Data Mining and Machine Learning Techniques Applied to Digital Marketing Domain Needs

Ahsain, Sara; Kbir, M. Aït

doi:10.1007/978-3-030-66840-2_55

Cited by 6 publications

(1 citation statement)

References 22 publications

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“…For most of these domains, if not for all, marketing is one of the backbones that increase sales and revenue by creating product awareness among customers. The impact of IoT on digital marketing will imply greater data control for consumers, with them being digital natives or digital refugees, and marketers must make efforts to collect the data from devices [3] to gain insights into client behaviour [4].…”

Section: Introductionmentioning

confidence: 99%

Towards Marketing 4.0: Vision and Survey on the Role of IoT and Data Science

et al. 2023

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According to KPMG, Internet of Things (IoT) technology was among the top 10 technologies of 2019. It has been growing at a significant pace, influencing and disrupting several application domains. It is expected that by 2025, 75.44 billion devices will be connected to the Internet. These devices generate massive amounts of data which, when harnessed using the power of data science (DS) techniques and approaches such as artificial intelligence (AI) and machine learning (ML), can provide significant benefits to economy, society, and people. Examples of areas that are being disrupted are digital marketing and retail commerce services in smart cities. This paper presents a vision for Marketing 4.0 that is underpinned by disruptive digital technologies such as IoT and DS. We present an analysis of the current state of the art in IoT and DS via the three pillars of marketing: namely, people, products, and places. We propose a blueprint architecture for developing a Marketing 4.0 solution that is underpinned by IoT and DS. We conclude the paper by highlighting the open challenges that need to be addressed in order to realise the Marketing 4.0 blueprint architecture, including supporting the integration of IoT data concerning people, products, and places and using DS to make efficient and effective recommendations.

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Section: Introductionmentioning

confidence: 99%