This talk discusses the recent developments in automation of marketing, with an emphasis in the field of Higher Education. It focuses on the following topics:

1. Campaign Measurement: Presentation of the academic knowledge available on the effectiveness of advertising campaign measurement.
   AI-based models, often offered by advertising platforms, are known to sometimes misrepresent and/or overestimate the effects of marketing campaigns. Based on large-scale field experiments, we will discuss the reasons why returns of ad campaigns are mis-estimated and will discuss known remedies.
    
2. Search Engine Dynamics & SE: Discussion about the proliferation of automated meta rankings.
   In the context of higher education, broad search terms (e.g., “Master’s in Management”) often yield generic search results, some of which with informal and/or automated rankings. Moreover, the integration of AI in Google’s search results provides an additional integration on program discoverability. Ironically, the proliferation of automated rankings is likely to make a few reputable rankings more important, as prospective students look for certainty as they consider alternatives.
    
3. Predictive Analytics: Considerations about student admissions with the support of AI systems
   The fact that predictive analytics models are widespread enables schools to screen students based on a large amount of data. In addition to the dangers of automatic admissions, the ability to engage in screening automation shines a light on the tradeoffs the school faces. We will discuss the need to clarify the school’s mission and ensure that the admissions team has incentives to not rely on automation completely.
    
4. The future: Navigating AI developments
   We expect new capabilities related with student attraction and teaching to come to the market, including chatbots and conversational agents, technologies of enhanced learning, etc. We will discuss how an institution may navigate the investment opportunities in this space.