Constructing a Mathematical Model of Product Color Design Based on Data Mining: Case Study of a Thermos Cup
Source abstract
Abstract
In product design, color is the first element that acts on the human visual senses and significantly influences consumer decisions. This study aimed to analyze consumers’ color preferences for products and explore the mathematical patterns of product color design. Firstly, sales data and images of popular thermos cups from Tmall and Jingdong (JD), two prominent e-commerce platforms in China, were obtained through data mining. Subsequently, this research focused on single-color thermos cups with high sales as the research subject, extracting the hue (H), saturation (S), and value (V) for each cup from the product images. Furthermore, a 3D scatter plot of HSV values was generated using Origin Pro, visually representing the consumers’ color preferences. Finally, this study examined the relationships among HSV values of the popular product colors through multiple regression analysis and constructed a mathematical model for HSV. This method enables manufacturers to gain valuable insights into consumer color preferences, facilitating digital color design and enhancing design efficiency and accuracy.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Color perception and design · Consumer Packaging Perceptions and Trends · Color Science and Applications
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
College of Furnishing and Industrial Design, Nanjing Forestry University, Nanjing 210037, China
Co-Innovation Center of Efficient Processing and Utilization of Forest Resources, Nanjing Forestry University, Nanjing 210037, China
Funding
National Nature Science Foundation of China · China Postdoctoral Science Foundation
References (30)
Show source-provided references
1. A design decision-making support model for customized product color combination10.1016/j.compind.2006.11.001 ↗
2. Bayesian analysis of color preferences: An application for product and product line design10.1002/col.21982 ↗
3. Product designing and big data
4. Development of computer aided color customization system for sofa
5. A computer-assisted colour selection system based on aesthetic measure for colour harmony and fuzzy logic theory10.1002/col.20417 ↗
6. Color preferences for different topics in connection to personal characteristics10.1002/col.21845 ↗
7. An ecological framework for temporal and individual differences in color preferences10.1016/j.visres.2017.01.010 ↗
8. Color preference and contributing factors of urban architecture based on the selection of color samples—Case study: Shanghai10.1002/col.22731 ↗
9. Hue, chroma, and lightness preference in chinese adults: Age and gender differences10.1002/col.22426 ↗
10. The impact of color preference on adolescent children’s choice of furniture10.1002/col.22507 ↗
11. The role of individual colour preferences in consumer purchase decisions10.1002/col.22180 ↗
12. Colour associations and consumer product-colour purchase decisions10.1002/col.22659 ↗
13. Evaluation of the color aesthetics of fine wood based on perceptual cognition10.15376/biores.16.2.4126-4148 ↗
14. A study of colors of court robes of the eldest direct descendants of Confucius in the Ming and Qing dynasty based on HSV color model
15. An exploration of color preference in modern interior design
16. Development of a selection system for the colour of wardrobe furniture10.15376/biores.17.3.3912-3928 ↗
17. Zhao, L., Wang, Z., Zuo, Y., and Hu, D. (2021). Comprehensive evaluation method of ethnic costume color based on K-Means clustering method. Symmetry, 13.10.3390/sym13101822 ↗
18. The revision of conversion for mulafrom RGB color space to HSV color space
19. Relationship between Saturation and Brightness Value in HSV Color Spac
20. Hand, D., Mannila, H., and Smyth, P. (2001). Principles of Data Mining, The Mit Press.
21. Squire, M. (2016). Mastering Data Mining with Python, Packt Publishing.
22. Jiang, S., Li, X., and Zheng, Q. (2011). Principle and Practice of Data Mining, Publishing House of Electronics Industry.
23. The effect of new package design on product attention, categorization and evaluation10.1016/S0167-4870(97)00008-1 ↗
24. The pedigree study on the color of industrial design product:a case study of radio in shanghai
25. Individual differences and their implications for color perception10.1016/j.cobeha.2019.05.002 ↗
26. Sensory, computational and cognitive components of human colour constancy10.1098/rstb.2005.1633 ↗
27. The Influence of Luminance Level on Visual Sensitivity to Color Differences10.1364/JOSA.41.000684 ↗
28. Berthold, M.R., Borgelt, C., and Höppner Frank Klawonn, F. (2023). Intelligent Data Analysis, Springer.
29. The role of package colour in influencing purchase intent of bottled water
30. Product color design based on big data