Understanding Customer Insights Through Big Data: Innovations in Brand Evaluation in the Automotive Industry

Abstract

Abstract. Insights gained from social media platforms are pivotal for businesses to understand their products’ present position. While it is possible to use consulting services focusing on surveys about a product or brand, such methods may yield limited insights. By contrast, on social media, people frequently express their individual and unique feelings about products openly and informally. With this in mind, we aim to provide rigorous methodologies to enable businesses to gain significant insights on their brands and products in terms of representations on social media. This study employs conjoint analysis to lay the analytical groundwork for developing positive and negative sentiment frameworks to evaluate the brands of three prominent emerging automotive companies in Indonesia, anonymized as “HMI,” “YMI,” and “SMI.” We conducted a survey with a sample size of n=67 to analyze the phrasings of importance for our wording dictionary construction. A series of data processing operations were carried out, including the collection, capture, formatting, cleansing, and transformation of data. Our study’s findings indicate a distinct ranking of the most positively and negatively perceived companies among social media users. As a direct management-related implication, our proposed data analysis methods could assist the industry in applying the same rigor to evaluating companies’ products and brands directly from social media users’ perspective. Keywords: Brand image, social media, data analytics, sentiment analysis, conjoint analysis