Identifying Brand Consistency by Product Differentiation Using CNN.

This paper presents a new method of using a convolutional neural network (CNN) in machine learning to identify brand consistency by product appearance variation. In Experiment 1, we collected fifty mouse devices from the past thirty-five years from a renowned company to build a dataset consisting of product pictures with pre-defined design features of their appearance and functions. Results show that it is a challenge to distinguish periods for the subtle evolution of themouse devices with such traditionalmethods as time series analysis and principal component analysis (PCA). In Experiment 2, we applied deep learning to predict the extent to which the product appearance variation ofmouse devices of various brands. The investigation collected 6,042 images ofmouse devices and divided them into the Early Stage and the Late Stage. Results show the highest accuracy of 81.4% with the CNN model, and the evaluation score of brand style consistency is 0.36, implying that the brand consistency score converted by the CNN accuracy rate is not always perfect in the real world. The relationship between product appearance variation, brand style consistency, and evaluation score is beneficial for predicting new product styles and future product style roadmaps. In addition, the CNN heat maps highlight the critical areas of design features of different styles, providing alternative clues related to the blurred boundary. The study provides insights into practical problems for designers, manufacturers, and marketers in product design. It not only contributes to the scientific understanding of design development, but also provides industry professionals with practical tools and methods to improve the design process and maintain brand consistency. Designers can use these techniques to find features that influence brand style. Then, capture these features as innovative design elements and maintain core brand values. [ABSTRACT FROM AUTHOR]