Search or ask a question

Can guest personalization data be used to predict future booking behaviors and preferences?

Unsupervised learning

Market segmentation

Discrete choice

Best insight from top research papers

Guest personalization data can indeed be utilized to forecast future booking behaviors and preferences. Various methodologies, such as clustering techniques, deep learning, and ensemble learning frameworks, have been proposed to address this challenge. For instance, a hybrid model incorporating deep and shallow neural network embeddings into a gradient boosting tree model has shown promising results in predicting booking probability and value at the traveler level. Additionally, the use of unsupervised machine learning algorithms, like hierarchical clustering, on guest profiles can aid in creating tailored marketing strategies for personalized marketing within the hospitality industry. These approaches not only enhance prediction accuracy but also provide valuable insights for businesses to tailor their offerings, personalize marketing strategies, and ultimately drive profits by meeting the evolving preferences and behaviors of guests.

Answers from top 5 papers

PDF

Open Access

More filters

Papers (5)	Insight
Open access•Journal Article•DOI DeepPredict: A Zone Preference Prediction System for Online Lodging Platforms Yihan Ma, Hua Sun, Yang Chen, Jiayun Zhang, Yang Xu, Xin Wang, Pan Hui - Show less +6 more 16 Feb 2021 5 Citations	Yes, guest personalization data can be used to predict future booking behaviors and preferences, as demonstrated by DeepPredict in online lodging platforms with remarkable performance.
Journal Article•DOI A class-specific soft voting framework for customer booking prediction in on-demand transport Le Minh Kieu, Yuming Ou, Long T. Truong, Chen Cai - Show less +3 more 01 May 2020-Transportation Research Part C-emerging Technologies 8 Citations	Yes, guest personalization data can be utilized to predict future booking behaviors and preferences in On-Demand Transport services, as demonstrated by the proposed Class-specific Soft Voting framework.
Journal Article•DOI Estimating Personalized Demand with Unobserved No-purchases using a Mixture Model: An Application in the Hotel Industry Sanghoon Cho, Mark Ferguson, Pelin Pekgün, Andrew Vakhutinsky - Show less +3 more 11 Sep 2021-Social Science Research Network 3 Citations	Yes, personalized demand estimation using a mixture model can predict future booking behaviors and preferences based on guest characteristics, travel attributes, and room features in the hotel industry.
Open access•Posted Content Deep Personalized Re-targeting Meisam Hejazinia, Pavlos Mitsoulis-Ntompos, Serena Zhang - Show less +2 more 03 Jul 2019-arXiv: Information Retrieval	Yes, guest personalization data can be utilized to predict future booking behaviors and preferences by infusing deep and shallow neural network embeddings into a gradient boosting tree model.
Open access•Journal Article•DOI Data-driven market segmentation in hospitality using unsupervised machine learning 01 Dec 2022-Machine learning with applications	Yes, guest personalization data can be clustered using unsupervised machine learning to predict future booking behaviors and preferences in the hospitality industry, as outlined in the study.

My columns

Related Questions

. Hotels are rapidly using AI technologies to improve guest experiences, optimise operations, and enhance overall efficiency?4 answersHotels are indeed rapidly adopting AI technologies to enhance guest experiences, streamline operations, and boost efficiency. Studies highlight AI's impact on guest satisfaction, operational processes, and decision-making in the hospitality industry. AI applications like personalized guest experiences, chatbots, and virtual assistants have been shown to increase guest satisfaction. However, the implementation of AI in hotels comes with ethical, legal, and social considerations that need careful reflection. Guests' acceptance of AI-based systems making decisions for them is influenced by perceived ethics, benefits, and risks. Furthermore, AI's role in reengineering internal processes, enabling data-driven competitiveness, and transforming customer services is crucial for hotel marketing. Overall, AI is revolutionizing the hotel industry by offering opportunities for innovation, efficiency, and improved guest interactions.

What factors influence customer preference in hotel bookings?5 answersFactors influencing customer preference in hotel bookings include service quality dimensions like tangibility, empathy, and reliability. Post-pandemic, guests prioritize value for money, cleanliness, sanitization, and safety & security. Additionally, factors such as ease of use, price, promotion, perceived privacy/security, and online reviews of booking platforms significantly impact hotel booking intentions. During global health crises, self-efficacy and trust relating to consumer health play crucial roles in influencing booking intentions through hotel mobile apps. Moreover, hedonic and utilitarian aspects influence e-loyalty through trust, emphasizing the importance of website content for quick information access and enhancing consumer enjoyment. These factors collectively shape customer preferences in the dynamic landscape of hotel bookings.

Which personalization practices influence guest loyalty in the hospitality industry?5 answersPersonalization practices that influence guest loyalty in the hospitality industry include the use of artificial intelligence (AI) technologies like chatbots and virtual assistants, as well as the implementation of personalized services tailored to meet customers' individual needs and provide unforgettable experiences. Studies have shown that personalization not only enhances guest satisfaction but also increases their loyalty by creating a sense of pride and satisfaction, ultimately leading to repeat business and improved competitive advantage for hotels. Additionally, the importance of balancing AI with human interaction is highlighted, as many guests still value the personal touch and human connection in traditional hospitality experiences. By focusing on providing personalized experiences, streamlining operations, and enhancing convenience and comfort, hotels can effectively influence guest loyalty in the hospitality industry.

How does guest personalization impact customer satisfaction and loyalty in hotels?5 answersGuest personalization in hotels significantly impacts customer satisfaction and loyalty. Personalization practices are crucial for enhancing guest satisfaction, loyalty, and overall experience, especially in the competitive post-COVID era. The design process of personalization in hotels involves understanding customer segments, implementation challenges at the frontline, and corporate culture, with on-site improvisation playing a key role in effectiveness. Implementing innovative applications like an ontological framework can enhance hotels' capabilities in offering personalized services based on guest characteristics and needs, leading to improved satisfaction and loyalty. Additionally, artificial intelligence technologies such as chatbots and virtual assistants can revolutionize guest satisfaction by streamlining operations and providing personalized experiences, ultimately increasing loyalty and competitive advantage. Balancing AI with human interaction is essential to maintain the personal touch valued by guests.

What are the benefits of launching an application in terms of personalization for a lodging company?4 answersLaunching an application for personalization can bring several benefits to a lodging company. Firstly, it allows the company to offer customized services and experiences to individual customers, enhancing their satisfaction and loyalty. Secondly, personalization helps in building strong consumer relationships, as it enables the company to provide tailored information and recommendations to customers based on their preferences. Thirdly, it can improve the company's image by showing that they value the interests and needs of their customers, leading to a positive perception among the public. Lastly, personalization can contribute to the economic interests of the company by attracting more loyal customers, increasing repeat business, and gaining a larger market share. Overall, launching an application for personalization can be a strategic move for a lodging company to stay competitive and meet the evolving demands of customers.

What are the different levels of personalization in a customer experience?5 answersPersonalization in customer experience can occur at different levels. One level is personalization in voting behavior, where individuals shift their attention and power from collective actors to themselves. Another level is hyper-personalization in omnichannel business models, which involves gathering and transforming customer data into personalized experiences. Hyper-personalization strategies can also be used in e-commerce to address users' real-time needs and provide better customer-centric marketing. In the context of retail websites, actual personalization and perceived personalization both enhance the playful customer experience, with actual personalization influencing objective measures and perceived personalization influencing subjective measures. Personalization in customer experience also has positive and negative consequences, impacting customer responses and the overall success of firm-customer communication.

See what other people are reading

How the ensemble deep learning paves the way in the medical image classification specially histopathology images?

Ensemble deep learning significantly advances medical image classification, particularly in histopathology, by leveraging multiple models to enhance accuracy and reliability. This approach addresses the challenges of classifying complex histopathological images, which are crucial for diagnosing diseases like breast cancer and colorectal cancer. For instance, an ensemble model combining VGG16, Xception, ResNet50, and DenseNet201 demonstrated superior performance in classifying breast histopathological images, achieving an accuracy of 98.90%, outperforming traditional Transformer and MLP models. Similarly, a hybrid deep learning model integrating a dilated ResNet structure with an attention module and DeepSVM for colorectal cancer tissue classification achieved remarkable accuracies of 98.75% and 99.76% on CRC datasets. The effectiveness of ensemble methods is further underscored by their ability to compress histopathological images while retaining meaningful representations, facilitating efficient classification without compromising accuracy. Additionally, Deep Fastfood Ensembles, which combine deep features from various CNN models using random projections, offer a fast and effective solution for histopathology image analysis, demonstrating the versatility of ensemble approaches in handling large, complex datasets. Innovative ensemble techniques, such as combining pre-trained image transformers with graph convolutional networks for classifying tissue patches in vulvar cancer, highlight the potential of ensemble models to incorporate sequential and neighborhood information for improved classification outcomes. Moreover, customized deep-learning models, like the combination of Resnet50 and Densenet201 for oral squamous cell carcinoma detection, showcase the adaptability of ensemble methods in achieving high classification accuracy. The hybrid combination of Inspection-ResNetv2 and EfficientNetV2 for breast cancer histopathology image classification further exemplifies the strength of ensemble learning in achieving high accuracy rates, demonstrating the method's potential in early cancer detection. Lastly, ensemble learning's application in medical image segmentation, where multiple DNN models are combined to enhance segmentation accuracy, underscores its broad applicability and effectiveness in medical image analysis. Together, these studies illustrate how ensemble deep learning paves the way in medical image classification, especially in histopathology, by improving accuracy, efficiency, and reliability across various applications.How negative disconfirmation affect customer satisfaction?

Negative disconfirmation, such as when customers' expectations are not met, can significantly impact customer satisfaction. Research indicates that negative disconfirmation leads to lower repurchase intentions and higher switching intentions among customers. Moreover, the level of disconfirmation experienced by customers, whether online or offline, can result in dissatisfaction, especially when perceived as unfair. However, effective service recovery processes can moderate the adverse effects of negative disconfirmation, leading to increased satisfaction and loyalty among customers. Therefore, understanding and addressing negative disconfirmation through appropriate service recovery strategies are crucial for businesses to maintain customer satisfaction and retention in the face of service failures.What technological advancements have been implemented in Manila's hotel industry in recent years?

Recent technological advancements in Manila's hotel industry include the adoption of cyber-physical systems, the internet of things, and the internet of systems as part of the 4th industrial revolution (4IR). These advancements aim to enhance guest experiences and improve operational efficiency. Additionally, the industry has been integrating Artificial Intelligence (AI) to replace human labor in various departments such as communication, financial transactions, food and beverage operations, marketing, and service delivery. Furthermore, the implementation of robotics and automation, particularly in services like concierge and housekeeping, is gaining traction to provide impeccable and personalized experiences for guests while increasing service quality and profitability. The hotel industry's focus on innovation and technology underscores its commitment to staying competitive and meeting evolving customer expectations.What specific technological advancements have been implemented in Manila's hotel industry in recent years?

In recent years, the hotel industry in Manila has seen significant technological advancements. These include the adoption of cyber-physical systems, the internet of things, and the internet of systems as part of the 4th industrial revolution (4IR). Hotels have been utilizing technology to enhance various aspects of their operations, such as communication mediums, financial transactions, food and beverage operations, marketing, and promotional tools, as well as incorporating Artificial Intelligence (AI) to improve service delivery and replace human labor. Additionally, the industry has been introducing automation and robotics in services like concierge and housekeeping to improve efficiency and service quality, ultimately aiming to exceed customer expectations and provide a more personalized experience.Segment Anything Model在衛照影像運用為何?

Segment Anything Model (SAM)在醫學影像領域的應用包括提高對醫學異常的準確性評估，並擴展到不需要人類標註的情況。SAM是第一個通用影像分割基礎模型，通過自動一切和手動提示兩種主要模式實現零樣本影像分割，對各種自然影像分割任務取得了令人印象深刻的成果。SAM的零樣本分割能力有助於減少標註時間，推動醫學影像分析的發展。此外，SAM的能力在某些特定對象和模態下表現出色，但在其他情況下可能不完善甚至完全失敗。因此，SAM在醫學影像中的應用對於提高準確性、節省時間並推動醫學影像分析領域的發展具有潛在價值。Segment Anything Model在building footprint運用如何?

The Segment Anything Model, a segmentation approach, is applied in building footprint extraction from remote sensing images. This model integrates convolutional neural networks (CNN) and graph models to enhance accuracy and reliability in generating building footprints. By combining CNN as a feature extractor with a feature pairwise conditional random field (FPCRF) graph model, spatial correlations are considered, leading to sharper boundaries and finer segmentation results. Additionally, neural networks and Markov Random Fields (MRF) are utilized in another methodology for automatic building footprint extraction from normalized Digital Surface Model (nDSM) and satellite images within urban areas. This approach involves learning unary terms with a neural network and minimizing the energy function using a max flow algorithm, resulting in efficient building extraction with high accuracy.Customer Behavior in digital world?

Customer behavior in the digital world has evolved significantly due to the rise of digital services and online platforms. The digitalization of society has led to changes in consumer behavior, creating new opportunities and challenges for businesses. Strategies such as content marketing, User Experience (UX), influencers marketing, User-Generated Content (UGC), and Electronic Word of Mouth (eWOM) play a crucial role in engaging customers online. The use of digital technologies, including mobile applications, has transformed how customers interact with businesses, influencing their purchasing decisions and preferences. The shift towards online shopping platforms has been driven by factors such as a wide range of choices, product exploration, privacy, and time-saving benefits, impacting customer behavior and preferences towards online retail markets.What are the key communication skills that are crucial for effective teamwork in a pre-COVID-19 workplace setting?

Effective communication skills play a vital role in fostering teamwork in a pre-COVID-19 workplace setting. Studies have highlighted the significance of communication, teamwork, and leadership as essential skills for healthcare workers in providing quality patient care. Research on essential workers with COVID-19 infections emphasized the importance of accessing accurate and timely information, navigating excessive data, and evolving perceptions of information providers. Furthermore, a study on healthcare worker teamwork climate during the pandemic revealed that communication aspects like speaking up, conflict resolution, and interdisciplinary coordination were crucial for maintaining well-being and safety culture. Therefore, in a pre-COVID-19 workplace, honing communication skills such as transparency, timeliness, and mutual support can enhance teamwork effectiveness and overall organizational resilience.What are new trends in federated learning?

New trends in federated learning include the integration with other learning algorithms to enhance the vanilla federated averaging algorithm, termed as federated x learning, which encompasses multitask learning, meta-learning, transfer learning, unsupervised learning, and reinforcement learning. Additionally, there is a shift towards adapting federated learning to real-world scenarios with dynamic task sequences and strict storage constraints through the introduction of new generation federated learning (NGFL). NGFL focuses on continuous or incremental learning, where each client not only trains the server but also learns private tasks independently of server communication, addressing challenges like aggregation of heterogeneous output layers and task transformation mutual knowledge problem. These advancements aim to make federated learning more adaptable and efficient in practical applications.How effective are novel medical image enhancement and segmentation techniques in detecting COVID-19?

Novel medical image enhancement and segmentation techniques have shown significant effectiveness in detecting COVID-19. These techniques leverage deep learning models like U-Net and Modified Deep Convolutional Neural Networks (MD-CNN) to accurately segment infected areas in lung scans, improving diagnostic accuracy. Studies have demonstrated that these methods outperform traditional approaches, achieving high accuracy rates ranging from 97.95% to 98.60%. By utilizing advanced image processing algorithms and deep learning architectures, such as ResNet-U-Net and VGG16-U-Net, these techniques enable precise localization of infections, aiding physicians in diagnosing and monitoring COVID-19 with enhanced efficiency and accuracy. The integration of these innovative approaches enhances the capabilities of medical imaging for COVID-19 detection, offering promising advancements in disease diagnosis and management.What ai can do in the feild of physiotherapy?

Artificial intelligence (AI) holds significant potential in the field of physiotherapy by enhancing patient outcomes, reducing workloads, and automating tasks. AI applications in physiotherapy include personalized exercise guidance, monitoring patient progress, and predicting outcomes for individuals with neurologic disorders. Machine learning (ML), a subset of AI, can automate decision-making processes, analyze patient data, and provide clinical decision support in musculoskeletal medicine. Physiotherapists recognize the importance of integrating AI into practice but express concerns about the lack of educational resources and training opportunities. By incorporating AI and robotic coaches, physiotherapists can engage patients more effectively in rehabilitation exercises, leading to improved self-efficacy and motivation for therapy sessions. Overall, AI in physiotherapy has the potential to revolutionize care delivery, improve patient outcomes, and shape the future of healthcare.