AI-Based Digital Marketing Strategies—A Review

Kumar, B. R. Arun

doi:10.1007/978-981-33-4305-4_70

Cited by 5 publications

(3 citation statements)

References 2 publications

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“…Digital intelligence techniques are applied in marketing and can be analyzed to predict consumer behavior. It is important to apply artificial positioning strategies to effectively target the customer to understand their behavior and find their expectations in relation to product features [ 3 ] . Artificial Intelligence and Deep Learning have evolved marketing, opening avenues for more personalized marketing so that the consumer is not the recipient of random advertising [1].…”

Section: Market Researchmentioning

confidence: 99%

The Impact of Digital Intelligence on Business Marketing Processes

Xu Dan,

Li Wei

2023

JMHR

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Accompanied by the rapid development of digital intelligence technology, enterprises are urgent to shift from traditional marketing to digital intelligence marketing. Based on the literature search analysis, this paper studies the framework design analysis of digital intelligent marketing in the enterprise marketing process, aiming at reconstructing the enterprise marketing structure and enhancing the innovation of the whole marketing process and universities. It provides a reference significance for enterprises to innovate the new mode of digital intelligent marketing, and at the same time, it thinks about the negative impact of digital intelligent marketing on enterprises in the case of imperfect establishment of the system. Therefore, this paper can provide a certain degree of reference for the improvement of marketing competitiveness of Chinese enterprises in the Internet era and the use of Internet technology based on social stability.

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Section: Market Researchmentioning

confidence: 99%

The Impact of Digital Intelligence on Business Marketing Processes

Xu Dan,

Li Wei

2023

JMHR

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“…They feed digital advertising systems by integrating the traces left on different platforms for improved analyses. Google Ads mixes traces from Google Analytics, Google search, Chrome, Gmail, YouTube, and the rest of Google services, allowing more nuanced predictions about the users' (advertising) preferences and tailored ads (Kumar, 2021). The more platforms feed the online advertising system, the better their deemed predictive power.…”

Section: Online Behavioral Advertisingmentioning

confidence: 99%

“…Those systems provide no data to the client but automatize decisions on when and where to display a given paid promotion. Google Ads is the most popular of these services in Western countries (Kumar, 2021), which relies on reusing digital traces collected on different Google platforms, from Google Analytics to Google search and Google maps, or from YouTube to Gmail.…”

Section: Introductionmentioning

confidence: 99%

Quality Assessment and Biases in Reused Data

Fernández-Ardèvol

Rosales

2022

American Behavioral Scientist

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This article investigates digital and non-digital traces reused beyond the context of creation. A central idea of this article is that no (reused) dataset is perfect. Therefore, data quality assessment becomes essential to determine if a given dataset is “good enough” to be used to fulfill the users’ goals. Biases, a possible source of discrimination, have become a relevant data challenge. Consequently, it is appropriate to analyze whether quality assessment indicators provide information on potential biases in the dataset. We use examples representing two opposing sides regarding data access to reflect on the relationship between quality and bias. First, the European Union open data portal fosters the democratization of data and expects users to manipulate the databases directly to perform their analyses. Second, online behavioral advertising systems offer individualized promotional services but do not share the datasets supporting their design. Quality assessment is socially constructed, as there is not a universal definition but a set of quality dimensions, which might change for each professional context. From the users’ perspective, trust/credibility stands out as a relevant quality dimension in the two analyzed cases. Results show that quality indicators (whatever they are) provide limited information on potential biases. We suggest that data literacy is most needed among both open data users and clients of behavioral advertising systems. Notably, users must (be able to) understand the limitations of datasets for an optimal and bias-free interpretation of results and decision-making.

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