Prediction of Customer Movements in Large Tourism Industries by the Means of Process Mining

Customer movements in large tourism industries (such as public transport systems, attraction parks or ski resorts) can be understood as business processes. Their processes describe the flow of persons through the networked systems, while Information Systems log the different steps. The prediction of how large numbers of customers will behave in the near future is a complex and yet unsolved challenge. However, the possible business benefits of predictive analytics in the tourism industry are manifold. We propose to approach this task with the yet unexploited appli-cation of predictive process mining. In a prototypical use case, we work together with two major European ski resorts. We implement a predictive process mining algorithm towards the goal of predicting near future lift arrivals of skiers within the ski resort in real-time. Furthermore, we present the results of our prototypical implementation and draw conclusions for future research in the area

Predictors of active loyalty: The case of hotel group X

Loyalty programs are now considered industry standards in the hotel sector. Such programs aim to encourage repeat purchases, attract new customers, reward loyal ones, increase retention rates and market share, and collect customer information. Nonetheless, simple participation in a loyalty program does not imply active loyalty. This in-company project seeks to identify Hotel Group X's active loyal customers and provide the company with insights into who these guests are today and who may become one in the future, allowing them to design appropriate marketing strategies. The CRISP-DM methodology was employed in this study, and its data mining goals were to uncover the most important predictors of reward redemptions, which translate into active loyalty. Two predictive models were used in this study – C&RT and Logistic Regression. According to the C&RT model, reservations made on the company's website are the best predictor of reward redemptions, followed by stays in the Algarve region and city hotels. The Logistic Regression model suggests that there is a significant predictive power for the corporate customers, followed by all the direct booking channels. Our results can help enhance the practical direction for hotel managers who deal with vast volumes of data that can be further integrated into the model built in this study to generate novel insights on consumers.Os programas de fidelização são, atualmente, considerados padrões da indústria no sector hoteleiro. Tais programas visam encorajar compras recorrentes, recompensar clientes fiéis, assim como atrair novos, aumentar as taxas de retenção e a quota de mercado, e melhorar a recolha de informação sobre os clientes. No entanto, a simples participação num programa de fidelização não implica uma lealdade ativa. Este projeto in-company procura identificar os clientes leais ativos do Grupo Hoteleiro X, fornecendo à empresa informações sobre quem são agora esses hóspedes e quais poderão vir a sê-lo no futuro, permitindo-lhes conceber estratégias de marketing apropriadas. Neste estudo foi utilizada a metodologia CRISP-DM com o principal objetivo de descobrir as variáveis que mais influenciam a troca de pontos por recompensas, e que, por sua vez, se traduzem em lealdade ativa. Foram utilizados dois modelos: C&RT e a Regressão Logística. De acordo com os resultados do C&RT, as reservas feitas no website da empresa são as preditoras mais importantes de recompensas redimidas, seguidos de estadias na região do Algarve e estadias em hotéis urbanos. Já no modelo de Regressão Logística foi possível concluir que os clientes corporate são muito significativos nesta previsão. Para além disso, pudemos concluir que todos os canais diretos de marcação de estadias são, também, preditores. Os nossos resultados podem, assim, ajudar a melhorar a direção prática da empresa, que lida com um grande volume de dados, podendo estes serem eventualmente integrados nos modelos construídos neste estudo, de forma a gerar novos conhecimentos sobre os consumidores

Application Of Bayesian Networks In Consumer Service Industry

Gao, Yuan. M.S.I.E., Purdue University. December 2014. Application of Bayesian Networks in Consumer Service Industry. Major professor: Vincent G. Duffy The purpose of the present study is to explore the application of Bayesian networks in the consumer service industry to model causal relationships within complex risk factor structures using aggregate data. An analysis of the Hawaii tourism market was conducted to find out how visitor characteristics affect their behavior and experience as consumers during the trips, and influence the tourism market outcomes represented by measurable factors. Two hypotheses were proposed regarding the use of aggregate data and the influence of visitor origin, and were verified through the analysis. The source data came from the Hawaii Tourism Authority\u27s official website, including monthly tourists highlight reports over a period of 36 months. The analysis verified the hypotheses that visitor origin, as a symbol of cultural background, plays an important role in their behavior, preferences, decisions and experience in consuming. The results were validated both statistically and against literature and expert opinion. In the increasingly segmented tourism market, such findings can help tourism service providers improve consumer satisfaction and loyalty with assistance in policy-making, investment decision-making, resource planning, and strategic marketing

Business intelligence and big data in hospitality and tourism: a systematic literature review

Purpose This paper aims to examine the extent to which Business Intelligence and Big Data feature within academic research in hospitality and tourism published until 2016, by identifying research gaps and future developments and designing an agenda for future research. Design/methodology/approach The study consists of a systematic quantitative literature review of academic articles indexed on the Scopus and Web of Science databases. The articles were reviewed based on the following features: research topic; conceptual and theoretical characterization; sources of data; type of data and size; data collection methods; data analysis techniques; and data reporting and visualization. Findings Findings indicate an increase in hospitality and tourism management literature applying analytical techniques to large quantities of data. However, this research field is fairly fragmented in scope and limited in methodologies and displays several gaps. A conceptual framework that helps to identify critical business problems and links the domains of business intelligence and big data to tourism and hospitality management and development is missing. Moreover, epistemological dilemmas and consequences for theory development of big data-driven knowledge are still a terra incognita. Last, despite calls for more integration of management and data science, cross-disciplinary collaborations with computer and data scientists are rather episodic and related to specific types of work and research. Research limitations/implications This work is based on academic articles published before 2017; hence, scientific outputs published after the moment of writing have not been included. A rich research agenda is designed. Originality/value This study contributes to explore in depth and systematically to what extent hospitality and tourism scholars are aware of and working intendedly on business intelligence and big data. To the best of the authors’ knowledge, it is the first systematic literature review within hospitality and tourism research dealing with business intelligence and big data

The competitive productivity (CP) of tourism destinations: an integrative conceptual framework and a reflection on big data and analytics

Purpose: The purpose of this study is twofold. First, this study elaborates an integrative conceptual framework of tourism destination competitive productivity (TDCP) by blending established destination competitiveness frameworks, the competitive productivity (CP) framework and studies pertaining to big data and big data analytics (BDA) within destination management information systems and smart tourism destinations. Second, this study examines the drivers of TDCP in the context of the ongoing 4th industrial revolution by conceptualizing the destination business intelligence unit (DBIU) as a platform able to create sustained destination business intelligence under the guise of BDA, useful to support destination managers to achieve the tourism destination’s economic objectives. Design/methodology/approach: In this work, the authors leverage both extant literature (under the guise of research on CP, tourism destination competitiveness [TDC] and destination management information systems) and empirical work (in the form of interviews and field work involving destination managers and chief executive officers of destination management organizations and convention bureaus, as well as secondary data) to elaborate, develop and present an integrative conceptual framework of TDCP. Findings: The integrative conceptual framework of TDCP elaborated has been found helpful by a number of destination managers trying to understand how to effectively and efficiently manage and market a tourism destination in today’s fast-paced, digital and hypercompetitive environment. While DBIUs are at different stages of implementation, often as part of broader smart destination initiatives, it appears that they are increasingly fulfilling the purpose of creating sustained destination business intelligence by means of BDA to help tourism destinations achieve their economic goals. Research limitations/implications: This work bears several practical implications for tourism policymakers, destination managers and marketers, technology developers, as well as tourism and hospitality firms and practitioners. Tourism policymakers could embed TDCP into tourism and economic policies, and destination managers and marketers might build and make use of platforms such as the proposed DBIU. Technology developers need to understand that designing destination management information systems in general and more specifically DBIUs requires an in-depth analysis of the stakeholders that are going to contribute, share, control and use BDA. Originality/value: To the best of the authors’ knowledge, this study constitutes the first attempt to integrate the CP, TDC and destination management information systems research streams to elaborate an integrative conceptual framework of TDCP. Second, the authors contribute to the Industry 4.0 research stream by examining the drivers of tourism destination CP in the context of the ongoing 4th industrial revolution. Third, the authors contribute to the destination management information systems research stream by introducing and conceptualizing the DBIU and the related sustained destination business intelligence