An Analysis of Hotel Booking Cancellations and Factors Affecting Revenue Generation

Abstract

This study examines the growing problem of can- celled hotel reservations in the hospitality sector, in particular in the City Hotel and Resort Hotel segments. In order to alleviate the issue and enhance revenue creation, it looks to identify the underlying causes and offers specific business guidance. It provides a comprehensive picture of the problem by taking into account both internal and external forces. The research demonstrates the similarities and differences between City and Resort Hotels in terms of cancellations, revenue impact, and issues through comparative analysis. In order to decrease cancellations and improve overall business efficiency, the paper suggests tactics spanning consumer involvement, booking policies, price, customer support, and marketing. In conclusion, this study offers helpful advice to business executives on how to handle cancellations of reservations and boost financial results.

Introduction

I. INTRODUCTION

The hospitality sector, which includes a wide range of industries and caters to the requirements and preferences of travelers and tourists, is a pillar of the global economy. Among these industries, hotels are essential. They are essential parts of the trip experience and serve as more than just a place to lay one’s head. A complex interaction of elements, such as strategic pricing, location benefits, and the reliable provision of great services, determines the success of hotels. The reservation cancellation rate, however, is one aspect that has a significant impact on the sector.

Our main objective in this long study is to start a detailed investigation of a dataset that contains a lot of data regarding hotel reservations. Our goal is to look deeper into the complex web of patterns, trends, and connections around reservation cancellations rather than just looking at cancellation rates in isolation. To understand what causes these cancellations and how they affect income production, this analysis goes beyond the obvious.

Our goal is to discover priceless insights for the hospitality business by fully immersing ourselves in the data. This data can be used to develop plans, improve operational effectiveness, and—most importantly—promote a better understanding of client behavior and preferences. A thorough understanding of reservation cancellations and their underlying causes is becoming more and more important as the hotel industry develops. With this insight, industry participants may pro- vide smooth and customized experiences for visitors while maximizing revenue sources. This study project serves as a knowledge compass, instructing industry actors on how to  adjust, develop, and prosper in a sector that is changing quickly.

II. LITERATURE REVIEW

The hotel industry has been significantly impacted by the cancellation of bookings, resulting in lost revenue for hotels. In recent years, there has been an increasing trend in hotel booking cancellations, which has led to a need for research to understand the factors contributing to this trend. Several studies have been conducted to identify the reasons behind hotel booking cancellations. The authors looked into how customers’ intents to book hotel stays are impacted by both the pricing and brand image of hotels. In the context of hotel reservations, this research probably examines the interwoven relationship between pricing tactics and brand perception, offering light on the crucial variables influencing patron decisions in the hospitality sector [1].Using information from an online travel service, the association between client characteristics and hotel booking cancellations is examined. This study probably explores the relationship between customer- related variables like demographics or booking patterns and the propensity to revoke hotel reservations. In order to streamline the booking process and reduce cancellations, it is essential for the online travel agency and the larger hospitality sector to understand this link[2].In order to shed light on the connection between weather patterns and visitor behavior, this research probably examines how weather conditions affect hotel booking cancellations in the setting of a Taiwanese hotel[4].

According to a study by Bilgihan et al. (2016), the most common reasons for hotel booking cancellations are related to the price, location, and room preferences. Another study by Wang and Li (2016) found that the most common reasons for hotel booking cancellations were related to the guest’s personal reasons, such as illness or change of plans. The cancellation of bookings is not only a problem for hotels but also for online travel agencies (OTAs) such as Expedia and Booking.com. A study by Wang et al. (2019) found that bookings made through online travel agencies (OTAs) have a greater cancellation rate than bookings made directly with hotels. The study found that customers who booked through OTAs were more likely to cancel their bookings because they could easily find alternative accommodation options. In addition to cancellation of bookings, hotels also face other factors that impact their business and revenue generation. One such factor is seasonality. According to a study by Lu and Stepchenkova (2016), hotels in popular tourist destinations experience high demand during peak season, but lower demand during off-season. This results in a fluctuation of revenue, which makes it difficult for hotels to plan their budget and make pricing decisions. Another factor that affects the hotel industry is the emergence of new technologies. With the rise of online booking platforms, hotels must adapt to new technologies to remain competitive. A study by Buhalis and Law (2008) found that hotels that embrace new technologies are more likely to increase their revenue and remain competitive in the market. In conclusion, hotel booking cancellations have become a significant problem for hotels, and identifying the factors contributing to this trend is crucial for the industry. Additionally, other factors such as seasonality and the emergence of new technologies also impact the hotel industry’s business and revenue generation. Understanding these factors is necessary to assist hotels in making pricing and promotional decisions that can help them remain competitive and profitable in the market.

Study[7] examines the causes of hotel reservations that are canceled, with an emphasis on information from a Chinese online travel service. It provides insightful information on the causes of cancellations and their larger effects on the hospitality and online travel sectors. This study[8] looks at how trust, perceived utility, and usability of online reviews affect hotel booking intentions. We now have a better grasp of how online reviews affect consumer choices when making hotel reservations as a result of the data it provides. J. Zhang and his team present data and insights on how social media affects the performance of upscale hotels in China, providing important information for study in the hospitality sector, especially when looking at upscale hotels in China[9]. When hotel customer receive room rates that are higher than the rates given to other customer, they perceive the pricing practice to be less fair than when they receive the same room rate as others[12]

III. METHODOLOGY

The list of tasks outlined in this process demonstrates a thorough and methodical management of a data set focused on hotel reservations. It begins by importing a number of necessary Python modules, each of which plays a specific part in manipulating and visualizing data. An organized and effective environment for handling data is provided by loading the dataset into a pandas Data Frame. The early exploration stage involves many different aspects. It entails going deeper into the data’s structure in addition to simply providing the first and last few rows of the data for a rapid initial review. We can gauge the size of the dataset by looking at its dimensions, which represent the number of rows and columns. The column names are also displayed, enabling us to rapidly refer to particular data attributes. To comprehend the nature and quality of the data set, data types and the existence of missing values are investigated. Importantly, the cleaning procedure makes sure that the data is ready for further study. The ‘reservation status date’ column is converted into a datetime format in this step, which is essential if time-based analysis or visualization is anticipated. To preserve data integrity, rows with missing values are removed from the dataset. Columns with a high percentage of missing data, including” company” and ”agent,” are carefully removed because they are unlikely to be useful for analysis. The procedure also entails removing values from the ”adr” column that are regarded to be unreasonable. This can be inaccurate data or outliers that influence the results of the analysis. The elimination of these values guarantees that the data is correct and relevant. The tasks in the analysis domain aim to glean insights from the dataset. This involves figuring out the percentage of cancelled reservations, which is a key metric for comprehending booking patterns. The distribution of reservation statuses can be seen clearly and intuitively by using visual representations like bar graphs. This visual perception is extremely helpful for identifying trends and patterns. Additionally, the data is intelligently divided into hotels in cities and resorts. Due to the fact that the percentage of canceled reservations is calculated independently for each hotel type, this divide enables a more sophisticated analysis of the dataset. This methodical technique enables us to determine whether cancellation trends differ between city and resort hotels, which could help us develop specialized tactics for each kind. These painstakingly carried out stages work together to get the dataset ready for more intricate and focused investigations. In addition to making the information more understandable and instructive for researchers and analysts in the field, they ensure data quality and provide invaluable insights into the features and dynamics of hotel bookings.

To advance the data analysis process, an alternative strategy might be adopted rather than strictly sticking to the previously described procedures. This alternate approach employs more sophisticated and occasionally effective strategies to address the subtleties of the dataset. For instance, sophisticated imputation methods can be used in place of manually removing rows with missing information. The completeness of the dataset is preserved while the effects of missing data are reduced thanks to mean or median imputation, which enables the estimation of missing values based on statistical measures. Regression imputation takes it a step further by providing a more data- centric approach by predicting and replacing missing values using the relationships between variables. It is also possible to adopt a more sophisticated strategy to dealing with outliers. The Interquartile Range (IQR) method, a reliable statistical methodology, can be used to systematically detect and manage outliers rather than the oversimplified elimination of extreme values. With this approach, extreme data points can be handled in a data-driven manner, ensuring that they are considered during analysis rather than being rejected without consideration. A dynamic and interactive way to study and present data is also provided by advanced visualization tools like Tableau or Power BI. These systems offer the capacity to build interactive dashboards that enable more thorough and approachable data study, going beyond the static graphs and charts. They are very helpful when disseminating information and insights to a larger audience. Machine learning libraries like Scikit-Learn or TensorFlow can be used to create prediction models, spot intricate patterns, and divide data into several categories for more intricate investigations. These libraries make it possible to employ algorithms that provide predictive capabilities and hidden insights, which is notably useful for forecasting and pattern detection in data. AWS, Azure, or Google Cloud are a few examples of cloud computing platforms that can make processing large datasets and complicated calculations easier. They have the benefit of scalable and on-demand processing capabilities, which provide efficient management of massive amounts of data. Including these additional techniques expands the set of tools available for data analysis. It makes it possible to explore data in a more sophisticated and nuanced way, improving comprehension of large datasets and enabling data professionals and researchers to make more accurate, educated, and data-driven decisions in a variety of contexts and applications.

IV. RESEARCH WORK AND HYPOTHESIS

Hotels are key contributors to the development of the hospitality sector, which is a vibrant and constantly changing industry. However, hotels face a variety of difficulties, with the increased number of cancellations of reservations standing out as one of the most significant. Since this phenomenon inescapably affects hotel operators’ revenue streams and profit margins, it has attracted a great deal of attention. We explore the complex web of factors that affect hotel cancellations in this research study with the goal of giving hotel owners insightful information they can use to make wise pricing and promotional choices.

Research Question 1: Untangling the Factors Affecting Hotel Cancellations is the first research question. In the complex web of the hospitality sector, cancellation rates have become a key measure that directly affects hotel revenue. Our research dives into the complex variables that influence the trends in hotel cancellations. We carefully examine information from two separate datasets that represent hotels in cities and resort areas. We determine the percentage of canceled reservations for each type of hotel through a thorough analysis and disentangle the intricate connections between cancellation rates and a wide range of variables. These variables cover a wide range, including booking lead times, arrival dates, room kinds, and clientele classifications. Our careful investigation demonstrates that when it comes to cancellation rates, booking lead time is a crucial variable. Close to their intended arrival date, customers have a much higher risk of canceling their reservations, which has a negative impact on hotel revenue. We also discover the significant influence of client demographics and accommodation types on cancellation rates. Customers who choose more opulent lodgings exhibit greater levels of commitment and are less likely to cancel their reservations. In contrast, people who book through third-party websites are discovered to be more likely to cancel, providing a special problem for hotel operators.

Research question 2: Orienting Hotel Pricing and Promotional Decisions Hotel operators have to traverse a tricky maze of pricing and marketing choices every day. A persistent issue is striking a precise balance between increasing consumer attraction and revenue optimization. We provide a set of well- informed recommendations based on our understanding of the factors influencing hotel cancellations to assist hotel operators in these crucial decisions. First and foremost, we advise hotels to think about rewarding guests who opt to make direct reservations through their official websites. This consumer segment exhibits a noticeably decreased risk of cancellation, making them a valuable resource for hotels. Additionally, we advise hotels to create more accommodating cancellation procedures for clients who make last-minute reservations. This tactical move helps to reduce the danger of revenue loss that comes with last-minute cancellations. Finally, we suggest that hotel owners use a dynamic pricing strategy, which involves monitoring the availability of rooms and modifying prices in response to changes in demand. This dynamic strategy not only maximizes profits but also makes sure that pricing stays competitive in a market that is always changing.

In conclusion, our study highlights the indisputable importance of cancellation rates in the complex web of the hospitality sector. It uncovers a voluminous web of factors that affect these rates, giving hotel owners a road map for wise pricing and marketing choices. Hotels may improve their revenue optimization plans and foster improved client experiences by taking note of these insights, which will ultimately provide them with a better position in the competitive hospitality industry.

The significance of data-driven decision-making in the contemporary hospitality scene must be emphasized to further amplify the insights and suggestions offered in the research. To continuously monitor and adjust to shifting consumer behavior and market dynamics, hotel operators should make an investment in reliable data collecting and analysis systems. As a result, they can continuously improve their pricing and marketing tactics and stay one step ahead of the competition. The need of tailored marketing and client connection must also be emphasized. It is possible to increase customer retention and brand loyalty by targeting discounts and incentives at particular consumer segments, such as regular direct bookers or loyal customers. In addition, utilizing technology, such as customer relationship management (CRM) systems, can assist hotels in tracking and managing visitor preferences, enabling a more customized and positive visitor experience.

Finally, the research results need to motivate hotel owners to promote teamwork within divisions including revenue management, marketing, and customer service. By ensuring that decisions on pricing and promotions are in line with the larger objectives of improving guest pleasure, effective cross- functional communication can ultimately result in better revenue outcomes and a competitive advantage in the hospitality sector.

V. ASSUMPTIONS

1. There don’t seem to have been any noteworthy or uncommon occurrences that could have had a significant impact on the data utilized for study over the time from 2015 to 2017. It is helpful because the data is reasonably constant due to its temporal consistency, which makes it a solid foundation for developing theories and methods. The quality of the data for analyzing trends and patterns in the hotel business is improved by the absence of unusual fluctuations or disruptions throughout this time.
2. The available data is remarkably current, making it a useful tool for quickly analyzing a hotel’s possible strategy. A real-time analysis of market conditions, visitor habits, and industry developments is possible because to the information’s timeliness. Making decisions based on this current viewpoint helps hotel management quickly adjust to the hospitality industry’s constantly changing terrain.
3. Notably, it doesn’t seem that using the suggested methods or strategies will have any unanticipated consequences or unfavorable effects on hotels. This finding highlights the potential advantages of the suggested strategies and suggests that they are compatible with the hotels’ current goals and surroundings. Such alignment reduces risks and strengthens the viability of putting these recommended solutions into action.
4. The fact that the hotels under examination do not currently use the suggested remedies is an important finding. This gives these businesses the chance to investigate and perhaps adopt similar tactics. The fact that these recommendations have not yet been implemented shows that there may be unrealized potential for the hotels to improve their operations, increase income, and improve guest experiences by taking these recommendations into consideration and putting them into practice. This emphasizes how pertinent and timely the proposed solutions are.

VI. RESULTS AND DISCUSSIONS

By showing the percentage of bookings that have been canceled and those that have not, the given bar graph provides a clear view of the cancellation dynamics within the dataset. A clear pattern shows that a sizable percentage of reservations have held without being canceled. This sector makes up a sizeable 63% of customers who have kept their bookings, indicating a steady stream of income for the hotels. It’s equally noteworthy that 37% of customers have chosen to cancel their appointments, though. The earnings of the hotels are directly impacted by this choice. This statistic’s importance cannot be overstated because it shows a prospective revenue loss that might be stopped through deliberate actions. Because it directly affects the financial success of their hotels, hotel owners should rigorously look into ways to lower this cancellation rate. Every percentage point reduction in cancellations could result in a sizable boost in income.

A noticeable tendency emerges when we contrast resort hotels with city hotels. Compared to their resort equivalents, city hotels seem to receive more bookings. This finding implies that city hotels tend to draw a bigger percentage of visitors, maybe as a result of convenience, accessibility, and close proximity to metropolitan attractions and economic districts. The variance in booking volumes between these two types of hotels may be explained by their different pricing philosophies. With services, picturesque settings, and a focus on leisure and relaxation, resort hotels frequently provide a distinctive experience. Compared to city hotels, which primarily serve business travellers, tourists, and those seeking more affordable accommodations, these distinctive offers may be more expensive. As a result, the prospective cost difference can affect visitors’ reservation choices. Due to financial constraints, some visitors would choose city hotels, while those willing to spend more money for a more upscale experience might lean toward resort hotels. This pricing difference illustrates the variety of options available within the hotel sector, enabling visitors to choose accommodations that suit their preferences, needs, and budget.

The shown line graph offers a perceptive look at the typical daily rates for both city hotels and resort hotels over a period of time. The variability in these prices becomes instantly clear, with city hotels occasionally providing cheaper average daily rates than their resort equivalents. Furthermore, this pattern isn’t constant; it changes on different days, with some showing much more obvious price variations. This variation leads logically to the conclusion that weekends and holidays have a substantial impact on resort hotel rates. The rates at resort hotels frequently increase during these festive times. This pattern can be ascribed to the increased demand for lodging on the weekends and during major holidays, when people are looking for relaxing vacations and experiences that resorts are best suited to deliver. On the other hand, city hotels usually provide more affordable rates during regular weekdays and non-holiday times, making them a desirable choice for business travelers or vacationers looking for affordable options. This finding emphasizes how flexible pricing methods are in the hotel business, which adjusts them to the ebb and flow of demand related to days and seasons.

With a focus on how these reservations are dispersed among various reservation statuses, the painstakingly created grouped bar graph is a potent tool for understanding the ebb and flow of reservation levels over different months. August clearly stands out as a crucial month because it has the highest number of confirmed and cancelled reservations. This phenomenon emphasizes the importance of August in the hospitality sector, which experiences a boom in visitor activity, both in terms of confirmed reservations and those that don’t. In contrast, we find a different situation when we focus on the month of January. While January shines as the month with the largest number of cancelled reservations, August shines as the month with the highest number of confirmed reservations. This surprising finding prompts inquiries regarding the particular variables affecting booking decisions in these two diametrically opposed months. It may imply that the beginning of the new year in January may involve a larger percentage of cancellations, presumably as a result of changes in plans or post-holiday considerations, compared to August, which may be driven by vacation and holiday plans. Hotel management may successfully strategize to take advantage of peak months like August and handle problems connected with other months like January with the help of the information gained from this analysis. The market is able to optimize its offerings, pricing, and promotional methods in accordance with these dynamic monthly patterns thanks to the ability to recognize these trends.

  The bar graph that is being provided reveals an important relationship between the frequency of cancelled reservations and the cost of lodging. It is abundantly evident that cancellations are more frequent when lodging costs are at their maximum ebb, whereas they are less frequent when costs are lower. This direct link emphasizes how pricing has a big impact on whether or not bookings are canceled. The price of lodging appears as a key determinant of this decision-making process, with visitors displaying increased reluctance when confronted with higher costs. Now that we are looking at things from a bigger picture, we try to determine which nation has the most cancellations of reservations. Portugal is clearly the top in this category, showing the most cancellations out of all the nations looked at. This finding highlights the significance of grasping regional and national booking behaviors, which can be influenced by a number of variables including economic situations, travel trends, and cultural preferences. Understanding these variances enables the hospitality sector to customize tactics and policies to efficiently reduce cancellations while taking into account the distinctive dynamics of various geographic areas.

Our investigation focuses on where hotel visitors come from and their preferred methods of booking, revealing whether they book independently, with others, through internet travel agencies, or through traditional travel agents. The research reveals fascinating insights into how travellers select their lodging. The ease of online travel agencies is chosen by a sizeable share of about 46% of customers, illustrating the growing reliance on digital platforms for hotel reservations. This choice indicates how easily accessible and convenient online booking channels have made it for customers to interact with the hotel sector. It’s interesting to see that almost 27% of customers prefer the group booking option, which may mean they’re a part of scheduled tours, events, or larger gatherings. This pattern emphasizes how important group dynamics are in the decision- making process, with the attractiveness of group reservations standing out as a significant element. On the other hand, only 4% of customers decide to make direct hotel reservations by going to the hotels themselves. This trend towards remote and digital booking techniques is highlighted by the minority preference for in-person reservations. Collectively, these results highlight the constantly changing hotel reservation scene, which is significantly influenced by the digital era, the dominance of online travel agents, and the various booking habits of various client segments. Hotel management must be aware of these trends in order to customize their marketing and distribution plans to meet the wide range of client preferences.

VII. FUTURE SCOPE

Our research offers several directions for future studies. Firstly, we only analyzed two specific hotels in Portugal, and the findings may not be generalizable to other regions or countries. It would be worthwhile to replicate the study in different contexts to examine the applicability of the findings. Secondly, while we investigated several variables, we did not consider all the possible factors that could affect cancellation rates. For instance, customer demographics, nationality, and the purpose of travel could also be important factors to investigate. Thirdly, we only focused on quantitative data analysis. It would be interesting to employ qualitative research methods such as interviews and surveys to explore customer perceptions and attitudes towards hotel booking cancellations.

Finally, we only analyzed past data, and future research can be conducted to predict cancellation rates and evaluate the effectiveness of different revenue management strategies using machine learning algorithms. Overall, our research provides a valuable contribution to the hospitality industry, and there is still much to explore in this area.

Conclusion

In a nutshell, our inquiry explores the complex world of hotel reservation cancellations and offers insightful information to hotel management. The difference in cancellation rates between city and resort hotels, which calls for specific marketing plans for each, is the most notable finding. We emphasize how booking channels have an impact on hoteliers’ pricing and marketing strategies, particularly given the higher cancellation rates associated with online travel agents. In order to support data-driven revenue management, our study additionally emphasizes the significance of lead time, length of stay, and room type in affecting cancellation rates. We advise flexible pricing and cancellation policies to accommodate last- minute bookings and different room kinds. To improve revenue management and guest experiences in this era of dynamic pricing and competition, hotel managers must continue to be flexible and adaptive. Hotels must embrace these data-driven insights if they want to succeed in the hospitality sector, which is constantly evolving.

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Copyright

Copyright © 2023 Aditya Dole. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.