Real-time big data processing and automation of interactions as a driver and booster of online customer engagement

Detalhes bibliográficos
Autor(a) principal: Juster, Michelle Amorim
Data de Publicação: 2023
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/152537
Resumo: Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Marketing Intelligence
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spelling Real-time big data processing and automation of interactions as a driver and booster of online customer engagementReal-time processingInteraction AutomationPersonalized ResponsesCustomer Online EngagementDissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Marketing IntelligenceThe present study discusses how can real-time big data processing and automation of interactions drive and improve customer engagement. Since the marketing departments are experiencing a transition from ad-hoc decisions to decisions based on data usage, these AI-Driven innovations are also transforming customers' online engagement experiences. The present proposal aims to study the impact of marketing decisions based on AI tools on online customer engagement. Real-time big data processing enables instant decision-making, and consequently, the automation of interactions will impact the customer. With the large gap in studies from a customer perspective, this research explores more in this scenario. Customer engagement is an everchanging subject, organizations should have a fast and efficient approach, and to soften the impact of this constant change, big data analytics in a fast, efficient, and accurate method is crucial. With the large gap in studies from a customer perspective, this research explores more in this scenario. A theoretical model was proposed with determinant facts and artificial tools that can lead and impact customers' online engagement. Those concerns are related to artificial intelligence, product recommendations, delivery of personalized responses, and customer satisfaction with artificial intelligence services. Lastly, the model also includes how higher customer engagement can lead to purchasing intention. This study was tested using quantitative methods, an online survey with a sample of 169 answers, only approaching people who use social networks since the questionnaire was distributed through this channel. This study will contribute to understanding, from the consumer's perspective, what drives them and if the tools mentioned above can be boosters for more significant interaction with the pages, and consequently, if this greater interaction with the pages, motivated by personal factors and tools, leads to the purchase intention. This study supports that when consumers receive product recommendations enter a page, receive personalized responses, and are satisfied with the artificial intelligence services provided, they tend to interact more with online pages. Finally, building on prior work, this paper aims to contribute to a far-reaching understanding of the perceptions and motivations when interacting online, answering the abovementioned research objectives with qualitative methods, literature review, and quantitative methods, a questionary.Rita, Paulo Miguel Rasquinho FerreiraRamos, Ricardo Filipe CarreiraRUNJuster, Michelle Amorim2023-04-102026-04-10T00:00:00Z2023-04-10T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/152537TID:203268776enginfo:eu-repo/semantics/embargoedAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T05:34:49Zoai:run.unl.pt:10362/152537Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:54:55.874407Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Real-time big data processing and automation of interactions as a driver and booster of online customer engagement
title Real-time big data processing and automation of interactions as a driver and booster of online customer engagement
spellingShingle Real-time big data processing and automation of interactions as a driver and booster of online customer engagement
Juster, Michelle Amorim
Real-time processing
Interaction Automation
Personalized Responses
Customer Online Engagement
title_short Real-time big data processing and automation of interactions as a driver and booster of online customer engagement
title_full Real-time big data processing and automation of interactions as a driver and booster of online customer engagement
title_fullStr Real-time big data processing and automation of interactions as a driver and booster of online customer engagement
title_full_unstemmed Real-time big data processing and automation of interactions as a driver and booster of online customer engagement
title_sort Real-time big data processing and automation of interactions as a driver and booster of online customer engagement
author Juster, Michelle Amorim
author_facet Juster, Michelle Amorim
author_role author
dc.contributor.none.fl_str_mv Rita, Paulo Miguel Rasquinho Ferreira
Ramos, Ricardo Filipe Carreira
RUN
dc.contributor.author.fl_str_mv Juster, Michelle Amorim
dc.subject.por.fl_str_mv Real-time processing
Interaction Automation
Personalized Responses
Customer Online Engagement
topic Real-time processing
Interaction Automation
Personalized Responses
Customer Online Engagement
description Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Marketing Intelligence
publishDate 2023
dc.date.none.fl_str_mv 2023-04-10
2023-04-10T00:00:00Z
2026-04-10T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/152537
TID:203268776
url http://hdl.handle.net/10362/152537
identifier_str_mv TID:203268776
dc.language.iso.fl_str_mv eng
language eng
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instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
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