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Original Articles

Advanced Customer Analytics: Strategic Value Through Integration of Relationship-Oriented Big Data

References

  • Abbasi, A.; Albrecht, C.; Vance, A.; and Hansen, J. Metafraud: A meta-learning framework for detecting financial fraud. MIS Quarterly, 36, 4 (2012), 1293–1327.
  • Abbasi, A.; Sarker, S.; and Chiang, R.H.L. Big data research in information systems: Toward an inclusive research agenda. Journal of the Association of Information Systems, 17, 2 (2016), 1–32.
  • Abbasi, A.; Zahedi, F.; Zeng, D.; Chen, Y.; Chen, H.; and Nunamaker, J.F. Enhancing predictive analytics for anti-phishing by exploiting website genre information. Journal of Management Information Systems, 31, 4 (2015), 109–157.
  • Abbasi, A.; Zhou, Y.; Deng, S.; and Zhang, P. Text analytics for sense-making in social media: A language-action perspective. MIS Quarterly, forthcoming.
  • Agarwal, R., and Dhar, V. Big data, data science, and analytics: The opportunity and challenge for IS research. Information Systems Research, 25, 3 (2014), 443–448.
  • Ballings, M., and Van Den Poel, D. Customer event history for churn prediction: How long is long enough? Expert Systems with Applications, 39, 18 (2012), 13517–13522.
  • Bolton, R.N. A dynamic model of the duration of the customer’s relationship with a continuous service provider: The role of satisfaction. Marketing Science, 17, 1 (1998), 45–65.
  • Bolton, R.N.; Lemon, K.N.; and Verhoef, P.C. The theoretical underpinnings of customer asset management: A framework and propositions for future research. Journal of the Academy of Marketing Science, 32, 3 (2004), 271–292.
  • Buckinx, W., and Van den Poel, D. Customer base analysis: Partial defection of behaviourally loyal clients in a non-contractual FMCG retail setting. European Journal of Operational Research, 164, 1 (2005), 252–268.
  • Burges, C.J. A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery, 2, 2 (1998), 121–167.
  • Cappiello, C.; Francalanci, C.; and Pernici, B. Time-related factors of data quality in information multichannel systems. Journal of Management Information Systems, 20, 3 (2003), 71–91.
  • Chen, D.Q.; Preston, D.S.; and Swink, M. How the use of big data analytics affects value creation in supply chain management. Journal of Management Information Systems, 32, 4 (2015), 4–39.
  • Chen, H., and Storey, V.C. Business intelligence and analytics: From big data to big impact. MIS Quarterly, 36, 4 (2012), 1165–1188.
  • Chen, P.S., and Hitt, L.M. Measuring switching costs and the determinants of customer retention in internet-enabled businesses: A study of the online brokerage industry. Information Systems Research, 13, 3 (2002), 255–74.
  • Collins, M., and Duffy, N. Convolution kernels for natural language. In Advances in Neural Information Processing Systems, (2002), Vancouver, British Columbia, Canada. pp. 625–632.
  • Coussement, K., and De Bock, K.W. Customer churn prediction in the online gambling industry: The beneficial effect of ensemble learning. Journal of Business Research, 66, 9 (2013), 1629–1636.
  • Crosby, L.A., and Stephens, N. Effects of relationship marketing on satisfaction, retention, and prices in the life insurance industry. Journal of Marketing Research, 24, 4 (1987), 404–411.
  • Davenport, T.H. Analytics 3.0. Harvard Business Review, December 2013, 64–72.
  • van Doorn, J.; Lemon, K.N.N.; Mittal, V.; et al. Customer engagement behavior: Theoretical foundations and research directions. Journal of Service Research, 13, 3 (2010), 253–266.
  • Fader, P.S.; Hardie, B.G.S.; and Lee, K.L. Counting your customers the easy way: An alternative to the Pareto/NBD model. Marketing Science, 24, 2 (2005), 275–284.
  • Gázquez-Abad, J.C.; Canniére, M.H. De; and Martínez-López, F.J. Dynamics of customer response to promotional and relational direct mailings from an apparel retailer: The moderating role of relationship strength. Journal of Retailing, 87, 2 (2011), 166–181.
  • Goes, P.B. Big data and IS research. MIS Quarterly, 38, 3 (2014), iii–viii.
  • Goodhue, D.L.; Kirsch, L.J.; Quillard, J.A; and Wybo, M.D. Strategic data planning: Lessons from the field. MIS Quarterly, 16, 1 (1992), 11–34.
  • Goodhue, D.L., Wybo, M.D., and Kirsch, L.J. The impact of data integration on the costs and benefits of information systems. MIS Quarterly, 16, 3 (1992), 293–311.
  • Gunarathne, P.; Rui, H.; and Seidmann, A. Whose and what social media complaints have happier resolutions? Evidence from Twitter. Journal of Management Information Systems, 34, 2 (2017), 314–340.
  • Gupta, S.; Hanssens, D.; Hardie, B.; et al. Modeling customer lifetime value. Journal of Service Research, 9, 2 (2006), 139–155.
  • Heudecker, N., and White, A. The data lake fallacy: All water and little substance. Gartner, July 2014, 6.
  • Hevner, A.R.; March, S.T.; Park, J.; and Ram, S. Design science in information systems research. MIS Quarterly, 28, 1 (2004), 75–105.
  • Hitt, L.M., and Frei, F.X. Do Better customers utilize electronic distribution channels? The case of PC banking. Management Science, 48, 6 (2002), 732–748.
  • Juola, P., and Baayen, H. A controlled-corpus experiment in authorship identification by cross-entropy. Literary and Linguistic Computing, 20 (2005), 59–67.
  • Karimi, J., and Walter, Z. The role of dynamic capabilities in responding to digital disruption: A factor-based study of the newspaper industry. Journal of Management Information Systems, 32, 1 (2015), 39–81.
  • Keane, T.J., and Wang, P. Applications for the lifetime value model in modern newspaper publishing. Journal of Direct Marketing, 9, 2 (1995), 59–66.
  • Koppel, M.; Akiva, N.; and Dagan, I. Feature instability as a criterion for selecting potential style markers. JASIST, 57, 11 (2006), 1519–1525.
  • Kunz, W.; Aksoy, L.; Bart, Y.; et al. Customer engagement in a big data world. Journal of Services Marketing, 31, 2 (2017), 161–171.
  • Laney, D. Why and how to measure the value of your information assets. Gartner, August 2015. 1–22
  • Lemmens, A., and Croux, C. Bagging and boosting classification trees to predict churn. Journal of Marketing Research, 43, 2 (2006), 276–286.
  • Lodhi, H.; Saunders, C.; Shawe-Taylor, J.; Cristianini, N.; and Watkins, C. Text classification using string kernels. Journal of Machine Learning Research, 2 (2002), 419–444.
  • Lu, Y., and Ramamurthy, K. Understanding the link between information technology capability and organizational agility: An empirical examination. MIS Quarterly, 35, 4 (2011), 931–954.
  • Lyytinen, K., and Grover, V. Management misinformation systems: A time to revisit? Journal of the Association for Information Systems, 18, 3 (2017), 206–230.
  • McAfee, A., and Brynjofsson, E. Big data: The management revolution. Harvard Business Review, October 2012, 60–68.
  • Miglautsch, J. Application of RFM principles: What to do with 1–1–1 customers? Journal of Database Marketing, 9, 4 (2002), 319–324.
  • Miguéis, V.L.; Van den Poel, D.; Camanho, A.S.; and Falcão e Cunha, J. Modeling partial customer churn: On the value of first product-category purchase sequences. Expert Systems with Applications, 39, 12 (2012), 11250–11256.
  • Mithas, S.; Ramasubbu, N.; and Sambamurthy, V. How information management capability influences firm performance. MIS Quarterly, 35, 1 (2011), 237–256.
  • Mittal, V., and Kamakura, W.A. Satisfaction, repurchase intent, and repurchase behavior: Investigating the moderating effect of customer characteristics. Journal of Marketing Research, 38 ( February 2001)., 131–142.
  • Neslin, S.A.; Taylor, G.A.; Grantham, K.D.; and McNeil, K.R. Overcoming the “recency trap” in customer relationship management. Journal of the Academy of Marketing Science, 41, 3 (2013), 320–337.
  • Neslin, S.A; Gupta, S.; Kamakura, W.; Lu, J.; and Mason, C.H. Defection detection: Measuring and understanding the predictive accuracy of customer churn models. Journal of Marketing Research, 43 ( May 2006), 204–211.
  • Nunamaker, J.F.; Briggs, R.O.; Derrick, D.C.; and Schwabe, G. The last research mile: Achieving both rigor and relevance in information systems research. Journal of Management Information Systems, 32, 3 (2015), 10–47.
  • Palmer, A. The evolution of an idea: An environmental explanation of relationship marketing. Journal of Relationship Marketing, 1, 1 (2002), 79–94.
  • Prat, N.; Comyn-Wattiau, I.; and Akoka, J. A taxonomy of evaluation methods for information systems artifacts. Journal of Management Information Systems, 32, 3 (2015), 229–267.
  • Ransbotham, B.S., and Kiron, D. Analytics as a source of business innovation. MIT Sloan Management Review, February 2017, 1–16.
  • Ransbotham, S.; Kiron, D.; and Prentice, P.K. Minding the analytics gap. MIT Sloan Management Review, 56, ( Spring 2015), 63–68.
  • Reinartz, W.J., and Kumar, V. The impact of customer relationship characteristics on profitable lifetime duration. Journal of Marketing, 67 (January 2003), 77–99.
  • Roberts, N., and Grover, V. Leveraging information technology infrastructure to facilitate a firm’s customer agility and competitive activity: An empirical investigation. Journal of Management Information Systems, 28, 4 (2012), 231–270.
  • Sambamurthy, V.; Bharadwaj, A.; and Grover, V. Shaping agility through digital options: Reconceptualizing the role of information technology in contemporary firms. MIS Quarterly, 27, 2 (2003), 237–263.
  • Schmittlein, D.C.; Morrison, D.G.; and Colombo, R. Counting your customers: Who are they and what will they do next? Management Science, 33, 1 (1987), 1–24.
  • Shmueli, G., and Koppius, O.R. Predictive analytics in information systems research. MIS Quarterly, 35, 3 (2011), 553–572.
  • Teo, T.S.H., and King, W.R. Integration between business planning and information systems planning: An evolutionary-contingency perspective. Journal of Management Information Systems, 14, 1 (1997), 185–214.
  • Vanderveld, A., and Han, A. An engagement-based customer lifetime value system for e-commerce. In 22nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 2016.
  • Venkatesan, R., and Kumar, V. Framework for customer selection. Journal of Marketing, 68 (October 2004), 106–125.
  • Verbraken, T.; Verbeke, W.; and Baesens, B. A novel profit maximizing metric for measuring classification performance of customer churn prediction models. IEEE Transactions on Knowledge and Data Engineering, 25, 5 (2013), 961–973.
  • Verhoef, P.C. Understanding the effect of customer relationship management efforts on customer retention and customer share development. Journal of Marketing, 67 ( October 2003).
  • Voss, G.B.; Godfrey, A.; and Seiders, K. How complementarity and substitution alter the customer satisfaction–repurchase link. Journal of Marketing, 74 (November 2010), 111–127.
  • Wagner, C., and Majchrzak, A. Enabling customer-centricity using wikis and the wiki way. Journal of Management Information Systems, 23, 3 (2007), 17–43.
  • Walls, J.G.; Widmeyer, G.R.; and Sawy, O.A. El. Building an information system design theory for vigilant EIS. Information Systems Research, 3, 1 (1992), 36–59.
  • Wang, K., and Skadron, K. Association rule mining with the Micron Automata Processor. IEEEInternational Parallel and Distributed Processing SymposiumHyderabad, India. pp. 689–699, 2015.
  • Wixom, B., and Ross, J. How to monetize your data. MIT Sloan Management Review, January 2017, 10–13.

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