Leveraging Event Marketing Performance using AI in Facial Recognition

Abstract

With the advances in technology and the rapid changes in human-technology interactions, the event marketing field has seen major developments over the past years. Despite its remarkable growth, many aspects of event marketing do not yet align with the best available technologies. In this paper, we aim to leverage event marketing performance using artificial intelligence techniques. We design a framework that optimizes attendee-feedback generation using a facial-recognition algorithm. The framework measures attendees’ engagement levels by periodically extracting attendee facial features during a session, categorizing them into seven states of emotions (anger, disgust, fear, happiness, neutral, sadness, and surprise), and then analyzing session engagements based on the obtained results. These measurements are then used to give insights about an event’s performance during and after sessions, thus improving the overall performance of a given event. The proposed framework is easy-to-implement, time-efficient, and cost-effective.