Artificial Intelligence (AI) enables processing of large amounts of data to achieve a desired goal, simulating the way a human would carry on a similar task, but much more efficiently and often more effectively.
In marketing, AI has the power to radically transform the way brands communicate to current and prospective customers, providing a more personalized experience. This requires listening to what customers are saying about their encounters with products at scale, capturing the elements that factor into aggregate and segment specific sentiment. That sentiment can then be used to develop and evolve content, targeting strategy, and even to optimize engagement. However, an AI is only as smart as the machine learning model that powers it analyzes, which is based in part on the quality of the data used to build and feed the model. Therefore, it stands to reason that industry specific, purpose-built AI and machine learning models, optimized to listen for a particular customer voice, will be the most successful tools.
In healthcare, marketers can used transactional (such as claims) , longitudinal or real-world (such as EHR) data, traditional market research such as ATU and focus groups, to build profiles of customer cohorts to inform content and campaigns. But this structured data is limited in the dimensionality required to understand the patients’ journey. Today, patients and consumers are more vocal about their disease and drug experiences than ever before, thanks to social media. This provides an opportunity to enrich the marketers’ customer profile and develop a comprehensive understanding of patients, which matures the machine models and thereby allows for AI powered engagement, ultimately leading to not just greater affinity for healthcare companies and their brands, but improved health outcomes as well.