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.

营销是 ChatGPT 等大型语言模型 (LLM) 已找到实际应用的领域之一。本研究考察了营销提示——营销人员创建的文本输入，用于指导法学硕士生成所需的输出。通过结合营销文献的见解和法学硕士的最新研究，该研究围绕营销提示的三个关键特征开发了一个概念框架：提示域（提示针对的具体营销行为）、提示吸引力（提示的预期输出）信息性或情感性）和提示格式（提示的预期输出是通用的或上下文的）。该研究收集了 X（以前称为 Twitter）上共享的数百个营销提示模板，并结合自然语言处理技术和描述性统计对它们进行了分析。研究结果表明，提示模板针对广泛的营销领域——总共大约 16 个领域。同样，研究结果表明，大多数营销提示旨在产生信息输出（而不是情感投入的输出）。此外，研究结果表明，营销提示旨在生成通用输出和上下文输出的平衡组合。研究进一步发现，提示诉求和提示格式的使用因提示领域而异。