marketing | latest digital marketing in ai – ueducate

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The rapid progress of artificial intelligence has opened up a new frontier for many industries, especially in the field of marketing. With the improvement in AI technologies, they are endowed with the power to process huge sets of data, identify patterns, predict, and even make decisions with minimal human interference. Digital, with its ever-changing environment and use of real-time information, can gain significantly from the promise of AI.

However, the interaction between marketing and AI has been the focus of many studies, and the fast pace of development in both areas calls for ongoing investigation. The integration of artificial intelligence into marketing has greatly revolutionized and transformed business operations, ushering in a new era of innovation and expansion in business models. Chin Alapati and Pandey extensively investigated the role and influence of AI in modern marketing, highlighting its transformative power. In the Their mastic literature review, they five central functional topics in marketing where AI has been prominently deployed integer rated digital, content marketing, experiential, marketing operations, ai and market testing.

They have examined a total of 170 use cases from the literature, illuminating the sheer breadth of ways AI has been used to improve the quality and effectiveness of outputs overall. Similarly, Verma et al. acknowledged the revolutionary power of disruptive technologies, particularly AI, in reshaping business. With their emphasis on the role of AI, their study aimed to provide an all-encompassing picture of the topic over nearly four decades, from 1982 to 2020. Based on the systematic analysis of a whopping 1,580 papers, Verma and coauthors aimed to present the most dominant authors and sources that have been instrumental in constructing the narrative surrounding AI in marketing.

Materials and Methods

We used the PRISMA framework for this systematic literature review of the interaction between AI and marketing. We ran an exhaustive search on the Scopus database in July 2023, searching the TITLE-ABS-KEY fields with the combined keywords Artificial Intelligence AND Marketing. This yielded 3327 results. This narrowed our set to 849 articles. In our research into the synergies between AI and digital marketing, a strategic choice was made in the early stages of our systematic literature review.
Knowing the vast domains of both AI, we decided to use a wide-ranging keyword approach by looking for Artificial Intelligence AND Marketing in the Scopus database, latest ai, latest ai marketing, best ai and digital marketing, marketing.

This was done with the deliberate intent to cast a wide net so that we did not end up missing relevant studies that could be tangentially tagged. Yet, to keep the focus on our main area of interest, digital, a careful screening phase ensued. In this stage, records not directly about digital marketing were carefully excluded. This approach of starting with a wide search and then narrowing it down guaranteed both the depth and breadth of our research scope, capturing a comprehensive picture of the convergence of AI and digital marketing in the literature.

Literature Clustering

The progression of scholarly inquiry in interdisciplinary AI and digital marketing has facilitated prominent thematic groupings. Bibliometric analysis, involving the critical analysis of research literature to detect trends and patterns, has mooted a variety of influential themes framing the scenario of existing studies within this landscape. Let’s now delve further into these themes more intensively. Our dataset was clustered using the bibliophile application and graphical user interface for the Bibliometric R package specifically for bibliometric analysis.

To guarantee the integrity and specificity of our clusters, we followed a systematic process to optimize the dataset and the clustering parameters. We chose particular parameters to direct the clustering process in our analysis. We fixed the minimum cluster frequency at nine, which set the smallest group size that we used, and we concentrated our analysis on the most important and frequent themes. The community repulsion was fixed at zero, which influenced the group formation in our analysis, where a lower value results in wider, more general groups. For the clustering algorithm, we employed the Walk trap algorithm.

The use of these parameters was made possible by the automated clustering features of a bibliophile, which utilizes sophisticated algorithms to cluster the data efficiently. The selection of the combination of minimum cluster frequency, repulsion of communities, and Walk trap algorithm played a crucial role in sustaining an optimal balance between the granularity and completeness of clusters, thus allowing us to derive useful patterns and themes from the latest AI database.

Social Media Cluster

The latest developments in artificial intelligence have introduced novel methods to analyze online consumer behavior, especially in the social media marketing space. Villegas-Ch et al.. ventured into the immense data reservoirs of social media platforms to infer users’ personalities. With extensive use of artificial intelligence and sentiment analysis of Twitter data, they created a machine learning model that could forecast a user’s personality from their activity. Their results were encouraging, showing that if approached in the right way, social media marketing data contains valuable information. More than simple personal insights, the potential uses range from coming up with marketing campaigns to streamlining recruitment processes.

Aguilar and Garcia entered the complex arena of social media, targeting Facebook. Given the problems encountered by firms to maximize their campaigns, they created a smart system based on data mining strategies. Their system not only assists in generating ads but is also adaptive, where automatic optimization adjustments are made for better ad performance. Such developments seek to solve problems such as high expenses, long design hours, data mining, social media marketing, AI and database marketing, latest marketing and ai, and complexity in advertisement creation for more efficient delivery of advertisements on platforms as huge as Facebook.

Conclusion

Artificial intelligence marketing has greatly revolutionized the field of online advertising in many new and innovative ways. One of Guerriero et al.’s studies highlighted how AI, especially via intelligent speakers, has introduced new methods of advertising that enable human-like conversations with customers. Their study, which had 326 participants, identified that consumers’ acceptance of such AI devices’ data mining is dependent on the perceived utility of the smart assistant and hedonic needs.

There is a rider here consumers’ ease of use of these smart speakers diminishes when consumers believe there are potential privacy threats. Stepping onto slightly different ground, Guo ventures into the arena of voice data mining and how it will impact e-commerce advertising.
With the development of AI, advertising has ceased to be purely the creation of human intellect and expertise. With the applications of association rule models and neural networks, online ads can advance beyond mere clicks, Guo proposes. Online ads can activate users with multi-sensory interactions, with a richer, more engaging experience. Such an approach requires both eloquent diction and content harmony and concise and beautiful ad language.