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Dissertations / Theses on the topic 'Big data in marketing'
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Liu, Xiao. "Marketing Solutions Enabled by Big Data Using Structural Models." Research Showcase @ CMU, 2015. http://repository.cmu.edu/dissertations/512.
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Digital marketing has brought in enormous capture of consumer data. In quantitative marketing, researchers have adopted structural models to explain the (dynamic) decision process and incentives of consumers. However, due to computational burdens, structural models have rarely been applied to big data. Machine learning algorithms that perform well on big data, however, hardly have any theoretical support. Equipped with both economics perspective and machine leaning techniques, in this dissertation, I aim to combine rigorous economic theory with machine learning methods and apply them to data on a large scale. First of all, I leverage structural models to understand the behaviors of consumers, firms and the entire market. In my first essay “An Empirical Analysis of Consumer Purchase Behavior of Base Products and Add-ons Given Compatibility Constraints”, I model consumers as forward-looking utility maximizers who consider the product bundle of base products and add-ons jointly. I derive the add-on-to-base effect from the solution of the dynamic programming problem and then quantified it by model estimates. The underlying theory of switching cost caused by incompatibility constraints helps me rationalize the high market share of Sony’s despite its high price. Without this theoretical foundation, I could not explain the data puzzle and might have fallen into false causality. Doing empirical research in this time of an explosion of the quantity and quality of data is fortuitous. With the help of “Big Data”, I am able to explore new areas of research, like social media. In my second essay “A Structured Analysis of Unstructured Big Data Leveraging Cloud Computing”, I conduct analysis on a staggering volume of nearly two billion tweets to predict TV show ratings. Different from the traditional economics data that can be easily stored in a spreadsheet, the unstructured format and sheer volume of the tweet data challenges traditional information extraction and selection methods. But after careful sifting the data through a combination of methods from cloud computing, machine learning and text mining, the rich content information imbedded in the text provides us with much better explanatory and predictive power. The beauty of structural models comes with a cost of computational burden as manifested in estimating dynamic choice models. The problem of curse of dimensionality is exacerbated when I estimate the model at the individual level on a large sample, as I do in the third essay “Overhaul Overdraft Fees: Creating Pricing and Product Design Strategies with Big Data”. In this project I build a model where I assume consumers perform financial planning and spend their money rationally subject to monitoring cost and heavy discounting tendency. As consumers exhibit significantly different behavior patterns, I need to estimate the model at the individual level in order to design targeted strategies. But to do this with the standard estimation methods takes prohibitively large amount of time. To solve this problem I employ a new parallel computing algorithm to conduct parallel MCMC to speed up the estimation. This facilitates the marriage of applying the structural model on Big Data.
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Nybacka, A. (Aino). "Privacy concerns of consumers in big data management for marketing purposes:an integrative literature review." Bachelor's thesis, University of Oulu, 2016. http://urn.fi/URN:NBN:fi:oulu-201605261989.
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This bachelor’s thesis is a literature review about big data, privacy concerns for individual consumers and how these two overlap together in a way to possibly explain what are the privacy concerns that customers have, and the companies maybe should think about, when they utilize data as a marketing tool.
The thesis introduces an integrative framework for the privacy concerns emerging from the process of big data management for marketing purposes and gives insights for phases in this process first separately from companies point of view, then from the consumers point of view regarding the privacy issues and then together by bringing these two issues together.
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Brauer, Jimmy, and Eriksson Björn Linnala. "Blockchain's influence on digital marketing : An exploratory study examining blockchain in relation to big data and digital marketing." Thesis, Umeå universitet, Företagsekonomi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-172806.
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Today's society has grown to be highly digitalized where technologies are playing a large role in everyone's lives. Moreover, society is still developing at a rapid pace, with a new innovation around every corner and with more and more business conducted online. By using this as a starting point, the authors of this thesis choose to examine how blockchain can influence the use of big data in digital marketing. Moreover, as previous articles have perceived GDPR as an obstacle for implementing blockchain within this context, the authors perceived it as necessary to examine blockchains’ relation to GDPR. Thus, this thesis has set out to both identify challenges and opportunities that exist when applying blockchain within the context of digital marketing. This exploratory thesis applies a qualitative data collection method in order to fulfill the purpose of the study and to answer the following components of the research question; (1) How will blockchain influence the use of big data within digital marketing? (2) How will blockchain in a digital marketing context cope with the regulations of GDPR? (3) How will blockchain influence the future of digital marketing? The qualitative collection method chosen is semi-structured interviews and has included six participants from Europe. The findings of this thesis suggest that blockchain will influence big data and thus, digital marketing will be influenced because it is heavily data-driven. Furthermore, blockchain will influence the companies’ knowledge of the customer, allow more reliable data to be obtained, and allow consumers to retain ownership over their data. Thus, blockchain places a higher demand on companies to deliver relevant information and match the interest of customers in order for the companies to gain access to customers’ data. Additionally, the findings suggest that blockchain has the ability to remove intermediaries and eliminate fraudulent activities, such as deep fakes, illegitimate reviews, click-fraud, within digital marketing. Despite that blockchain has a lot of potentials, it also faces a lot of challenges and obstacles. For instance, large companies such as Google may feel threatened by the blockchain and thus, these companies may have incentives for disrupting the implementation of the technology. Moreover, practical challenges have been identified. These were related to non-scalable algorithms and limited capacities to store large amounts of data.
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Nybacka, A. (Aino). "The role of consumers’ knowledge and attitudes in determining the importance of privacy in big data marketing." Master's thesis, University of Oulu, 2018. http://urn.fi/URN:NBN:fi:oulu-201806062496.
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The world has changed due to the digital revolution and this means that the marketing environment has changed and that it has to keep changing. Even though the data has always existed and been a part of marketing, due to this digital revolution the marketing done based on customer data has evolved immensely as well. Big data offers marketers opportunities that seem endless, due to the exponentially growing amount of data and the new innovations that are made to harness it, but what has to be remembered is that big data marketing is not a solution without its problems.
In this research we focus on big data marketing and its implications on consumer privacy; what are the knowledge and attitudes of consumers towards privacy in big data marketing and what is their role in determining the importance of consumer privacy for the companies. The question we ask and answer is: how consumers’ knowledge of and attitudes towards privacy in big data marketing affect the reasons why companies should take privacy as a part of their strategy? The aim of the research is to explain why consumers’ knowledge and attitudes towards privacy in big data marketing are important and thus something that the companies should take into account in their strategy when looking at it from this particular point of view. These questions are examined through asking a group of informants about their knowledge and attitudes towards privacy in big data marketing and analyzing the data in its entirety before drawing conclusions from it. The research is conducted with a qualitative questionnaire sent to this group of informants and the findings are a direct result of these answers collected from them.
The findings of the research include both theoretical contributions and managerial implications. Theoretical contributions of this work presents a model that introduces a framework that explains how the knowledge and attitudes of consumers are interwoven with companies’ actions and what are the major aspects in each of the parts that have an effect on how the privacy in big data marketing is seen and this plays directly to the reasons why companies should take privacy as a part of their strategy.
In the managerial implications we explain these things further and learn how either positive or negative outlook towards big data marketing from the consumers’ point of view can affect them in a larger scale and not just on how the customers’ see them. These results can be used to evaluate how the consumers see privacy in big data marketing in a specific company and what are the things that can be done to help the situation; what the companies can do to help consumers’ see privacy in big data marketing in a better light and help them understand the possible ramifications from negative outlook and benefits from a positive one.
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Dervan, Philip. "Social networking theory and the rise of digital marketing in the light of big data." Thesis, University of Southampton, 2015. https://eprints.soton.ac.uk/378356/.
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The topic of this thesis is the use of ‘Big Data’ as a catalyst for true precision target marketing, where online advertisements across all communication channels are so timely and relevant that they are welcomed by the consumer because they improve the customer experience. In particular, the research has been directed to demonstrate the link between investment in digital branding and sales revenue at the company level. This thesis includes a review of the accumulation of ‘Big Data’ from a plethora of social networks, and an assessment of its current use and application by marketing and sales departments and emerging others. The hypothesis tested was that companies most advanced in processing ‘Big Data’ by rules-based, algorithmic, digital analysis are the companies realizing the greatest return on investment in the use of ‘Big Data’. The research was conducted using a questionnaire and interviews with the top people working in large consultancy and related firms who are actively engaged in the utilization of social media and large datasets. As there is a lack of understanding within companies in terms of using social media, and many obstacles have to be overcome, the research was meant to unearth some insights into the effective use of data. The research indicated that companies that had certain organizational and operational characteristics actively use social media, although the utilization is often limited in scope. However companies that do use them effectively gain measurable ROI and tend to track users across many venues. The companies using advanced ‘Big Data’ analytical tools to describe and predict user characteristics, applying the intelligence to target, time, tailor and trigger the release of cogent content to the ‘dynamic throng of individual audiences’ are experiencing the highest return on social media investment. This thesis makes a contribution to the wider understanding of social media use by the large business entities, and to the current and future problems that this explosion of data is creating and is likely to create.
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Gibson, Scott D. "Advertising planning in the age of big data. An exploratory study." Thesis, Queensland University of Technology, 2018. https://eprints.qut.edu.au/122469/1/Scott_Gibson_Thesis.pdf.
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The impact that big data has on relationships between advertising agencies and clients is explored from the planning perspective. Using diffusion of innovations theory, findings indicate that the complexities and expectations associated with using data in advertising decision-making both inhibits and accelerates its use. Clients are attracted to the promise of competitive advantage, driving agencies to respond, often with short-term gains at the expense of long-term brand building. Planners develop advertising strategies primarily using traditional research. However those who can meaningfully merge this with big data will create a winning edge for both clients and agencies.
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De, Koker Louise. "Fostering collaboration amongst business intelligence, business decision makers and statisticians for the optimal use of big data in marketing strategies." University of the Western Cape, 2019. http://hdl.handle.net/11394/6780.
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Philosophiae Doctor - PhD<br>The aim of this study was to propose a model of collaboration adaptable for the optimal use of big data in an organisational environment. There is a paucity of knowledge on such collaboration and the research addressed this gap. More specifically, the research attempted to establish whether leadership, trust and knowledge sharing influence collaboration among the stakeholders identified at large organisations.
The conceptual framework underlying this research was informed by collaboration theory and organisational theory. It was assumed that effective collaboration in the optimal use of big data possibly is associated with leadership, knowledge sharing and trust. These concepts were scientifically hypothesised to determine whether such associations exist within the context of big data.
The study used a mixed methods approach, combining a qualitative with a quantitative study. The qualitative study was in the form of in-depth interviews with senior managers from different business units at a retail organisation in Cape Town. The quantitative study was an online survey conducted with senior marketing personnel at JSE-listed companies from various industries in Cape Town. A triangulation methodology was adopted, with additional in-depth interviews of big data and analytics experts from both South Africa and abroad, to strengthen the research.
The findings of the research indicate the changing role of the statistician in the era of big data and the new discipline of data science. They also confirm the importance of leadership, trust and knowledge sharing in ensuring effective collaboration. Of the three hypotheses tested, two were confirmed. Collaboration has been applied in many areas. Unexpected findings of the research were the role the chief data officer plays in fostering collaboration among stakeholders in the optimal use of big data in marketing strategies, as well as the importance of organisational structure and culture in effective collaboration in the context of big data and data science in large organisations.
The research has contributed to knowledge by extending the theory of collaboration to the domain of big data in the organisational context, with the proposal of an integrated model of collaboration in the context of big data. This model was grounded in the data collected from various sources, establishing the crucial new role of the chief data officer as part of the executive leadership and main facilitator of collaboration in the organisation. Collaboration among the specified stakeholders, led by the chief data officer, occurs both horizontally with peers and vertically with specialists at different levels within the organisation in the proposed model. The application of such a model of collaboration should facilitate the successful outcome of the collaborative efforts in data science in the form of financial benefits to the organisation through the optimal use of big data.
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Lager, Joel, and Pontus Hermansson. "Marknadsföringsmixens fortsatta betydelse, med hänsyn till digitaliseringen. : En systematisk litteraturstudie." Thesis, Högskolan Dalarna, Företagsekonomi, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:du-27860.
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Syfte: Syftet med denna studie är att diskutera hur marknadsföringsmixen kunnat bevarat sin betydelse över tid inom området marknadsföring, med hänsyn till de förändringar som digitaliseringen inneburit. Metod: Studien genomfördes som en systematisk litteraturstudie. Studien baserades på över 50 vetenskapliga artiklar som är relevanta för syftet. Artiklarna samlades in via akademiska databaser. Resultat: Studien visar på att marknadsföringsmixen fortsatt är en aktuell modell inom marknadsföring tack vare sin pedagogiska enkelhet och förmåga att anpassa sig till rådande förutsättningar. De fyra P:na står fortsatt för Produkt, Pris, Plats och Påverkan men förändringen ligger i vad som innefattas i de ständigt växande och föränderliga subkategorierna. Det som motståndarna till marknadsföringsmixen menar är dess svaghet, att kriterierna som de olika kategorierna vilar på aldrig blivit specificerade, tycks också vara modellens styrka. Utan den efterfrågade specificeringen kan de fyra P:na anpassas efter användaren och de förhållanden som råder, vilket har gjort att den bevarat sin betydelse trots digitaliseringen och de nya förutsättningarna.<br>Purpose: The purpose of this study is to discuss how the marketing mix could have retained its importance over time in the field of marketing, in consideration of changes that digitalization meant. Method: The study is conducted as a systematic literature study. The study is based on more than 50 scientific articles relevant to the purpose. The articles were collected through academic databases. Result: The articles show that the marketing mix is still an up-to-date marketing tool thanks to its educational simplicity and ability to adapt to the prevailing conditions. The four P:s still stand for Product, Price, Place and Promotion, but the change lies in what is included in the ever-growing and changing subcategories. What the opponents of the marketing mix mean is its weakness, that the criteria that the different categories rest on have never been specified, also seems to be its greatest strength. Without the requested specification, the four P:s can be adapted to the user and the conditions prevailing, which has made it survive in spite of digitization and the new conditions.
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Shi, Fanjuan. "A Smart Marketing Personalization Methodology Based On Real-Time Contextualisation In The Era of Big Data." Thesis, Lyon, 2017. http://www.theses.fr/2017LYSE3019/document.
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La personnalisation peut améliorer la pertinence et l'utilité des communications marketing B2C. Cependant, ce n'est pas la panacée pour tous les défis marketing B2C. Dans le but de renforcer la pertinence et l'utilité des contenus de marketing personnalisés, nous proposons une approche de personnalisation marketing contextuelle qui permet d'analyser les comportements d’achat cross-canaux, d'interpréter les intentions du consommateur, de détecter l'état cognitif des consommateurs et d'utiliser la stratégie de personnalisation appropriée pour générer et afficher des contenus marketing personnalisés. Notre approche prend en compte le volume, la variété et la variabilité des données de consommateurs dans un scénario de shopping cross-canaux. En utilisant un mécanisme d'apprentissage, notre approche permet à un système de personnalisation marketing d'apprendre de la préférence des consommateurs pour le moment et le moyen de contact afin qu'il puisse ajuster sa stratégie de personnalisation dynamiquement. Le test A / B indique que notre approche peut améliorer la pertinence et l'utilité des contenus de marketing personnalisés et la connaissance des voyages d'achat cross-canaux peuvent remodeler la façon dont les entreprises planifient, exécutent et évaluent leurs événements marketing<br>Personalization can enhance the relevance and usefulness of B2C marketing communications. However, it is not the panacea for all the B2C marketing challenges. With an aim to further enhance the relevance and usefulness of personalized marketing contents, we propose a context-aware marketing personalization approach that can analyze cross-channel shopping behaviors, interpret consumer intentions, detect consumers cognitive state, and use the appropriate personalization strategy to generate and deliver personalized marketing contents to them. Our approach takes into account the volume, variety, and variability of consumer data in a cross-channel shopping scenario. Using a learning mechanism, our approach allows a marketing personalization system to learn from consumers’ preference for the moment and medium of contact so that it can adjust its personalization strategy dynamically. A/B test indicates that our approach can significantly enhance the relevance and usefulness of personalized marketing contents and the knowledge of cross-channel shopping journeys may reshape the way firms plan, execute, and evaluate marketing events
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Sacchetti, Giacomo. "L’impatto degli Analytics: come i Big Data stanno cambiando le aziende." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2022.
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In questa tesi affronto il mondo dei big data e di come stanno avendo impatto sulle aziende. Vengono affrontati casi di studio come Cambridge Analytica, le elezioni Americane del 2016 e la Brexit. Si affronta anche vari aspetti di gestione e estrazione dei dati negli algoritmi di suggestione dei vari provider di servizi, quali netflix, amazon etc...
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Salvador, Alexandre Borba. "O uso das informações de big data na gestão de crise de marca." Universidade de São Paulo, 2015. http://www.teses.usp.br/teses/disponiveis/12/12139/tde-01102015-144508/.
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As crises de marca não só experimentam um crescimento em quantidade como também passam a ter sua visibilidade aumentada pelas redes sociais. A repercussão de uma crise de imagem de marca afeta negativamente tanto o brand equity como as vendas no curto prazo. Mais do que isso, gera custosas campanhas para minimização dos efeitos negativos. Se por um lado o avanço tecnológico aumenta a visibilidade da crise, por outro, possibilita acesso a uma série de informações, internas e externas, que podem ajudar na definição de um plano de ação. Big Data é um termo recentemente criado para designar o crescimento das informações, grandes em volume, diversificadas em formato e recebidas em alta velocidade. No ambiente de marketing, o sistema de informação de marketing (SIM) tem por objetivo fornecer as informações ao tomador de decisão de marketing. Informação relevante, confiável e disponibilizada em um curto espaço de tempo é fundamental para que as decisões sejam tomadas rapidamente, garantindo a liderança do processo de gestão de crise. A partir da pergunta \"qual o uso das informações provenientes do big data na gestão de crise de marca?\" e com o objetivo de \"verificar como gestores fazem uso das informações provenientes de big data na gestão de crise\", elaborou-se este estudo exploratório, empírico, qualitativo e com uso de entrevistas em profundidade com executivos de marketing com experiência em gestão de crise de marca. As entrevistas com seis gestores com experiência em crise e dois especialistas possibilitaram verificar uma grande diferença no uso das informações de big data na gestão de crises de marca, nas diferentes etapas da crise identificadas no referencial teórico: identificação e prevenção, gestão da crise, recuperação e melhorias e aprendizados.<br>The brand crises not only experience a growth in quantity but also now have increased their visibility through social networks. The impact of a brand crisis negatively affects the brand equity and short-term sales. It also generates costly campaigns to minimize its negative effect. If technological advancement increases the visibility of the crisis, it also provides access to a wealth of internal and external information that can help define an action plan. The term Big Data refers to the growth of information volume, diversification of formats and production and reception in real time, so that a traditional processing system could not store and analyze them. In the marketing environment, marketing information system (MIS) aims to provide information to the marketing decision maker. Relevance, reliability and availability of information is critical for the decision process. It also could ensures the leadership of the crisis management process. From the question \"what is the use of information from big data in brand crisis management?\" and in order to \"verify how managers make use of information from big data in crisis management\", this study exploratory empirical, qualitative, using interviews with marketing executives with experience in brand crisis management was elaborated. Interviews with six managers with experience in crisis and two experts made it possible to verify a big difference in the use of big data information on brand management of crises in the different stages of the crisis identified in the theoretical framework: identification and prevention, crisis management, recovery and improvement and learning.
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Afzal, Samra. "Big data analysis of Customers’ information: A case study of Swedish Energy Company’s strategic communication." Thesis, Uppsala universitet, Medier och kommunikation, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-388552.
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Big data analysis and inbound marketing are interlinked and can play a significant role in the identification of target audience and in the production of communication content as per the needs of target audience for strategic communication campaigns. By introducing and bringing the marketing concepts of big data analysis and inbound marketing into the field of strategic communication this quantitative study attempts to fill the gap in the limited body of knowledge of strategic communication research and practice. This study has used marketing campaigns as case studies to introduce a new strategic communication model by introducing the big data analysis and inbound marketing strategy into the three staged model of strategic communication presented by Gulbrandsen, I. T., & Just, S. N. in 2016. Big data driven campaigns are used to explain the procedure of target audience selection, key concepts of big data analysis, future opportunities, practical applications of big data for strategic communication practitioners and researchers by identifying the need for more academic research and practical use of big data analysis and inbound marketing in the strategic communication area. The study shows that big data analysis has potential to contribute in the field of strategic and target oriented communication. Inbound marketing and big data analysis has been used and considered as marketing strategy but this study is an attempt to shift the attention towards its role in strategic communication so there is a need to study big data analysis and inbound marketing with an open mind without confining it with some particular fields.
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Peyne, Benjamin, and Ariane Chan. "Data-driven decision making in Marketing : A theoretical approach." Thesis, Högskolan i Halmstad, Akademin för ekonomi, teknik och naturvetenskap, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-33545.
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Customer insight is at the heart of the big data era. This revolution makesit possible to directly obtain high potential data and in large quantitiesabout customers. Thus we take note that, more than ever, a large volumeof big data is collected by companies.We observe that big data have become a necessary tool within marketing.More and more companies orient their decisions according to theinformations provided by data, with the aim of quickly having betterresults.Nevertheless, in order to integrate these big data in a better way and gaina competitive advantage, companies must face new challenges. Tomeasure and understand the impact of big data in marketing decisions,we propose, with the support of our scientific and theoretical resources, areasoning, demonstrating all the issues. Big data is increasinglyubiquitous and necessary for companies (I). Their impact in decisionsneeds to be taken into account (II) and their use is leading to amanagement revolution (III). Moreover, it modifies the close relationbetween decision and intuition. (IV). In this article, we present aperspective that study all these concepts. We close by offering a modeland a conclusion answering our problematic.
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Giannini, Andrea. "Social Network Analysis: Architettura Streaming Big Data di Raccolta e Analisi Dati da Twitter." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2022. http://amslaurea.unibo.it/25378/.
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Negli ultimi anni i social media, come ad esempio Facebook, Twitter, WhatsApp, YouTube, si sono diffusi a macchia d'olio.
Ormai quasi tutti accedono giornalmente su almeno uno di questi per informarsi, esprimere opinioni e interagire con altri utenti.
Per questa ragione sono diventati fondamentali per i reparti marketing delle aziende essendo non solo un ottimo canale di comunicazione, ma anche una fonte di informazioni sui clienti e potenziali tali.
La tesi si focalizza proprio su quest'ultimo aspetto.
Il progetto Social Network Analysis (SNA) vuole essere infatti uno strumento attraverso il quale è possibile visionare e analizzare per intero le reti di interazione tra utenti.
Ci si è posti l'obiettivo di realizzare SNA in modo che raccogliesse e si aggiornasse in tempo reale, così da essere sempre al passo con le ultime novità, data la dinamicità delle informazioni all'interno dei social media.
Un progetto come SNA comporta dover affrontare diversi ostacoli.
Oltre a quello di riuscire a realizzare un'architettura che accolga un flusso continuo di informazioni, uno degli ostacoli più importanti è quello di gestire la grande mole di dati.
Per farlo ci si è affidati ad un'architettura distribuita e facilmente scalabile che comprende l'uso di elaborazioni in cluster, di funzioni serverless e di database NoSQL approvvigionati attraverso il servizio cloud di Microsoft, Azure.
In questa tesi SNA è stato progettato e implementato basandosi su Twitter, ma è possibile sfruttare la stessa idea su tanti altri social media.
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Krusing, Markus, and Leo Tiricke. "Big datas påverkan på beslutsfattande av kundsegment : - En fallstudie av organisationer inom resebranschen." Thesis, Uppsala universitet, Företagsekonomiska institutionen, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-329506.
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Through an ever-increasing connectivity, the digital imprints left behind are opening new possibilities for organizations. Big data contains large amounts of consumer data and through analysis this data is transformed into information. This has enabled organizations new forms of how to implement big data to the decision-making process regarding segmentation of consumers. Through a qualitative survey and semi-structured interviews, a case study is used for the research of Destination Gotland and TUI. The purpose of this study is to investigate how big data affects the decision-making process for segmentation at two organizations in the travel industry. The theoretical framework has been designed after thorough research of relevant literature. The Analysis model used in the thesis is a product of the theoretical framework. The results of this study show that big data is affecting how organizations are identifying customer segments. Big data is used a foundation for decision-making but still relies on knowledgeable decision-makers. The type of market the organization is operating in is crucial to the nature decision that can be made regarding segmentation.<br>Till följd av den digitala utvecklingen har organisationer börjat inhämta och lagra stora mängder digital data. Den digitala datan som samlats in av organisationer benämns som big data och utgörs av personlig information om individer. En grundläggande del vid ett tillämpande av big data i organisationers arbete är en analys av insamlad information. Med hjälp av en analys transformeras datan till förståelig information som sedan kan appliceras i beslutsfattandeprocesser inom organisationer. Beslutsfattare inom organisationer använder information som är utvunnen från big data för att stärka deras argument vid situationer där beslut ska fattas. Big data har utvecklat och möjliggjort nya former av hur organisationer genomför beslutsfattandeprocessen gällande segmentering av konsumenter. Med avseende på det bredare utbud av information som finns tillgänglig om individer tilltar organisationers möjligheter för en ökad förståelse om individen. Informationen som samlas in av organisationer kan ha både dagsaktuell och historisk karaktär. Syftet med uppsatsen är att med hjälp av en undersökning granska två svenska reseföretags användande av big data i deras beslutsfattade vid identifiering av nya segment. Studien utgår från en fallstudiedesign som undersöker två svenska företag inom resebranschen med hjälp av kvalitativa intervjuer. Det teoretiska ramverk uppsatsen bygger på har utformats utefter problemformuleringen och tar därför upp big data, beslutsfattande och segmentering. Detta leder fram till en analysmodell som förklarar hur dessa begrepp förhåller sig till varandra. De resultat undersökningen påvisar är att organisationers identifiering av nya kundsegment påverkas av big data på olika sätt. Big data används som underlag för beslut men är fortfarande i behov av kompetenta beslutsfattare med sakkunskap om marknaden och konsumenter. Beroende på vilken marknad organisationen är verksam på är typen av data avgörande för vilka beslut gällande identifiering av segment som kan fattas.
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Wright, Michelle Christine. "Social Media Data Strategies Bankers Use to Increase Customer Market Share." ScholarWorks, 2019. https://scholarworks.waldenu.edu/dissertations/7356.
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Banking leaders who fail to implement social media data-driven marketing strategies lose opportunities to increase customer market share through customer acquisition and retention, improved customer service, heightened brand awareness, customer cocreation, and relationship management. The purpose of this multiple case study was to explore strategies banking leaders used to integrate social media analytics into marketing strategies to increase customer market share. The target population was 6 senior bankers from 2 banks in the mid-Atlantic region of the United States, with a significant social media presence, including 25,000 or more followers across 2 social media platforms. The disruptive innovation theory served as the conceptual framework for the study. Data were collected from semistructured interviews and a review of the organizations' public business documents, websites, and social media websites. Data were analyzed using coding to determine themes. By analyzing these sources and performing methodological triangulation, 8 key themes emerged that were categorized into 4 groupings: (a) social media knowledge management, (b) social media marketing strategy implementation, (c) social media data challenges and communication, and (d) social media competitive gain and future enhancements. The implications of this study for positive social change include social and environmental benefits such as creating jobs and economic growth through a corporate social responsibility initiative. Current and prospect customer bases, local communities, bankers, and stakeholders might benefit from the findings of this study.
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Tan, Lujiao. "Data-Driven Marketing: Purchase Behavioral Targeting in Travel Industry based on Propensity Model." Thesis, Blekinge Tekniska Högskola, Institutionen för industriell ekonomi, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-14745.
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By means of data-driven marketing as well as big data technology, this paper presents the investigation of a case study from travel industry implemented by a combination of propensity model and a business model “2W1H”. The business model “2W1H” represents the purchasing behavior “What to buy”, “When to buy”, and “How to buy”. This paper presents the process of building propensity models for the application in behavioral targeting in travel industry. Combined the propensity scores from predictive analysis and logistic regression with proper marketing and CRM strategies when communicating with travelers, the business model “2W1H” can perform personalized targeting for evaluating of marketing strategy and performance. By analyzing the business model “2W1H” and the propensity model on each business model, both the validation of the model based on training model and test data set, and the validation of actual marketing activities, it has been proven that predictive analytics plays a vital role in the implementation of travelers’ purchasing behavioral targeting in marketing.
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Bezuidenhout, Werner. "How to incorporate the use of big data in a marketing strategy within South Africa to gain competitive advantage." Thesis, Stellenbosch : Stellenbosch University, 2013. http://hdl.handle.net/10019.1/95635.
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Thesis (MBA)--Stellenbosch University, 2013.<br>The research study aims to show how South African companies within the retail, financial services and cellular industries can incorporate Big Data into their marketing strategies to gain a competitive advantage.
The literature review sets out to explain what Big Data is and how it can fit into a marketing strategy as well as exploring the challenges and benefits companies around the world experience when working with Big Data.
The findings from the literature review are used to create a proposed framework that can guide South African companies to incorporate Big Data into their marketing strategy.
The validity of the framework is tested against a sample of South African companies across various industries that are using Big Data within their organisations.
Some of the findings from the research study that are highlighted relating to how South African companies use Big Data to gain a competitive advantage is that social media seems to be the most pervasive Big Data type being used across the different industries. Another trend seems to be that most companies use Big Data for sentiment analysis but that sentiment analysis tools are not mature enough yet to correctly calculate the sentiment. Currently South African companies use Big Data as part of their targeting, segmenting, differentiation marketing strategies and that they feel that by incorporating Big Data into their marketing strategies that they have gained better insight into the market and their customers which has helped to grow their market share and realise cost savings.
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Solenne, Andrea. "Machine Learning nell'era del Digital Marketing." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2020. http://amslaurea.unibo.it/20476/.
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Il Machine Learning è una branca dell’intelligenza artificiale avente lo scopo di migliorare autonomamente l’identificazione di pattern nei dati. L'obiettivo di questo elaborato è capire se gli strumenti che mette a disposizine il machine learning possano fare la differenza nella creazione di strategie di digital marketing. Per rispondere a questa domanda sono stati presi in considerazione come casi studio, aziende che hanno utilizzato o utilizzano algoritmi di machine learning per il raggiungimento o miglioramento delle proprie strategie di marketing. Sulla base dei casi analizzati, si è dedotto che tali algoritmi non solo migliorano queste strategie ma bensì ne identificano di nuove, proponendo previsioni sempre più accurate a chi le utilizza.
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Carlsson, Ricky, and Alexander Vilhelmsson. "Nej tack till onödig reklam! : En studie om riktad marknadsföring via Big Data från ett konsumentperspektiv." Thesis, Linköpings universitet, Institutionen för ekonomisk och industriell utveckling, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-176771.
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Title: No thanks to unnecessary advertising! -A study on targeted marketing via Big Data in a consumer perspective Authors: Ricky Carlsson and Alexander Vilhelmsson Supervisor: Anders Parment Key words: Targeted marketing, Big Data, Customer segmentation, Buying process, Integrity concern, Customer relationship management, Marketing communication, Strategic management, Big Data management, Online Behavioural Targeting Introduction: In a world that is globalizing and where digital development is advancing, companies have had to adapt. In recent times with the increasingly more digital world, technology has become an increasingly more relevant factor, not least in marketing. A digital method that has emerged is Big Data, whichmakes it possible forcompanies tocollect large amounts of information about consumers. By analysing the information extracted from Big Data, it is easier to find and understand consumers' needs and what motivates their buying process. It is important that companies analyse the information correctly so that they do not run the risk of creating negative effects from targeted marketing via Big Data. Purpose: To investigate Swedish consumers' attitudestowards targeted marketing via Big Data and to find out how companies that sell goods and services to consumers can improve their use of Big Data in targeted marketing from a consumer perspective. Method: The study is a cross-sectional study of a qualitative and quantitative nature. The qualitative empirical data consists of 11 semi-structured interviews with students in Sweden. The quantitative empirical data consists of 203 survey answers collected from consumers around Sweden. The study is based on an abductive approach and has a hermeneutic approach. Conclusion: The result of the study shows that there are both opportunities and challenges for companies when using Big Data in targeted marketing. Targeted marketing with the help of Big Data that is performed correctly should only have a positive impact on the targeted marketing and something that creates value for both the consumers and the companies, but this is not the case today. The population of the study perceives that marketing often does not match their needs; this shows that companies must become better at analysing the data. If the data extracted from Big Data is analysed in a better way, the segmentation of consumers will also be better.<br>Titel: Nej tack till onödig reklam! - En studie om riktad marknadsföring via Big Data i ett konsumentperspektiv. Författare: Ricky Carlsson och Alexander Vilhelmsson Handledare: Anders Parment Bakgrund: I en värld som globaliseras och där den digitala utvecklingen går framåt har företag varit tvungna att anpassa sig. På senare tid i takt med den ständigt mer digitaliserade världen har teknologi blivit en alltmer relevant faktor, inte minst inom marknadsföring. En digital metod som har vuxit fram är Big Data genom vilken företag har möjlighet att samla in stora mängder information om konsumenter. Genom att analysera informationen som utvinns från Big Data går det att lättare finna och förstå konsumenters behov och vad som motiverar deras köpprocess. Det är viktigt att företag analyserar informationen på rätt sätt för att inte löpa risken att skapa negativa effekter av den riktade marknadsföringen via Big Data. Syfte: Att undersöka svenska konsumenters attityder till riktad marknadsföring via Big Data samt ta reda på hur företag som säljer varor eller tjänster till konsumenter kan förbättra användningen av Big Data inom riktad marknadsföring utifrån ett konsumentperspektiv. Metod: Studien är en tvärsnittsstudie av kvalitativ och kvantitativ karaktär. Den kvalitativa empirin består av 11 semi-strukturerade intervjuer med studenter i Sverige. Den kvantitativa empirin består av 203 insamlade enkätsvar från konsumenter runt om i Sverige. Studien grundas i en abduktiv ansats och har ett hermeneutiskt synsätt. Slutsatser: Resultatet i studien visar på att det finns möjligheter och utmaningar för företag vid användning av Big Data inom riktad marknadsföring. En riktad marknadsföring medhjälp av Big Data som utförs på rätt sätt borde enbart ha en positiv påverkan på den riktade marknadsföringen och något som skapar värde för konsumenter och företag, men så är inte fallet idag. Då studiens population uppfattar att den riktade marknadsföringen ofta inte matchar deras behov bör företag bli bättre på att analysera data. Om data som utvinns från Big Data analyseras på ett bättre sätt kommer även segmenteringen av konsumenter att bli bättre.
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Hansson, Madelene, and Adam Manfredsson. "“We Traded Our Privacy for Comfortability” : A Study About How Big Data is Used and Abused by Major International Companies." Thesis, Linnéuniversitetet, Institutionen för marknadsföring (MF), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-96480.
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Due to digitalization, e-commerce and online presence is something most of us take for granted. Companies are moving more towards an internet-based arena of sales, rather than traditional commerce in physical stores. This development has led to that firms’ choses to market themselves through various online channels such as social media. Big data is our digital DNA that we leave behind on every part of the internet that we utilize. Big data has become an international commodity that can be sold, stored and used. The authors of this thesis have investigated the way international firms extract and use big data to construct customized marketing for their customers. This thesis has also examined the ethical perspective of how this commodity is handled and used, and people’s perception regarding the matter. This is interesting to investigate since very few researches has been previously conducted combining big data usage with ethics. To accomplish the aim of this thesis, significant theory has been reviewed and accounted for. Also, a qualitative research has been conducted, where two large companies that are working closely with big data has been investigated through a case-study. The authors have also conducted six semi-structured interviews with people between the age of 20-30 years old. The outcome of this thesis shows the importance of implementing ethics within the concept and usage of big data and provide insight into the mind of the consumer that has been lacking in previous research of this subject.
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MacFarlane, Gregory Stuart. "Using big data to model travel behavior: applications to vehicle ownership and willingness-to-pay for transit accessibility." Diss., Georgia Institute of Technology, 2014. http://hdl.handle.net/1853/51804.
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The transportation community is exploring how new "big'' databases constructed by companies or public administrative agencies can be used to better understand travelers' behaviors and better predict travelers' responses to various transportation policies. This thesis explores how a large targeted marketing database containing information about individuals’ socio-demographic characteristics, current residence attributes, and previous residential locations can be used to investigate research questions related to individuals' transportation preferences and the built environment. The first study examines how household vehicle ownership may be shaped by, or inferred from, previous behavior. Results show that individuals who have previously lived in dense ZIP codes or ZIP codes with more non-automobile commuting options are more likely to own fewer vehicles, all else equal. The second study uses autoregressive models that control for spatial dependence, correlation, and endogeneity to
investigate whether investments in public transit infrastructure are associated
with higher home values. Results show that willingness-to-pay estimates obtained from the general spatial Durbin model are less certain than comparable estimates obtained through ordinary least squares. The final study develops an empirical framework to examine a housing market's resilience to price volatility as a function of transportation accessibility. Two key modeling frameworks are considered. The first uses a spatial autoregressive model to investigate the relationship between a home's value, appreciation, and price stability while controlling for endogenous missing regressors. The second uses a latent class model that considers all these attributes simultaneously, but cannot control for endogeneity.
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Qassim, Niazy. "L'utilisation du big data, de l'intelligence artificielle et des technologies XR dans les campagnes politiques : Une étude comparative Franco-Allemande." Electronic Thesis or Diss., Université Côte d'Azur, 2022. http://www.theses.fr/2022COAZ2033.
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Ces dernières années, les nouvelles technologies ont rendu l'information politique facilement accessible. Dans le même temps, l'information est devenue une matière première stratégique que les acteurs politiques cherchent à maîtriser pour obtenir des avantages compétitifs. En parallèle, les technologies artificielles d'intelligence et de réalité étendue (XR) ont commencé à révolutionner les formes et les modes de communication politique. Dans cette thèse, nous avons étudié comment les candidats et les partis politiques en France et Allemagne utilisent ces technologies lors des campagnes électorales. Nous avons analysé les élections fédérales allemandes de 2021 et les élections présidentielles françaises de 2022, en combinant une recherche qualitative à l'étude des sites Web de la campagne et des données des médias sociaux, évaluant aussi la « modernisation » des campagnes. Nous avons trouvé que les personnalités politiques françaises sont plus dynamiques, agressives et diversifiées que leurs homologues allemands. Nous avons également trouvé que les candidats et les partis politiques en France sont plus innovants dans l'utilisation du Big data, de l'intelligence artificielle et des technologies XR dans leurs campagnes de communication que leurs homologues allemands. Mais malgré les progrès de cette professionnalisation des campagnes en ligne dans les deux pays, l'utilisation de ces technologies dans les campagnes reste faible et imparfaite<br>In the last few years, new information and communication technology has made information easily accessible. At the same time, information has become a strategic raw material that political actors seek to master in order to gain competitive advantages. In parallel, artificial intelligence and virtual reality have started to revolutionize the forms and the modes of political communication. Therefore, in this thesis, we studied how French and German political candidates and parties use big data, artificial intelligence, and virtual reality during political campaigns. Based on mixed research during the 2021 German federal election and 2022 French presidential elections. We found that political campaigns in France are more dynamic, aggressive and diverse than German political campaigns. We also found that French political candidates and parties are more creative and innovative in terms of using big data, artificial intelligence and virtual reality in political campaigns than German political candidates and parties. Despite the progress of professionalized campaigning in both countries, the use of big data, artificial intelligence and virtual reality in political campaigns until now weak and not perfect
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Loverus, Anna, and Paulina Tellebo. "There ain ́t no such thing as a free lunch : What consumers think about personal data collection online." Thesis, Uppsala universitet, Företagsekonomiska institutionen, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-315656.
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This study examines how consumers reason and their opinions about personal data collection online. Its focus is to investigate whether consumers consider online data collection as an issue with moral implications, and if these are unethical. This focus is partly motivated by the contradiction between consumers’ stated opinions and actual behavior, which differ. To meet its purpose, the study poses the research question How is personal data collection and its prevalence online perceived and motivated by consumers?. The theoretical framework consists of the Issue-Contingent Model of Ethical Decision-Making by Jones (1991), thus putting the model to use in a new context. Collection of data for the study was done by conducting focus groups, since Jones’ model places ethical decision- making in a social context. The results of the study showed that consumers acknowledge both positive and negative aspects of online data collection, but the majority of them do not consider this data collection to be unethical. This result confirms partly the behaviour that consumers already display, but does not explain why their stated opinions do not match this. Thus, this study can be seen as an initial attempt at clarifying consumer reasoning on personal data collection online, with potential for future studies to further investigate and understand consumer online behaviour.<br>Denna uppsats undersöker hur konsumenter resonerar och tänker kring insamling av personlig data på Internet. Fokus är att utreda ifall konsumenter anser att denna insamling har konsekvenser, och ifall dessa anses vara oetiska. Detta fokus baseras delvis på resultat som visar på skillnader i vad konsumenter uttrycker för åsikter kring detta ämne, och deras faktiska beteende på Internet. Undersökningen utgår ifrån forskningsfrågan som lyder Hur uppfattar och motiverar konsumenter insamling av personlig data på Internet? Studiens teoretiska ramverk består av modellen An Issue-Contingent model of Ethical Decision- Making som är utvecklad av Jones (1991), och modellen används därmed i en ny kontext. Studiens data samlades in genom fokusgrupper. Detta val baserades på Jones (1991) modell, som menar att etiskt beslutsfattande alltid sker i en social kontext. De resultat som kommit fram visar att konsumenter ser både positiva och negativa aspekter och konsekvenser av att ha sin personliga data insamlad, däremot utan att anse att insamlingen i sig är oetisk. Detta bekräftar delvis tidigare resultat, men förklarar inte varför de åsikter konsumenter uttrycker kring ämnet inte stämmer överens med hur de sedan faktiskt beter sig. Därmed kan den här uppsatsen ses som ett första försök att klargöra hur konsumenter resonerar kring insamling av personlig data på Internet. Det har bedömts finnas mycket potential för framtida studier inom samma område, för att fortsatt undersöka och förstå konsumenters beteende på Internet.
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Economou, Emmanuel. "How institutional pressures influence data-driven corporate communications." Thesis, Queensland University of Technology, 2021. https://eprints.qut.edu.au/209153/1/Emmanuel_Economou_Thesis.pdf.
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This study explores the use of data to guide corporate communications, a practice of which empirical research is limited, and is the first to investigate how institutional pressures influence practitioners in their work. In-depth interviews were conducted with 15 participants from a wide range of organisations in Brisbane, Australia. Additionally, the study illustrates the current state of research on big data in corporate communication in a systematic literature review. The study’s findings indicate that institutional pressures influence data-driven corporate communications and are generally consistent with the results of practitioner studies around the world.
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Dell, Molina Mauricio Henry. "Transferencia tecnológica del big data hacia la gestión cultural : pertinencia del uso de indicadores de marketing digital en la industria de la música popular chilena." Tesis, Universidad de Chile, 2015. http://repositorio.uchile.cl/handle/2250/138165.
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Magíster en gestión cultural<br>Dado que durante los últimos 50 años las industrias culturales y creativas se han validado
cada vez más como áreas diferenciadas de la economía y el desarrollo, el gestor cultural
profesional enfrenta también un creciente desafío profesional y requiere de herramientas,
conocimientos y capacidades afines que le permitan impulsar las actividades culturales en
tanto sector productivo.
Junto con lo anterior, el explosivo desarrollo de las nuevas tecnologías de la información y
las comunicaciones alojadas en Internet originaron en los últimos 20 años el fenómeno
conocido hoy como big data1, que ha revolucionado el acceso, el alcance, la magnitud y la
variedad de los datos existentes. Esta revolución de los datos es otra fuente de desafíos para
el músico y el gestor cultural quienes deberán estar informados y actualizados de cómo
estos avances pueden beneficiar o afectar sus labores
Actualmente en Chile es posible acceder de manera abierta y democrática a un creciente
cuerpo de datos estadísticos para el estudio y comprensión de las industrias culturales. Sin
embargo, estos registros de fuentes institucionales dejan brechas que deben ser cubiertas
por otras fuentes y modelos de acceso a la información. Con esta necesidad se genera la
oportunidad de proponer una transferencia de conocimientos y tecnologías de producción y
procesamiento de información, desde otras actividades como el marketing, campo que es
afín a la gestión cultural en tanto actividad para el desarrollo económico e industrial.
Las afinidades que existen entre el marketing y la gestión cultural nos permitirán proponer
la factibilidad de extrapolar prácticas y conocimientos desde un sector al otro a modo de
transferencia tecnológica. Específicamente buscaremos dar con un sub-grupo de
indicadores de marketing que, proviniendo del territorio de los nuevos medios y el big data,
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Pettersson, Thorell Sandro, and Fredrik Björkman. "Digital direktmarknadsföring och personlig integritet : En obeaktad gråzon?" Thesis, Uppsala universitet, Företagsekonomiska institutionen, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-414731.
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I takt med en tilltagande användning av internet och teknologiska enheter har tillgången till användargenererad data om individer ökat. Denna personliga data kan, genom fenomen som Big Data, vidare exploateras av företag som grund för att utforma och anpassa marknadsföring i linje med konsumenters individuella preferensmönster. Detta utgör delar inom digital direktmarknadsföring. Emellertid existerar det en problematik med specifikt utformad direktmarknadsföring i digital miljö. Konsumenter som mottager denna typ av marknadsföring har en tendens till att uppleva att företagen kommer för nära inpå privatlivet. Tidigare studier visar att företag kan få tillgång till för känslig information om konsumenter och deras aktiviteter i vardagen. I många fall sker detta utan att konsumenten är medveten om hur denna information har erhållits och en konsekvens av detta är att den personliga integriteten kan bli föremål för intrång. Uppsatsens syfte tar grund i en strävan efter att undersöka hur konsumenter uppfattar den personliga integriteten vid exponering mot individuellt riktad digital marknadsföring. Mer specifikt utforskas det eventuella sambandet mellan personlig integritet och digital direktmarknadsföring. Den teoretiska referensramen behandlar teorier och begrepp som är relevanta för få en övergripande förståelse kring ämnet. Under avsnittet presenteras bland annat Westins teori om den personliga integriteten och dess olika tillstånd. Därtill behandlas även beskrivningar av begrepp såsom digital direktmarknadsföring, Big data, Surveillance Capitalism och dataskyddsförordningen (GDPR). Här beaktas variablerna känslighet på direktmarknadsföring, omfattning av direktmarknadsföring, ålder, utbildningsnivå, kön samt kontrollvariabelninternetanvändning. Utifrån dessa variabler samt tidigare forskning har fem hypoteser formulerats. Den empiriska undersökningen baserades på en tvärsnittsstudie av deduktiv karaktär där det genomfördes en digital enkätundersökning. Totalt deltog 203 respondenter i denna studie. Enkäten innefattade frågor som baserades på uppsatsens variabler. För att se om det går att finna stöd eller inte för dessa hypoteser används en multivariat regressionsanalys. Undersökningens resultat visar att uppfattningen av den personliga integriteten påverkas av direktmarknadsföring. Det kan utläsas att både känsligheten och omfattningen på direktmarknadsföringen har en jämnstark och statistiskt signifikant effekt på uppfattningen av den personliga integriteten. Därtill indikeras det även att ålder är en variabel som har en effekt på hur den personliga integriteten påverkas, där äldre individer tenderar att vara mer oroade över den personliga integriteten än vad yngre individer är. Utifrån denna undersökning går det vidare att uttyda att det föreligger tendenser till intrång på den personliga integriteten i den digitala miljön vid exponering mot digital direktmarknadsföring.<br>Given the increased availability of internet and technological devices, the access to user-generated data has increased. This personal data is made accessible to companies and through phenomena such as Big Data it can be exploited and adapted to lay the foundation for marketing aimed directly at individual consumers. This is also known as direct marketing. However, it seems as if there exists a tendency for digital direct marketing to result in companies entering the individual’s personal space and as a consequence – the personal integrity is jeopardized. The purpose of this thesis is to examine the way consumers on the Swedish retail market perceive the personal integrity when being exposed to digital direct marketing. The study is of cross-sectional deductive character and it investigates the possible relationship between personal integrity and digital direct marketing. This is done through a digital survey with questions based on five hypotheses which in turn reflect the following variables that have been defined; Sensibility to direct marketing,Extent of direct marketing, Age, Level of education, and Gender. In addition to this, the control variable Internet-usage is added. The hypotheses are then tested in a multiple regression analysis. From the discussion and conclusion of this thesis the results show that consumers experience that the personal integrity is affected by digital direct marketing. The findings indicate further that both sensibility to direct marketing, the extent of direct marking, and age have a statistically significant effect on the individual’s perception of the personal integrity in this context. Given the result from the survey, the regression analysis and its application on the hypotheses, this study finds implications for violation of the consumer’s personal integrity.
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Fürsten, Cardell Joshua, and Maya Källström. "Maskininlärning inom digital marknadsföring : En studie om hur maskininlärning hjälper eller stjälper digitala marknadsföringsbyråer, och vilka faktorer som bör tas i beaktning vid användning av maskininlärning." Thesis, Södertörns högskola, Medieteknik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:sh:diva-44948.
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Problem: Although AI tools have come to facilitate and influence digital marketing in several ways, there is a continuing need for research on how AI can alleviate marketing problems and how the use of AI tools for marketing purposes can be facilitated for digital marketing agencies. Purpose: The purpose of the study is to demonstrate how and why machine learning can both benefit and harm digital marketing agencies, as well as what factors digital marketing agencies need to take into account if they intend to use machine learning in digital marketing. Method: The study is based on a qualitative research strategy, with an inductive approach where six semi-structured interviews were conducted. Semi-structured interviews have been conducted to create flexibility in the individual being interviewed, and thus new insights and thoughts can arise. Thematic analysis has been used as an analysis method for the study, where the focus was aimed on identifying patterns and themes. Conclusion: Machine learning can benefit digital marketing agencies in digital marketing by streamlining work procedures by processing data and information, freeing up time for employees and also gaining the opportunity to achieve competitive advantage. Machine learning can harm digital marketing agencies in digital marketing by excluding human participants in the work system, reducing trust in the organization and creating discomfort with active personalization if used unethically. Factors that should be taken into account by digital marketing agencies if they intend to use machine learning in digital marketing are, that digital marketing agencies can not replace human interaction, human integrity is important insofar as digital marketing agencies follow laws and regulations, the size of the business determines which priorities is made between using previous knowledge and new technology, organizations need to be transparent and AI must be used throughout the organization in order for the organization to benefit in the long term<br>Problem: Trots att AI-verktyg har kommit att underlätta samt påverka den digitala marknadsföringen på flertalet olika sätt, så finns det ett fortsatt behov för forskning kring hur AI kan underlätta marknadsföringsproblem samt hur användningen av AI-verktyg i marknadsföringssyfte kan underlättas för digitala marknadsföringsbyråer. Syfte: Studiens syfte är att påvisa hur och varför maskininlärning både hjälper och stjälper digitala marknadsföringsbyråer, samt vilka faktorer som digitala marknadsföringsbyråer behöver ta i beaktning om de avser att använda maskininlärning inom digital marknadsföring. Metod: Studien utgår från en kvalitativ forskningsstrategi, med ett induktivt tillvägagångssätt där sex genomförda semistrukturerade intervjuer utförts. Semistrukturerade intervjuer har genomförts för att skapa flexibilitet hos individen som blir intervjuad, och således kan nya insikter och tankar uppstå. Tematisk analys har använts som analysmetod för studien, där fokus legat på att identifiera mönster och teman. Slutsats: Maskininlärning hjälper digitala marknadsföringsbyråer inom digital marknadsföring med att effektivisera arbetsprocesser genom att bearbeta data och information, frigöra tid för anställda samt möjligheten att erhålla konkurrensfördelar. Maskininlärning stjälper digitala marknadsföringsbyråer inom digital marknadsföring genom att exkludera mänskliga deltagare inom arbetssystemet, minska förtroende för organisationen samt skapa obehag vid aktiv personalisering om det används oetiskt. Faktorer som bör tas i beaktning av digitala marknadsföringsbyråer om de avser att använda maskininlärning inom digital marknadsföring är att digitala marknadsföringsbyråer inte kan ersätta mänsklig interaktion, mänsklig integritet är viktigt i det mån att digitala marknadsföringsbyråer efterföljer lagar och regler, storleken av verksamheten avgör vilka prioriteringar som görs mellan att nyttja tidigare kunskap och ny teknologi, organisationer behöver vara transparenta och AI måste nyttjas genomgående i organisationen för att organisationen ska gynnas på lång sikt.
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Klingler, Tina. "Entscheidungsunterstützung des Marketing-Managements durch Marketingsinformationssysteme." [S.l. : s.n.], 2004. http://www.bsz-bw.de/cgi-bin/xvms.cgi?SWB11675735.
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Klee, Christopher. "AI in Marketing – Curse or Blessing? : Impacts of Programmatic Advertising and Personalized Content on Society." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-299572.
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With the help of Programmatic Advertising and the resulting personalized content, consumers can be targeted precisely and with the help of Artificial Intelligence. The associated use of customer data creates ethical conflicts. Therefore, the research question is asked: How does Programmatic Advertising influence consumer's data security and diversity of opinion and what effect does this have on the further development of the technology? For the purpose of elaborating the research problem a literature analysis and expert interviews are carried out. The analysis shows that Programmatic Advertising has already taken up the majority of digital advertising activities. This results in advantages for advertisers, since consumers can be addressed in a targeted manner. Nevertheless, this provokes data law issues and the demand for more data security for the individual customer, which, among other things, is given more attention by big tech companies. Due to the constant change within this trend, new possibilities arise, such as contextual targeting, in order to continue to do an efficient display of advertising. Nonetheless, this work calls for more regulations to be able to give customers a better overview and control of their used data and to avoid restricted diversities of opinion, which can be promoted through microtargeting and therefore the addressed display of content. The prospects of Programmatic Advertising, however, are predicted with a steady increase because other areas within the media landscape will be pervaded by this technology in the future.<br>Med hjälp av programmatisk reklam och artificiell intelligens får kunder och individer reklam som är specifikt utformade för just dem. Användningen av underliggande användardata ger upphov till etiska dilemman. Således, har vi följande problemformulering: Hur påverkar programmatisk reklam användarnas data och åsikter, och vilken effekt har detta på den fortsatta utvecklingen av området? Med avsikt att vidareutveckla problemformuleringen utfördes expertintervjuer samt en litteraturstudie. Analysen visar att programmatisk reklam utgör majoriteten av all digital marknadsföring. Detta har resulterat i fördelar för marknadsförare, ty användarna kan bli bemötta mer precist. Icke desto mindre, medför detta legala problem relaterat till användardata och ett ökat kraf av dataskydd för användaren, vilket är något som får stort fokus av stora tech företag. På grund av den konstanta utvecklingen av denna metod, föds nya möjligheter, exempelvis "kontextuell riktad marknadsföring", för att fortsätta vara en effektiv marknadsföringsmetod. Därmed, redogör denna rapport för en mer reglerad spelplan där användarna får en bättre överblick och kontroll över hur deras användardata utnyttjas, samt en mindre inskränkt åsiktspåverkan, vilket är något som skulle kunna ske genom knappnålsfin riktad marknadsföring. Dock är utvecklingen för programmatisk reklam förutspådd ljus, då andra områden inom medielandskapet kommer att genomsyras av denna teknologi i framtiden
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FORLEO, GIANROBERTO. "Digital AgriFood – Conversazioni online e Big Data per lo sviluppo della comunicazione strategica e progettuale del sistema produttivo marchigiano. Abstract." Doctoral thesis, Urbino, 2023. https://hdl.handle.net/11576/2710331.
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Milišauskas, Paulius. "Elektroninės komercijos, naudojant didžiuosius duomenis, rinkodaros modelis." Master's thesis, Lithuanian Academic Libraries Network (LABT), 2014. http://vddb.library.lt/obj/LT-eLABa-0001:E.02~2014~D_20140218_132530-89125.
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Baigiamajame magistro darbe analizuojamos elektroninės komercijos, tradicinės ir skaitmeninės rinkodaros bei duomenų analizės teorijos. Identifikuojamos pragrindinės probleminės duomenų analizės vietos. Pristatomi mažų ir vidutinių Lietuvoje veikiančių elektroninių parduotuvių savininkų apklausos skaitmeninės rinkodaros ir vartotojų kuriamų duomenų analizės temomis rezultatai. Sudaromas teorinis rinkodaros modelis, o vėliau papildomas apklausos rezultatų įžvalgomis, nurodomos pagrindinės išspręstos probleminės sritys. Remiantis nauju rinkodaros modeliu siūlomas verslo modelis. Darbo pabaigoje yra pateikiamos išvados. Darbą sudaro 6 dalys: įvadas, teorijos analizė, tiriamasis-analitinis skyrius, projektinis skyrius, išvados ir siūlymai, literatūros sąrašas. Darbo apimtis – 63p. teksto be priedų, 9 paveiksl., 3 lent., 79 bibliografiniai šaltiniai. Atskirai pridedami darbo priedai.<br>This master’s thesis begins with an analysis of e-commerce, traditional and electronic marketing, and data analytics theories. Later on the results of Lithuanian based small and medium sized electronic shop owner’s query on digital marketing and data analytics are being presented. Theoretical marketing model is being presented and later updated depending on the results of previously presented results of query. Also main solutions for previously clarified problems are being proposed. Depending on new marketing model a new business model is also proposed. At the end of this bachelor’s thesis there are conclusions being brought, considering all three parts of the paper. Structure: introduction, theory analysis, analytical research, project, conclusions and suggestions, references. Thesis consist of: 63 p. text without appendixes, 9 pictures, 3 tables, 79 bibliographical entries. Appendixes included.
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Gabel, Sebastian. "One-to-One Marketing in Grocery Retailing." Doctoral thesis, Humboldt-Universität zu Berlin, 2019. http://dx.doi.org/10.18452/20084.
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In der akademischen Fachliteratur existieren kaum Forschungsergebnisse zu One-to-One-Marketing, die auf Anwendungen im Einzelhandel ausgerichtet sind. Zu den Hauptgründen zählen, dass Ansätze nicht auf die Größe typischer Einzelhandelsanwendungen skalieren und dass die Datenverfügbarkeit auf Händler und Marketing-Systemanbieter beschränkt ist.
Die vorliegende Dissertation entwickelt neue deskriptive, prädiktive und präskriptive Modelle für automatisiertes Target Marketing, die auf Representation Learning und Deep Learning basieren, und untersucht deren Wirksamkeit in Praxisanwendungen.
Im ersten Schritt zeigt die Arbeit, dass Representation Learning in der Lage ist, skalierbar Marktstrukturen zu analysieren. Der vorgeschlagene Ansatz zur Visualisierung von Marktstrukturen ist vollständig automatisiert und existierenden Methoden überlegen. Die Arbeit entwickelt anschließend ein skalierbares, nichtparametrisches Modell, das Produktwahl auf Konsumentenebene für alle Produkte im Sortiment großer Einzelhändler vorhersagt. Das Deep Neural Network übertrifft die Vorhersagekraft existierender Benchmarks und auf Basis des Modells abgeleitete Coupons erzielen signifikant höhere Umsatzsteigerungen.
Die Dissertation untersucht abschließend eine Coupon-Engine, die auf den entwickelten Modellen basiert. Der Vergleich personalisierter Werbeaktionen mit Massenmarketing belegt, dass One-to-One Marketing Einlösungsraten, Umsätze und Gewinne steigern kann. Eine Analyse der Kundenreaktionen auf personalisierte Coupons im Rahmen eines Kundenbindungsprogrammes zeigt, dass personalisiertes Marketing Systemnutzung erhöht. Dies illustriert, wie Target Marketing und Kundenbindungsprogramme effizient kombiniert werden können.
Die vorliegende Dissertation ist somit sowohl für Forscher als auch für Praktiker relevant. Neben leistungsfähigeren Modellansätzen bietet diese Arbeit relevante Implikationen für effizientes Promotion-Management und One-to-One-Marketing im Einzelhandel.<br>Research on one-to-one marketing with a focus on retailing is scarce in academic literature. The two main reasons are that the target marketing approaches proposed by researchers do not scale to the size of typical retail applications and that data regarding one-to-one marketing remain locked within retailers and marketing solution providers.
This dissertation develops new descriptive, predictive, and prescriptive marketing models for automated target marketing that are based on representation learning and deep learning and studies the models’ impact in real-life applications.
First, this thesis shows that representation learning is capable of analyzing market structures at scale. The proposed approach to visualizing market structures is fully automated and superior to existing mapping methods that are based on the same input data. The thesis then proposes a scalable, nonparametric model that predicts product choice for the entire assortment of a large retailer. The deep neural network outperforms benchmark methods for predicting customer purchases. Coupon policies based on the proposed model lead to substantially higher revenue lifts than policies based on the benchmark models.
The remainder of the thesis studies a real-time offer engine that is based on the proposed models. The comparison of personalized promotions to non-targeted promotions shows that one-to-one marketing increases redemption rates, revenues, and profits. A study of customer responses to personalized price promotions within the retailer’s loyalty program reveals that personalized marketing also increases loyalty program usage. This illustrates how targeted price promotions can be integrated smoothly into loyalty programs.
In summary, this thesis is highly relevant for both researchers and practitioners. The new deep learning models facilitate more scalable and efficient one-to-one marketing. In addition, this research offers pertinent implications for promotion management and one-to-one marketing.
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Gremlin, Therese, and Vlada Rusakova. "What do consumers really think about advertising chasing them online? : A quantitative study about consumers’ perception of behavioural marketing." Thesis, Luleå tekniska universitet, Institutionen för ekonomi, teknik och samhälle, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-74811.
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Behavioral marketing is something that has received lots of attention due to growing awareness around artificial intelligence (AI) and companies using this technology for marketing purposes. However, the data collection practices have been receiving lots of critique for a while now and therefore it is more important than ever to gain an insight of how the consumers perceive behavioral online marketing. From this standpoint two research questions have been developed: How do consumers’ attitudes towards behavioral marketing, affect their purchase intention? and How does exposure to behavioral marketing affects purchase intention? In order to answer these research questions, the experimental vignette methodology was used to study digital natives, namely individuals between 18 and 30 years old. The experiment provided the basis for the study combined with the questionnaire the participants were asked to complete after the experiment. The experiment process consisted of two purchase scenarios that had to be performed online: a purchase of a low degree behavioral marketing product (city bike) and a purchase of a high degree marketing product (sneakers). One of the most important findings of this research regarding consumers’ attitude’s impact on purchase intention was the fact that trust has a bigger impact on the high involvement products compared to the low involvement product. When it came to exposure to behavioral marketing, the test objects that were exposed to behavioral ads had a higher likelihood to purchase a sponsored item. Therefore, there is a positive relationship between how much behavioral marketing a company use and the likelihood of a user purchasing a sponsored item. One of the most important practical implications that can be derived from this study is that businesses need to prioritize building a strong brand image prior to utilizing behavioral marketing.<br>Beteendemässig marknadsföring är något som fått större uppmärksamhet i och med betydelsen som artificiell intelligens (AI) har fått samt något som företag använder sig av i marknadsföringssyfte. Däremot har insamling av data länge kritiserats och frågan om hur kunder faktiskt uppfattar annonser online har blivit mer och mer relevant. Utifrån detta resonemang har dessa forskningsfrågor tagits fram: Hur påverkar konsumenternas attityd gentemot beteendemässig marknadsföring deras köpavsikt? samt Hur påverkar exponeringen av beteendemässig marknadsföring gentemot konsumenter deras köpavsikt? För att kunna uppnå detta syfte har experimentell vignettes metodik använts för att studera millenniegenerationen, alltså personer i åldrarna 18-30. Ett experiment gjorde grunden till studien, tillsammans med ett frågeformulär som de var ombedda att göra efter experimentet. Experimentet bestod av två olika köp som deltagarna skulle göra online; en produkt med låg grad av beteendemässig marknadsföring (cykel) och en produkt med hög grad av beteendemässig marknadsföring (skor). Det viktigaste som kunde tas från denna undersökning när attityder undersöktes var att förtroende gentemot ett företag eller varumärke hade en stark påverkan på köpavsikten. Däremot är inverkan för förtroende starkare vid köpa av en produkt med hög grad av beteendemässig marknadsföring jämfört med en produkt med låg grad av beteendemässig marknadsföring. När det gäller exponeringen av beteendemässig marknadsföringen var den största upptäckten att de deltagare som hade blivit utsatta för annonser till en högre grad hade en större chans att köpa en produkt som var annonserad. Alltså, ju mer företag använder sig av beteendemässig marknadsföring som är av relevans för konsumenten, desto mer sannolikt är det att de köper just deras produkt. Den största praktiska implikationen som kunde utläsas var att innan beteendemässig marknadsföring kan bli optimalt för företag måste de prioritera att bygga ett starkt varumärke.
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Wong, Chi-chiu Albert, and 黃志超. "A marketing plan for the provision of public data services in Hong Kong." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1989. http://hub.hku.hk/bib/B31264542.
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Wong, Shing-tat, and 黃承達. "Disaggregate analyses of stated preference data for capturing parking choice behavior." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2006. http://hub.hku.hk/bib/B36393678.
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Asp, Sandin Agnes. "Individuell marknadsföring : En kvalitativ studie om konsumenters uppfattning kring individanpassad marknadsföring gällande personlig integritet." Thesis, Högskolan i Skövde, Institutionen för informationsteknologi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-18770.
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Digitaliseringen av samhället har ökat kraftigt det senaste decenniet och hela 95% av Sveriges befolkning uppger att de använder internet dagligen. Det har också lett till att data som personer lämnar efter sig på nätet även den har ökat. Denna ökning i kombination med smartare analysverktyg för att utvinna information ur dessa datamängder har möjliggjort en ny typ av marknadsföring, den individanpassade marknadsföringen. Det innebär att företag riktar sina erbjudanden och annonser för den specifika konsumenten med hjälp av dennes egen data även kallat digitala fotspår. De digitala fotspår som företag använder sig utav kan vara köpbeteende, GPS- koordinater och tidigare sökhistorik på exempelvis Google. Dock finns det en risk att företag kan kränka den personliga integriteten när den individanpassade reklamen blir för specifik eller är baserad på känslig information om konsumenten. Detta leder in på studiens forskningsfråga som är ” Vad är konsumentens uppfattning kring individanpassad marknadsföring gällande den personliga integriteten?”. Denna forskningsfråga har även två delfrågor som är ” Vilka faktorer bidrar till att konsumenten i mindre utsträckning uppfattar individanpassad reklam som integritetskränkande?” och ” Vad uppfattas som integritetskränkande för konsumenten?”. Dessa delfrågor ska tillsammans hjälpa till att svara på huvudfrågan. Ordet konsument avser i denna studie de som handlar digitalt och använder sig av olika digitala tjänster där de blir exponerade för individanpassad reklam.Forskningsfrågan besvaras med hjälp av en kvalitativ metod och samtalsintervjuer där en induktiv med inslag av deduktiv tematisk analys används för att analysera materialet. Studien kommer fokusera på att undersöka individanpassad marknadsföring gällande integritet utifrån ett konsumentperspektiv och kommer därmed inte gå in på hur företag resonerar kring denna fråga. Syftet med studien är att ge företag en inblick i vilken information som konsumenter tycker är okej att de använder sig av för att individanpassa reklam samt när de anser att det blir integritetskränkande.Det resultat som studien kom fram till är att olika åtgärder från företag så som att ge konsumenten ökad kontroll över sin personliga information samt en ökad transparens kring vilken information som samlas in och används leder till ett ökat förtroende för företaget vilket i sin tur bidrar till ett positivare mottagande av reklamen. Vidare uppfattas reklam som är baserat på konsumentens köpbeteende och tidigare sökningar3som minst integritetskränkande medan reklam som baseras på ljudupptag, GPS- koordinater samt vilka konsumenten integrerar med som mest integritetskränkande. Företaget som ligger bakom en reklam som uppfattas som integritetskränkande kan få en skadad image vilket kan leda till att konsumenten i framtiden väljer ett annat företag.
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李安敏 and On-man Andrew Lee. "Some thoughts on the applications of management science in sales and marketing activities on the professional products." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1996. http://hub.hku.hk/bib/B31267397.
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Chen, Xi, and 陳熹. "Using data mining techniques to discover customer behavioral patterns for direct marketing in mobile telecommunication industry." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2008. http://hub.hku.hk/bib/B40987942.
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Chintagunta, Pradeep Kumar. "Issues in panel data analysis a theoretical and empirical investigation /." access full-text, 1990. http://libweb.cityu.edu.hk/cgi-bin/ezdb/umi-r.pl?9114533.pdf.
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Benamara, Issam. "Artificial intelligence for the configuration and optimization of digital advertising campaigns." Electronic Thesis or Diss., Paris 13, 2024. http://www.theses.fr/2024PA131029.
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Avec l'évolution du marketing moderne, la publicité numérique est devenue un élément central, permettant aux entreprises d'atteindre des audiences plus larges et diversifiées avec une précision inédite. Les plateformes de publicité numérique offrent des avantages comme des coûts contrôlables, un ciblage précis et la mesurabilité des performances. Cependant, la complexité croissante de ces écosystèmes pose des défis majeurs pour l'optimisation des campagnes. Les méthodes traditionnelles, dépendantes de l'expertise humaine, deviennent insuffisantes face à cette complexité. D'où une dépendance accrue aux solutions algorithmiques et à l'intelligence artificielle pour améliorer les résultats.Les travaux actuels se concentrent principalement sur l'optimisation des campagnes après leur lancement, telles que la prévision des indicateurs clés de performance (KPI) et des coûts pour optimiser les agents de bidding en temps réel (RTB) qui visent à affiner l'allocation budgetaire et maximiser l'efficacité des campagnes en cours. Malgré les progrès réalisés, l'efficacité de ces méthodes dépend de la bonne configuration préalable des campagnes publicitaires, spécifiquement en termes de ciblage d'audiences et un bon paramètrage. Une tâche que nous appelons Conception de Stratégie Publicitaire.Malheureusement, en raison de l'échelle et de la complexité inhérentes à la tâche, il y a un manque considérable dans l'optimisation des campagnes avant leur lancement. La conception de stratégie publicitaire repose encore fortement sur de l'expertise humaine, ce qui conduit souvent à un ciblage sous-optimal, et dégrade la prise de décision ainsi que la performance globale de la campagne, soulignant un domaine potentiel d'amélioration.Cette thèse vise à exploiter les méthodes d'intelligence artificielle pour la configuration et l'optimisation des campagnes publicitaires numériques. À cette fin, nous intégrons des approches d'apprentissage profond dans les phases initiales de planification de la campagne dans la tâche de conception de stratégie publicitaire. Dans cette thèse, nous contribuons d'abord un système novateur et un modèle de réseau de neurones génératif qui exploite le mécanisme d'attention des transformers pour contextuellement générer des stratégies publicitaires optimales tout en évitant l'explosion combinatoire. Nous évaluons nos résultats sur un ensemble de données public iPinYou ainsi que sur les données de l'entreprise en mesurant la proximité des stratégies générées avec les ensembles de données (Distance Cosinus et Hamming) ainsi que leur performance KPI estimée. En l'absence de méthodes directement comparables, nous avons comparé nos résultats à des méthodes principales d'autres domaines, adaptées à cette tâche spécifique. Nous affinons ensuite notre contribution, en améliorant la diversité générative, en améliorant la robustesse contre le mode-collapse — une condition où le modèle tend à générer une gamme limitée de sorties — et en introduisant un mode exploratoir au moment de l'inférence via des techniques de quantification vectorielle et l'apprentissage de métriques. Nous proposons également un protocole d'évaluation amélioré pour notre système. Nous proposons finalement une méthodologie novatrice axée sur des tokens de signalisation pour le contrôle génératif flexible dans les modèles basés sur les transformers qui prends en entrée des signaux suggestifs, ce qui permet à notre modèle de considérer les préférences utilisateur tout en conservant l'autonomie de s'en écarter si elles ne produisent pas de résultats optimaux, les intégrant dans le processus génératif comme des paramètres suggestifs plutôt que des directives strictes. Des expériences étendues ont été menées pour évaluer l'efficacité de notre approche, qui a produit des résultats exceptionnels et confirmé son applicabilité dans divers domaines utilisant des modèles de transformateurs<br>In the evolving landscape of modern marketing, digital advertising has emerged as a pivotal component, enabling businesses to expand their reach to larger, more diverse audiences with unprecedented precision. Digital advertising platforms offer advantages such as controllable costs, accurate audience targeting, and measurable feedback. However, the escalating complexity of digital advertising ecosystems poses significant challenges in optimizing the performance of advertising campaigns. Traditional methodologies, heavily reliant on human expertise, are increasingly inadequate in addressing the multifaceted nature of these digital environments. Consequently, there is a growing dependence on algorithmic solutions and artificial intelligence (AI) to navigate this complexity and enhance campaign outcomes.Current works predominantly focus on post-launch campaign optimization, such as key performance indicator (KPI) and cost forecasting to optimize real-time bidding (RTB) agents which aim to refine budget allocation and maximize the effectiveness of ongoing campaigns. Despite the achieved progress, the effectiveness of these methods depends on the accurate configuration of advertising campaigns, specifically in terms of targeting the appropriate audiences with the correct parameters. A task that we call Advertising Strategy Design.Unfortunately, due to the inherent scale and complexity of the task, there is a noticeable gap in pre-launch campaign optimization process. Advertising strategy design still relies heavily on human expertise, which often leads to sub-optimal targeting and decision-making. This affects the overall campaign performance, underscoring a potential area of improvement.This thesis aims at leveraging artificial intelligence methods for the configuration and optimization of digital advertising campaigns. For this purpose, we integrate deep learning approaches in the initial phases of campaign planning in the task of advertising strategy design. In this thesis, we first contribute a novel framework and generative neural network model which leverages the attention mechanism through transformers to contextually generate optimal advertising strategies while avoiding combinatorial explosion. We evaluate our results on a public dataset iPinYou as well as the company's private dataset by measuring the closeness of the generated strategies to the datasets (using Cosine and Hamming distances) as well as their estimated KPI performance. In the absence of directly comparable methods, we benchmarked our results against prominent methods from other fields, adapted for this specific task. We further refined our approach by enhancing the generative diversity, improving robustness against mode collapse—a condition where the model tends toward generating a limited range of outputs—and introducing an inference-time exploration mode employing vector quantization techniques and learned metrics. An improved evaluation protocol for our framework was also developed. We finally propose a novel token-driven methodology for flexible generative control in transformer-based models. This method includes a suggestive input mechanism that allows the model to take user preferences into account while maintaining the freedom to deviate from them if they do not lead to optimal outcomes, treating these inputs as guiding suggestions rather than strict rules. Extensive experiments were conducted to assess the effectiveness of our approach, which yielded outstanding results and confirmed its applicability across various domains utilizing transformer models
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Snygg, Oliver, and Carl Torberger. "Vilka nya markanadsmodeller kanfå producerande företag att gå motcirkulär ekonomi?" Thesis, KTH, Industriell produktion, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-255347.
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Miljömedvetenheten i världen ökar. Samhällsdebatten formas allt mer av ett miljöperspektiv på flera olika fronter. Allt fler börjar förstå att jordens resurser är ändliga och vi måste tänka om hur vi lever idag. Den producerande industrin står för stor dela avbrukandet av råmaterial, men hur kommer industrin se ut i framtiden?Framtidens industri beskrivs ofta som en cirkulär ekonomi där minimala mängder råmaterial bryts och den största parten av det material som faller ur användning går återin i produktionen. Verkligheten vi lever i idag är en annan, större parten av världensindustri fortsätter med linjär produktion där råmaterial brukas i tillverkningen och produkter deponeras vid slutet av dess livscykel. Projektgruppen undersöker byggstenarna inom cirkulär ekonomi och var vi egentligen befinner oss i utveckling mot en hållbarare hantering av råvaror. Syftet med rapporten är att utreda vilka ekonomiska drivkrafter som finns för företag att själva gå mot cirkulär ekonomi i form av nya marknadsmodeller. Möjligheter till dessa marknadsmodeller växer ofta fram med hjälp av ny teknik från Industri 4.0 som Internet of Things och Big Data. Vi har nått slutsatsen att marknadsmodeller som pay-per-use där företagen behåller ägandet av produkten ofta är enkla att implementera i cirkulär ekonomi, då företaget själva kan kontrollera hela kretsloppet för produkten. De underlättar om man ska basera sin produktion på retur av produkter om man själv har kontroll över volymerna. Dessa modeller bygger ofta på att man säljer service-baserade tjänster som är beroende av Internet of Things och Big Data.<br>Environmental awareness in the world is increasing. The social debate is increasingly formed by an environmental perspective on several fronts. More and more people are beginning to understand that the earth's resources are finite and we must think about how we live today. The producing industry accounts for a large part of the use of raw materials, but how will the industry change in the future? The industry of the future is often described as a circular economy where minimal amounts of raw materials are broken and the majority of the material that is in use goes back to production. The reality we live in today is another, the greater part of the world's industry continues with linear production where raw materials are used in manufacturing and products are deposited at the end of its lifecycle. The project group examines the building blocks of circular economy and where we are today in the development towards sustainable management of raw materials. The purpose of the report is to investigate the economic advantages for companies to change to circular economy in the form of new business models. Opportunities for these business models often grow with the help of new technology from Industry 4.0 like Internet ofThings and Big Data. We have the conclusion that business models such as pay-per-use where companies maintain ownership of the product, often are easy to implement in circular economics, since the company itself can control the entire circuit for the product. They make it easier to base their production on return of products if they control the volumes of the returned products themself. These models are often based on selling services that are dependent on the Internet of things and Big Data.
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Snygg, Oliver, and Carl Torberger. "Vilka nya marknadsmodeller kan få producerande företag att gå mot cirkulär ekonomi?" Thesis, KTH, Skolan för industriell teknik och management (ITM), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-264557.
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Miljömedvetenheten i världen ökar. Samhällsdebatten formas allt mer av ett miljöperspektiv på flera olika fronter. Allt fler börjar förstå att jordens resurser är ändliga och vi måste tänka om hur vi lever idag. Den producerande industrin står för stor dela av brukandet av råmaterial, men hur kommer industrin se ut i framtiden? Framtidens industri beskrivs ofta som en cirkulär ekonomi där minimala mängder råmaterial bryts och den största parten av det material som faller ur användning går åter in i produktionen. Verkligheten vi lever i idag är en annan, större parten av världens industri fortsätter med linjär produktion där råmaterial brukas i tillverkningen och produkter deponeras vid slutet av dess livscykel. Projektgruppen undersöker byggstenarna inom cirkulär ekonomi och var vi egentligen befinner oss i utveckling mot en hållbarare hantering av råvaror. Syftet med rapporten är att utreda vilka ekonomiska drivkrafter som finns för företag att själva gå mot cirkulär ekonomi i form av nya marknadsmodeller. Möjligheter till dessa marknadsmodeller växer ofta fram med hjälp av ny teknik från Industri 4.0 som Internet of Things och Big Data. Vi har nått slutsatsen att marknadsmodeller som pay-per-use där företagen behåller ägandet av produkten ofta är enkla att implementera i cirkulär ekonomi, då företaget själva kan kontrollera hela kretsloppet för produkten. De underlättar om man ska basera sin produktion på retur av produkter om man själv har kontroll över volymerna. Dessa modeller bygger ofta på att man säljer service-baserade tjänster som är beroende av Internet of Things och Big Data.<br>Environmental awareness in the world is increasing. The social debate is increasingly formed by an environmental perspective on several fronts. More and more people are beginning to understand that the earth's resources are finite and we must think about how we live today. The producing industry accounts for a large part of the use of raw materials, but how will the industry change in the future? The industry of the future is often described as a circular economy where minimal amounts of raw materials are broken and the majority of the material that is in use goes back to production. The reality we live in today is another, the greater part of the world's industry continues with linear production where raw materials are used in manufacturing and products are deposited at the end of its lifecycle. The project group examines the building blocks of circular economy and where we are today in the development towards sustainable management of raw materials. The purpose of the report is to investigate the economic advantages for companies to change to circular economy in the form of new business models. Opportunities for these business models often grow with the help of new technology from Industry 4.0 like Internet of Things and Big Data. We have the conclusion that business models such as pay-per-use where companies maintain ownership of the product, often are easy to implement in circular economics, since the company itself can control the entire circuit for the product. They make it easier to base their production on return of products if they control the volumes of the returned products themself. These models are often based on selling services that are dependent on the Internet of things and Big Data.
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Alfaro, Vásquez Máximo, Núñez Juan Pablo Coloma, and Villanueva Ronald Rengifo. "Uso de publicidad programatica para rentabilizar un diario deportivo digital." Master's thesis, Universidad Peruana de Ciencias Aplicadas (UPC), 2004. http://hdl.handle.net/10757/655867.
Full textAbstract:
En las tres últimas décadas la llamada cultura .COM ha causado una revolución tan grande como la de la imprenta en su momento, revolución que potenció las comunicaciones y por ende la manera de comerciar y consumir.
Con un crecimiento mundial continuo y orgánico ha logrado migrar la costumbre de los consumidores obligando a los medios de comunicación tradicionales a reinventarse o desaparecer.
Nuestro país no es ajeno a este cambio registrando un aumento importante en los 15 últimos años y aunque todavía se considera la web como soporte del negocio original, la exposición de productos nacionales a nivel mundial, y de negocios transversales han crecido exponencialmente, muchas veces obteniendo márgenes en mercados donde no se esperaba una penetración.
En el 2010 el número de peruanos con acceso a información digital era de 34.8%, para el 2017 este número ya era del 48.8% con un crecimiento promedio de 2% por año; esto coloca a los medios de comunicación tradicional en una encrucijada; pero también nos brinda una gran oportunidad.
Bajo esta oportunidad presentamos nuestro proyecto: Una plataforma digital de comunicación deportiva, la cual gracias a tecnologías de información (Big Data) dirigiría sus contenidos a los mercados que mejor paguen por los espacios de publicidad.
Los contenidos se ajustarán en tiempo real según la información que obtengamos vía SEM y serán trabajados en tiempo real por redactores especializados en generar contenido deportivo polémico y divertido.
Esto permitiría captar visitas que sean fieles y regresen, y además rentabilizaría por la venta de espacios vía publicidad programática, gracias a la cual venderemos nuestros espacios a los mejores postores en tiempo real.<br>In the last three decades the so-called .COM culture has caused a revolution as big as the printing press in its time, a revolution that enhanced communications and therefore the way we trade and consume.
With a continuous and organic worldwide growth, it has managed to migrate the consumers' habits, forcing traditional media to reinvent themselves or disappear.
Our country is no stranger to this change, registering a significant increase in the last 15 years and although the web is still considered as the support of the original business, the exposure of national products worldwide, and transversal businesses have grown exponentially, often obtaining margins in markets where penetration was not expected.
In 2010 the number of Peruvians with access to digital information was 34.8%, by 2017 this number was already 48.8% with an average growth of 2% per year; this places the traditional media at a crossroads; but also gives us a great opportunity.
Under this opportunity we present our project: a digital platform for sports communication, which thanks to information technologies (Big Data) would direct its contents to the markets that best pay for advertising spaces.
The contents will be adjusted in real time according to the information we obtain via SEM and will be worked on in real time by writers specialized in generating controversial and entertaining sports content.
This would allow us to capture visits that are loyal and return, and would also be profitable through the sale of spaces via programmatic advertising, thanks to which we will sell our spaces to the best bidders in real time.<br>Trabajo de investigación
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Alfaro, Vásquez Máximo, Villanueva Ronald Rengifo, and Núñez Juan Pablo Javier Coloma. "Uso de publicidad programatica para rentabilizar un diario deportivo digital." Master's thesis, Universidad Peruana de Ciencias Aplicadas (UPC), 2021. http://hdl.handle.net/10757/655867.
Full textAbstract:
En las tres últimas décadas la llamada cultura .COM ha causado una revolución tan grande como la de la imprenta en su momento, revolución que potenció las comunicaciones y por ende la manera de comerciar y consumir.
Con un crecimiento mundial continuo y orgánico ha logrado migrar la costumbre de los consumidores obligando a los medios de comunicación tradicionales a reinventarse o desaparecer.
Nuestro país no es ajeno a este cambio registrando un aumento importante en los 15 últimos años y aunque todavía se considera la web como soporte del negocio original, la exposición de productos nacionales a nivel mundial, y de negocios transversales han crecido exponencialmente, muchas veces obteniendo márgenes en mercados donde no se esperaba una penetración.
En el 2010 el número de peruanos con acceso a información digital era de 34.8%, para el 2017 este número ya era del 48.8% con un crecimiento promedio de 2% por año; esto coloca a los medios de comunicación tradicional en una encrucijada; pero también nos brinda una gran oportunidad.
Bajo esta oportunidad presentamos nuestro proyecto: Una plataforma digital de comunicación deportiva, la cual gracias a tecnologías de información (Big Data) dirigiría sus contenidos a los mercados que mejor paguen por los espacios de publicidad.
Los contenidos se ajustarán en tiempo real según la información que obtengamos vía SEM y serán trabajados en tiempo real por redactores especializados en generar contenido deportivo polémico y divertido.
Esto permitiría captar visitas que sean fieles y regresen, y además rentabilizaría por la venta de espacios vía publicidad programática, gracias a la cual venderemos nuestros espacios a los mejores postores en tiempo real.<br>In the last three decades the so-called .COM culture has caused a revolution as big as the printing press in its time, a revolution that enhanced communications and therefore the way we trade and consume.
With a continuous and organic worldwide growth, it has managed to migrate the consumers' habits, forcing traditional media to reinvent themselves or disappear.
Our country is no stranger to this change, registering a significant increase in the last 15 years and although the web is still considered as the support of the original business, the exposure of national products worldwide, and transversal businesses have grown exponentially, often obtaining margins in markets where penetration was not expected.
In 2010 the number of Peruvians with access to digital information was 34.8%, by 2017 this number was already 48.8% with an average growth of 2% per year; this places the traditional media at a crossroads; but also gives us a great opportunity.
Under this opportunity we present our project: a digital platform for sports communication, which thanks to information technologies (Big Data) would direct its contents to the markets that best pay for advertising spaces.
The contents will be adjusted in real time according to the information we obtain via SEM and will be worked on in real time by writers specialized in generating controversial and entertaining sports content.
This would allow us to capture visits that are loyal and return, and would also be profitable through the sale of spaces via programmatic advertising, thanks to which we will sell our spaces to the best bidders in real time.<br>Trabajo de investigación
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Wöldern, Lars. "Discovery and Analysis of Social Media Data : How businesses can create customized filters to more effectively use public data." Thesis, Linnéuniversitetet, Institutionen för datavetenskap och medieteknik (DM), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-75275.
Full textAbstract:
The availability of prospective customer information present on social media platforms has led to many marketing and customer-facing departments utilizing social media data in processes such as demographics research, and sales and campaign planning. However, if your business needs require further filtration of data, beyond what is provided by existing filters, the volume and rate at which data can be manually sifted, is constrained by the speed and accuracy of employees, and their digital competency. The repetitive nature of filtration work, lends itself to automation, that ultimately has the potential to alleviate large productivity bottlenecks, enabling organizations to distill larger volumes of unfiltered data, faster and with greater precision. This project employs automation and artificial intelligence, to filter Linkedin profiles using customized selection criteria, beyond what is currently available, such as nationality and age. By introducing the ability to produce tailored indices of social media data, automated filtration offers organizations the opportunity to better utilize rich prospective data for more efficient customer review and targeting.
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MAZHAR, FAIZAN. "EXPLOITING DIVERSE DATA SOURCES TO INVESTIGATE EMERGING SAFETY ISSUES OF DRUGS IN POST-MARKETING PHASE: A RESEARCH FRAMEWORK FOR CLINICAL PHARMACOLOGIST." Doctoral thesis, Università degli Studi di Milano, 2021. http://hdl.handle.net/2434/812797.
Full textAbstract:
I farmaci di neo-immissione in commercio vengono approvati sulla base delle prove di efficacia e sicurezza dimostrate nel corso dei trial clinici pre-registrativi. Gli studi clinici randomizzati e controllati di fase 3 sono infatti considerati il mezzo più rigoroso per studiare il profilo di efficacia/sicurezza dei farmaci. Vari aspetti legati alla sicurezza di un medicinale possono però non essere individuati durante la conduzione di tali studi clinici e vengono resi noto solo quando il prodotto viene utilizzato nella pratica clinica quotidiana.
Le attività di ricerca del farmacologo clinico nel prevedere la sicurezza e l'efficacia dei farmaci richiedono un approccio multidimensionale. Un tale approccio multidimensionale dovrebbe includere tutte le discipline correlata alla farmacologia clinica, come la farmacoepidemiologia, la farmacovigilanza e la farmacia clinica, che possono fornire gli strumenti per una valutazione rigorosa dei benefici e dei possibili effetti collaterali dei farmaci. Le evidenze scaturite dai dati del mondo reale (RWD) forniscono il potenziale per affrontare gli aspetti legati all'efficacia e sicurezza dei trattamenti. Grazie al numero crescente di fonti di RWD, il farmacologo clinico potrebbe sfruttare appieno ed efficacemente la possibilità di utilizzare queste fonti, in accordo allo specifico quesito clinico.
A tal fine, l’elaborato di tesi è incentrato sulla proposta e validazione di un modello di approccio multidisciplinare per la farmacologia clinica, in modo tale da verificare se tali strategie possono produrre prove affidabili per rispondere a determinate quesiti clinici sfruttando e combinando prove da diverse fonti di dati, vale a dire i sistemi di segnalazione spontanea, la letteratura e la conduzione di studi osservazionali. Sono stati condotti diversi studi per affrontare determinati quesiti clinici. Il lavoro descritto in questa tesi si basa su cinque studi originali.
Negli studi I-II, è stata studiata l’associazione tra l'uso di farmaci psicotropi (antipsicotici/antidepressivi) e l'insorgenza di iponatriemia. La fisiopatologia dell'iponatriemia indotta da farmaci psicotropi rimane poco chiara. Per acquisire conoscenze su questa patologia rara e grave, abbiamo eseguito uno studio che combina i dati di monitoraggio post-marketing con le proprietà farmacodinamiche dei farmaci in oggetto. Questi studi evidenziano i potenziali ruoli dei bersagli recettoriali dei vari agenti psicotropi nella comparsa di iponatriemia. Nel complesso, questi studi contribuiscono a una migliore comprensione ti tale associazione e all’identificazione di potenziali bersagli di interesse che devono essere ulteriormente esplorati.
Nello studio III, è stata analizzata la potenziale associazione tra l'uso di inibitori della dipeptidil peptidasi-4 (DPP-4) e la comparsa di pemfigoide bolloso. A partire dal 2012, è stato segnalato un aumento del rischio di pemfigoide bolloso tra gli utilizzatori di farmaci inibitori della DPP-4 nell’ambito di studi di farmacovigilanza e caso-controllo. Ad oggi, il meccanismo con cui gli inibitori della DPP-4 inducono la BP non è stato caratterizzato. Questo è il primo studio volto a valutare il ruolo potenziale delle proprietà farmacologiche delle diverse gliptine nella comparsa di pemfigoide bolloso a seguito dell'esposizione a tali farmaci, utilizzando i dati provenienti dal sistema di segnalazione spontanea internazionale. Nell’analizzare diversi bersagli molecolari, abbiamo riscontrato una rilevanza clinica della selettività delle gliptine per la isoforma DDP-4 nello sviluppo del pemfigoide bolloso come risultato dell'esposizione a questi farmaci.
Negli studi IV-V, abbiamo studiato l'associazione tra i cambiamenti antropometrici nei pazienti con malattia infiammatoria intestinale (IBD) e la terapia con farmaci inibitori del fattore di necrosi tumorale (TNF)-α utilizzando tre diverse metodologie.
Nello studio IV, abbiamo eseguito una revisione sistematica e una meta-analisi della letteratura sull’utilizzo dei farmaci anti-TNF-α negli adulti e nei pazienti pediatrici con IBD che riportavano cambiamenti nei parametri antropometrici. Successivamente, è stata eseguita un’analisi longitudinale di case series per valutare i cambiamenti antropometrici e glicometabolici nei pazienti pediatrici con IBD dopo il trattamento con anti-TNF-α nella pratica clinica quotidiana. Infine, per mappare un profilo di sicurezza più ampio della terapia anti-TNF-α in un contesto reale, in parallelo, abbiamo effettuato un'analisi del database di farmacovigilanza per quantificare l'associazione tra la terapia anti-TNF-α e il tasso di cambiamenti antropometrici.
In conclusione, la ricerca presentata in questo elaborato di tesi ha prodotto diversi nuovi spunti di discussione e ha fornito nuove risposte ai numerosi quesiti clinici irrisolti sulla sicurezza dei farmaci, sviluppando contestualmente un approccio multidisciplinare che include metodi tecnici e statistici per esaminare gli ADE e/o gli effetti benefici dei farmaci che potrebbero verificarsi tra nuovi e vecchi utilizzatori. Questo lavoro fornisce al farmacologo clinico una strategia di intervento pluridisciplinare per la valutazione dei rischi e benefici dei farmaci da adattare potenzialmente ad altri ambiti di ricerca prioritari, così come ad altre popolazioni e fonti dati in grado di contribuire alla caratterizzazione dei profili di sicurezza dei farmaci.<br>New therapeutic products are typically approved based on their demonstrated efficacy and safety in a series of clinical trials. Randomized, controlled, phase 3 studies are considered to be the most rigorous means for studying the efficacy/safety profile of drugs. Various aspects of a medicinal product’s safety may not be known from clinical trials and until the product is used in routine clinical settings. Research efforts by clinical pharmacologist towards predicting safety, efficacy and effectiveness of drugs require a multidimensional approach. Such a multidimensional approach should encompass related discipline of clinical pharmacology, such as pharmacoepidemiology, pharmacovigilance and clinical pharmacy that can provide the tools for rigorous assessment of the good and harm that specific medications provide.
Real-world data (RWD) and evidence provide the potential to address the effectiveness and safety of drugs. With the emerging RWD sources, clinical pharmacologist should able to effectively and comprehensively exploit RWD sources depending on the research problem. Towards this goal, this dissertation has proposed and investigated a research framework for clinical pharmacology, if this would produce reliable evidence to answer given research question by exploiting and combining evidence from different data sources, namely pharmacovigilance and observational studies. To answer the initial research questions, several studies were conducted. The work described in this thesis is based on five original studies.
In study I-II, an association between psychotropic drugs (anti-psychotics/antidepressants) use and hyponatremia was studied. The pathophysiology of hyponatremia induced by psychotropic drug remains unclear. To gain knowledge into this rare and severe pathology, we performed a study combining a real-world pharmacovigilance safety data and the pharmacodynamics properties of given drug class. These studies highlight the potential roles of receptor targets in psychotropic induced by hyponatremia. Overall, these studies contribute to a better understanding of hyponatremia induced psychotropic and to identify the potential target of interest that needs to be further explored.
In study III, the potential association between the use of dipeptidyl peptidase-4 (DPP-4) inhibitors and bullous pemphigoid was analysed. Since 2012, an increased risk of bullous pemphigoid among DPP-4 inhibitor users has been reported in case series, pharmacovigilance reports, and case-control studies. To date, the mechanism by which DPP-4 inhibitors induce BP is not well understood. This is the first study aimed at evaluating the potential role of pharmacological properties of different gliptins in the occurrence of BP as a result of exposure to DPP-4 inhibitors using the global SRS. Several molecular targets were analysed and we found a clinical relevance of gliptins selectivity for DDP-4 in the development of BP as a result of exposure to these drugs.
In study IV-V, we studied the association between anthropometric changes in inflammatory bowel disease (IBD) patients following anti-tumour necrosis factor (TNF)- α therapy using three different methodologies. In study IV, we performed a systematic review and meta-analysis of the studies on anti-TNF-α in adults and paediatric IBD patients reporting changes in anthropometric parameters. Subsequently, A longitudinal case series analysis was performed to assess anthropometric and glucometabolic changes in paediatric IBD patients following anti-TNF treatment. Finally, to map a broader safety profile of anti-TNF-α therapy in a real-world setting, we carried out a comprehensive analysis of pharmacovigilance database in parallel to quantify the association between TNF-α therapy and the rate of body-related changes.
In conclusion, the research presented in this dissertation has produced several novel insights and provided new answers towards the challenging drug safety questions. This dissertation developed research framework including technical and statistical methods for examining ADEs and/or beneficial effect of drugs that might occur among new or established users of pharmaceuticals. This work provides a template to clinical pharmacologist in establishing a continuous learning system for other priority conditions and drug classes, other populations, and other databases which could potentially unveil drug safety profiles and novel adverse events (AEs).
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王達才 and Tat-choi Wong. "The strategic importance of information systems to airline revenue management." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1995. http://hub.hku.hk/bib/B31266873.
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容勁 and King Stanley Yung. "Application of multi-agent technology to supply chain management." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1999. http://hub.hku.hk/bib/B31223886.
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Hansen, Simon, and Erik Markow. "Big Data : Implementation av Big Data i offentlig verksamhet." Thesis, Högskolan i Halmstad, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-38756.
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