Density-based spatial clustering and ordering points approach for characterizations of tourist behaviour

Knowledge about the spots where tourist activity is undertaken, including which segments from the tourist market visit them, is valuable information for tourist service managers. Nowadays, crowdsourced smartphones applications are used as part of tourist surveys looking for knowledge about the tourist in all phases of their journey. However, the representativeness of this type of source, or how to validate the outcomes, are part of the issues that still need to be solved. In this research, a method to discover hotspots using clustering techniques and give to these hotspots a data-driven interpretation is proposed. The representativeness of the dataset and the validation of the results against existing statistics is assessed. The method was evaluated using 124,725 trips, which have been gathered by 1505 devices. The results show that the proposed approach successfully detects hotspots related with the most common activities developed by overnight tourists and repeat visitors in the region under study

Managerial Segmentation of Service Offerings in Work Commuting, MTI Report WP 12-02

Methodology to efficiently segment markets for public transportation offerings has been introduced and exemplified in an application to an urban travel corridor in which high tech companies predominate. The principal objective has been to introduce and apply multivariate methodology to efficiently identify segments of work commuters and their demographic identifiers. A set of attributes in terms of which service offerings could be defined was derived from background studies and focus groups of work commuters in the county. Adaptive choice conjoint analysis was used to derive the importance weights of these attributes in available service offering to these commuters. A two-stage clustering procedure was then used to explore the grouping of individual’s subsets into homogeneous sub-groups of the sample. These subsets are commonly a basis for differentiation in service offerings that can increase total ridership in public transportation while approximating cost neutrality in service delivery. Recursive partitioning identified interactions between demographic predictors that significantly contributed to the discrimination of segments in demographics. Implementation of the results is discussed

Analysis of out-of-town expenditures and tourist trips using credit card transaction data

Credit card transaction data contains a vast amount of valuable information that can indicate consumer behaviour patterns and mark out human mobility. In this study we analyse the transactions carried out by a sample of 10.000 Istanbul-based customers of a Turkish bank to scrutinize expenditures incurred out of Istanbul. In our preliminary descriptive analysis, we examine the relation between demographic attributes and spending measures, as well as investigate the extent to which the population and the number of points of interest imply higher or lower credit card expenditure by visitors. We develop a methodology to extract tourist trips from consecutive credit card transactions. Subsequently, we implement a hierarchical clustering method to evaluate what the purpose of these trips might have been. Our results indicate 5 clusters of purpose: ’Leisure’, ’Business’, ’Acquisition’, ’Visiting Friends and Relative’ and ’Package Holiday’. The same clustering method is applied to segment provinces of Turkey based on which product and service categories visitors prefer. We deploy a number of predictive models to estimate tourist expenditure and whether a person would embark on a trip in the upcoming months. The predictive power of these models are generally moderate; nevertheless, several of the most useful predictors are behavioural or are related to previous trips, factors that have not been considered in literatur

Segmenting Markets by Bagged Clustering: Young Chinese Travelers to Western Europe.

Market segmentation is ubiquitous in marketing. Hierarchical and nonhierarchical methods are popular for segmenting tourism markets. These methods are not without controversy. In this study, we use bagged clustering on the push and pull factors of Western Europe to segment potential young Chinese travelers. Bagged clustering overcomes some of the limitations of hierarchical and nonhierarchical methods. A sample of 403 travelers revealed the existence of four clusters of potential visitors. The clusters were subsequently profiled on sociodemographics and travel characteristics. The findings suggest a nascent young Chinese independent travel segment that cannot be distinguished on push factors but can be differentiated on perceptions of the current independent travel infrastructure in Western Europe. Managerial implications are offered on marketing and service provision to the young Chinese outbound travel market

Supplier Selection and Relationship Management: An Application of Machine Learning Techniques

Managing supply chains is an extremely challenging task due to globalization, short product life cycle, and recent advancements in information technology. These changes result in the increasing importance of managing the relationship with suppliers. However, the supplier selection literature mainly focuses on selecting suppliers based on previous performance, environmental and social criteria and ignores supplier relationship management. Moreover, although the explosion of data and the capabilities of machine learning techniques in handling dynamic and fast changing environment show promising results in customer relationship management, especially in customer lifetime value, this area has been untouched in the upstream side of supply chains. This research is an attempt to address this gap by proposing a framework to predict supplier future value, by incorporating the contract history data, relationship value, and supply network properties. The proposed model is empirically tested for suppliers of public works and government services Canada. Methodology wise, this thesis demonstrates the application of machine learning techniques for supplier selection and developing effective strategies for managing relationships. Practically, the proposed framework equips supply chain managers with a proactive and forward-looking approach for managing supplier relationship

Customer Demographic Segmentation Based On Telecom Behavioral Data

In the modern world, digitalization becomes ubiquitous and covers almost every aspect of the business and daily life. Telecom services providers have a major role in these processes due to their involvement in collecting, storing and processing enormous amounts of customer data. This also includes personal telecom services usage data, which if correctly interpreted, might be used for many different purposes. Using telecom data to predict certain demographic characteristics of the customers is helpful in more than one aspect: 1) It could add the acquired knowledge into customer segmentation to better target different customer groups. 2) Such data could be used in cases where traditional historic data is not available- the potential strength of predicting customer credit worthiness based on behavior data is still not fully explored. 3) Last but definitely not least, is the use of data for verifying customer identification in fraud detection. In this paper, an overview of some successful use of telecom data for non-telecom services is shown, as well as with a set of real telco data, statistical techniques are used to demonstrate the relation between mobile telecom services usage and subscription owners’ age. Use of alternative customer data could have enormous implication both on traditional predictive models and could alter the role of the telecoms, making them one of the most important information sources for financial institutions, which operate with sensitive customer data

How do they pay as they go?: Learning payment patterns from solar home system users data in Rwanda and Kenya

Pay-as-you-go (PAYGo) financing models play a vital role in boosting the distribution of solar-home-systems (SHSs) to electrify rural Sub-Saharan Africa. This financing model improves the affordability of SHSs by supporting the payment flexibility required in these contexts. Such flexibility comes at a cost, and yet the assumptions that guide the PAYGo model design remain largely untested. To close the gap, this paper proposes a methodology based on unsupervised machine learning algorithms to analyse the payment records of over 32,000 Rwandan and 25,000 Kenyan SHS users from Bboxx Ltd., and in so doing gain detailed insights into users' payment behavioural patterns. More precisely, the method first applies three clustering algorithms to automatically learn the main payment behavioural groups in each country separately; it then determines the preferred customer segmentation through a validation procedure which combines quantitative and qualitative insights. The results highlight six behavioural groups in Rwanda and four in Kenya; however, several parallels can be made between the two country profiles. These groups highlight the diversity of payment patterns found in the PAYGo model. Further analysis of their payment performance suggests that a one-size-fits-all approach leads to inefficiencies and that tailored plans should be considered to effectively cater to all SHS users

A model to improve the Evaluation and Selection of Public Contest´s Candidates (Police Officers) based on AI technologies

Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Business AnalyticsThe number of candidates applying to Public Contests is increasing compared to the number of Human Resources employees required for selecting them for Police Forces. This work intends to perceive how those Public Institutions can evaluate and select their candidates efficiently during the different phases of the recruitment process, and for achieving this purpose AI approaches will be studied. This paper presents two research questions and introduces a corresponding systematic literature review, focusing on AI technologies, so the reader is able to understand which are most used and more appropriate to be applied to Police Forces as a complementary recruitment strategy of the National Criminal Investigation Police agency of Portugal – Polícia Judiciária. Design Science Research (DSR) was the methodological approach chosen. The suggestion of a theoretical framework is the main contribution of this study in pair with the segmentation of the candidates (future Criminal Inspectors). It also helped to comprehend the most important facts facing Public Institutions regarding the usage of AI technologies, to make decisions about evaluating and selecting candidates. Following the PRISMA methodology guidelines, a systematic literature review and meta-analyses method was adopted to identify how can the usage and exploitation of transparent AI have a positive impact on the recruitment process of a Public Institution, resulting in an analysis of 34 papers published between 2017 and 2021. The AI-based theoretical framework, applicable within the analysis of literature papers, solves the problem of how the Institutions can gain insights about their candidates while profiling them; how to obtain more accurate information from the interview phase; and how to reach a more rigorous assessment of their emotional intelligence providing a better alignment of moral values. This way, this work aims to advise the improvement of the decision making to be taken by a recruiter of a Police Force Institution, turning it into a more automated and evidence-based decision when it comes to recruiting the adequate candidate for the place