KeynoteCognitive Content Strategy: How to Use Artificial Intelligence to Model Audience Intent
Keywords help content strategists audit existing content and identify new content opportunities. By using audience intent modeling, content strategists can map content plans to buy-cycle states for their target personas. But audience intent modeling requires lots of trial and error and other manual processes, because natural language is full of ambiguities and meaning is malleable. Enter cognitive content strategy, which uses natural language processing and machine learning to map keywords to job roles and buy cycle states. This automation allows IBM to scale intent modeling across the enterprise without employing an army of intent modelers.
This presentation shows how to do audience intent modelling in the context of a case study within IBM. Attendees can expect to come away with insights they can take back and implement immediately.
James Mathewson has 20 years of experience in applying content strategy to build web audiences. As editor in chief (EIC) of ComputerUser.com, James built the audience from 0 to 1.8 million unique visitors per month in six months. As EIC of ibm.com, James helped double engagement with IBM content. As IBM's Distinguished Technical Marketer, he runs a center of excellence (COE) to help the many and various IBM brands and business units improve their digital content effectiveness. In this role, he has helped build business unit microsites from scratch that account for 80 percent of the organic marketing traffic and engagement for IBM. James is lead author of Audience, Relevance, and Search: Targeting Web Audiences With Relevant Content (2010) and Outside-In Marketing: Using Big Data to Guide Your Content Marketing (2016).