An Advertising Real-Time Intelligent and Scalable Framework for Profiling Customers’ Emotions

• Autores: Leandro Alves, Pedro Oliveira, João Henriques, Marco V. Bernardo, Cristina Wanzeller, Filipe Caldeira
• Localización: New Trends in Disruptive Technologies, Tech Ethics and Artificial Intelligence: The DITTET 2022 Collection / Daniel Hernández de la Iglesia (ed. lit.), Juan Francisco de Paz Santana (ed. lit.), Alfonso José López Rivero (ed. lit.), 2023, ISBN 978-3-031-14858-3, págs. 55-68
• Idioma: español
• Texto completo no disponible (Saber más ...)
• Resumen

  • The advertising industry is continuously looking up for effective ways to communicate to customers to impact their purchasing. Usually, profiling them is a time-consuming offline activity. Therefore, it becomes necessary to reduce costs and time to address consumers’ needs. This work proposes a scalable framework enabled by a Machine Learning (ML) model to profile customers to identify their emotions to help to drive campaigns. A multi-platform mobile application continuously profiles consumers crossing the front stores. Profiling customers according to their age and hair color, the color of their eyes, and emotions (e.g. happiness, sadness, disgust, fear) will help companies to make the most suitable advertisement (e.g. to predict whether the advertising tones on the front store are the adequate ones). All that data are made available in web portal dashboards, wherein subscribers can take their analysis. Such results from the analysis data help them to identify tendencies regarding the culture and age, and drive companies to fit front stores accordingly (e.g. to discover the right tones for the season). This framework can help to develop new innovative cost-effective business models at scale by driving in real-time the advertisements to a huge number of consumers to maximize their impact and centralizing the data collected from a large number of stores to design future campaigns.