Business intelligence using the fuzzy-Kano model

Soumaya Lamrharia, Hamid Elghazi, Abdellatif El Faker

Abstract


Today, understanding customer satisfaction is becoming a difficult and complex task for companies due to the explosive growth of the voice of the customer in online reviews. This has pushed companies to rethink their business strategies and resort to business intelligence techniques in order to help them in analyzing customer requirements and market trends. This paper proposes a decision support framework for dynamically transforming the voice of the customer data into actionable insight. The framework measures the customer satisfaction by extracting key products’ aspects along with customers’ sentiments from online reviews using a text mining technique: the latent Dirichlet allocation approach. We apply the Fuzzy-Kano model to classify the real customer requirements, then, map them dynamically to the SWOT matrix. The proposed approach is extensively tested on an empirical dataset based on several performance metrics including accuracy, precision, recall, and F-score. The reported results showed that latent Dirichlet allocation approach has correctly extracted aspects with 97.4% accuracy and 92.4 % precision.

Keywords


Business intelligence, customer satisfaction, decision support framework, Fuzzy-Kano model, latent Dirichlet allocation, online reviews, text mining, voice of the customer, web intelligence

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References


Søilen, K.S. (2019) How managers stay informed about the surrounding world. Journal of Intelligence Studies in Business. 9 (1) 28-35.

Lashgari, M., Sutton-Brady, C., Solberg Søilen, K., & Ulfvengren, P. (2018). Adoption strategies of social media in B2B firms: a multiple case study approach. Journal of Business & Industrial Marketing, 33(5), 730-743.

Tontini, G., & Söilen, K. S. (2017). Nonlinear antecedents of customer satisfaction and loyalty in third-party logistics services (3PL). Asia Pacific Journal of Marketing and Logistics, (just-accepted), http://www.emeraldinsight.com/doi/pdfplus/10.1108/APJML-09-2016-0173

Søilen, K. S. (2017). Why care about competitive intelligence and market intelligence? The case of Ericsson and the Swedish Cellulose Company. Journal of Intelligence Studies in Business, 7(2).

Söilen, K. S. (2017). Why the social sciences should be based in evolutionary theory: the example of geoeconomics and intelligence studies. Journal of Intelligence Studies in Business, 7(1).

Solberg Søilen, K. (2016). Economic and industrial espionage at the start of the 21st century–Status quaestionis. Journal of Intelligence Studies in Business, 6(3).

Solberg Søilen, K. (2016). Users’ perceptions of Data as a Service (DaaS). Journal of Intelligence Studies in Business, 6(2), 43-51.

Solberg Søilen, K. (2016). A research agenda for intelligence studies in business. Journal of Intelligence Studies in Business, 6(1), 21-36.

Gedda, David, Nilsson, Billy, Såthén, Zebastian and Solberg Søilen, Klaus (2016). Crowdfunding: Finding the optimal platform for funders and entrepreneurs. Technology Innovation Management Review, 6, 3, pp. 31-40

Solberg Søilen, Klaus (2015). A place for intelligence studies as a scientific discipline. Journal of Intelligence Studies in Business, Vol. 5, No 3, pp. 34-46.

Oubrich, Mourad, Aziza, Amine, Solberg Søilen, Klaus (2015). The impact of CRM on QoE: An exploratory study from mobile phone industry in Morocco. Journal of Intelligence Studies in Business, Vol 5, No 2, pp. 22-35.

Drozdz, Sebastian, Dufwa, Marcus, Meconnen, Robiel, Solberg Søilen, Klaus (2015). An assessment of Customer Shared Value in the restaurant industry. Theoretical and Applied Economics, No. 4, 605, pp. 85-98

Granquist, C., Strömberg, F., Solberg Søilen, K. (2015). Games as a marketing channel – the impact of players and spectators. International Journal of Electronic Business Management, Vol. 13, No. 1, pp. 57-65

Vriens, Dirk, Solberg Søilen, Klaus (2014). Disruptive Intelligence - How to gather Information to deal with disruptive innovations. Journal of Intelligence Studies in Business, Vol. 4, No 3, pp. 63-78

Solberg Søilen, Klaus (2014). A survey of users’ perspectives and preferences as to the value of JISIB – A spot-check. Journal of Intelligence Studies in Business, Vol. 4, No 2, pp. 61-65

Svensson, B., Frestad Solér, M. Solberg Søilen, K. (2014). Bara segrar

Tontini, G., Söilen, K. S., & Zanchett, R. (2017). Nonlinear antecedents of customer satisfaction and loyalty in third-party logistics services (3PL). Asia Pacific Journal of Marketing and Logistics, 29(5), 1116-1135.

Agostino, Alessandro, Solberg Søilen, Klaus, Gerritsen, Bart (2013). Cloud solution in Business Intelligence for SMEs –vendor and customer perspectives, Journal of Intelligence Studies in Business Vol 3, No 3, pp. 5-28

Solberg Søilen, K. (2013). An overview of articles on Competitive Intelligence in JCIM and CIR. Journal of Intelligence Studies in Business Vol 3, No 1, pp. 44-58.

Solberg Søilen, K., Jenster, P. (2013). The Relationship between Strategic Planning and Company Performance – A Chinese perspective. Journal of Intelligence Studies in Business, Vol 3, No 1, pp. 15-30.

Tontini, G., Solberg Søilen, K., Silveira, A. (2013). How interactions of service attributes affect customer satisfaction: A study of the Kano model’s attributes. Total Quality Management & Business Excellence, Volume 24, Issue 11-12, pages 1253-1271

Solberg Søilen, K., Nerme, P., Stemström, C., Darefelt, N. (2013). Usage of internet banking among different segments – trust and information needs. Journal of Internet Banking and Commerce, Vol 18, No 2, pp. 2-18

Fri, W., Pehrsson, T., Solberg Søilen, K. (2013). How the phases of cluster development are associated with innovation – the case of China. International Journal of Innovation Science, Vol. 5, Nr. 1, pp. 31-43.

Hansson, L., Wrangmo, A. Solberg Søilen, K. (2013). Optimal ways for companies to use Facebook as a marketing channel. Journal of Information, Communication and Ethics in Society. Vol. 11 Iss: 2, pp. 112 – 126.

Solberg Søilen, K., Tontini, G. (2013). Knowledge Management systems and Human Resource Management policies for Innovation benchmarking: a study at ST Ericsson. Internatinal Journal of Innovation Science, Vol 5, No 3, pp. 159-171

Yasmina, A., Solberg Søilen, K., Vriens, D. (2012). Using the SSAV model to evaluate Business Intelligence Software. Journal of Intelligence Studies in Business, Vol 2, No 1, pp. 29-40.

Solberg Søilen, K. Hasslinger, A. (2012). Factors shaping vendor differentiation in the Business Intelligence software industry. Journal of Intelligence Studies in Business, Vol 2, No 3, pp. 48-54.

Sabanovic, A., Solberg Søilen, K. (2012). Customers’ Expectations and Needs in the Business Intelligence Software Market. Journal of Intelligence Studies in Business, Vol 2, No 1, pp. 5-20.

Solberg Søilen, K. (2012). The Fallacy of the Service Economy. European Business Review, Vol 24, Iss: 4, pp. 308-319.

. Solberg Søilen, K. (Planned for 2020) Digital Marketing. Springer: Heidelberg/Berlin

Solberg Søilen, K. (2013). Exhibit Marketing & Trade Show Intelligence - Successful Boothmanship and Booth Design. Springer Verlag, Berlin

Solberg Søilen, K. (2012). Geoeconomics. Ventus Publishing ApS/Bookboon, London (50 000+ downloads per year)

Jenster, P., Solberg Søilen, K. (2009). Market Intelligence: Building Strategic Insight. Copenhagen Business School Press, Denmark

Solberg Søilen, K. and Huber, S. (2006). 20 svenska studier för små och medelstora företag – pedagogik och vetenskaplig metod. Studentlitteratur, Lund

Solberg Søilen, K. (2005). Introduction to Public and Private Intelligence. Studentlitteratur, Lund

Solberg Søilen, K. (2005). En liten bok i Logikk. Hvordan lære å tenke. GRIN Humanities, Norderstedt, Germany

Solberg Søilen, K. (2005). Wirtschaftsspionage in Verhandlungen aus Informationsökonomischer Perspektive - Eine Interdisziplinäre Analyse. Dissertation. Faculty of Economics/Wirtschaftswissenshaftlichen Fakultät Universität Leipzig, Germany

Aguwa, C.C., Monplaisir, L., Turgut, O., 2012. Voice of the customer: Customer satisfaction ratio based analysis. Expert Systems with Applications 39, 10112–10119. https://doi.org/10.1016/j.eswa.2012.02.071

Alghamdi, R., Alfalqi, K., 2015. A survey of topic modeling in text mining. Int. J. Adv. Comput. Sci. Appl.(IJACSA) 6.

Berger, C.C., Blauth, R.E., Boger, D., 1993. kano’s methods for understanding customerdefined quality. Blei, D.M., 2012. Probabilistic Topic Models. Commun. ACM 55, 77–84. https://doi.org/10.1145/2133806.2133826

Carulli, M., Bordegoni, M., Cugini, U., 2013. An approach for capturing the Voice of the Customer based on Virtual Prototyping. J Intell Manuf 24, 887–903. https://doi.org/10.1007/s10845-012-0662-5

Culotta, A., Cutler, J., 2016. Mining Brand Perceptions from Twitter Social Networks. Marketing Science 35, 343–362. https://doi.org/10.1287/mksc.2015.0968 Darling, W.M., 2011. A theoretical and practical implementation tutorial on topic modeling and gibbs sampling, in: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies. pp. 642–647.

Das, S.R., Chen, M.Y., Agarwal, T.V., Brooks, C., Chan, Y., Gibson, D., Leinweber, D., Martinezjerez, A., Raghubir, P., Rajagopalan, S., Ranade,

A., Rubinstein, M., Tufano, P., 2001. Yahoo! for amazon: Sentiment extraction from small talk on the web, in: 8th Asia Pacific Finance Association Annual Conference.

Decker, R., Trusov, M., 2010. Estimating aggregate consumer preferences from online product reviews. International Journal of Research in Marketing 27, 293–307. https://doi.org/10.1016/j.ijresmar.2010.09.001

Farhadloo, M., Patterson, R.A., Rolland, E., 2016. Modeling customer satisfaction from unstructured data using a Bayesian approach. Decision Support Systems 90, 1–11. https://doi.org/10.1016/j.dss.2016.06.010

Farhadloo, M., Rolland, E., 2013. Multi-Class Sentiment Analysis with Clustering and Score Representation, in: 2013 IEEE 13th International Conference on Data Mining Workshops. Presented at the 2013 IEEE 13th International Conference on Data Mining Workshops, pp. 904–912. https://doi.org/10.1109/ICDMW.2013.63

Gioti, H., Ponis, S.T., Panayiotou, N., 2018. Social business intelligence: Review and research directions. Journal of Intelligence Studies in Business 8.

Goodman, J., 2014. Customer experience 3.0: High-profit strategies in the age of techno service.

Amacom. Guo, Y., Barnes, S.J., Jia, Q., 2017. Mining meaning from online ratings and reviews: Tourist satisfaction analysis using latent dirichlet allocation. Tourism Management 59, 467–483. https://doi.org/10.1016/j.tourman.2016.09.009

Hofmann, T., 2017. Probabilistic Latent Semantic Indexing. SIGIR Forum 51, 211–218. https://doi.org/10.1145/3130348.3130370

Hu, M., Liu, B., 2004a. Mining Opinion Features in Customer Reviews, in: AAAI. Hu, M., Liu, B., 2004b. Mining and Summarizing Customer Reviews, in: Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’04. ACM, New York, NY, USA, pp. 168–177. https://doi.org/10.1145/1014052.1014073

Jia, S.S., 2018. Leisure Motivation and Satisfaction: A Text Mining of Yoga Centres, Yoga Consumers, and Their Interactions. Sustainability 10, 4458.

KANO, N., 1984. Attractive quality and must-be quality. Hinshitsu (Quality, the Journal of Japanese Society for Quality Control) 14, 39– 48.

Lee, H., Han, J., Suh, Y., 2014. Gift or threat? An examination of voice of the customer: The case of MyStarbucksIdea. com. Electronic Commerce Research and Applications 13, 205–219.

Lee, Y.-C., Huang, S.-Y., 2009. A new fuzzy concept approach for Kano’s model. Expert Systems with Applications 36, 4479–4484. https://doi.org/10.1016/j.eswa.2008.05.034

Lu, Y., Mei, Q., Zhai, C., 2011. Investigating task performance of probabilistic topic models: an empirical study of PLSA and LDA. Inf Retrieval 14, 178–203. https://doi.org/10.1007/s10791-010-9141-9

Miller, G.A., 1995. WordNet: a lexical database for English. Communications of the ACM 38, 39–41.

Nyblom, M., Behrami, J., Nikkilä, T., Solberg Søilen, K., 2012. An evaluation of Business Intelligence Software systems in SMEs-a case study. Journal of Intelligence Studies in Business 2, 51–57. Park, Y.,

Lee, S., 2011. How to design and utilize online customer center to support new product concept generation. Expert Systems with Applications 38, 10638–10647. https://doi.org/10.1016/j.eswa.2011.02.125

Phadermrod, B., Crowder, R.M., Wills, G.B., 2019. Importance-Performance Analysis based SWOT analysis. International Journal of Information Management 44, 194–203. https://doi.org/10.1016/j.ijinfomgt.2016.03.009 PromptCloud: Fully Managed Web Scraping Service, n.d. URL https://www.promptcloud.com/ (accessed 9.24.19).

Qi, J., Zhang, Z., Jeon, S., Zhou, Y., 2016. Mining customer requirements from online reviews: A product improvement perspective. Information & Management, Big Data Commerce 53, 951–963. https://doi.org/10.1016/j.im.2016.06.002

Rese, A., Sänn, A., Homfeldt, F., 2015. Customer integration and voice–of–customer methods in the German automotive industry. International Journal of Automotive Technology and Management.

Reyes, G., 2016. Understanding non response rates: insights from 600,000 opinion surveys.

Sabanovic, A., Søilen, K.S., 2012. Customers’ Expectations and Needs in the Business Intelligence Software Market. Journal of Intelligence Studies in Business 2.

Saura, J.R., Palos-Sanchez, P., Grilo, A., 2019. Detecting indicators for startup business success: Sentiment analysis using text data mining. Sustainability 11, 917.

Søilen, K.S., Tontini, G., Aagerup, U., 2017. The perception of useful information derived from Twitter: A survey of professionals. Journal of Intelligence Studies in Business, 7(3).

Szolnoki, G., Hoffmann, D., 2013. Online, face-toface and telephone surveys—Comparing different sampling methods in wine consumer research. Wine Economics and Policy 2, 57–66. https://doi.org/10.1016/j.wep.2013.10.001

Ting, K.M., 2017. Confusion Matrix, in: Sammut, C., Webb, G.I. (Eds.), Encyclopedia of Machine Learning and Data Mining. Springer US, Boston, MA, pp. 260–260. https://doi.org/10.1007/978-1-4899-7687-1_50

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, 463–479. https://doi.org/10.1509/jmr.12.0106

Tontini, G., Solberg Søilen, K., Silveira, A., 2013. How interactions of service attributes affect customer satisfaction: A study of the Kano model’s attributes. Total Quality Management & Business Excellence 24, 1253–1271.

Ullah, A.M.M.S., Tamaki, J., 2011. Analysis of Kano-model-based customer needs for product development. Systems Engineering 14, 154– 172. https://doi.org/10.1002/sys.20168

Umoh, U.A., Isong, B.E., 2013. Fuzzy logic based decision making for customer loyalty analysis and relationship management. International Journal on Computer Science and Engineering 5, 919.

Xiao, S., Wei, C.-P., Dong, M., 2016. Crowd intelligence: Analyzing online product reviews for preference measurement. Information & Management 53, 169–182. https://doi.org/10.1016/j.im.2015.09.010

Xu, X., Li, Y., 2016. The antecedents of customer satisfaction and dissatisfaction toward various types of hotels: A text mining approach. International Journal of Hospitality Management 55, 57–69. https://doi.org/10.1016/j.ijhm.2016.03.003


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