Business-to-business (B2B) sellers need to enhance content marketing and analytics in an online environment. The challenge is that sellers have data but do not know how to utilize it. In this study, we develop a neural content model to match the content that B2B sellers are providing with the type of content that buyers are seeking. The model was tested with two experiments using a dataset that combines cookie-based browsing data from 74 B2B seller companies over a period of fourteen months. In total, the data comprises 180 million browsing sessions tracked via 11.44 million cookies from 34,170 buyer companies. In the first experiment, we study the content in the sellers' own channels, and in the second experiment we study paid channels. With these experiments, we illustrate that browsing data can be combined with marketing content data to evaluate and improve the content-marketing efforts of B2B seller firms. Since the development of digital information technologies (DITs) has made the B2B buying process more buyer driven, our neural content modeling approach can be used to create B2B analytics that re-empower the sellers.

企业对企业（B2B）卖家需要在在线环境中增强内容营销和分析能力。挑战在于卖家拥有数据，但不知道如何利用它。在这项研究中，我们开发了一种神经内容模型，以将B2B卖方提供的内容与买方寻求的内容类型进行匹配。通过使用数据集的两个实验对该模型进行了测试，该数据集结合了来自74个B2B卖方公司基于Cookie的浏览数据，历时14个月。数据总共包括1.8亿个浏览会话，通过来自34,170个买方公司的1144万个cookie进行了跟踪。在第一个实验中，我们研究卖方渠道中的内容，在第二个实验中，我们研究付费渠道。通过这些实验，我们说明，浏览数据可以与营销内容数据结合起来，以评估和改进B2B卖方公司的内容营销工作。由于数字信息技术（DIT）的发展使B2B购买过程更加受买方驱动，因此我们的神经内容建模方法可用于创建重新赋予卖方权力的B2B分析。