Artificial intelligence (AI) is widely deployed to solve problems related to
marketing attribution and budget optimization. However, AI models can be quite
complex, and it can be difficult to understand model workings and insights
without extensive implementation teams. In principle, recently developed large
language models (LLMs), like GPT-4, can be deployed to provide marketing
insights, reducing the time and effort required to make critical decisions. In
practice, there are substantial challenges that need to be overcome to reliably
use such models. We focus on domain-specific question-answering, SQL generation
needed for data retrieval, and tabular analysis and show how a combination of
semantic search, prompt engineering, and fine-tuning can be applied to
dramatically improve the ability of LLMs to execute these tasks accurately. We
compare both proprietary models, like GPT-4, and open-source models, like
Llama-2-70b, as well as various embedding methods. These models are tested on
sample use cases specific to marketing mix modeling and attribution.

人工智能广泛应用于解决与市场归因和预算优化相关的问题，然而由于模型复杂，很难在没有完整的实施团队的情况下理解模型的运作和见解，理论上，最近开发的大型语言模型（如 GPT-4）可以用来提供营销见解，以减少做出关键决策所需的时间和精力。 实际上，要可靠地使用这些模型，需要克服一些重大挑战，我们聚焦于领域特定的问答、用于数据检索的 SQL 生成以及表格分析，并展示了如何通过语义搜索、提示工程和微调的结合来显著提高大型语言模型执行这些任务的准确性。我们比较了专有模型（如 GPT-4）和开源模型（如 Llama-2-70b），以及各种嵌入方法。这些模型在特定于市场混合建模和归因的样本用例上进行了测试。