Understanding Programmatic Creative: The Role of AI

Chen, Gang; Xie, Peihong; Dong, Jing; Wang, Tianfu

doi:10.1080/00913367.2019.1654421

Cited by 75 publications

(72 citation statements)

References 11 publications

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“…Computerization finds an important place in the context of content personalization. First of all, it opens up new possibilities for automating the personalization and adjusting content through contextualization (Chen, Xie, Dong and Wang, 2019). Just as the demographic data and indicators of the audience are usually stable, the needs and attitudes of viewers change from time to time, and interests shift daily.…”

Section: Mass-personalization Of Contentmentioning

confidence: 99%

“…In the context of artificial intelligence-assisted media production practices, most of the scientific works focus on journalistic practice (Bodó, 2019;Diakopoulos, 2018Diakopoulos, , 2019Hansen, Roca-Sales, Keegan and King, 2017;Stray, 2019), programmatic advertising (Busch, 2016;Huang, 2015), computational creativity (Chen, Xie, Dong and Wang, 2019;Jordanours, 2016) and broadly defined technological solutions (explored by numerous authors). In terms of audiovisual production, one can additionally find the reflections on machine-based support for decision-making (Fuschi and Badii, 2013;Ghiassi, Lio and Moon, 2015), as well as the role of metadata or computational narrative intelligence (Riedl, 2016).…”

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confidence: 99%

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"Did you know that David Beckham speaks nine languages?”: AI-supported production process for enhanced personalization of audio-visual content

Derda¹

2021

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The introduction of artificial intelligence (AI) into the media production process has contributed to the automation of selected tasks and stronger hybridization of man and machine in the process; however, the AI-supported production process has expanded from the traditional, three-stage model by a new phase of consumer evaluation and feedback collection, analysis, and application. This has opened a way for far-reaching content personalization and thus offers a new type of media experience. Powering the production process with a constant stream of consumer data has also affected the process itself and changed its nature from linear to cyclical.

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Section: Mass-personalization Of Contentmentioning

confidence: 99%

mentioning

confidence: 99%

"Did you know that David Beckham speaks nine languages?”: AI-supported production process for enhanced personalization of audio-visual content

Derda¹

2021

Preprint

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“…This learning enables it to refine its advertising and increase its effectiveness (Kietzmann, Paschen, and Treen 2018). AI also enables online customer assistance (Pantano and Pizzi 2020) and programmatic creative and advertising optimization (Bakpayev et al 2020;Chen et al 2019;Deng et al 2019).…”

Section: Ai: Definitions and Application To Advertisingmentioning

confidence: 99%

“…AI revolutionized how marketers communicate with consumers (Qin and Jiang 2019) and with whom they communicate (Kumar et al 2019). Through the facilitation of data, algorithms, and computing power, marketers could create personalized communication in real time, based on consumers' profiles, the prediction of their preference, and their social surroundings through programmatic advertising (Bakpayev et al 2020;Chen et al 2019). Subsequently, such communication could be delivered at a specific time and under specific contexts.…”

Section: Introductionmentioning

confidence: 99%

Stimulating or Intimidating: The Effect of AI-Enabled In-Store Communication on Consumer Patronage Likelihood

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Cui

Jain

2020

Journal of Advertising

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Artificial intelligence (AI) has penetrated the marketing landscape and is having a profound impact on businesses' communication strategies. With AI coming under the spotlight, we know surprisingly little about its impact on consumers' patronage likelihood. This research attempts to address this void by investigating the "just-walk-out" retail technology in cohort with in-store communication. Across three studies, conducted online and in the field, the authors demonstrate that, compared to self-service checkouts, AI-enabled checkouts lead to significantly higher consumers' patronage likelihood. Furthermore, sensory stimulation stemming from in-store communication (environmental cues including assortment, advertising, and technology) underlies this impact. Importantly, the extent to which consumers perceive AI technology to be threatening is revealed as a boundary condition to these effects. These findings advance our understanding of how AI-enabled checkouts and in-store communication influence consumers' patronage likelihood and the boundary condition that moderates their impact.

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“…Teaching a machine to automatically place ads in context is challenging (Chen et al, 2019). The machine must first analyze the textual content of a given medium (like a website) and then choose an appropriate advertisement based on keywords provided by the advertiser.…”

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confidence: 99%

Uncovering the Sources of Machine-Learning Mistakes in Advertising: Contextual Bias in the Evaluation of Semantic Relatedness

Watts

Adriano

2020

Journal of Advertising

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Imagine a beer advertisement next to an article about drunk driving, or a coupon for a free dinner embedded in an article about food poisoning. While humans are quite good at seeing the error in these examples, the machine-learning algorithms that place advertisements online continue to struggle with this type of contextual nuance. We argue that this shortcoming stems from the manner in which these machines are taught about semantic relatedness-the conceptual distance between words in the human mind. Specifically, we hypothesize that there is a difference in how humans view semantic relatedness when context is present versus when it is absent and that this difference is missing from the data used by machines to place advertisements online. To test this hypothesis, we adapt existing best practices to create a new, context-aware database and then compare it to the current state of the art. We find substantial differences in the distribution of semantic relatedness scores for context-aware versus context-free databases. We also find that the nature and scope of these differences are likely to lead to the types of mistakes observed in practice.

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Understanding Programmatic Creative: The Role of AI

Cited by 75 publications

References 11 publications

"Did you know that David Beckham speaks nine languages?”: AI-supported production process for enhanced personalization of audio-visual content

"Did you know that David Beckham speaks nine languages?”: AI-supported production process for enhanced personalization of audio-visual content

Stimulating or Intimidating: The Effect of AI-Enabled In-Store Communication on Consumer Patronage Likelihood

Uncovering the Sources of Machine-Learning Mistakes in Advertising: Contextual Bias in the Evaluation of Semantic Relatedness

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