TY - GEN
T1 - Designing the future of personal fashion
AU - Vaccaro, Kristen
AU - Agarwalla, Tanvi
AU - Shivakumar, Sunaya
AU - Kumar, Ranjitha
N1 - Publisher Copyright:
© 2018 Copyright held by the owner/author(s).
PY - 2018/4/20
Y1 - 2018/4/20
N2 - Advances in computer vision and machine learning are changing the way people dress, and buy clothes. Given the vast space of fashion problems, where can data-driven technologies provide the most value? To understand consumer pain points and opportunities for technological interventions, this paper presents the results from two independent need-finding studies that explore the gold-standard of personalized shopping: interacting with a personal stylist. Through interviews with five personal stylists, we study the range of problems they address and their in-person processes for working with clients. In a separate study, we investigate how styling experiences map to online settings by building and releasing a chatbot that connects users to one-on-one sessions with a stylist, acquiring more than 70 organic users in three weeks. These conversations reveal that in-person and online styling sessions share similar goals, but online sessions often involve smaller problems that can be resolved more quickly. Based on these explorations, we propose future personalized, online interactions that address consumer trust and uncertainty, and discuss opportunities for automation.
AB - Advances in computer vision and machine learning are changing the way people dress, and buy clothes. Given the vast space of fashion problems, where can data-driven technologies provide the most value? To understand consumer pain points and opportunities for technological interventions, this paper presents the results from two independent need-finding studies that explore the gold-standard of personalized shopping: interacting with a personal stylist. Through interviews with five personal stylists, we study the range of problems they address and their in-person processes for working with clients. In a separate study, we investigate how styling experiences map to online settings by building and releasing a chatbot that connects users to one-on-one sessions with a stylist, acquiring more than 70 organic users in three weeks. These conversations reveal that in-person and online styling sessions share similar goals, but online sessions often involve smaller problems that can be resolved more quickly. Based on these explorations, we propose future personalized, online interactions that address consumer trust and uncertainty, and discuss opportunities for automation.
KW - Chatbots
KW - Conversational agents
KW - Fashion
KW - Need-finding
UR - http://www.scopus.com/inward/record.url?scp=85046959133&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85046959133&partnerID=8YFLogxK
U2 - 10.1145/3173574.3174201
DO - 10.1145/3173574.3174201
M3 - Conference contribution
AN - SCOPUS:85046959133
T3 - Conference on Human Factors in Computing Systems - Proceedings
BT - CHI 2018 - Extended Abstracts of the 2018 CHI Conference on Human Factors in Computing Systems
PB - Association for Computing Machinery
T2 - 2018 CHI Conference on Human Factors in Computing Systems, CHI 2018
Y2 - 21 April 2018 through 26 April 2018
ER -