Using AI predicted personality to enhance advertising effectiveness

Shumanov, Michael; Cooper, Holly; Ewing, Mike

doi:10.1108/ejm-12-2019-0941

Cited by 29 publications

(15 citation statements)

References 82 publications

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“…This more holistic assessment of the consumer using AI is less prevalent than preference construction. But recent research has demonstrated the potential of integrating personality traits to improve the personalization and persuasiveness of AI-based marketing (Shumanov et al 2021). More importantly, AI has shown great promise in deciphering consumers' personality traits from a variety of data.…”

Section: Personality Assessmentmentioning

confidence: 99%

Artificial empathy in marketing interactions: Bridging the human-AI gap in affective and social customer experience

Liu‐Thompkins

Okazaki

2022

J. of the Acad. Mark. Sci.

136

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Artificial intelligence (AI) continues to transform firm-customer interactions. However, current AI marketing agents are often perceived as cold and uncaring and can be poor substitutes for human-based interactions. Addressing this issue, this article argues that artificial empathy needs to become an important design consideration in the next generation of AI marketing applications. Drawing from research in diverse disciplines, we develop a systematic framework for integrating artificial empathy into AI-enabled marketing interactions. We elaborate on the key components of artificial empathy and how each component can be implemented in AI marketing agents. We further explicate and test how artificial empathy generates value for both customers and firms by bridging the AI-human gap in affective and social customer experience. Recognizing that artificial empathy may not always be desirable or relevant, we identify the requirements for artificial empathy to create value and deduce situations where it is unnecessary and, in some cases, harmful.

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Section: Personality Assessmentmentioning

confidence: 99%

Artificial empathy in marketing interactions: Bridging the human-AI gap in affective and social customer experience

Liu‐Thompkins

Okazaki

2022

J. of the Acad. Mark. Sci.

136

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“…In this paper, bp model is used to analyze the cam of injection pump of single cylinder direct injection diesel engine. Through data simulation, through continuous bp network training, the best parameters are obtained, and the injection pump cam is optimally designed [15][16].…”

Section: Cam Of Single Cylinder Direct Injection Diesel Enginementioning

confidence: 99%

Optimization Design of Injection Pump Cam of Single Cylinder Direct Injection Diesel Engine Combined with Artificial Intelligence Algorithm

2022

KME

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Most models of low-power off-road diesel engines in China are single-cylinder diesel engines. The production, use and export of single-cylinder diesel engines are one of the characteristics of internal combustion engine industry in China. As the emission standards of off-road diesel engines are becoming stricter in China, it is of academic significance and engineering application value to study the low emission mechanism and optimization of low emission technical scheme of single-cylinder diesel engines. The purpose of this paper is to study the optimal design of injection pump cam of single cylinder direct injection diesel engine combined with artificial intelligence algorithm. The problems existing in the fuel injection system of domestic large bore single cylinder diesel engine are analyzed. The measures to improve the injection pressure of single cylinder engine are put forward. The basic principle and method of oil pump cam design are given. The artificial intelligence technology is used to simulate and model the new oil pump cam. According to the calculation, the cam design of single-cylinder engine fuel injection pump has its own characteristics. In the design, not only the general technology of the cam design of fuel injection pump, but also the special requirements of the installation and adjustment of single-cylinder engine should be considered to meet the needs of the overall layout of diesel engine. Based on the optimization scheme, the influence of key parameters changes on diesel engine emissions in the simulated production process is studied. The calculation results show that in order to ensure the effective control of emissions, performance and production consistency, it is necessary to reduce the change range of key parameters in the production process. The calculation and analysis show that the fuel supply advance angle, compression clearance and injector extension height are controlled within ±0.5°CA, ±0.08mm and ±0.1mm respectively. The increase percentage of NOx emission is within 9.8%, and the increase percentage of Soot emission is within 8.4%. The calculation results can guide the control of parameters and dimensional tolerance of parts in diesel engine production.

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“…This coalescence presents many opportunities to integrate PC and ML, both to discover more about the effects of PC and to better tailor products and services to individuals. Technologies that currently use PC include AI agents, user profiling and predictive models, with applications in fields such as business, education, health and psychology (Shumanov et al , 2021; Wang et al , 2019; Yang et al , 2019; Zarouali et al , 2022).…”

Section: Introductionmentioning

confidence: 99%

Persuasive communication systems: a machine learning approach to predict the effect of linguistic styles and persuasion techniques

Braca

Dondio

2023

JSIT

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Purpose Prediction is a critical task in targeted online advertising, where predictions better than random guessing can translate to real economic return. This study aims to use machine learning (ML) methods to identify individuals who respond well to certain linguistic styles/persuasion techniques based on Aristotle’s means of persuasion, rhetorical devices, cognitive theories and Cialdini’s principles, given their psychometric profile. Design/methodology/approach A total of 1,022 individuals took part in the survey; participants were asked to fill out the ten item personality measure questionnaire to capture personality traits and the dysfunctional attitude scale (DAS) to measure dysfunctional beliefs and cognitive vulnerabilities. ML classification models using participant profiling information as input were developed to predict the extent to which an individual was influenced by statements that contained different linguistic styles/persuasion techniques. Several ML algorithms were used including support vector machine, LightGBM and Auto-Sklearn to predict the effect of each technique given each individual’s profile (personality, belief system and demographic data). Findings The findings highlight the importance of incorporating emotion-based variables as model input in predicting the influence of textual statements with embedded persuasion techniques. Across all investigated models, the influence effect could be predicted with an accuracy ranging 53%–70%, indicating the importance of testing multiple ML algorithms in the development of a persuasive communication (PC) system. The classification ability of models was highest when predicting the response to statements using rhetorical devices and flattery persuasion techniques. Contrastingly, techniques such as authority or social proof were less predictable. Adding DAS scale features improved model performance, suggesting they may be important in modelling persuasion. Research limitations/implications In this study, the survey was limited to English-speaking countries and largely Western society values. More work is needed to ascertain the efficacy of models for other populations, cultures and languages. Most PC efforts are targeted at groups such as users, clients, shoppers and voters with this study in the communication context of education – further research is required to explore the capability of predictive ML models in other contexts. Finally, long self-reported psychological questionnaires may not be suitable for real-world deployment and could be subject to bias, thus a simpler method needs to be devised to gather user profile data such as using a subset of the most predictive features. Practical implications The findings of this study indicate that leveraging richer profiling data in conjunction with ML approaches may assist in the development of enhanced persuasive systems. There are many applications such as online apps, digital advertising, recommendation systems, chatbots and e-commerce platforms which can benefit from integrating persuasion communication systems that tailor messaging to the individual – potentially translating into higher economic returns. Originality/value This study integrates sets of features that have heretofore not been used together in developing ML-based predictive models of PC. DAS scale data, which relate to dysfunctional beliefs and cognitive vulnerabilities, were assessed for their importance in identifying effective persuasion techniques. Additionally, the work compares a range of persuasion techniques that thus far have only been studied separately. This study also demonstrates the application of various ML methods in predicting the influence of linguistic styles/persuasion techniques within textual statements and show that a robust methodology comparing a range of ML algorithms is important in the discovery of a performant model.

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Using AI predicted personality to enhance advertising effectiveness

Cited by 29 publications

References 82 publications

Artificial empathy in marketing interactions: Bridging the human-AI gap in affective and social customer experience

Artificial empathy in marketing interactions: Bridging the human-AI gap in affective and social customer experience

Optimization Design of Injection Pump Cam of Single Cylinder Direct Injection Diesel Engine Combined with Artificial Intelligence Algorithm

Persuasive communication systems: a machine learning approach to predict the effect of linguistic styles and persuasion techniques

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