What attracts vehicle consumers’ buying:A Saaty scale-based VIKOR (SSC-VIKOR) approach from after-sales textual perspective?

Purpose: The increasingly booming e-commerce development has stimulated vehicle consumers to express individual reviews through online forum. The purpose of this paper is to probe into the vehicle consumer consumption behavior and make recommendations for potential consumers from textual comments viewpoint. Design/methodology/approach: A big data analytic-based approach is designed to discover vehicle consumer consumption behavior from online perspective. To reduce subjectivity of expert-based approaches, a parallel Naïve Bayes approach is designed to analyze the sentiment analysis, and the Saaty scale-based (SSC) scoring rule is employed to obtain specific sentimental value of attribute class, contributing to the multi-grade sentiment classification. To achieve the intelligent recommendation for potential vehicle customers, a novel SSC-VIKOR approach is developed to prioritize vehicle brand candidates from a big data analytical viewpoint. Findings: The big data analytics argue that “cost-effectiveness” characteristic is the most important factor that vehicle consumers care, and the data mining results enable automakers to better understand consumer consumption behavior. Research limitations/implications: The case study illustrates the effectiveness of the integrated method, contributing to much more precise operations management on marketing strategy, quality improvement and intelligent recommendation. Originality/value: Researches of consumer consumption behavior are usually based on survey-based methods, and mostly previous studies about comments analysis focus on binary analysis. The hybrid SSC-VIKOR approach is developed to fill the gap from the big data perspective

Sentiment Analysis for Social Media

Sentiment analysis is a branch of natural language processing concerned with the study of the intensity of the emotions expressed in a piece of text. The automated analysis of the multitude of messages delivered through social media is one of the hottest research fields, both in academy and in industry, due to its extremely high potential applicability in many different domains. This Special Issue describes both technological contributions to the field, mostly based on deep learning techniques, and specific applications in areas like health insurance, gender classification, recommender systems, and cyber aggression detection

SizeNet: Weakly Supervised Learning of Visual Size and Fit in Fashion Images

Finding clothes that fit is a hot topic in the e-commerce fashion industry. Most approaches addressing this problem are based on statistical methods relying on historical data of articles purchased and returned to the store. Such approaches suffer from the cold start problem for the thousands of articles appearing on the shopping platforms every day, for which no prior purchase history is available. We propose to employ visual data to infer size and fit characteristics of fashion articles. We introduce SizeNet, a weakly-supervised teacher-student training framework that leverages the power of statistical models combined with the rich visual information from article images to learn visual cues for size and fit characteristics, capable of tackling the challenging cold start problem. Detailed experiments are performed on thousands of textile garments, including dresses, trousers, knitwear, tops, etc. from hundreds of different brands.Comment: IEEE Conference on Computer Vision and Pattern Recognition Workshop (CVPRW) 2019 Focus on Fashion and Subjective Search - Understanding Subjective Attributes of Data (FFSS-USAD

Customer Churn Rate Analysis in Fashion E-Commerce Using Logistic Regression and Random Forest

E-commerce companies think about long-term customer relationships in terms of conversion rates and repeat purchase rates. It is more cost-effective to retain existing customers than to acquire new ones, which is why it is crucial to track customers at high risk of turnover and target them with retention strategies. This research aims to identify what factors play a significant role in e-commerce to the customer churn rate in the next month. This study is based on 77,841 transactions data collected from Indonesia's fashion company through their e-commerce sales channel. In processing the data, descriptive statistics and predictive analytics with logistic regression and random forest models are used to achieve the research's objective, as both models have a good level of accuracy in making predictions and classifications. This study shows several factors such as gender, customer length of stay, order amount, and shipping cost significantly influencing the churn rate. This study recommends that the company and the fashion e-commerce industry manage customer churn by made appropriate strategic and business steps after knew the factors that cause it. Keywords: Customer Churn Rate; E-Commerce; Big Data; Logistic Regression; Random Fores