Data analytics dilemma at Alpen Hotel

2019 ◽  
Vol 9 (2) ◽  
pp. 58-63
Author(s):  
Tammy Wee ◽  
Arif Perdana ◽  
Detlev Remy
Keyword(s):  
Customer Relationship Management ◽  
Data Analytics ◽  
Data Privacy ◽  
Hospitality Industry ◽  
Service Providers ◽  
Relationship Management ◽  
Customer Relationship ◽  
Using Data ◽  
Privacy And Confidentiality

Data analytics is currently the buzzword for the hospitality industry to stay ahead of their competitors. Service providers use data analytics to ensure their brand remains relevant for customers. Using data analytics in customer relationship management is a relatively novel initiative for the hospitality industry to enhance the efforts of customer relationship management. Obtaining customers’ data (i.e. customers’ hotel stay and preferences) provides both opportunity and challenges for the hospitality industry. Data analytics helps the hospitality industry to quickly, effectively, and efficiently pursue data-driven decision-making. At the same time, acquiring relevant customers’ data is a challenge, for example, data privacy and confidentiality. This case study is based on Alpen Hotel (pseudonym), a luxury hotel in Singapore with a good standing in the hospitality industry. This case is focused on the issues they experienced in implementing data analytics as part of the hotel’s customer relationship management efforts. This case study aims to highlight data analytics dilemma at the hotel and may create an opportunity for hospitality educators to work interdisciplinary with faculties from an information systems or technology discipline. Finally, the case study may enhance knowledge and minimise the practice gap between industry and academia.

scholarly journals THE ANTECEDENT OF CUSTOMER SATISFACTION

2020 ◽  
Author(s):  
Andala Rama Putra Barusman ◽  
Evelin Putri Rulian ◽  
Susanto Susanto
Keyword(s):  
Customer Satisfaction ◽  
Structural Equation Model ◽  
Customer Relationship Management ◽  
Structural Equation ◽  
Hospitality Industry ◽  
Relationship Management ◽  
Customer Retention ◽  
Equation Model ◽  
Customer Relationship

Taking a case study of tourism as hospitality industry in Lampung Province in Indonesia, we analyze the antecedent of customer satisfaction and its impact on customer retention. Using Structural Equation Model (SEM), we find that customer relationship management has a significant impact on service quality, customer satisfaction and customer retention.

Customer Relationship Management in Service Mediary-Driven Mobile Services

2011 ◽  
pp. 67-86
Author(s):  
Timo Saarinen ◽  
Jukka Kallio ◽  
Markku Tinnila ◽  
Jarkko Vesa
Keyword(s):  
Business Model ◽  
Customer Relationship Management ◽  
Service Providers ◽  
Relationship Management ◽  
Business Environment ◽  
Customer Relationship ◽  
Critical Elements ◽  
The Future ◽  
Specialized Service

In this chapter we introduce the concept of Service Mediary, which provides a conceptual and technical platform for electronic services. Our analysis shows that it pays off to outsource customer relationship management in electronic channels to a specialized service provider. The I-mode case study demonstrates clearly the benefits this kind of cooperation offers to content and service providers. Especially the built-in billing and revenue sharing logic are critical elements to I-mode’s success story. Even more encouraging are the benefits of collaboration to customers, who will enjoy the fruits of the new networked e-business economy. A Service Mediary is in a position to understand the customer requirements and building a service offering which optimally matches those requirements. In the multi-channel business environment of tomorrow, no single company can develop cost efficiently all required services for every channel. The future belongs to specialized, closely cooperating companies, which can offer superior customer experience at a competitive price. A Service Mediary like NTT DoCoMo with its I-mode service can offer a true win-win-win business model: service providers, customers and the Service Mediary all win in this game. Based on the analysis of the I-mode case, we can conclude that Service Mediary as a business model has a great potential in the future.

scholarly journals The Importance of Human Domain Knowledge and Business Data Analytics to Support Modern Financial Decisions

2021 ◽  
Vol 4 (1) ◽  
pp. p1
Author(s):  
Nadia Delanoy Ph.D. ◽  
Karina Kasztelnik, Ph.D., MBA, CPA, CTP
Keyword(s):  
Social Media ◽  
Customer Relationship Management ◽  
Data Analytics ◽  
Domain Knowledge ◽  
Relationship Management ◽  
Customer Relationship ◽  
Financial Decision ◽  
Business Data ◽  
Media Narratives

The purpose of this study is to identify how importance is human domain knowledge and business data analytics to support modern financial decision. Understanding whether social media narratives could provide a value-add to current customer relationship management practices could be quite valuable. Design/methodology/approach-An analysis of the literature was undertaken and based on an assessment of the literature, conceptual states and pragmatic approaches as well as existing theoretical understandings and frameworks. An explorative case study approach based on Yin’s design will be utilized as a framework as well as a demographic survey to distill even further the characteristics of the sampling from a customer, management and social media user perspective. Furthermore, a customer relationship management framework which would include the adjoining of data analytics and social media narratives will be discussed in context of the research findings. This will help researchers and practitioners more readily explore the shared value framework which the study will be based and contribute to a more fulsome consideration of customer relationship management practice shifts within a technological and social media-oriented age. The contributions of this research will also help reiterate the importance of context in data management as well as the importance of the paradigmatic power shifts reflected in consumer usage of social media, product or service offerings, social consciousness and ethical practice as it relates to the influence of consumer intentions and subsequent purchasing intentions.The purpose of this qualitative exploratory case study was to gain common understandings of how importance is human domain knowledge and business data analytics to support modern financial decision. In order to support reliable and valid research, a purposive sample of customer relations managers, business analysts who have customer relations management (CRM) roles, and customers who utilize social media for the purposes of product or service development was attained.

scholarly journals A Frame Work for Customer Relationship Management in Nigerian Banks Using Data Analytics

2019 ◽  
Vol 4 (2) ◽  
Author(s):  
Olatayo M Olaniyan ◽  
Emeka Ojukwu ◽  
Cyril Ogude
Keyword(s):  
Customer Satisfaction ◽  
Customer Relationship Management ◽  
Management System ◽  
Data Analytics ◽  
Empirical Test ◽  
Big Data Analytics ◽  
Relationship Management ◽  
Customer Relationship ◽  
New Strategy ◽  
Using Data

One of the most crucial challenges that Nigeria banks have to face is in the jurisdiction of customers’ satisfaction. Customers’ satisfaction has become one of the most important factors of success in today’s banking industry in Nigeria. Today Nigeria banks customer’s increases every day, as it is essential for many Nigerian to have proper savings with any bank of their choice; if the performance of bank falls short of their expectations, the very survival of such bank would be difficult. In this paper, a   framework for customer relationship management for Nigeria banks using big data analytics approach was developed. Qualitative research was used to identify customer satisfaction through customer management system information publish annually.  The data were collected from complaint data for financial report 2017 from the Customer Relationship Management System for WEMA Bank Plc. The data were analyzed using excel spread sheet and later converted into CSV and ARFF file format respectively. Data were  exported into WEKA for data analytics which then generated results. The formulated hypotheses are subjected to empirical test using Logistic regression and Machine learning. This new strategy provided solution of these problems identified. Keywords: Data Analytics, Linear regression, Banking,  Customer Satisfaction

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