Investigating Consumer Behavior to Create Expected Customer Value, Using Big Data Analysis

Document Type : Research Paper

Authors

1 Ph.D. Candidate, Business Management, Faculty of Administrative Sciences & Economics, Isfahan

2 Associate Prof., Department of Business Management, Faculty of Administrative Sciences & Economics, University of Isfahan, Isfahan, Iran

10.34785/J018.2020.467

Abstract

The concept of “value” in a business is a potential product or service that a business promises to deliver to the customer and in general, it is the reason why a customer chooses a brand and prefers it over competing brands. In recent years, with the increasing presence of consumers in social networks, it has become possible to access data related to the interests and expected values of consumers. The purpose of this study is to identify the components of value, to create and provide value to the customer, by analyzing the opinions and user-generated content in social networks. For this purpose, 41904 costumers review relating cell phone from the Digikala online shopping site using machine learning algorithms and topic extraction by inductive approach is analyzed. According to this study, five main groups of values were detected: 1. Functional values, 2. Economic values, 3. Qualitative values, 4. Emotional values, 5. Social values. Also, the components related each group was identified. The results show that by using big data analytics, it is possible to obtain a clearer image than the expected values of the customer with less waste of resources and produce a commodity tailored to customer values.

Keywords


Aaker, D. (2012). Building Strong Brands. London: Pocket Simon & Schuster.
Abbaszadeh, H., Alamtabriz, A., Irandoost, M., Salavati, A. (2019). Branding to create consumer value in the Iranian banking system. Consumer Behavior Studies Journal, 6(1), 193-211. (in Persian)
Asgari, P. (2014). Fundamentals of Research Methods in the Humanities. Ahwaz: Islamic Azad University Press. (in Persian)
Baldassarre, B., Calabretta, G., Bocken, N. M. P., & Jaskiewicz, T. (2017). Bridging sustainable business model innovation and user-driven innovation: A process for sustainable value proposition design. Journal of Cleaner Production147, 175-186.
Bazargan, A. (2016). Introduction to Qualitative and Mixed Research Methods: A Conventional Approach in the Behavioral Sciences. Tehran: Didar Press. (in Persian)
Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent dirichlet allocation. Journal of machine Learning research3(Jan), 993-1022.
Chang, V. (2018). A proposed social network analysis platform for big data analytics. Technological Forecasting and Social Change130, 57-68.
Clinton, L., & Whisnant, R. (2019). Business model innovations for sustainability. In Managing Sustainable Business (pp. 463-503). Springer, Dordrecht.
Dahle, Y., Toscher, B., Duc, A. N., Steinert, M., & Reuther, K. (2019, June). An analysis of Core Competence and Unique Value Proposition as normative entrepreneurship elements. In 2019 IEEE International Conference on Engineering, Technology and Innovation (ICE/ITMC) (pp. 1-10). IEEE.
Eggert, A., Kleinaltenkamp, M., & Kashyap, V. (2019). Mapping value in business markets: An integrative framework. Industrial Marketing Management, 79, 13-20.
Eid, R., & El-Gohary, H. (2015). The role of Islamic religiosity on the relationship between perceived value and tourist satisfaction. Tourism Management46, 477-488.
Gholami, N., Aghaei, N., Mohammad Kazemi, R., Saffari, M. (2019). Value Proposition to the Customer in Sport Business Model. Sport Management Studies, 11(53), 83-98. (in Persian)
Grönroos, C. (1997). Value‐driven relational marketing: from products to resources and competencies. Journal of Marketing Management, 13(5), 407-419.
Guo, Y., Barnes, S. J., & Jia, Q. (2017). Mining meaning from online ratings and reviews: Tourist satisfaction analysis using latent dirichlet allocation. Tourism Management59, 467-483.
Halawani, F. M., Soh, P. C., & Muthaiyah, S. (2019). The Effect of Social Media on Hotels' Business Performance in the Lebanese Hotel Sector: Effect of Social Media on Hotels' Business Performance. Journal of Electronic Commerce in Organizations (JECO)17(3), 54-70.
Han, J., Pei, J., & Kamber, M. (2017). Data mining: concepts and techniques. Tehran: Niyaz Danesh Press. (in Persian)
Hastie, T., Friedman, J., & Tisbshirani, R. (2017). The Elements of statistical learning: data mining, inference, and prediction. New York: Springer.
Hidayanti, I., Herman, L. E., & Farida, N. (2018). Engaging customers through social media to improve industrial product development: the role of customer co-creation value. Journal of Relationship Marketing17(1), 17-28.
Khalouzadeh Mobarakeh, S., Manian, A., Hasangholipour Yasori, T. (2019). Designing a customer experience and response improvement model using social media marketing. Consumer Behavior Studies Journal, 6(1), 287-309. (in Persian)
Kim, S., Ham, S., Moon, H., Chua, B. L., & Han, H. (2019). Experience, brand prestige, perceived value (functional, hedonic, social, and financial), and loyalty among GROCERANT customers. International Journal of Hospitality Management, 77, 169-177.
Kotler, P., Armstrong, G., & Opresnik, M. (2018). Principles of Marketing. Harlow, England: Pearson.
Liu, A. H., Leach, M. P., & Bernhardt, K. L. (2005). Examining customer value perceptions of organizational buyers when sourcing from multiple vendors. Journal of business research, 58(5), 559-568.
Liu, B. (2015). Sentiment Analysis: Mining Opinions, Sentiments, and Emotions. Cambridge, England: Cambridge University Press.
Loshin, D. (2013). Big data analytics: from strategic planning to enterprise integration with tools, techniques, NoSQL, and graph. Elsevier.
Lüdeke-Freund, F., Bohnsack, R., Breuer, H., & Massa, L. (2019). Research on Sustainable Business Model Patterns: Status quo, Methodological Issues, and a Research Agenda. In Sustainable Business Models (pp. 25-60). Palgrave Macmillan, Cham.
Masoudi, B. & Rahati Ghouchani, S. (2015). An LDA Topic Model for Farsi Word Sense Disambiguation. Signal and Data Processing. 36(4), 117-125. (in Persian)
Mobini Dehkordi, A., Rezvani, M., Davari, A., Forozanfar, F. (2014). Innovative business model for B2C distribution's companies (Case Study: Golrang-pakhsh). Journal of Entrepreneurship Development, 7(3), 569-588. (in Persian)
Mostafa, M. M. (2013). More than words: Social networks’ text mining for consumer brand sentiments. Expert Systems with Applications40(10), 4241-4251.
Osterwalder, A., & Pigneur, Y. (2010). Business model generation: a handbook for visionaries, game changers, and challengers. John Wiley & Sons.
Payne, A., Frow, P., & Eggert, A. (2017). The customer value proposition: evolution, development, and application in marketing. Journal of the Academy of Marketing Science, 45(4), 467-489.
Pihlström, M., & Brush, G. J. (2008). Comparing the perceived value of information and entertainment mobile services. Psychology & Marketing, 25(8), 732-755.
Raschka, S., & Mirjalili, V. (2019). Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow 2. Packt Publishing Ltd.
Rintamäki, T., Kuusela, H., & Mitronen, L. (2007). Identifying competitive customer value propositions in retailing. Managing Service Quality: An International Journal17(6), 621-634.
Rohani, S. & Hoseini, S. (2015). Big Data Analytics. Tehran: Niyaz Danesh Press. (in Persian)
Sakyi-Gyinae, K., & Holmlund, M. (2018). What do business customers value? An empirical study of value propositions in a servitization context. Technology Innovation Management Review, 8(5).
Sanchez, J., Callarisa, L., Rodriguez, R. M., & Moliner, M. A. (2006). Perceived value of the purchase of a tourism product. Tourism management27(3), 394-409.
Sheehan, N. T., & Bruni-Bossio, V. (2015). Strategic value curve analysis: Diagnosing and improving customer value propositions. Business Horizons, 58(3), 317-24.
Shekari, S. & Masumi, B. (2016). Semantic text clustering using “Latent Dirichlet allocation” and Genetic Algorithm. Proceeding of the 4th International Conference on Research in Science and Technology. Russia: Saint Petersburg. (in Persian)
Sheth, J. N., Newman, B. I., & Gross, B. L. (1991a). Why we buy what we buy: A theory of consumption values. Journal of business research22(2), 159-170.
Silge, J., & Robinson, D. (2017). Text mining with R: A tidy approach. " O'Reilly Media, Inc.".
Sohrabi Yourtchi, B. & Iraj, H. (2015). Big Data Management in Private and Public Sectors. Tehran: Samt Press. (in Persian)
Stahl, F., Gaber, M. M., & Adedoyin-Olowe, M. (2014). A survey of data mining techniques for social media analysis. Journal of Data Mining & Digital Humanities2014.
Sweeney, J. C., & Soutar, G. N. (2001). Consumer perceived value: The development of a multiple item scale. Journal of retailing77(2), 203-220.
Syam, N., & Sharma, A. (2018). Waiting for a sales renaissance in the fourth industrial revolution: Machine learning and artificial intelligence in sales research and practice. Industrial Marketing Management69, 135-146.
Tonidandel, S., King, E. B., & Cortina, J. M. (2018). Big data methods: Leveraging modern data analytic techniques to build organizational science. Organizational Research Methods21(3), 525-547.
Valos, M. J., Haji Habibi, F., Casidy, R., Driesener, C. B., & Maplestone, V. L. (2016). Exploring the integration of social media within integrated marketing communication frameworks: Perspectives of services marketers. Marketing Intelligence & Planning34(1), 19-40.
Verma, T., Renu, R., & Gaur, D. (2014). Tokenization and filtering process in RapidMiner. International Journal of Applied Information Systems7(2), 16-18.
Yang, S., & Zhang, H. (2018). Text mining of Twitter data using a latent Dirichlet allocation topic model and sentiment analysis. International Journal of Computer and Information Engineering12(7), 525-529.
Yee Liau, B., & Pei Tan, P. (2014). Gaining customer knowledge in low cost airlines through text mining. Industrial management & data systems114(9), 1344-1359.
Zauner, A., Koller, M., & Hatak, I. (2015). Customer perceived value-Conceptualization and avenues for future research. Cogent psychology, 2(1), 1061782.
Zhang, T. C., Gu, H., & Jahromi, M. F. (2019). What makes the sharing economy successful? An empirical examination of competitive customer value propositions. Computers in Human Behavior95, 275-283.