Interest-based segmentation of online video platforms' viewers using semantic technologies

To better connect supply and demand for various products, marketers needed novel ways to segment and target their customers with relevant adverts. Over the last decade, companies that collected a large amount of psychographic and behavioural data about their customers emerged as the pioneers of hyper-targeting. For example, Google can infer people’s interests based on their search queries, Facebook based on their thoughts, and Amazon by analysing their shopping cart history. In this context, the traditional channel used for advertising – the media market – saw its revenues plummeting as it failed to infer viewers’ interests based on the programmes they are watching, and target them with bespoke adverts. In order to propose a methodology for inferring viewers’ interests, this study adopted an interdisciplinary approach by analysing the problem from the viewpoint of three disciplines: Customer Segmentation, Media Market, and Large Knowledge Bases. Critically assessing and integrating the disciplinary insights was required for a deep understanding of: the reasons for which psychographic variables like interests and values are a better predictor for consumer behaviour as opposed to demographic variables; the various types of data collection and analysis methods used in the media industry; as well as the state of the art in terms of detecting concepts from text and linking them to various ontologies for inferring interests. Building on these insights, a methodology was proposed that can fully automate the process of inferring viewers interests by semantically analysing the description of the programmes they watch, and correlating it with data about their viewing history. While the methodology was deemed valid from a theoretical point of view, an extensive empirical validation was also undertaken for a better understanding of its applicability. Programme metadata for 320 programmes from a large broadcaster was analysed together with the viewing history of over 50,000 people during a three-year period. The findings from the validation were eventually used to further refine the methodology and show that is it possible not only to infer individual viewers interests based on the programmes watched, but also to cluster the audience based on their content consumption habits and track the performance of various topics in terms of attracting new viewers. Having an effective way to infer viewers’ interests has various applications for the media market, most notably in the areas of better segmenting and targeting audiences, developing content that matches viewers’ interests, or improving existing recommendation engine

New perspectives in relationship marketing conceptualization

Relationship marketing represents a concept that has been disputed in the last 15 years as being a true paradigm of marketing thinking, or just a new method to apply marketing techniques to the new requirements and transformations into the socio-economic field of contemporary economy. Developing the conceptualization of relationship marketing has involved a wide area of research both in the theoretical and practical background. But despite many controversies about how to apply relationship marketing, at a profound level, the openness to new modalities for managing relationships with consumers, in the context of developing a new type of consumer – the postmodern consumer – is one of the undeniable strengths of relationship marketing.The present article is trying to capture some of the possible directions of development of relationship marketing techniques considered by the author as being a kind of future trends of this complex scientific approach. In a brief we consider as appropriate for companies in the consumer markets to develop relationship marketing strategies around the concept of “consumer personal brands basket”. Considering this, every company should try to put together strategic resources and develop common activities with other producers from the brands basket for a certain consumer. Due to the technological development and diminishing costs for management of large and complex consumer databases, developing such a strategic orientation could be not only an illusion but a simple solution for consumers and tomorrow’s competitive environment

Semantic discovery and reuse of business process patterns

Patterns currently play an important role in modern information systems (IS) development and their use has mainly been restricted to the design and implementation phases of the development lifecycle. Given the increasing significance of business modelling in IS development, patterns have the potential of providing a viable solution for promoting reusability of recurrent generalized models in the very early stages of development. As a statement of research-in-progress this paper focuses on business process patterns and proposes an initial methodological framework for the discovery and reuse of business process patterns within the IS development lifecycle. The framework borrows ideas from the domain engineering literature and proposes the use of semantics to drive both the discovery of patterns as well as their reuse

A solution to the hyper complex, cross domain reality of artificial intelligence: The hierarchy of AI

Artificial Intelligence (AI) is an umbrella term used to describe machine-based forms of learning. This can encapsulate anything from Siri, Apple's smartphone-based assistant, to Tesla's autonomous vehicles (self-driving cars). At present, there are no set criteria to classify AI. The implications of which include public uncertainty, corporate scepticism, diminished confidence, insufficient funding and limited progress. Current substantial challenges exist with AI such as the use of combinationally large search space, prediction errors against ground truth values, the use of quantum error correction strategies. These are discussed in addition to fundamental data issues across collection, sample error and quality. The concept of cross realms and domains used to inform AI, is considered. Furthermore there is the issue of the confusing range of current AI labels. This paper aims to provide a more consistent form of classification, to be used by institutions and organisations alike, as they endeavour to make AI part of their practice. In turn, this seeks to promote transparency and increase trust. This has been done through primary research, including a panel of data scientists / experts in the field, and through a literature review on existing research. The authors propose a model solution in that of the Hierarchy of AI

Strategic Assortment Decisions in Omnichannel Retailing: The Design and Evaluation of an Omnichannel Assortment Ontology for Consumer Confusion.

Consumer confusion is a phenomenon observed in retail settings where consumers feel irritation or frustration during the shopping journey. Consumers can be overwhelmed by assortment size, complex product variety, brand similarities, information inconsistencies or by intense stimuli from store atmospherics inducing information overload, leading to adverse reactions. Oftentimes, these experiences result in various negative short- and long-term consequences such as helplessness, purchase abandonment, dissatisfaction, or loss of trust or loyalty, thus representing a crucial challenge for retailers to prevent or mitigate. Consumer confusion has been studied extensively in a single-channel context, for instance, by investigating information overload phenomena in online shopping situations or examining increased choice sets resulting from large assortment sizes in physical stores. However, although omnichannel retailing has become the current state-of-the-art in the retail industry today, consumer confusion research from an omnichannel perspective is still very scarce. With the increased adoption of the omnichannel strategy by retailers that allow free switching behaviour for their customers during their shopping journeys, a new dimension to the consumer confusion phenomenon is observed. Customers are not only exposed to potential confusion at a specific retail situation in a single channel but are now confronted with potential new negative experiences while comparing products, prices, or information across channels. Particularly, when confronted with assortment inconsistencies across channels while switching channels, customers can experience irritation, frustration, or annoyance if the desired item is not to be found on the other channel, leading to adverse reactions that can potentially impact the retailer's financial performance. Prior literature has considered consumer confusion induced by assortment size, variety, or layout, but neglected its occurrence from assortment inconsistencies across channels from a channel switching perspective so far. This thesis focuses on the consumer confusion phenomenon resulting from assortment inconsistencies across channels from a channel-switching perspective in omnichannel retailing. Strategic assortment decisions in omnichannel retailing involve the coordination of the assortment between channels. Retailers can decide to realise a “Full”, “Asymmetric”, or “No Integration” approach for their assortment across channels. These strategic assortment decisions are taken at the Marketing-Operations-Interface (MOI), an interface harmonizing oftentimes conflicting relationships between objectives of the marketing and operations functions of the retailer. Although identical assortment across channels seems to be the desired solution to prevent consumer confusion (representing an objective from the marketing function), retailers oftentimes apply partial integration to benefit from channel-specific advantages such as the Long Tail effect (representing an objective from the operations function) which is detrimental to consumer confusion prevention. Retailers seem to neglect the significance of consumer confusion while making strategic assortment decisions at the MOI indicating that the phenomenon is not sufficiently explored or captured in an omnichannel context. Retailers appear to lack knowledge of the relevant concepts, dimensions, and consequences of the consumer confusion phenomenon. As a result, retailers are likely to fail in addressing and preventing the occurrence of the consumer confusion phenomenon in an omnichannel context. Current studies on strategic assortment decisions and consumer confusion in omnichannel retailing are very scarce and primarily based on experimental studies with a strong lack of empirical contributions. More importantly, none of the studies considers channel switching behaviour in the context of consumer confusion although representing the primary condition for the phenomenon to occur. There is a need for the integration and alignment of knowledge capturing the domains for strategic assortment decisions, the consumer confusion concept, and its short- and long-term consequences from a channel switching behaviour perspective in order to inform strategic assortment decisions at the MOI. Ontologies are explicit and formal specifications of shared conceptualisations that can structure and link information of specific domains and thus are a suitable technique for knowledge representation. Grounded on a Design Science project, this research designs and develops an ontology-based knowledge representation that captures and aligns domain knowledge on strategic assortment decisions, the consumer confusion concept and its consequences from a channel switching behaviour perspective in an omnichannel retailing context. The literature- and practitioner-informed Omnichannel Assortment Ontology for Consumer Confusion is able to integrate and represent relevant concepts and their relationships at the MOI in order to inform omnichannel retailers on the link between strategic assortment decisions and the consumer confusion phenomenon. The ontology is instantiated and evaluated through a System Dynamics model based on a case study that demonstrates successfully its ability to inform omnichannel retailers on strategic assortment decisions and the consumer confusion concept at the MOI. This study contributes to theory and practice in various ways. From a theoretical perspective, this is the first study to link strategic assortment decisions with the consumer confusion concept from a channel switching behaviour perspective. The solution design embodies novel design knowledge on the construction of an ontology-based knowledge representation. Moreover, the study enhances the fields of omnichannel assortment, consumer confusion, and channel switching behaviour research by introducing novel concepts, tools, and an improved understanding of the domains and their interplay with each other. From a managerial perspective, the ontology effectively serves as a knowledge reference that is able to guide strategic decision-making in assortment integration for omnichannel retailers at the MOI. This allows omnichannel retailers to identify and mitigate potential adverse consumer reactions induced by consumer confusion, thus eventually preventing financial impact on their retail performance

New Fundamental Technologies in Data Mining

The progress of data mining technology and large public popularity establish a need for a comprehensive text on the subject. The series of books entitled by "Data Mining" address the need by presenting in-depth description of novel mining algorithms and many useful applications. In addition to understanding each section deeply, the two books present useful hints and strategies to solving problems in the following chapters. The contributing authors have highlighted many future research directions that will foster multi-disciplinary collaborations and hence will lead to significant development in the field of data mining

2015 Colloquium Booklet Complete Papers

Papers submitted by DBA and DMC students to the UoG Doctoral Colloquium 2015

Database marketing intelligence methodology supported by ontologies and knowlegde discovery in databases

Tese de doutoramento em Tecnologias e Sistemas de InformaçãoActualmente as organizações actuam em ambientes caracterizados pela inconstância, elevada competitividade e pressão no desenvolvimento de novas abordagens ao mercado e aos clientes. Nesse contexto, o acesso à informação, o suporte à tomada de decisão e a partilha de conhecimento tornam-se essenciais para o desempenho organizativo. No domínio do marketing têm surgido diversas abordagens para a exploração do conteúdo das suas bases de dados. Uma das abordagens, utilizadas com maior sucesso, tem sido o processo para a descoberta de conhecimento em bases de dados. Por outro lado, a necessidade de representação e partilha de conhecimento tem contribuído para um crescente desenvolvimento das ontologias em áreas diversas como sejam medicina, aviação ou segurança. O presente trabalho cruza diversas áreas: tecnologias e sistemas de informação (em particular a descoberta de conhecimento), o marketing (especificamente o database marketing) e as ontologias. O objectivo principal desta investigação foca o papel das ontologias em termos de suporte e assistência ao processo de descoberta de conhecimento em bases de dados num contexto de database marketing. Através de abordagens distintas foram formuladas duas ontologias: ontologia para o processo de descoberta de conhecimento em bases de dados e, a ontologia para o processo database marketing suportado na extracção de conhecimento em bases de dados (com reutilização da ontologia anterior). O processo para licitação e validação de conhecimento, baseou-se no método de Delphi (ontologia de database marketing) e no processo de investigação baseada na revisão de literatura (ontologia de descoberta de conhecimento). A concretização das ontologias suportou-se em duas metodologias: metodologia methontology, para a ontologia de descoberta de conhecimento e metodologia 101 para a ontologia de database marketing. A última, evidencia a reutilização de ontologias, viabilizando assim a reutilização da ontologia de descoberta de conhecimento na ontologia de database marketing. Ambas ontologias foram desenvolvidas sobre a ferramenta Protege-OWL permitindo não só a criação de toda a hierarquia de classes, propriedades e relações, como também, a realização de métodos de inferência através de linguagens baseadas em regras de Web semântica. Posteriormente, procedeu-se à experimentação da ontologia em casos práticos de extracção de conhecimento a partir de bases de dados de marketing. O emprego das ontologias neste contexto de investigação, representa uma abordagem pioneira e inovadora, uma vez que são propostas para assistirem em cada uma das fases do processo de extracção de conhecimento em bases de dados através de métodos de inferência. È assim possível assistir o utilizador em cada fase do processo de database marketing em acções tais como de selecção de actividades de marketing em função dos objectivos de marketing (e.g., perfil de cliente), em acções de selecção dados (e.g., tipos de dados a utilizar em função da actividade a desenvolver) ou mesmo no processo de selecção de algoritmos (e.g. inferir sobre o tipo de algoritmo a usar em função do objectivo definido). A integração das duas ontologias num contexto mais lato permite, propor uma metodologia com vista ao efectivo suporte do processo de database marketing baseado no processo de descoberta de conhecimento em bases de dados, denominado nesta dissertação como: Database Marketing Intelligence. Para a demonstração da viabilidade da metodologia proposta foi seguido o método action-research com o qual se observou e testou o papel das ontologias no suporte à descoberta de conhecimento em bases de dados (através de um caso prático) num contexto de database marketing. O trabalho de aplicação prática decorreu sobre uma base de dados real relativa a um cartão de fidelização de uma companhia petrolífera a operar em Portugal. Os resultados obtidos serviram para demonstrar em duas vertente o sucesso da abordagem proposta: por um lado foi possível formalizar e acompanhar todo o processo de descoberta de conhecimento em bases de dados; por outro lado, foi possível perspectivar uma metodologia para um domínio concreto suportado por ontologias (suporte á decisão na selecção de métodos e tarefas) e na descoberta de conhecimento em bases de dados.Nowadays, the environment in which companies work is turbulent, very competitive and pressure in the development of new approaches to the market and clients. In this context, the access to information, the decision support and knowledge sharing become essential for the organization performance. In the marketing domain several approaches for the exploration of database exploration have emerged. One of the most successfully used approaches has been the knowledge discovery process in databases. On the other hand, the necessity of knowledge representation and sharing and contributed to a growing development of ontologies in several areas such as in the medical, the aviation or safety areas. This work crosses several areas: technology and information systems (specifically knowledge discovery in databases), marketing (specifically database marketing) and ontologies in general. The main goal of this investigation is to focus on the role of ontologies in terms of support and aid to the knowledge discovery process in databases in a database marketing context. Through distinct approaches two ontologies were created: ontology for the knowledge discovery process in databases, and the ontology for the database marketing process supported on the knowledge extraction in databases (reusing the former ontology). The elicitation and validation of knowledge process was based on the Delphi method (database marketing ontology) and the investigation process was based on literature review (knowledge discovery ontology). The carrying out of both ontologies was based on two methodologies: methontology methodology, for the knowledge discovery process and 101 methodology for the database marketing ontology. The former methodology, stresses the reusing of ontologies, allowing the reusing of the knowledge discovery ontology in the database marketing ontology. Both ontologies were developed with the Protege-OWL tool. This tool allows not only the creation of all the hierarchic classes, properties and relationships, but also the carrying out of inference methods through web semantics based languages. Then, the ontology was tested in practical cases of knowledge extraction from marketing databases. The application of ontologies in this investigation represents a pioneer and innovative approach, once they are proposed to aid and execute an effective support in each phase of the knowledge extraction from databases in the database marketing context process. Through inference processes on the knowledge base created it was possible to assist the user in each phase of the database marketing process such as, in marketing activity selection actions according to the marketing objectives (e.g., client profile) or in data selection actions (e.g., type of data to use according to the activity to be preformed. In relation to aid in the knowledge discovery process in databases, it was also possible to infer on the type of algorithm to use according to the defined objective or even according to the type of data pre-processing activities to develop regarding the type of data and type of attribute information. The integration of both ontologies in a more general context allows proposing a methodology aiming to the effective support of the database marketing process based on the knowledge discovery process in databases, named in this dissertation as: Database Marketing Intelligence. To demonstrate the viability of the proposed methodology the action-research method was followed with which the role of ontologies in assisting knowledge discovery in databases (through a practical case) in the database marketing context was observed and tested. For the practical application work a real database about a customer loyalty card from a Portuguese oil company was used. The results achieved demonstrated the success of the proposed approach in two ways: on one hand, it was possible to formalize and follow the whole knowledge discovery in databases process; on the other hand, it was possible to perceive a methodology for a concrete domain supported by ontologies (support of the decision in the selection of methods and tasks) and in the knowledge discovery in databases.Fundação para a Ciência e a Tecnologia (FCT) - SFRH/BD/36541/200