Online content match-making in B2B markets: Application of neural content modeling

Abstract 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.

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