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The aim of this chapter is to present in a pedagogical way the main digital tools used by tourism-related businesses, especially by hospitality businesses. The main purpose of this chapter is to illustrate our discussion with concrete examples and to give a set of advices for efficient use of those tools.

Literature review was conducted on conceptual issues, as well as managerial and marketing aspects of digital tools, their value and use in the hospitality industry.

This chapter highlights the fact that needs in terms of digitalisation depend on the size of the hotel. The main differences can be explained by differences in terms of hotel capacity, and digital technologies should be customised to different types of structures.

This chapter is exploratory in nature, based on a literature review.

It provides clear and practical guidance about the way independent hospitality businesses could use digital tools for marketing purposes. It also suggests the most efficient digital technologies to improve their performance in the field of marketing and customer relationship management.

The chapter demonstrates the huge gap between best practices in the hospitality industry and the way independent enterprises really use, in practice, the digital tools for marketing purposes. It shows how digital technologies could be used in a more efficient way, to take advantage of their full potential.

Hotels, the second largest component of online travel, have traditionally collected customer data through loyalty or customer relationship management programmes, compiling data about customers and using it to improve customer service and marketing efforts (Piccoli & O’Connor, 2003). Recent growth in online sales has given them access to even more granular and topical data. In particular technologies such as cookies, web bugs and beacons allow data on an individual’s browsing (e.g. the pages they have visited or the searches they have conducted) to be collected, consolidated and analysed to reveal detailed insights about customers which can subsequently be used for marketing purposes (Gilbert, 2008). While previous studies have investigated data collection by hotels, for the most part these have focussed on data surrendered voluntarily, with none addressing data collection through automated means. This chapter therefore attempts to fill this research gap, investigating whether and how hotel companies use such techniques, and whether consumers are adequately informed about this data collection process. Using the top 50 global hotel brands as a population, it explores both cookie disclosure and cookie practice by global hotel brands. The findings demonstrate that hotel brands, particularly those with their headquarters in the USA, are making extensive use of online monitoring technologies, typically for marketing purposes. In most cases, this data collection is happening surreptitiously, with the data protection notices that exist overtly long as well as crouched in highly technical language. Despite legislative changes that amplify the need to inform consumers about data collection, few hotel chains currently comply with the principles of informed consent, leaving many open to potential sanction.

Budget hotels are becoming an emerging industry for convenience and affordability, where consumer sentiments are of paramount importance. Tourism has become increasingly dependent on social media and online platforms to gather travel-related information, purchase travel products, food, lodging, etc., and share views and experiences. The user-generated data helps companies make informed decisions through predictive and behavioural analytics.

Design/Methodology/Approach: This study uses text mining, deep learning, and machine learning techniques for data collection and sentiment analysis based on 117,151 online reviews of the customers posted on the TripAdvisor website from May 2004 to May 2019 from 197 hotels of five prominent budget hotel groups spread across India using Feedforward Neural Network along with Keras package and Softmax activation function.

Findings: The word-of-mouth turns into electronic word-of-mouth through social networking sites, with easy access to information that enables customers to pick a budget hotel. We identified 20 widely used words that most customers use in their reviews, which can help managers optimise operational efficiency by boosting consumer acceptability, satisfaction, positive experiences, and overcoming negative consumer perceptions.

Practical Implications: The analysis of the review patterns is based on real-time data, which is helpful to understand the customer’s requirements, particularly for budget hotels.

Originality/Value: We analysed TripAdvisor reviews posted over the last 16 years, excluding the Corona period due to industry crises. The findings reverberate in consonance with the performance improvement theory, which states feed-forward a neural network enhances organisational, process, and individual-level performance in the hospitality industry based on customer reviews.

Online reputation manager has become increasingly important in tourism industry. Managers, regardless of working for a hospitality structure or a tourism destination, are paying more and more attention in respect of the importance of reputational levels. Online reputation, in fact, originates in visitor's user-generated contents (UGCs) but reverberates on the whole web, on successive visitors' attitude and behavior, and on managed organization performances. How to manage online reputation in tourism and destination management anyway mostly stayed an anecdotal topic for many years. While best practices exist, indeed, literature has frequently neglected their systematization. Building on this need, this book will try to improve and organize the existing body of knowledge on this topic to help future hotel and destination managers to better deal with the mounting environmental complexity.

An essential part of the transfer of knowledge in the tourism and hospitality industry, destination image is defined as the expression of objective knowledge, imagination, and the subjective emotions of the tourist. Social media is profoundly changing the way the tourist images and interacts with the destination environment. In turn, firms in the industry are seeking to leverage the power of social media to gain insights into tourist cognition and behavior. In this chapter, we analyze various social media to investigate knowledge transfer relating to two groups of hotels in Philadelphia, and we propose a methodology to predict future lodging demand from empirical data in line with the objectives of the t-Forum.

There is no doubt that social media have had a powerful impact on the way people experience the world around them. People love sharing new experiences and telling social networks about upcoming travel plans and reminiscing about them after they have returned. People prefer to buy products and services from brands they trust. Social media allow people to share written testaments to the brand's most amazing qualities, and share pictures and videos of their experiences. Social media make it easy to approach the enterprises and interact with them.

Connecting reputation management and social media is the process of monitoring and handling user-generated social media content such as reviews to impact the way a company is recognized. Tangible aspects of services and products are evaluated by customers of tourism and hospitality enterprises in an individual manner, and relevant opinions are shared on various social media platforms. The cost of a negative experience is rather high – the “eWOM” (electronic word-of-mouth) through social media essentially could destroy a business. For many businesses, a bad review or comment on one social networking site could spread to other social media tools.

The pattern of use of social media by customers of tourism and hospitality organizations can be divided into three stages: pretrip, during the trip, and posttrip use of social media. Customer experiences during each of these stages can be reflected on different social media networks. The manner of the use of social media by customers at all three stages could influence on performances of tourism and hospitality organizations as advantages and challenges. In this article authors are discussing both positive and negative impacts depending on consumer experiences and a range of other factors. Social media implementing blogs, social networking sites, photo and video stories, chats, forums, and other forms of communication need to be used by management of tourism and hospitality enterprises in an integrated manner. Great opportunities offered by viral marketing are fully appreciated and acted upon by managers of hospitality organizations.

It is important for the enterprise to develop an engaged audience by creating and sharing relevant content on social platforms. In addition to relevant content it is important to pay attention to interacting with the audience. Focus must be on products and services, but without interacting and engaging with the audience the enterprise might lose them. Social media are an option for tourism and hospitality to get the opportunity to control and present their contents including their products and services, and using it positively, solve their core values and draw and retain back their customers. Using social media channels can help enterprises to generate more profit. Setting special procedures or documents on how to respond to negative and positive comments guarantees correct and consistent execution. In this chapter the reader could find some real examples from tourism and hospitality enterprises.

Online reputation management (ORM) plays a significant role in the tourism industry. Tourists are more and more interested to express their opinions about their experiences/satisfaction not only with their friends but also on social media. ORM is largely used not only by tourist destinations but also by other companies operating in the tourism industry. This chapter aims to draw attention to the importance of intermediaries in tourism, their reputation in general, and especially their ORM and its specifics. This contribution also characterizes different types of intermediaries and their different roles in the distribution process of tourism services. These roles are important and can be even more significant in the “new normal” post-COVID-19 time. In the scientific literature and research, there is not much attention given to intermediaries as a whole and even less to their ORM and its specific solutions. But practical contributions can be found. Due to the specific activities and roles of different types of intermediaries, also their reputation is influenced not only by tourists but also by their suppliers. Their ORM has also some specifics and needs specific solutions. Their reputation is depending not only on customers' satisfaction with their own services but also on the reputation of tourism service providers, whose services they offer and mediate either individually or in the form of their own product, e.g., package tours. Specific attention in this chapter is given to intermediaries such as OTAs (Online Travel Agencies/Agents) and tour operators. At some time, these two types of intermediaries help to increase the reputation of tourism services providers, e.g., hotels. The chapter describes the situation in the field of intermediaries with a conceptual model, their ORM, and summarizes its specifics.

The objective of this chapter is to establish the positioning on Booking.com of hotels located in the cities of Tehran, Kashan, Abyaneh, Isfahan, Yazd, Persepolis, and Shiraz, the main tourism destinations in the Islamic Republic of Iran. The methodology is quantitative, consisting of the development of a “Spyder” that allows content to be identified and timely information to be extracted from the web. The data obtained allow to establish the average rating by the evaluation categories stated by the platform, while contributing to determining the strengths and weaknesses that the establishments present. These relevant data can thus contribute to the improvement of the services and the destination in general.

The chapter proposes to investigate online reputation of hospitality brands and its measurements. Brand reputation is generally defined as an overall appraisal of a company by its stakeholders, which is the result of the company's past actions and predictions about the company's future (Ferguson, Deephouse, & Ferguson, 2000). Being viewed as the opinion shared among a group of stakeholders (Dowling, 2008), it plays an important role in the tourism industry. With the progress of Information Communication Technologies (ICTs), reviews and user-generated contents of destinations and of hospitality companies together with the related emerging brand reputation can influence consumers' behaviors and choices. Brand reputation analysis could be more useful in the hospitality brand management when integrated with brand image and brand identity analysis, mainly because in tourism businesses and destinations, brands are typically affected by an inherent fragility determined by the service nature of products (Casarin, 1996). According to Biel (1991), the meanings that consumers assign to a brand are synthesized into brand associations formed by the components perceived to underlie the brand's image. As well as brand reputation, strong, positive and unique associations reinforce a brand and increase its equity that requires significant internal brand identity efforts, which should create a corresponding brand image through integration in overall marketing programmes (Keller, 2003). It makes sense to develop an analytical research approach that compares online brand reputation (OBR) with brand association matching as a measure of the alignment between brand identity and brand image in hospitality companies. This comparative analysis emerging from brand reputation, brand image and brand identity analysis can reveal divergent situations (i.e., high brand reputation and low brand association matching) and orient brand managers in reviewing online brand communication. Brand reputation and brand image analysis will be contextualized in an online community as a social setting that is considered to be a new type of market (Muniz & O'Guinn, 2001). We focus on hospitality online communities populated by consumers and other actors such as influencers and bloggers: their brand perception could be separately compared with brand identity that we will extract from company communications including presentational information and brand-related press releases found on websites, nonfinancial narrative from annual reports, and interviews with managers published in mainstream media sources. In our analysis we will focalize on a cluster of luxury hospitality companies integrating a netnographic and text-mining techniques. We will use both the techniques in order to (1) extract and study brand associations in terms of brand reputation, brand image, and brand identity; (2) develop indicators of brand reputation and brand association matching; and (3) discuss their utility in the management of the hospitality company brands.