Fuzzy Logic Infused Intelligent Scent Dispenser for Creating Memorable Customer Experienceof Long-Tail Connected Venues
Publication Date
11-7-2018
Document Type
Conference Proceeding
Publication Title
Proceedings of 2018 International Conference on Machine Learning and Cybernetics
DOI
10.1109/ICMLC.2018.8527046
First Page
149
Last Page
154
Abstract
In today's competitive business environment, creating memorable experiences and emotionalconnections with consumers is critical to win customer spending and long term brand loyalty. Brands want their customers to be in pleasing, subliminal scented environments. Even a few micro particles of scent can do a lot of marketing's heavy lifting, from improving consumer perceptions of quality to increasing the number of visits. Hence, high roller venues such as Trump Towers and Caesars Palace of the World use digital connected intelligent scent disperser systems that deliver seamless olfactory experiences to connect with consumers on a deeper, emotionaland personal level.The challenges, however, for venues with limited digital connected infrastructure and deficient intelligent systems are lack of engagement with patrons at a personal and emotional level and thus miss recurring business opportunities and sustained long-Term brand loyalty.This research paper addresses the challenge by developing fuzzy control infused autonomous intelligent scent dispensers that bring connected intelligence to non-connected venues. The paperpresents prototyping solution design, as well as its application and certain experimental results.
Keywords
Artificial intelligent (AI) systems, Fuzzy logic, iDispenser, Internet of things, Long tail, Machine learning, Sensors, Venue analytics
Department
Computer Engineering
Recommended Citation
Chandrasekar Vuppalapati, Rajasekar Vuppalapati, Sharat Kedari, Anitha Ilapakurti, Jaya Shankar Vuppalapati, and Santosh Kedari. "Fuzzy Logic Infused Intelligent Scent Dispenser for Creating Memorable Customer Experienceof Long-Tail Connected Venues" Proceedings of 2018 International Conference on Machine Learning and Cybernetics (2018): 149-154. https://doi.org/10.1109/ICMLC.2018.8527046