The potential of generative AI for personalized persuasion at scale | Scientific Reports

The potential of generative AI for personalized persuasion at scale
 
 
 
 
 
 
 
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 Abstract

 Matching the language or content of a message to the psychological profile of its recipient (known as “personalized persuasion”) is widely considered to be one of the most effective messaging strategies. We demonstrate that the rapid advances in large language models (LLMs), like ChatGPT, could accelerate this influence by making personalized persuasion scalable. Across four studies (consisting of seven sub-studies; total N  = 1788), we show that personalized messages crafted by ChatGPT exhibit significantly more influence than non-personalized messages. This was true across different domains of persuasion (e.g., marketing of consumer products, political appeals for climate action), psychological profiles (e.g., personality traits, political ideology, moral foundations), and when only providing the LLM with a single, short prompt naming or describing the targeted psychological dimension. Thus, our findings are among the first to demonstrate the potential for LLMs to automate, and thereby scale, the use of personalized persuasion in ways that enhance its effectiveness and efficiency. We discuss the implications for researchers, practitioners, and the general public.

 
 
 

 
 
 

 
 
 
 
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 Introduction

 Financial analysts have described people’s digital behavioral data as “more valuable than oil” 1 , 2 . This is, in part, because such records afford one of the most effective forms of influence: personalized persuasion 3 , 4 . Compared to non-personalized communication, matching the content of a persuasive message (e.g., its language or visuals) to the psychological profile of its recipient enhances its effectiveness (e.g., 4 , 5 ). On the one hand, such personalization offers tremendous opportunities to promote desired behaviors, including a healthy lifestyle 6 , 7 , 8 , financial saving 9 , or support for environmentalism 10 . On the other hand, it can have a pernicious effect on societies 11 , for example, increasing the spread of disinformation 12 , manipulating political preferences 13 , 14 , or promoting maladaptive consumer decision-making 15 , 16 . We provide the first empirical evidence demonstrating how content generated by artificial intell

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