Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Computational Psychology to Embed Emotions into Product to Increase Customer Affinity
Abstract Customers take buying decisions on many factors. The emotional impact of the product on customer is one of the most important factors. Cognitive ergonomics tries to strike the balance between work, product and environment with human needs and capabilities. The utmost need to integrate emotions in the product cannot be denied. The idea is that product should be able to engage the customer on emotional and behavioral platform. While achieving this objective there is need to learn about customer behavior and use computational psychology while building product. This paper based on Machine Learning tries to map behavior of the customer with the products and also provide inputs for affective value for building personalized products. The affective value of the products is determined and products are mapped to customer. The algorithm suggests the most suitable product for customers while understanding emotional traits required for personalization. This work can be used to improve customer satisfaction through embedding emotions in the product. It can be used to map personal product range, personalized programs and ranking programs, products with reference to individuals.
Computational Psychology to Embed Emotions into Product to Increase Customer Affinity
Abstract Customers take buying decisions on many factors. The emotional impact of the product on customer is one of the most important factors. Cognitive ergonomics tries to strike the balance between work, product and environment with human needs and capabilities. The utmost need to integrate emotions in the product cannot be denied. The idea is that product should be able to engage the customer on emotional and behavioral platform. While achieving this objective there is need to learn about customer behavior and use computational psychology while building product. This paper based on Machine Learning tries to map behavior of the customer with the products and also provide inputs for affective value for building personalized products. The affective value of the products is determined and products are mapped to customer. The algorithm suggests the most suitable product for customers while understanding emotional traits required for personalization. This work can be used to improve customer satisfaction through embedding emotions in the product. It can be used to map personal product range, personalized programs and ranking programs, products with reference to individuals.
Computational Psychology to Embed Emotions into Product to Increase Customer Affinity
Kulkarni, Hrishikesh (Autor:in) / Joshi, Prachi (Autor:in) / Chande, Pradip (Autor:in)
28.06.2019
11 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
Machine learning , Artificial intelligence , Cognitive sciences , Computational psychology , Context , Computational behavior , Affective computing Engineering , Quality Control, Reliability, Safety and Risk , Performance and Reliability , Circuits and Systems , Operations Research, Management Science