Abstract
To react to new market dynamics, OEM, one of the largest marine equipment manufacturers in the world, was facing the task of transforming its aftersales business from key-account-manager-driven sales processes to more proactive and customer-centric processes. The company had recently implemented an organization-wide customer relationship management (CRM) system to facilitate this transformation. However, the system was not fully used because of a lack of proactive work practices that the system could support. Based on this diagnosis, we developed and applied a method for data-driven lead-generation that uses advanced analytics and automation to leverage internal and external data sources to identify and assess sales leads. To guide the design process, we ingrained the artifact with theory about data-driven decision-making (DDD) and value creation in the form of initial design principles. After several iterations of building the artifact, examining the organizational context, and evaluating the changes that those interventions introduced, we formalized a set of design principles and abstracted them to the broader class of DDD artifacts, highlighting decision quality but also the importance of model comprehensibility, domain knowledge, and actionability of results.
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Notes
- 1.
Earlier stages and iterations of the artifact of this ADR study have been reported in an unpublished master’s thesis and, with a focus on developed design theory, in the proceedings of the European Conference on Information Systems (Thiess & Müller, 2018).
References
Abe, M. (2008). ‘Counting your customers’ one by one: A hierarchical bayes extension to the Pareto/NBD model. Marketing Science, 28(3), 541–553.
Beverungen, D., Müller, O., Matzner, M., Mendling, J., & Vom Brocke, J. (n.d.). Conceptualizing smart service systems.
Brynjolfsson, E., Hitt, L. M., & Kim, H. H. (2011). Strength in numbers: How does data-driven decisionmaking affect firm performance? SSRN Electronic Journal.
Chandra, L., Seidel, S., & Gregor, S. (2015). Prescriptive knowledge in IS research: Conceptualizing design principles in terms of materiality, action, and boundary conditions. In: 2015 48th Hawaii International Conference on System Sciences (pp. 4039–4048). IEEE.
Davenport, T. H. (2013, December). ‘Analytics 3.0’.
Dearden, A. (2001). IDA-S: A conceptual framework for partial automation. In People and computers XV—interaction without frontiers (pp. 213–228). London: Springer.
Efron, B., & Morris, C. (1977). Stein’s paradox in statistics. Scientific American, 236(5), 119–127.
Fader, P. S., & Hardie, B. G. S. (2009). Probability models for customer-base analysis. Journal of Interactive Marketing, 23(1), 61–69.
Fader, P. S., & Hardie, B. G. (2013). The Gamma-Gamma model of monetary value.
Gregor, S., & Benbasat, I. (1999). Explanations from intelligent systems: Theoretical foundations and implications for practice. MIS Quarterly.
Hevner, A. (2007). A three cycle view of design science research. Scandinavian Journal of Information Systems, 19(2).
Hollander, E. P., Vroom, V. H., & Yetton, P. W. (1973). Leadership and decision-making. Administrative Science Quarterly.
International Maritime Organization. (2015). Survey Guidelines under the Harmonized System Of Survey And Certification (HSSC).
Kayande, U., De Bruyn, A., Lilien, G. L., Rangaswamy, A., & van Bruggen, G. H. (2009). How incorporating feedback mechanisms in a DSS affects DSS evaluations. Information Systems Research.
Kotter, J. P. (1995). Leading change: Why transformation efforts fail.
LaValle, S., Lesser, E., Shockley, R., Hopkins, M. S., & Kruschwitz, N. (2011). Big data, analytics and the path from insights to value. MIT Sloan Management Review, 52(2), 21.
Leek, J. T., & Peng, R. D. (2015). What is the question? Science, 347(6228), 1314–1315.
Müller, O., Fay, M., & vom Brocke, J. (2018). The effect of big data and analytics on firm performance: An econometric analysis considering industry characteristics. Journal of Management Information Systems, 35(2), 488–509.
Orlikowski, W. J., & Iacono, C. S. (2001). Research commentary: Desperately seeking the ‘IT’ in IT research—A call to theorizing the IT artifact. Information Systems Research, 12(2), 121–134.
Platzer, M., & Reutterer, T. (2016). Ticking away the moments: Timing regularity helps to better predict customer activity. Marketing Science, 35(5), 779–799.
Purao, S., Rossi, M., & Sein, M. K. (2010). On integrating action research and design research (pp. 179–194).
Ransbotham, S., Kiron, D., & Prentice, P. K. (2015). The talent dividend. MIT Sloan Management Review, 56(4), 1.
Rossi, P. E., & Allenby, G. M. (2003). Bayesian statistics and marketing. Marketing Science, (3), 304.
SAS. (2012). The evolution of decision making: How leading organizations are developing a data-driven culture—Sponsor content from SAS. Retrieved from https://hbr.org/sponsored/2016/04/the-evolution-of-decision-making-how-leading-organizations-are-developing-a-data-driven-culture.
Seidel, S., Chandra Kruse, L., Székely, N., Gau, M., & Stieger, D. (2018). Design principles for sensemaking support systems in environmental sustainability transformations. European Journal of Information Systems.
Sein, H., Purao, R., & Lindgren, R. (2011). Action design research. MIS Quarterly.
Shaikhina, T., & Khovanova, N. A. (2017). Handling limited datasets with neural networks in medical applications: A small-data approach. Artificial Intelligence in Medicine, 75, 51–63.
Sharma, R., Mithas, S., & Kankanhalli, A. (2014). Transforming decision-making processes: A research agenda for understanding the impact of business analytics on organisations. European Journal of Information Systems.
Shearer, C., Watson, H. J, Grecich, D. G., Moss, L., Adelman, S., Hammer, K., & Herdlein, S. a. (2000). The CRISP-DM model: The new blueprint for data mining. Journal of Data Warehousing.
Susman, G. I., & Evered, R. D. (1978). An assessment of the scientific merits of action research. Administrative Science Quarterly, 23(4), 582.
Thiess, T., & Müller, O. (2018). Towards design principles for data-driven decision making-an action design research project in the maritime industry. In Proceedings of the 26th European Conference on Information Systems (ECIS). Portsmouth: European Conference on Information Systems (ECIS).
Van De Schoot, R., Broere, J. J., Perryck, K. H., Zondervan-Zwijnenburg, M., & Van Loey, N. E. (2015). Analyzing small data sets using Bayesian estimation: The case of posttraumatic stress symptoms following mechanical ventilation in burn survivors. European Journal of Psychotraumatology.
vom Brocke, J., Debortoli, S., Müller, O., & Reuter, N. (2014). How in-memory technology can create business value: Insights from the hilti case. Communications of the Association for Information Systems.
Wagner, M. M., & Hogan, W. R. (1996). The accuracy of medication data in an outpatient electronic medical record. Journal of the American Medical Informatics Association, 3(3), 234–244.
Watson, H. J. (2014). Tutorial: Big data analytics: Concepts, technologies, and applications. CAIS, 34, 65.
Wu, L., & Hitt, L. M. (2016). How do data skills affect firm productivity: Evidence from process-driven versus innovation-driven practices.
Wu, L., Hitt, L. M., & Lou, B. (2017, February 28). Data analytics skills, innovation and firm productivity.
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Thiess, T., Müller, O. (2020). Setting Sail for Data-Driven Decision-Making an Action Design Research Case from the Maritime Industry. In: vom Brocke, J., Hevner, A., Maedche, A. (eds) Design Science Research. Cases. Progress in IS. Springer, Cham. https://doi.org/10.1007/978-3-030-46781-4_12
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