Understanding Customer Insights Through Big Data: Innovations in Brand Evaluation in the Automotive Industry
DOI:
https://doi.org/10.12695/ajtm.2022.15.1.4Keywords:
Brand image, social media, data analytics, sentiment analysis, conjoint analysisAbstract
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
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References
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Tirunillai, S., & Tellis, G. J. (2014). Mining marketing meaning from online chatter: Strategic brand analysis of big data using latent dirichlet allocation. Journal of marketing research, 51(4), 463-479.
Tripathi, S. N., & Siddiqui, M. H. (2010). An empirical study of tourist preferences using conjoint analysis. International Journal of Business Science & Applied Management (IJBSAM), 5(2), 1-16.
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Alsayat, A., & Elmitwally, N. (2020). A comprehensive study for Arabic sentiment analysis (Challenges and Applications). Egyptian Informatics Journal, 21(1), 7-12.
Alwakid, G., Osman, T., & Hughes-Roberts, T. (2017). Challenges in sentiment analysis for Arabic social networks. Procedia Computer Science, 117, 89-100.
Bak, A., & Bartlomowicz, T. (2009). Conjoint analysis method and its implementation in conjoint R package. Wroclaw: University of Economics.
Cattin, P., & Wittink, D. R. (1982). Commercial use of conjoint analysis: A survey. Journal of marketing, 46(3), 44-53.
Chiang, L. L. L., & Yang, C. S. (2018). Does country-of-origin brand personality generate retail customer lifetime value? A Big Data analytics approach. Technological Forecasting and Social Change, 130, 177-187.
Chong, A. Y. L., Ch’ng, E., Liu, M. J., & Li, B. (2017). Predicting consumer product demands via Big Data: the roles of online promotional marketing and online reviews. International Journal of Production Research, 55(17), 5142-5156.
Dong, R., O’Mahony, M. P., Schaal, M., McCarthy, K., & Smyth, B. (2015). Combining similarity and sentiment in opinion mining for product recommendation. Journal of Intelligent Information Systems, 46 (2), 285–312. doi:10.1007/s10844-015-0379-y
Eggers, F., Eggers, F., & Kraus, S. (2016). Entrepreneurial branding: measuring consumer preferences through choice-based conjoint analysis. International Entrepreneurship and Management Journal, 12(2), 427-444.
Green, P. E., & Krieger, A. M. (1993). Conjoint analysis with product-positioning applications. Handbooks in operations research and management science, 5, 467-515.
Green, P. E., & Krieger, A. M. (2002). What's right with conjoint analysis?. Marketing Research, 14(1), 24.
Hauser, J. R., & Rao, V. R. (2004). Conjoint Analysis, Related Modeling, and Applications. Marketing Research and Modeling: Progress and Prospects, 141–168. doi:10.1007/978-0-387-28692-1_7
Helmi, R. L. (2020). Knowledge Management Enabler (KME) to Promote Innovation Capabilities in Public R&D Centers in Indonesia. The Asian Journal of Technology Management, 13(2), 98-112.
Hemalatha, I., Varma, G. S., & Govardhan, A. (2013). Sentiment classification in online reviews using FRN algorithm. 357-362.
Hussein, D. M. E. D. M. (2018). A survey on sentiment analysis challenges. Journal of King Saud University-Engineering Sciences, 30(4), 330-338.
Jian Jin, & Ping Ji. (2015). Mining online product reviews to identify consumers’ fine-grained concerns. 12th International Symposium on Operations Research and Its Applications in Engineering, Technology and Management (ISORA 2015). doi:10.1049/cp.2015.0622
Kencanasari, R. A. M., Dhewanto, W., & Rustiadi, S. (2021). Digital Product Perception and User Satisfaction Relationship: Can They Create Feedback Intention?. The Asian Journal of Technology Management, 14(2), 109-127.
Khedr, A. E., & Yaseen, N. (2017). Predicting stock market behavior using data mining technique and news sentiment analysis. International Journal of Intelligent Systems and Applications, 9(7), 22.
Khoo, C. S. G., Nourbakhsh, A., & Na, J. C. (2012). Sentiment analysis of online news text: a case study of appraisal theory. Online Information Review, 36(6), 858-878.
Kralj Novak, P., Smailović, J., Sluban, B., & Mozetič, I. (2015). Sentiment of emojis. PloS one, 10(12), e0144296.
Kumar, S., & Kaur, K. (2016). Review of Data mining (Knowledge discovery) in the Future. International Journal of Advanced Research in Computer Science, 7(6).
Lehtinen, U., & Lehtinen, J. R. (1991). Two approaches to service quality dimensions. Service Industries Journal, 11(3), 287-303.
Lee, E., Lee, J. A., Moon, J. H., & Sung, Y. (2015). Pictures speak louder than words: Motivations for using Instagram. Cyberpsychology, behavior, and social networking, 18(9), 552-556.
Lim, S. Y., Harun, U. B., Gobil, A. R., Mustafa, N. A., Zahid, N. A., Amin-Nordin, S., ... & Shohaimi, S. (2021). Measuring customer satisfaction on the cleanliness of food premises using fuzzy conjoint analysis: A pilot test. Plos one, 16(9), e0256896.
Lin, D., Cao, D., Lv, Y., & Cai, Z. (2017). GIF video sentiment detection using semantic sequence. Mathematical Problems in Engineering, 2017, 1-11.
Liu, X., Zhou, Y., & Chen, X. (2018). Mining outlier data in mobile internet-based large real-time databases. Complexity, 2018, 1-12
Maglio, P. P., & Spohrer, J. (2008). Fundamentals of service science. Journal of the academy of marketing science, 36(1), 18-20.
Mariani, M. M., & Wamba, S. F. (2020). Exploring how consumer goods companies innovate in the digital age: The role of big data analytics companies. Journal of Business Research, 121, 338-352.
Marshall, D., Bridges, J. F., Hauber, B., Cameron, R., Donnalley, L., Fyie, K., & Reed Johnson, F. (2010). Conjoint analysis applications in health—how are studies being designed and reported?. The Patient: Patient-Centered Outcomes Research, 3(4), 249-256.
Medhat, W., Hassan, A., & Korashy, H. (2014). Sentiment analysis algorithms and applications: A survey. Ain Shams engineering journal, 5(4), 1093-1113.
Papadima, G., Genitsaris, E., Karagiotas, I., Naniopoulos, A., & Nalmpantis, D. (2020). Investigation of acceptance of driverless buses in the city of Trikala and optimization of the service using Conjoint Analysis. Utilities Policy, 62, 100994.
Reagan, A. J., Danforth, C. M., Tivnan, B., Williams, J. R., & Dodds, P. S. (2017). Sentiment analysis methods for understanding large-scale texts: a case for using continuum-scored words and word shift graphs. EPJ Data Science, 6, 1-21.
Sandoval-Almazan, R., & Valle-Cruz, D. (2018). Facebook impact and sentiment analysis on political campaigns. In Proceedings of the 19th Annual International Conference on Digital Government Research: Governance in the Data Age, 1-7.
Saura, J. R., Herráez, B. R., & Reyes-Menendez, A. (2019). Comparing a traditional approach for financial Brand Communication Analysis with a Big Data Analytics technique. IEEE Access, 7, 37100-37108.
Shayaa, S., Jaafar, N. I., Bahri, S., Sulaiman, A., Wai, P. S., Chung, Y. W., ... & Al-Garadi, M. A. (2018). Sentiment analysis of big data: Methods, applications, and open challenges. IEEE Access, 6, 37807-37827.
Shirdastian, H., Laroche, M., & Richard, M. O. (2019). Using big data analytics to study brand authenticity sentiments: The case of Starbucks on Twitter. International Journal of Information Management, 48, 291-307.
Smetanin, S. (2020). The applications of sentiment analysis for Russian language texts: Current challenges and future perspectives. IEEE Access, 8, 110693-110719.
Soliman, T. H., Elmasry, M. A., Hedar, A., & Doss, M. M. (2014). Sentiment analysis of Arabic slang comments on facebook. International Journal of Computers & Technology, 12(5), 3470-3478.
Sowmya, Y., Ratna, M. N., & Bindu, C. S. (2015). A Review on Big Data Mining, Distributed Programming Frameworks and Privacy Preserving Data Mining Techniques. International journal of advanced research in computer science, 6(1), 121-126.
Sun, X., Wagner, A. L., Ji, J., Huang, Z., Zikmund-Fisher, B. J., Boulton, M. L., ... & Prosser, L. A. (2020). A conjoint analysis of stated vaccine preferences in Shanghai, China. Vaccine, 38(6), 1520-1525.
Tirunillai, S., & Tellis, G. J. (2014). Mining marketing meaning from online chatter: Strategic brand analysis of big data using latent dirichlet allocation. Journal of marketing research, 51(4), 463-479.
Tripathi, S. N., & Siddiqui, M. H. (2010). An empirical study of tourist preferences using conjoint analysis. International Journal of Business Science & Applied Management (IJBSAM), 5(2), 1-16.
Troussas, C., Virvou, M., Espinosa, K. J., Llaguno, K., & Caro, J. (2013). Sentiment analysis of Facebook statuses using Naive Bayes classifier for language learning. In IISA 2013, 1-6.
Wang, L., & Wang, G. (2015). Data mining applications in big data. Computer Engineering and Applications Journal, 4(3), 143-152.
Youn, D. M., Lee, Y. H., & Lee, B. G. (2020). Proposal of Brand Evaluation Map through Big Data: Focus on The Hyundai Motor's Product Evaluation. Journal of Information Technology Services, 19(4), 1-11.
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Submitted
2021-12-16
Accepted
2022-04-22
Published
2022-04-28
How to Cite
Siahaan, B. P., & Prasetio, E. A. (2022). Understanding Customer Insights Through Big Data: Innovations in Brand Evaluation in the Automotive Industry. The Asian Journal of Technology Management (AJTM), 15(1), 49–66. https://doi.org/10.12695/ajtm.2022.15.1.4
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