The Potentialities of CRM to Increase Personalization in Hospitality

Introduction

Organizations are progressively implementing cutting-edge technologies and tactics to enhance the functionalities of Customer Relationship Management (CRM) systems in the ever-evolving domain of customer-centric business models (Rane et al., 2023). Also, the segmentation of databases based on CLV is the cornerstone of CRM. To implement CRM strategies, the hospitality industry relies heavily on loyalty programs to track customer behavior (Webb et al., 2022).

According to Alt & Klein (2011), there has been a movement in the marketplace toward customers having more power and control. Businesses need to use technology to interact with customers more personally since they are involved in production and consumption (Buhalis & Law, 2008; Pine et al., 1999).

Consequently, personalization makes it possible for clients to obtain tailored information more quickly and easily at any moment (Nyheim et al., 2015). Personalization provides a means for businesses to cultivate enduring relationships with their (Nyheim et al., 2015). Personalized services allow customers to spend less time looking for information and get services that are more specifically tailored to their needs based on the information they have provided (Nyheim et al., 2015; Piccoli et al., 2017). In order to provide customization, businesses must be able to discern their clients' wants, which may require them to divulge sensitive information such as purchasing patterns, location data, and personal profiles (Ho, 2012)

Gathering consumer data on preferences, buying habits, purchase histories, and demographics may provide a solid basis for creating effective marketing campaigns and gaining long-term competitive advantages (Erevelles et al., 2016). Additionally, Wattal, Telang, & Mukhopadhyay (2009) deduced that personalization is thought to boost the value of the entire consumption experience in e- and m-commerce contexts, especially by improving the fit between a product's qualities and the demands of the customer.

Furthermore, personalization was defined by Bilgihan, Kandampully, & Zhang (2016) as the extent to which information is customized to meet the demands of an individual user, and as such, it is a significant factor in determining pleasant experiences. Using data mining techniques, information is tailored to each customer's needs and tastes, potentially increasing their degree of interest in purchasing (Zhang, Edwards, & Harding, 2007). Furthermore, one of the essential elements frequently linked to machine learning is personalization, which focuses mainly on optimizing personalization applications and developing algorithmic decision and prediction models that are ever more precise (Zanker et al., 2019). Thus, brands can use predictive personalization, which is the process of adapting content in real time using data analysis and profiling tools based on artificial intelligence and machine learning.

This chapter's goal is to demonstrate how customer lifetime value (CLV) models can be used to classify potential customers and calculate their value by utilizing data about them from customer relationship management (CRM). Market segmentation and the distribution of marketing resources for acquisition, retention, and cross-selling can benefit from using CLV models. In addition, the authors suggested using personalization to make promotions for specific clients. The motivation of this chapter is to increase the competitiveness of the hotels and offer attractive services to achieve the loyalty of current and new clients. Also, to offer exclusive products or services to increase user satisfaction.

The format of this chapter is as follows. In the second section, various implementable customer lifetime value (CLV) models are reviewed and the relevant literature regarding customer relationship management (CRM) is presented. In the third section, personalization in the hospitality industry is discussed along with relevant literature, the distinction between customization and personalization, and personalization metrics. A few new methods for utilizing smart technologies and experiencing personalization are shown in Section 4. The chapter is concluded in section five.