of revenue generation. Nielsen (2016) reports that the category of ‘travel products and services’ is ranked in the top five (i.e. third place) for all regions worldwide (i.e. Africa, Asia-Pacific, Europe, Latin America, the Middle East and North America). Online channels are expected to become increasingly important in the future because of their popularity among online customers (Guo, Ling, Dong & Liang, 2013). The online travel agency Booking.com is recognised as one of the main hotel distribution channels and, together with Expedia, this site has become a major driver of change in this area. The present study sought to explore both hotels’ room prices and price counts (i.e. the number of prices or entries) regarding offers provided through online distribution channels. These offers are made, depending on product features, over a booking horizon of 300 days prior to check-in dates. Booking.com Sánchez-Lozano et al. (2020) / European Journal of Tourism Research 26, 2606 3 was selected as the source of data because it is a key player, accounting for 60% of all bookings made in Europe (Tom Dieck, Fountoulaki & Jung, 2018). From an academic perspective, this research aimed to enrich the literature on revenue management through an in-depth analysis of the online hotel room supply profile, with a special focus on two crucial variables of revenue management implementation: advance prices and total counts of prices. An urban destination was chosen as a case study. The research included taking the first steps towards conducting an analysis of combined offers of prices and features while considering an extensive booking horizon. For practitioners, the results provide information that can support the decision-making processes related to revenue management. The ability to analyse average prices by feature and hotel category could further improve managers’ ability to make appropriate decisions. This paper is structured into five sections. The next section presents the literature review. The third section sets out the methodology and describes the data retrieval and usage processes. Section four then describes the results. The conclusions offered in the final section highlight this research’s principal achievements and theoretical contributions, as well as proposing future research directions. Literature review This study focused on hotels’ price policies applied in online distribution channels. Pricing has long been considered a core part of revenue management (Baker & Collier, 1999; Chiang, Chen & Xu, 2007; McGill & van Ryzin, 1999; Weatherford & Bodily, 1992), but channel management is a relatively new area of research that has only recently gained relevance, especially in terms of hotels (Domingo-Carrillo et al., 2017; Guillet & Mohammed, 2015; Ivanov & Zhechev, 2012). Channel management is also an emerging field of study that has increasingly influenced revenue management practices and trends (Wang, Yoonjoung Heo, Schwartz, Legohérel & Specklin, 2015; Yeoman & McMahon-Beattie, 2017). Efficient distribution channel management must be implemented in complex scenarios (Kimes, 2016) that require both more time devoted to these activities and awareness of their impacts on costs. The Internet has radically changed the way that hoteliers inform customers about prices and room availability, but, simultaneously, this has entailed the assumption of new costs (Vinod, 2004). In addition, monitoring and controlling channel activities needs to be part of managing hotels in this competitive, constantly changing environment (Josephi, Stierand & van Mourik, 2016). Despite these challenges and channel management’s importance and impact on costs and revenue management performance measures, a gap still exists in the research in this area, especially from a supply perspective. Vives, Jacob and Payeras (2018) describe the current situation as a ‘new era of online distribution channels’. The online market has grown significantly, facilitating access to information and connections with customers. When these channels are properly utilised, opportunities arise for increasing revenues and profits (Talón-Ballestero & González-Serrano, 2013). Hotels already distribute approximately onethird of their bookings through the online market, and this figure may grow from 60 to 70% in the coming years (Law et al., 2015). Recent studies have used the data available in distribution channels and, specifically, on Booking.com to examine the behaviour of prices offered (Abrate & Viglia, 2016; Ivanov & Piddubna, 2016; Oses, Gerrikagoitia & Alzua, 2016; Pawlicz & Napierala, 2017). However, the existing research has only considered a maximum advance booking period of 90 days, which is significantly smaller than the 300- day period covered by the present study. As far as the literature review could determine, this study’s inclusion of hotels’ offer or price counts is also a novelty. Price count provides insights into the total and relative value of resources’ – or perishable assets’ – distribution through online channels. These Exploring online prices with an advance booking horizon on Booking.com 4 figures can be considered an indicator of the importance that hoteliers give to each of their online offers, thereby providing valuable information. Analyses of room prices and price counts according to booking conditions across the entire advance booking horizon enable general predictions to be made about hotels’ situation on any given day (i.e. the check-in date). Revenue managers can benefit from this overview, taking advance actions to achieve improved revenue. Thus, this research analysed both the average prices and price counts published on Booking.com based on a booking horizon of 300 days before customers’ arrival dates. In addition, the data analyses considered hotels’ official category and various variables published in the selected online travel agency, namely, day of the week, room type, number of guests per room and length of stay. Methodology This study focused on hotels located in Seville, a city in southern Spain famous for its rich cultural heritage. The destination was chosen for its greater importance compared to other Spanish cities. Seville is especially predominant in tourism as the city has been consistently ranked between third and sixth among all urban destinations in Spain, according to Exceltur (2013, 2016, 2017a, 2017b 2018). Its current prominence has recently been confirmed by being ranked first in Lonely Planet’s (2018) cities category. Regarding the hotels’ profile, Spain’s official hotel rating system classifies establishments into five categories indicated by the number of stars awarded, with 1 star being the lowest category and 5 stars the highest. Previous studies on revenue management in Spain have mainly focused on higher categories, that is, hotels with 3-, 4- and 5-star ratings (Chávez-Miranda, 2005; Domingo-Carrillo, 2016; Oteo-Iturmendi, 2013; Talón-Ballestero, 2010). This classification system was used in the present research. The hotel study population was first identified to facilitate the selection of a representative sample. Hostelmarket’s (2014) hotel census was compared to and completed with the hotels listed on Booking.com for Seville. The most recent census also provided the required information on hotels’ official star ratings, which meant that the population could be divided into 3 strata (i.e. 1 per hotel category). The total population was 94 hotels: 38 3-star, 51 4-star and 5 5-star hotels. The size of each strata of the sample was determined by a random sampling method with proportional allocation (5% margin of error; 95% confidence level; p = q). The resulting proportional allocation of the final sample of 31 hotels was, therefore, as follows: 12 3-star, 17 4-star and 2 5-star hotels. IBM SPSS Statistics software was used to obtain 3 independent random samples. This sampling method ensured that the results could be extrapolated both to hotel categories and the selected urban destination as a whole. To gather the required information, the data were retrieved from Booking.com by emulating the behaviours of consumers who visit the website to look at hotel prices. The dataset obtained was broadened to include hotels’ official hotel star ratings and number of rooms. Following previous related studies’ recommendations, an analysis was conducted of the main variable (i.e. price) and its influence 300 days in advance of check-in dates. The final dataset included room rates and offer-related conditions, namely, day of the week, type of room, number of guests per room and length of stay. The data were collected on 20 January 2017. No missing values were detected, and all values for each variable were collected. The variables covered by this study are presented in Table 1. Hotel category contains information about the sample hotels’ official star rating from 3 to 5 stars. The number of rooms was used to measure the hotels’ size, which ranged from 7 to 437 hotel rooms. The day of the week is a time variable that refers to the day of the week for which a specific price value is offered, so this variable ranges from Monday to Sánchez-Lozano et al. (2020) / European Journal of Tourism Research 26, 2606 5 Sunday. The variable of type of room enables the rooms offered to be put into similar categories of rooms: Standard, Superior, Superior Plus, Junior Suite and Suite. The number of guests per room or room PAX refers to the maximum number of guests allowed in a room, which went from 1 to 4. The length of stay gives information about the minimum number of nights that customers must stay in hotels for specific price offers to be valid, with values running from 1 to 4 hotel nights. Table 1. Summary of independent variables and their ranges Variables Categories Hotel category 3-, 4- and 5-star hotels Number of rooms 7–437 hotel rooms Day of the week 1–Monday, 2–Tuesday, 3–Wednesday, 4–Thursday, 5–Friday, 6–Saturday, 7– Sunday Type of room 1–Standard, 2–Superior, 3–Superior Plus, 4–Junior Suite, 5–Suite Number of guests 1–4 guests Length of stay 1–4 hotel nights In the selected set of variables, only type of room is a recoded variable. Hotels in Seville use more than 80 different types of room or ways of naming a room. On occasion, rooms that are almost identical in physical terms are named differently, which made the natural variable impractical. Based on two main criteria (i.e. probable room size or space and the room style, amenities and services included), all these room labels were reduced to five categories. Given this study’s aims, the results are presented in two ways: (1) average room price, which was calculated directly from the mean value of the variable of room price and (2) room price count, which is the sum total of prices retrieved from the Booking.com website. Additional results based on the hotels’ category are given in Table 2. A simple regression model was estimated to explain the relationship between room price and each independent variable. The model with the best fit was chosen based on the R-squared (R2) value. Number of rooms was the only variable not found to have a clear relationship with room price, it also presented numerous categories, so the results for that variable are not showed in Table 2 above. Finally, in line with similar previous research on price behaviour in distribution channels (Ivanov & Piddubna, 2016), the analysis of variance (ANOVA) and t-test for independent samples were applied to check for differences in average room prices between the independent variables. To summarise, the dataset contained 193,393 lines for each of the analysed variables including room price, which resulted in 1,353,751 records. This volume of data is remarkable for such a relatively small sample of hotels in Seville (31), which were able to produce a vast price offer database in a single online distribution channel on one day. These data were processed using both Microsoft Excel (2016 version) and IBM SPSS Statistics (version 24) software. In addition, Microsoft Power BI Desktop (version 2.73.5586.984) software was used to generate a dashboard with the categories, average room prices and maximum and minimum values per variable. Results Hotel category Given the 300-day booking horizon prior to check-in dates, an overview of the data on prices hotels’ offered through the selected online distribution channel shows an average room price of €177.80 (euros) for this urban destination. Figure 1 shows the average room prices by hotel category (i.e. 3–5 stars). The solid line indicates the average room price for each category, while the bars give the price count. The trendline (i.e. the dotted line) is discussed in greater detail below. Exploring online prices with an advance booking horizon on Booking.com 6 Table 2. Average room price per hotel category Hotel Category 3 4 5 Day of the week Monday 97.35 186.68 233.28 Tuesday 97.87 187.11 236.86 Wednesday 99.60 191.06 243.64 Thursday 98.61 192.26 246.65 Friday 105.60 199.59 246.52 Saturday 104.03 197.44 245.99 Sunday 93.60 183.03 233.18 ANOVA test F statistic 59.195 60.403 1.551 df 6 6 6 p-value <0.001 <0.001 <0.157 Type of room Standard 93.96 152.97 188.16 Superior 112.93 185.48 186.08 Superior plus 106.53 400.06 161.71 Junior suite 154.42 283.05 405.65 Suite 312.84 788.43 ANOVA test F statistic 1421.718 12865.092 5463.337 df 3 4 4 p-value <0.001 <0.001 <0.001 Room PAX 1 81.93 135.11 139.87 2 95.35 195.72 247.07 3 120.63 250.15 182.06 4 136.19 368.24 640.39 ANOVA test F statistic 2320.723 11144.915 408.660 df 3 3 3 p-value <0.001 <0.001 <0.001 Length of stay (price per night) 1 97.58 184.86 289.72 2 65.91 101.94 76.20 3 44.57 79.73 336.06 4 68.64 ANOVA test F statistic 793.668 7842.778 1606.841 df 2 3 2 p-value <0.001 <0.001 <0.001 Sánchez-Lozano et al. (2020) / European Journal of Tourism Research 26, 2606 7 Figure 1. Average room price and price count by hotel category Average room prices behave as expected with respect to the hotel categories. The price increases significantly as the star rating rises (F = 16,135.8; degrees of freedom1 [df1] = 2; df2 = 193,390; p-value < 0.001). However, this increase is not proportional since the average value for four-star hotels is almost double of the value found for the lowest category considered (92.0%). The mean room price offered for 5-star hotels, in turn, rises only by 26.0% in comparison with the preceding category. The average increase between categories is €70.60 (41.3%), which is 30 to 36% higher than the increase found by researchers in the Polish hotel industry (Pawlicz & Napierala, 2017). The higher the hotel category, the broader the price variability. In all cases, the price range is quite wide, that is, from €566.10 (3 stars) to €2,405.50 (5 stars). Notably, 3-star hotels sometimes ask prices that could be considered more suitable for 4-star hotels. Both 3- and 4-star hotels offer prices starting from similar minimum values (€33.90 and €32.40, respectively), but the maximum prices offered by 3-star hotels, for example, include values as high as €600.00. In addition, the highest category hotels offer rooms at €94.50, which could be considered a more appropriate price for 4-star establishments. Regarding the room price count, four-star hotels present the highest volume of price offer. They dominate the Seville market with over 66.6% of the total price count. This percentage exceeds the proportion of 4-star hotels in the sample (54.8%). Three-star hotels (price count of 26.0% out of 38.7% of the sample) and 5-star hotels (7.4% out of 6.5%) produce a lower percentage of price offers. Despite the well-documented dependence of 3-star hotels on Booking.com and the 38.7% they represent in the sample, their share in the price offer’s total volume is only 26.0% The average room price trend according to hotel category was estimated with a logarithmic function (R2 = 0.9996). The results clearly indicate that, as the number of stars rises, the average room rate also increases. This relationship was set out in Equation (1), in which y refers to the average room price and x to the hotel category (x=1 for 3 stars; x=2 for 4 stars; x=3 for 5 stars): y = 128.9ln(x) + 100.02. (1) 50 203 128 855 14 335 99,44 € 190,96 € 240,63 € 0 20 000 40 000 60 000 80 000 100 000 120 000 140 000 0 € 50 € 100 € 150 € 200 € 250 € 300 € 3-star hotels 4-star hotels 5-star hotels Room Price Count Average Room Price Exploring online prices with an advance booking horizon on Booking.com 8 Number of rooms The average room price was examined in relation to the number of rooms available at each hotel included in the study (see Figure 2). Some of the average room prices given are the result of multiple hotels having exactly the same size (2 hotels with 7 rooms, 2 hotels with 23 rooms and 2 hotels with 81 rooms). The data do not appear to show any particular behaviour pattern regarding hotel size and average room prices. However, the ANOVA results show that the average room price varies significantly by hotel size (F = 5,612.9; df1 = 26; df2 = 193,366; p-value < 0.001). When the results were analysed in detail, various individual cases were identified, which are discussed further below. In general, the maximum values correspond to 33- and 189-room hotels. Both are 4-star hotels, although they differ substantially in size (156 rooms). For example, the smallest hotel with 7 rooms sells these at a higher average rate than a 365-room hotel. Figure 2. Average room price and price count by number of rooms Regarding the room price count, the percentage expected for each hotel’s share is 3.2% of the total overall volume. However, this figure fails to measure the importance that each hotel places on its offer. Thus, the present analysis needed to consider the number of rooms’ influence on the number of price offers. To this end, the hotels’ fair share was calculated because this is an indicator used by revenue managers to compare hotels’ size (i.e. measured by number of rooms) with their competitive set of 4 to 7 hotels. The fair share can be calculated in an urban context by dividing the number of rooms in each hotel by the total number of rooms in the sample, which gave the expected average hotel offer for the current sample as 3.7%, which is reasonably similar to the expected offer (i.e. 3.2%). One further aspect that needs to be highlighted is the combined analysis of higher prices and price volume. The results include the individual cases mentioned previously. A third of the hotels (10) currently offer prices that exceed their expected percentage (a fair share above 3.7%), but only 3 – all 4– star hotels – offer a more advantageous average room price ranging from €200 to €250. One case is particularly interesting since this hotel is responsible for over 19% of the volume of price offers (i.e. 5 times more than the expected percentage). This anomaly can be partially explained by the hotel’s size 37 912 46 046 41 417 68 018 €158,46 €138,68 €173,28 €171,46 0 10 000 20 000 30 000 40 000 50 000 60 000 70 000 80 000 €- €20 €40 €60 €80 €100 €120 €140 €160 €180 €200 Less or equal to 50 rooms From 51 to100 From 101 to 200 Over 201 rooms Room Price Count Average Room Price Sánchez-Lozano et al. (2020) / European Journal of Tourism Research 26, 2606 9 (437 rooms) since it is the largest of all the hotels considered. An examination of these cases based on prior research’s results for the same destination (Chávez-Miranda, 2005) suggests that a mindful pricing strategy, tactics and, in general, proactive revenue management explain why these 3 hotels meet the 2 criteria of a higher volume of prices offered and success in terms of higher average prices. A second set of special cases was also be identified. The 2 hotels with the highest average prices (€400.00 to €465.00) offer fewer prices than expected (1.3% and 2.1% of the price count), but these establishments are different sizes (33 and 189 hotel rooms, respectively). A further examination of the present data and additional information about these specific cases revealed that their high average prices are unrelated to either of these hotels’ number of rooms or relative importance (i.e. their price offer) in the online distribution channel in question. The elevated prices may be due to price differentiation based on special hotel facilities as one is a boutique hotel and the other – with the highest average rate – is a fivestar hotel. Day of the week The results also confirmed that the average room price behaves differently depending on the day of the week (F = 64.5; df1 = 6; df2 = 193,386; p-value < 0.001). Figure 3 shows that prices rise at weekends and falls on weekdays (t = -17.7; df = 193,391; p-value < 0.001). An in-depth analysis (see Table 2 above) showed that the average room price significantly differs in all three hotel categories according to the day of the week. A similar analysis was also carried out for the remaining variables because the average room price varies significantly for hotels of different categories. Figure 3. Average room price and price count by day of the week A fictitious boundary was set at an average room price of €175.00, revealing two different rate categories. The average price offered for weekend days (Friday and Saturday) is €178.50, and the average price for weekdays is €167.90 (the remaining days), with a difference between the two rates of €10.60. This result is not unexpected since the primary users of Booking.com are tourists on holiday (Law et al., 2015). However, further analysis identified an additional price class in the middle of the week, so three price clusters were ultimately differentiated: (1) start of the week, (2) mid-week and (3) weekend. Average prices start a gradual upward trend on Wednesdays and Thursdays – immediately before the most significant change on Fridays, which signals the start of the weekend. Hotels in Seville tend to set higher 164,49 € 166,91 € 166,93 € 170,02 € 171,29 € 179,07 € 178,01 € 26 000 26 500 27 000 27 500 28 000 28 500 29 000 0 € 20 € 40 € 60 € 80 € 100 € 120 € 140 € 160 € 180 € 200 € Sunday Monday Tuesday Wednesday Thursday Friday Saturday Room Price Count Average Room Price Exploring online prices with an advance booking horizon on Booking.com 16 Limitations and future research directions While the present study’s results have some limitations, these simultaneously represent future lines of research. First, this research used online data from only one online distribution channel, although this channel has been shown to be the most important in Europe. Second, one further variable that future research needs to take into account is how average room price behaves according to number of days to check-in dates. Third, evidence has been found that online room prices increase as the date of bookings approaches, but researchers may get more useful results by confirming this pattern for the urban destination under study with reference to, among other variables, hotel category, room type, length of stay or tourism season. Last, this research explored only one city, albeit in great detail. Acknowledgements We would like to acknowledge the participation of the company Beonprice S.L. in the present research. We would also like to thank the Vice-Rectorate of Research of the University of Seville for providing financial support for this study through its ‘V Plan Propio de Investigación’. The authors are thankful for support from the Research Centre for Tourism, Sustainability and Well-being (CinTurs) [FCT Grant Number UIDP/SOC/04020/2020]. 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