Abstract
Marketing is one of the areas where large language models (LLMs) such as ChatGPT have found practical applications. This study examines marketing prompts—text inputs created by marketers to guide LLMs in generating desired outputs. By combining insights from the marketing literature and the latest research on LLMs, the study develops a conceptual framework around three key features of marketing prompts: prompt domain (the specific marketing actions that the prompts target), prompt appeal (the intended output of the prompts being informative or emotional), and prompt format (the intended output of the prompts being generic or contextual). The study collected hundreds of marketing prompt templates shared on X (formerly Twitter) and analyzed them using a combination of natural language processing techniques and descriptive statistics. The findings indicate that the prompt templates target a wide range of marketing domains—about 16 altogether. Likewise, the findings indicate that most of the marketing prompts are designed to generate informative output (as opposed to emotionally engaging output). Further, the findings indicate that the marketing prompts are designed to generate a balanced mix of generic and contextual output. The study further finds that the use of prompt appeal and prompt format differs by prompt domain.
Original language | English |
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Pages (from-to) | 790-805 |
Number of pages | 16 |
Journal | Journal of Marketing Analytics |
Volume | 12 |
Issue number | 4 |
DOIs | |
Publication status | Published - Dec 2024 |
Externally published | Yes |
Keywords
- AI
- Artificial intelligence
- Business
- Large language models
- Prompt engineering
ASJC Scopus subject areas
- Economics, Econometrics and Finance (miscellaneous)
- Strategy and Management
- Statistics, Probability and Uncertainty
- Marketing