Presentation 2021-12-18
Analysis of consumers by age and gender in POS data based on Taylor's law
Kazuki Koyama, Mariko Ito, Takaaki Ohnishi,
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Abstract(in Japanese) (See Japanese page)
Abstract(in English) System underlying purchase activity is hard to understand because of the heterogeneity in consumers’ behavior. However, we could expect to find a specific pattern if we divide purchase records by considering the feature of products and consumers. We, therefore, classified ID-POS data according to product categories, consumer’s attributes and seasons, and analyzed them based on the theory of Taylor’s law: the power-law relationship between the standard deviation and the mean of sales amounts. We found that the nature of Taylor’s law significantly depends on the category or brand of products.
Keyword(in Japanese) (See Japanese page)
Keyword(in English) Taylor’s law / ID-POS data / scaling law / cross-over / sales numbers
Paper # NLP2021-57
Date of Issue 2021-12-10 (NLP)

Conference Information
Committee NLP
Conference Date 2021/12/17(2days)
Place (in Japanese) (See Japanese page)
Place (in English) J:COM Horuto Hall OITA
Topics (in Japanese) (See Japanese page)
Topics (in English)
Chair Takuji Kosaka(Chukyo Univ.)
Vice Chair Akio Tsuneda(Kumamoto Univ.)
Secretary Akio Tsuneda(Kagawa Univ.)
Assistant Hideyuki Kato(Oita Univ.) / Yuichi Yokoi(Nagasaki Univ.)

Paper Information
Registration To Technical Committee on Nonlinear Problems
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Analysis of consumers by age and gender in POS data based on Taylor's law
Sub Title (in English)
Keyword(1) Taylor’s law
Keyword(2) ID-POS data
Keyword(3) scaling law
Keyword(4) cross-over
Keyword(5) sales numbers
1st Author's Name Kazuki Koyama
1st Author's Affiliation Rikkyo University(Rikkyo Univ.)
2nd Author's Name Mariko Ito
2nd Author's Affiliation Rikkyo University(Rikkyo Univ.)
3rd Author's Name Takaaki Ohnishi
3rd Author's Affiliation Rikkyo University(Rikkyo Univ.)
Date 2021-12-18
Paper # NLP2021-57
Volume (vol) vol.121
Number (no) NLP-307
Page pp.pp.61-66(NLP),
#Pages 6
Date of Issue 2021-12-10 (NLP)