Understanding audiences with machine learning

Arantxa Beiztegui

Machine learning (ML) and Artificial Intelligence (AI) can be used not only to analyze a company’s internal data but also to extract information from the external environment , revealing hidden experts and resources within an organization.

In a recent conversation for a podcast, Carlos Areia, Senior Data Scientist at Digital Science, Julia Mutygullina, Product Solutions Manager at Digital Science and Garth Sundem, Director of Communications and Marketing at Medical Affairs Professional Society (MAPS) discussed machine learning as a tool for understanding audiences, including audience segments/personas and individuals within these groups. They also touched on the limitations and data privacy concerns that distinguish what we “could” know from what we “should” uncover.

During the podcast episode held by MAPS in collaboration with Altmetric, Carlos Areia highlighted the differences between AI and machine learning. He described AI as a system or a set of technologies that use a vast pool of data and learning capabilities, citing GPT and Google as examples. ML, on the other hand, is a subset of AI, that uses algorithms and models.

Moreover, artificial intelligence operates almost in real-time, such as chat GPT, which responds instantly, unlike traditional machine learning that involves multiple steps and actions, including coding.

The episode delved into the potential of AI and machine learning in better understanding  audiences. Julia Mutygullina, who represented the client’s perspective, emphasized the importance of tailoring the approach based on the specific client needs, whether it involves understanding a particular group or key opinion leaders. 

Some clients require only access to  general information about large groups, without insights into personal profiles or details, for getting an overview of thier target audience. . Conversely, others need more specific insights and are interested in identifying key opinion leaders and healthcare practitioners who show the most enthusiasm or interest in their products. They may want to connect, have personal discussions, and explore potential collaborations. This raises questions about the extent to which personal information can be collected and stored, and Julia highlighted the importance of adhering to legal requirements and regulations.

For further insights on how machine learning can enhance audience understanding and the adjustments involved in the process, listen to the podcast.