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@Select Types{,
Journal = "Band-1",
Title= "Customer Data Mapping - A Method for data-driven Service Innovation",
Author= "Katharina Blöcher, Matthias Wittwer and Rainer Alt",
Doi= "https://doi.org/10.30844/wi_2020_j4-bloecher",
Abstract= "Service providers nowadays face a complex situation, which is characterized by highly-demanding customers on the one and a plethora of potentially relevant data on the other hand. Data-driven service offerings need to be based on a solid understanding of available data in order to design personal value propositions. This research proposes a visual approach to build up data understanding from a customer perspective and highlights the potential of customer data. Based on the Customer-Dominant Logic, it develops the method “Customer Data Mapping” which supports businesses in establishing customer understanding through a structured process in a collaborative setting. It guides participants from capturing customer data along the customer journey to deriving customer understanding as the foundation for data-driven services.
",
Keywords= "Customer data, personal data, data-driven services, service innovation, business transformation
",
}
Katharina Blöcher, Matthias Wittwer and Rainer Alt: Customer Data Mapping - A Method for data-driven Service Innovation. Online: https://doi.org/10.30844/wi_2020_j4-bloecher (Abgerufen 15.12.24)
Open Access
Abstract
Abstract
Service providers nowadays face a complex situation, which is characterized by highly-demanding customers on the one and a plethora of potentially relevant data on the other hand. Data-driven service offerings need to be based on a solid understanding of available data in order to design personal value propositions. This research proposes a visual approach to build up data understanding from a customer perspective and highlights the potential of customer data. Based on the Customer-Dominant Logic, it develops the method “Customer Data Mapping” which supports businesses in establishing customer understanding through a structured process in a collaborative setting. It guides participants from capturing customer data along the customer journey to deriving customer understanding as the foundation for data-driven services.
Keywords
Schlüsselwörter
Customer data, personal data, data-driven services, service innovation, business transformation
References
Referenzen
1. Huhtala, T.: Using Personal Data to Advance Preventive Healthcare Services. J. Serv. Sci. Res. 10, 77–115 (2018).
2. Alt, R., Ehmke, J.F., Haux, R., Henke, T., Mattfeld, D.C., Oberweis, A., Paech, B., Winter, A.: Towards customer-induced service orchestration – requirements for the next step of customer orientation. Electron. Mark. 29, 79–91 (2019).
3. Alt, R.: Electronic Markets on customer-orientation. Electron. Mark. 26, 1–5 (2016).
4. Demirkan, H., Bess, C., Spohrer, J., Rayes, A., Allen, D., Moghaddam, Y.: Innovations with Smart Service Systems: Analytics, Big Data, Cognitive Assistance, and the Internet of Everything. Commun. Assoc. Inf. Syst. 37, (2015).
5. Kunz, W., Aksoy, L., Bart, Y., Heinonen, K., Kabadayi, S., Ordenes, F.V., Sigala, M., Diaz, D., Theodoulidis, B.: Customer Engagement in a Big Data World. J. Serv. Mark. 31, 161–171 (2017).
6. Lim, C., Maglio, P.P.: Data-Driven Understanding of Smart Service Systems Through Text Mining. Serv. Sci. 10, 154–180 (2018).
7. Lim, C., Kim, M.-J., Kim, K.-H., Kim, K.-J., Maglio, P.: Customer Process Management. J. Serv. Manag. 30, 105–131 (2019).
8. Ostrom, A.L., Parasuraman, A., Bowen, D.E., Patrício, L., Voss, C.A.: Service Research Priorities in a Rapidly Changing Context. J. Serv. Res. 18, 127–159 (2015).
9. Schüritz, R., Satzger, G., Seebacher, S., Schwarz, L.: Datatization as the Next Frontier of Servitization – Understanding the Challenges for Transforming Organizations. In: Proceedings of the 38th International Conference on Information Systems (ICIS). pp. 1–21. , Seoul (2017).
10. Bhargava, R., D’Ignazio, C.: Designing Tools and Activities for Data Literacy Learners. In: Designing Tools and Activities for Data Literacy Learners. In Wed Science: Data Literacy Workshop. , Oxford (2015).
11. Böhmann, T., Kühne, B.: Data-Driven Business Models – Building the Bridge Between Data and Value. In: Proceedings of the 27th European Conference on Information Systems. , Stockholm (2019).
12. Lim, C., Kim, K.H., Kim, M.J., Heo, J.Y., Kim, K.J., Maglio, P.P.: From Data to Value: A Nine-Factor Framework for Data-based Value Creation in Information-intensive Services. Int. J. Inf. Manage. 39, 121–135 (2018).
13. Peters, C., Maglio, P., Badinelli, R., Harmon, R.R., Maull, R., Spohrer, J.C., Tuunanen, T., Vargo, S.L., Welser, J.J., Demirkan, H., Griffith, T.L., Moghaddam, Y.: Emerging Digital Frontiers for Service Innovation. Commun. Assoc. Inf. Syst. 39, 136–149 (2016).
14. Kollwitz, C., Mengual, M.P., Dinter, B.: Cross-Disciplinary Collaboration for Designing Data-Driven Products and Services. In: Pre-ICIS SIGDSA Symposium on Decision Analytics Connecting People, Data & Things. , San Francisco (2018).
15. Kühne, B., Zolnowski, A., Bornholt, J., Böhmann, T.: Making Data Tangible for Data-driven Innovations in a Business Model Context. In: Proceedings of the 25th Americas Conference on Information Systems. pp. 1–10. , Cancun (2019).
16. Engel, C., Ebel, P.: Data-driven Service Innovation: A Systematic Literature Review and Development of a Research Agenda. In: Proceedings of the 27th European Conference on Information Systems. , Stockholm (2019).
17. Brown, T.: Design Thinking. Harv. Bus. Rev. 86, 84 (2008).
18. Kronsbein, T., Müller, R.: Data Thinking: A Canvas for Data-driven Ideation Workshops. In: Proceedings of the 52nd Hawaii International Conference on System Sciences. pp. 561–570. , Maui (2019).
19. Wolff, A., Gooch, D., Montaner, J.J.C., Rashid, U., Kortuem, G.: Creating an Understanding of Data Literacy for a Data-driven Society. J. Community Informatics. 12, (2016).
20. De Götzen, A., Kun, P., Morelli, N.: Making Sense of Data in a Service Design Education. In: Proceedings of the 7th Service Design and Innovation Conference. pp. 177–187. , Milan (2018).
21. Mathis, K., Köbler, F.: Data-Need Fit – Towards data-driven business model innovation. In: Proceedings of the 5th Service Design and Innovation Conference. pp. 458–467. , Copenhagen (2016).
22. Kayser, V., Nehrke, B., Zubovic, D.: Data Science as an Innovation Challenge: From Big Data to Value Proposition. Technol. Innov. Manag. Rev. 8, 16–25 (2018).
23. Kim, M.-J., Lim, C.-H., Lee, C.-H., Kim, K.-J., Park, Y., Choi, S.: Approach to Service Design Based on Customer Behavior Data: a Case Study on Eco-Driving Service Design Using Bus Drivers’ Behavior Data. Serv. Bus. 12, 203–227 (2017).
24. Payne, A., Frow, P.: A Strategic Framework for Customer Relationship Management. J. Mark. 69, 167–176 (2005).
25. Catherine, S., Dittrich, Y., Grönvall, E.: Identification of Data Representation Needs in Service Design. Sel. Pap. Inf. Syst. Res. Semin. Scand. 8, (2017).
26. Maglio, P., Lim, C.-H.: Innovation and Big Data in Smart Service Systems. J. Innov. Manag. 4, 11–21(2016).
27. Heinonen, K., Strandvik, T., Mickelsson, K.J., Edvardsson, B., Sundström, E., Andersson, P.: A Customer-Dominant Logic of Service. J. Serv. Manag. 21, 531–548 (2010).
28. Heinonen, K., Strandvik, T.: Customer-Dominant Logic: Foundations and Implications. J. Serv. Mark. 29, 472–484 (2015).
29. Novak, J.D.: Concept Maps and Vee Diagrams: Two Metacognitive Tools to Facilitate Meaningful Learning. Instr. Sci. 19, 29–52 (1990).
30. Forbes: 2018 Restaurant Tech EcoSystem: Power Shift Underway With Decrease of the Data Gap, https://www.forbes.com/sites/themixingbowl/2018/05/21/2018-restaurant-tech-ecosystem-power- %0Ashift-underway-with-decrease-of-the-data-gap/#33e7dc6a4f6e.
31. Lusch, R.F., Nambisan, S.: Service Innovation: A Service-Dominant Logic Perspective. MIS Q. 39, 155–175 (2015).
32. Barrett, M., Davidson, E., Prabhu, J., Vargo, S.L.: Service Innovation in the Digital Age: Key Contributions and Future Directions. MIS Q. 39, 135–154 (2015).
33. Chapman, P., Clinton, J., Kerber, R., Khabaza, T., Reinartz, T., Shearer, C., Wirth, R.: CRISP 1.0 – Step-by-Step Data Mining Guide. (2000).
34. Larose, D.T.: Data Mining Methods and Models. John Wiley & Sons, Hoboken, New Jersey (2006).
35. Ogiela, L., Ogiela, M.R.: Knowledge management approaches for data understanding. Proc. – 2016 Int. Conf. Intell. Netw. Collab. Syst. IEEE INCoS 2016. 270–273 (2016).
36. Wand, Y., Weber, R.: Research Commentary: Information Systems and Conceptual Modeling – A Research Agenda. Inf. Syst. Res. 13, 363–376 (2002).
37. Gemino, A., Wand, Y.: A Framework for Empirical Evaluation of Conceptual Modeling Techniques. Requir. Eng. 9, 248–260 (2004).
38. Kim, Y.-G., March, S.T.: Comparing Data Modeling Formalisms. Commun. ACM. 38, 103–115 (1995).
39. Vessey, I., Conger, S.: Requirements Specification: Learning Object, Process, and Data Methodologies. Commun. ACM. 37, 102–113 (1994).
40. Lukyanenko, R.: Rethinking the role of conceptual modeling in the introductory IS curriculum. In: Proceedings of the International Conference on Information Systems. , San Francisco (2018).
41. Gemino, A., Wand, Y.: Evaluating Modeling Techniques Based on Models of Learning. Commun. ACM. 46, 79–84 (2003).
42. Topi, H., Venkataraman, R.: Human Factors Research on Data Modeling: A Review of Prior Research, an Extended Framework and Future Research Directions. J. Database Manag. 13, 3–19 (2002).
43. Frisendal, T.: Graph Data Modeling for NoSQL and SQL: Visualize Structure and Meaning. Technics Publications, Basking Ridge, N.J. (2016).
44. Mark, G., Lyytinen, K., Bergman, M.: Boundary Objects in Design: An Ecological View of Design Artifacts. J. Assoc. Inf. Syst. 8, 546–568 (2007).
45. Abbasi, A., Suprateek, S., Chiang, R.H.: Big Data Research in Information Systems: Toward an Inclusive Research Agenda. J. Assoc. Inf. Syst. 17, i–xxxii (2016).
46.Hills, T.: NoSQL and SQL Data Modeling: Bringing Together Data, Semantics, and Software. Technics Publications, Basking Ridge (2016).
47.Parsons, J., Wand, Y.: Extending Classification Principles From Information Modeling to Other Disciplines. J. Assoc. Inf. Syst. 14, 245–273 (2013)
48. Beverungen, D., Lüttenberg, H., Wolf, V.: Recombinant Service System Engineering. In: Proceedings der 13. Internationalen Tagung Wirtschaftsinformatik: Towards Thought Leadership in Digital Transformation. pp. 136–150. , St. Gallen (2017).
49. Pöppelbuß, J., Durst, C.: Smart Service Canvas – Ein Werkzeug zur strukturierten Beschreibung und Entwicklung von Smart-Service-Geschäftsmodellen. In: Bruhn, M. and Hadwich, K. (eds.) Dienstleistungen 4.0. pp. 91–110. Springer Gabler, Wiesbaden (2017).
50. Zheng, P., Xu, X., Chen, C.H.: A data-driven cyber-physical approach for personalised smart, connected product co-development in a cloud-based environment. J. Intell. Manuf. 24, 1–16 (2018).
51. Hevner, A.R., March, S.T., Park, J., Ram, S.: Design science in information system research. MIS Q. 28, 75–105 (2004).
52. Peffers, K., Tuunanen, T., Rothenberger, M. a., Chatterjee, S.: A Design Science Research Methodology for Information Systems Research. J. Manag. Inf. Syst. 24, 45–77 (2007).
53. Teixeira, J.G., Patrício, L., Tuunanen, T.: Bringing Design Science Research to Service Design. In: Proceedings of the International Conference on Exploring Service Science. pp. 373–384. Springer, Cham (2018).
54. March, S.T., Smith, G.F.: Design and Natural Science Research on Information Technology. Decis.Support Syst. 15, 251–266 (1995).
55. Gregor, S., Hevner, A.: Positioning and Presenting Design Science Research for Maximum Impact. Manag. Inf. Syst. Q. 37, 337–355 (2013).
56. Venable, J., Pries-heje, J., Baskerville, R.: A Comprehensive Framework for Evaluation in Design Science Research. In: Peffers, K., Rothenberger, M., and Kuechler, B. (eds.) Design Science Research in Information Systems. Advances in Theory and Practice. pp. 423–438. Springer, Berlin Heidelberg (2012).
57. Novak, J.D., Cañas, A.J.: The Theory Underlying Concept Maps and How to Construct and Use Them. Tech. Rep. IHMC C. (2008).
58. Butler-Kisber, L., Poldma, T.: The Power of Visual Approaches in Qualitative Inquiry: The Use of Collage Making and Concept Mapping in Experiential Research. J. Res. Pract. 6, 18 (2011).
59. Ausubel, D.P.: The psychology of meaningful verbal learning. Grune Strat. 99, 58 (1963).
60. Ausubel, D.P.: Educational psychology: A cognitive view. New York: Holt, Rinehart and Winston. Holt, Rinehart and Winston, Inc., New York (1968).
61. Saeed, S., Yousafzai, S., Paladino, A., De Luca, L.M.: Inside-out and outside-in orientations: A metaanalysis of orientation’s effects on innovation and firm performance. Ind. Mark. Manag. 47, 121–133 (2015).
62. Bitner, M.J., Ostrom, A.L., Morgan, F.N.: Service Blueprinting: A Practical Technique for Service Innovation. Calif. Manage. Rev. 50, 66–94 (2008).
63. Bettencourt, L.: Service Innovation: How to Go from Customer Needs to Breakthrough Services. McGraw Hill Education (2010).
64. Rosenbaum, M.S., Otalora, M.L., Ramirez, G.C.: How to Create a Realistic Customer Journey Map. Bus. Horiz. 60, 143–150 (2017).
65. Nemoto, Y., Uei, K., Sato, K., Shimomura, Y.: A Context-Based Requirements Analysis Method for PSS Design. Procedia CIRP. 30, 42–47 (2015).
66. Wittwer, M., Reinhold, O., Alt, R.: Customer Context and Social CRM – A Literature Review and Research Agenda. In: Proceedings of the 30th Bled eConference. , Bled (2017).
67. Rygielski, C., Jyun-Cheng, W., David, C.: Data Mining Techniques for Customer Relationship Management. Technol. Soc. 24, 483–502 (2002).
68. Brinkkemper, S.: Method Engineering: Engineering of Information Methods and Tools. 38, 275–280 (1996).
69. Venable, J., Pries-Heje, J., Baskerville, R.: FEDS: a Framework for Evaluation in Design Science Research. Eur. J. Inf. Syst. 25, 77–89 (2016).
70. Frank, U.: Domain-Specific Modeling Languages – Requirements Analysis and Design Guidelines. In: Reinhartz-Berger, I., Sturm, A., Clark, T., Cohen, S., and Bettin, J. (eds.) Domain Engineering: Product Lines, Languages, and Conceptual Models. pp. 133–157. Springer, Berlin, Heidelberg (2013).