Publicação
Real-time big data processing and automation of interactions as a driver and booster of online customer engagement
| dc.contributor.advisor | Rita, Paulo Miguel Rasquinho Ferreira | |
| dc.contributor.advisor | Ramos, Ricardo Filipe Carreira | |
| dc.contributor.author | Juster, Michelle Amorim | |
| dc.date.accessioned | 2023-05-09T16:27:39Z | |
| dc.date.embargo | 2026-04-10 | |
| dc.date.issued | 2023-04-10 | |
| dc.description | Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Marketing Intelligence | pt_PT |
| dc.description.abstract | The 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. | pt_PT |
| dc.identifier.tid | 203268776 | pt_PT |
| dc.identifier.uri | http://hdl.handle.net/10362/152537 | |
| dc.language.iso | eng | pt_PT |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | pt_PT |
| dc.subject | Real-time processing | pt_PT |
| dc.subject | Interaction Automation | pt_PT |
| dc.subject | Personalized Responses | pt_PT |
| dc.subject | Customer Online Engagement | pt_PT |
| dc.title | Real-time big data processing and automation of interactions as a driver and booster of online customer engagement | pt_PT |
| dc.type | master thesis | |
| dspace.entity.type | Publication | |
| rcaap.embargofct | "(…) ter a possibilidade de elaborar e publicar um artigo numa revista científica com base na dissertação de mestrado." | pt_PT |
| rcaap.rights | embargoedAccess | pt_PT |
| rcaap.type | masterThesis | pt_PT |
| thesis.degree.name | Mestrado em Gestão de Informação, especialização em Inteligência de Marketing | pt_PT |