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Computational Psychology to Embed Emotions into Product to Increase Customer Affinity

  • Hrishikesh KulkarniEmail author
  • Prachi Joshi
  • Pradip Chande
Conference paper

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.

Keywords

Machine learning Artificial intelligence Cognitive sciences Computational psychology Context Computational behavior Affective computing 

References

  1. 1.
    Osgood, C.E., Suci, G.J., Tannenbaum, P.H.: The Measurement of Meaning. University of Illinois Press, Illinois (1957)Google Scholar
  2. 2.
    Green, E.P., Srinivasan, V.: Conjoint analysis in consumer research. J. Consum. Res. 5 (1978)Google Scholar
  3. 3.
    Küller, R.: Semantisk Miljö Beskrivning (SMB). Psykologiförlaget AB Liber Tryck Stockholm, Stockholm (1975)Google Scholar
  4. 4.
    Akao, Y.: History of Quality Function Deployment in Japan. Hansa Publisher (1990)Google Scholar
  5. 5.
    Nagamachi, M.: Kansei Engineering. Kaibundo, Tokyo (1989)zbMATHGoogle Scholar
  6. 6.
    Encyclopaedia Britannica Online (2005). http://search.eb.com/
  7. 7.
    Titchener, E.B.: An Outline of Psychology. Thoemmes, Bristol (1998)Google Scholar
  8. 8.
    DeLancey, C.: Passionate Engines. Oxford University Press, Oxford (2002)CrossRefGoogle Scholar
  9. 9.
    Picard, R.: Affective Computing. Massachusetts Institute of Technology (1997)Google Scholar
  10. 10.
    Kleinginna, P.R., Kleinginna, A.M.: A categorized list of emotion definitions, with suggestions for consensual definition. Motiv. Emot. 5, 345–379 (1981)CrossRefGoogle Scholar
  11. 11.
    Kulkarni, H.: Intelligent context based prediction using probabilistic intent-action ontology and tone matching algorithm. In: IEEE Conference, ICACCI, Manipal (2017)Google Scholar
  12. 12.
    Kulkarni, H.: Thought process based team member selection using contextual sentiment closeness. In: IEEE Mumbai Chapter Conference, Third International Conference for Convergence in Technology, Pune, April 2018Google Scholar
  13. 13.
    Kulkarni, H.: Multi-graph based intent hierarchy generation to determine action sequence. In: Springer Conference, ICDECT, Pune, December 2017Google Scholar
  14. 14.
    Kulkarni, H., Marathe, M.: Context Based Machine Learning to Determine Similarity Index (CML-SI) for Team Selection (in press)Google Scholar
  15. 15.
    Barrett, L.F., Mesquita, B., Gendron, M.: Context in emotion perception. Curr. Dir. Psychol. Sci. 20(5), 286–290 (2011)CrossRefGoogle Scholar
  16. 16.
    Jack, R.E., Blais, C., Scheepers, C., Schyns, P.G., Caldara, R.: Cultural confusions show that facial expressions are not universal. Curr. Biol. 19, 1–6 (2009)CrossRefGoogle Scholar
  17. 17.
    Terwogt, M., Hoeksma, J.: Colors and emotions: preferences and combinations. J. Gen. Psychol. 122(1), 5–17 (2001)CrossRefGoogle Scholar
  18. 18.
    Singh, S.: Impact of color on marketing. Manag. Decis. 44(6), 783–789 (2006)CrossRefGoogle Scholar
  19. 19.
    Alt, M.: Emotional response to color associated with an advertisement, May 2008. https://etd.ohiolink.edu/!etd.send_file?accession=bgsu1206377243&disposition=inline. Accessed 16 Sept 2018
  20. 20.
    Khuong, M.N., Tram, V.N.B.: The effects of emotional marketing on consumer product perception, brand awareness and purchase decision—a study in Ho Chi Minh City, Vietnam. J. Econ. Bus. Manag. 3(5) (2015)Google Scholar
  21. 21.
    Ortony, A., Clore, G.L., Collins, A.: The Cognitive Structure of Emotions. Cambridge University Press, Cambridge (1988)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Hrishikesh Kulkarni
    • 1
    Email author
  • Prachi Joshi
    • 2
  • Pradip Chande
    • 2
  1. 1.PVG’s COETSPPUPuneIndia
  2. 2.iKnowlation Research LabsPuneIndia

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