Smart Generation System of Personalized Advertising Copy and Its Application to Advertising Practice and Research

Deng, Shasha; Tan, Chee‐Wee; Wang, Weijun; Pan, Yu

doi:10.1080/00913367.2019.1652121

Cited by 75 publications

(25 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Finally, since it is impossible to create hundreds or thousands of personalised ads within a second manually, programmatic creative is created to achieve this task automatically and at scale. (Chen et al 2019;Deng et al 2019. ) Similar to programmatic buying, programmatic creative is not a fully automatic process yet, so human intervention is required to ensure the appropriateness of system-generated ads (Li 2019).…”

Section: Overviewmentioning

confidence: 99%

“…Personalised ads are ads resonating with each consumer. Therefore, consumers interested in the same brand but having different online attributes will be likely to be exposed to different ad versions (Deng, Tan, Wang & Pan 2019). Furthermore, personalised ads are highly contextual, which implies that the same consumer could be exposed to different ad versions on different occasions and locations.…”

Section: Overviewmentioning

confidence: 99%

See 1 more Smart Citation

The planning and implementation process of Programmatic Advertising campaigns in emerging markets

Tiet¹

2021

Contemporary Issues in Digital Marketing

View full text Add to dashboard Cite

Section: Overviewmentioning

confidence: 99%

Section: Overviewmentioning

confidence: 99%

The planning and implementation process of Programmatic Advertising campaigns in emerging markets

Tiet¹

2021

Contemporary Issues in Digital Marketing

View full text Add to dashboard Cite

“…In contrast, CA scholars worry about issues including convenience censuses, algorithmic biases, and the effects of nonhuman (i.e., bot) traffic. There has been a recent movement in the computational creation of advertisements through which social media profiles and behaviors (Dragoni 2018) or browsing behaviors (Deng et al 2019) are used to generate creative content using artificial intelligence (AI). Although such a practice may provide benefits (e.g., less stress for humans to create millions of different advertisements for real-time bidding (RTB) marketplaces, Deng et al 2019), relying on AI for automatic content creation and recommendation can have detrimental effects from underrepresentation and bias standpoints.…”

Section: Bias In Data and Algorithmsmentioning

confidence: 99%

Challenges and Future Directions of Computational Advertising Measurement Systems

Yun

Segijn

Pearson

et al. 2020

Journal of Advertising

View full text Add to dashboard Cite

Computational advertising (CA) is a rapidly growing field, but there are numerous challenges related to measuring its effectiveness. Some of these are classic challenges where CA offers a new aspect to the challenge (e.g., multi-touch attribution, bias), and some are brand-new challenges created by CA (e.g., fake data and ad fraud, creeping out customers). In this article, we present a measurement system framework for CA to provide a common starting point for advertising researchers to begin addressing these challenges, and we also discuss future research questions and directions for advertising researchers. We identify a larger role for measurement: It is no longer something that happens at the end of the advertising process; instead, measurements of consumer behaviors become integral throughout the process of creating, executing, and evaluating advertising programs. Computational advertising (CA) presents an unprecedented opportunity for measuring the short-and long-term effectiveness of advertising. Simply defined, CA is personalized communication that uses computational power to match the right ads and advertisers with the right consumers at the right time in the right place with the right frequency to elicit the right response. Computational advertising-and the myriad digital media through which it is delivered-offers an explosion in the volume, variety, and velocity of data available; therefore, it provides new fuel for today's more powerful machine learning and analytical techniques. At the same time, CA is being deployed in environments where highly increased personal identification and tracking across touch points, formats, and media create an opportunity to measure effectiveness at a personal level across disparate elements of a campaign and over time. The nature of these touch points presents new types of data and presentation opportunities, from geotemporal data, search histories, and voice interaction to personalized placement opportunities embedded in other media. Together,

show abstract

“…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%

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

Esch

Cui

Jain

2020

Journal of Advertising

View full text Add to dashboard Cite

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.

show abstract

Smart Generation System of Personalized Advertising Copy and Its Application to Advertising Practice and Research

Cited by 75 publications

References 23 publications

The planning and implementation process of Programmatic Advertising campaigns in emerging markets

The planning and implementation process of Programmatic Advertising campaigns in emerging markets

Challenges and Future Directions of Computational Advertising Measurement Systems

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

Contact Info

Product

Resources

About