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Real-time big data processing and automation of interactions as a driver and booster of online customer engagement
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Resumo(s)
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.
Descrição
Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Marketing Intelligence
Palavras-chave
Real-time processing Interaction Automation Personalized Responses Customer Online Engagement