On September 26, the ARF held our third annual Creative Effectiveness conference where we discussed and debated questions around reclaiming creativity in the age of AI. Brand, agency, media and research sages showcased examples of how they are stimulating and measuring creative with various approaches and tools. Following the conference, attendees joined us for an evening of celebrating at the ARF David Ogilvy Awards — honoring research- and insights-driven advertising.
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This study explores the impact of using "containing language" in advertisements on perceived offer fairness and consumer behavior. Identifying useful phrases like "That's it!" and "Period!" can reduce perceived price complexity and enhance perceived offer fairness, leading to higher purchase intentions, the researchers conclude. These findings suggest that marketers can use such language to communicate prices more effectively and responsibly.
The study involved multiple experiments and a large-scale field study, demonstrating that containing language can positively influence consumer perceptions and responses. The research provides valuable insights for marketing practitioners on how to design advertisements that improve consumer trust and engagement.
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This study examines user interactions with AI assistants to infer purchase intent. By analyzing the text of user-initiated interactions, researchers build a bipartite network of nouns and verbs and measure the distance of specific words to "golden" purchasing words like "purchase," "buy" or "order." The study uses large language models, specifically Chat-GPT4, to annotate data with a measure of purchase intent and validates this method by comparing the results with cost-per-click (CPC) for keywords in Google Ads. The findings suggest that words used in an exchange with an AI assistant can predict purchase intent without customer tracking across interactions.
These findings have implications for using customized small versus large language models and can potentially inform advertising decisions. The study highlights the importance of understanding consumer behaviors in interactions with AI assistants. It provides a method to predict purchase intent based solely on the textual content of these interactions.
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