Digital marketing of standard essential patent licensing programs

Abstract

We present empirical evidence on the digital marketing choices of standard essential patent licensing programs on patent pool and licensor websites. We highlight the importance of dynamic learning in licensing negotiation events and strategic information revelation in the presence of asymmetric information. We document licensing schemes and licensed units adopted in patent licensing programs. We analyze the marketing strategies of licensing programs using applicable elements of the Marketing Mix framework. We observe significant variation in publicly available information across licensing programs. This suggests that licensors face trade-offs in deciding what information is revealed and anchored in pre-negotiations, as part of licensing program marketing, and during confidential licensing negotiations. Future studies could analyze how generative artificial intelligence (AI) systems may promote marketing and transparency of patent licensing programs.