Machine Learning to support Industrial Digitalization and Business Transformation

Bruzzone, Agostino G.; Massei, Marina; Sinelshchikov, Kirill; Fabbrini, Giuliano; Gotelli, M.; Molinari, Andrea

doi:10.46354/i3m.2019.emss.055

Cited by 7 publications

(15 citation statements)

References 3 publications

(3 reference statements)

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“…While in the past these solutions were common only in online stores, nowadays there is a grown interest in robotic assistants operating directly in physical stores. These conversational interfaces enhance the customer experience and increase the chances of conversions (Bruzzone et al, 2019).…”

Section: Natural Language Processing (Nlp)mentioning

confidence: 99%

Smart Customer Analysis for Personalized Promotion based on AI

2023

Proceedings of the 35th European Modeling &Amp; Simulation Symposium, EMSS

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The retail framework becomes always more complex and increasingly competitive, which makes retailers search for different approaches and their combinations, one of which is utilization of information about clients to propose individual promotions. The article highlights importance and potential of Artificial Intelligence (AI) in differentiation of customers, their clustering and generation of individual promotion and action plans.By analyzing vast amounts of client data, AI algorithms provide valuable insights, allowing retailers to produce targeted promotions, while covering as much as possible their customer base. Indeed, implementation of AI-driven customer segmentation, recommendation engines, and predictive algorithms allows retailers to deliver much more efficient personalized proposals, increasing the conversion and customer retention. Moreover, continuous fine-tuning of personalization, based on data fusion and prediction models, natural language processing, further improve customer experience, boosting brand loyalty. Indeed, by employing AI technologies, stores could create stronger connection with customers, consequently boosting revenue and remaining strongly competitive in the sector.

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Section: Natural Language Processing (Nlp)mentioning

confidence: 99%

Smart Customer Analysis for Personalized Promotion based on AI

2023

Proceedings of the 35th European Modeling &Amp; Simulation Symposium, EMSS

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“…Successful digital transformation strategies are highlighted in the literature, emphasizing the importance of leadership, customer-centric approaches, data-driven decision-making, and fostering an innovative organizational culture [6] .…”

Section: Strategies For Successful Digital Transformationmentioning

confidence: 99%

Navigating the digital wave: Transformative strategies in modern business with the integration of Artificial intelligence (AI) and machine learning (ML)

Singh

2022

Int. J. Comput. Artif. Intell.

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The advent of digital transformation has ushered in a new era of business strategy, fundamentally reshaping the landscape with the integration of Artificial Intelligence (AI) and Machine Learning (ML). This paper delves into the transformative strategies employed in modern businesses, examining the pivotal role of digital technologies in redefining business models and operational efficiencies. Through a comprehensive analysis of 15 seminal papers, this study explores the historical evolution, current trends, and future predictions of digital transformation in the business realm. It highlights the challenges and opportunities presented by this paradigm shift, offering insights into successful strategies and best practices for navigating the digital wave. The paper further presents case studies exemplifying effective digital transformation, thereby providing a holistic view of its impact on business sustainability and growth. This study contributes to the academic discourse by synthesizing existing literature and presenting a forward-looking perspective on digital transformation in business.

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“…One of possible fields of application of this approach is related to the production chain of shoes, which is typically composed from different suppliers of materials, third-party manufacturers responsible for specific parts of the preparation as well as principal production site in which the final product is assembled and tested (Hassan et al, 2019). At the same time, available data could be used to feed machine learning algorithm in order to have even better estimation of outcome (Bruzzone et al, 2020b;2019b).…”

Section: Figure 1 Shoe Production Chainmentioning

confidence: 99%