Advertisement

Entscheidungsunterstützung im Kundenbeziehungszyklus durch Maschinelle Lernverfahren

  • Andreas WelschEmail author
  • Verena Eitle
  • Peter Buxmann
Chapter
Part of the Edition HMD book series (EHMD)

Zusammenfassung

Die zunehmende Digitalisierung sowie die allgegenwärtige Verfügbarkeit von Daten verändern das Wirtschaftsleben, den Alltag des Einzelnen und die Gesellschaft als Ganzes. Vor diesem Hintergrund wird der Einsatz von Maschinellen Lernverfahren in vielen Bereichen von Wirtschaft und Gesellschaft zum Teil kontrovers diskutiert. Mit Hilfe des Einsatzes solcher Algorithmen lassen sich beispielsweise Prognosen verbessern sowie Entscheidungen bzw. Entscheidungsprozesse automatisieren. In diesem Artikel geben wir zum einen einen Überblick über die Grundprinzipien Maschinellen Lernens. Zum anderen diskutieren wir Anwendungsmöglichkeiten sowie Wirtschaftlichkeitspotenziale am Beispiel des Kundenbeziehungszyklus.

Schlüsselwörter

Automatisierung von Unternehmenssystemen Kundenbeziehungszyklus Vertriebspipelineprozess Kundenbindung Maschinelles Lernen 

Literatur

  1. Amazon Developer (2017) Alexa. https://developer.amazon.com/public/solutions/alexa. Zugegriffen am 29.10.2017
  2. Ang L, Buttle F (2006) Managing for successful customer acquisition: an exploration. J Mark Manag 22(3–4):295–317CrossRefGoogle Scholar
  3. Apple (2017) Siri. http://www.apple.com/ios/siri. Zugegriffen am 29.10.2017
  4. Batista G, Monard MC (2003) An analysis of four missing data treatment methods for supervised learning. Appl Artif Intell 17(5–6):519–533CrossRefGoogle Scholar
  5. Bitkom (2017) Künstliche Intelligenz verstehen als Automation des Entscheidens. https://www.bitkom.org/noindex/Publikationen/2017/Leitfaden/Bitkom-Leitfaden-KI-verstehen-als-Automation-des-Entscheidens-2-Mai-2017.pdf. Zugegriffen am 17.09.2017
  6. Breiman L (2001) Random forests. Mach Learn 45(1):5–32zbMATHCrossRefGoogle Scholar
  7. Bruhn M, Hadwich K (2012) Customer Experience – Eine Einführung in die theoretischen und praktischen Problemstellungen. In: Hadwich K (Hrsg) Customer experience. Springer Gabler, Wiesbaden, S 3–36CrossRefGoogle Scholar
  8. Brynjolfsson E, McAfee A (2017) The business of artificial intelligence. Harvard Business Review. https://hbr.org/cover-story/2017/07/the-business-of-artificial-intelligence. Zugegriffen am 29.09.2017
  9. Buckinx W, Van den Poel (2005) Customer base analysis: partial defection of behaviourally loyal clients in a non-contractual FMCG retail setting. Eur J Oper Res 164(1):252–268zbMATHCrossRefGoogle Scholar
  10. Burez J, Van den Poel D (2007) CRM at a pay-TV company: using analytical models to reduce customer attrition by targeted marketing for subscription services. Expert Syst Appl 32(2):277–288CrossRefGoogle Scholar
  11. Buttle F (2009) Managing the customer lifecycle: customer acquisition. In: Buttle F, Maklan S (Hrsg) Customer relationship management: concepts and technologies. Taylor & Francis, London, S 1–23Google Scholar
  12. Chen T, Guestrin C (2016) XGBoost: a scalable tree boosting system. arXiv preprint arXiv: 1603.02754Google Scholar
  13. Chesbrough H (2007) Business model innovation: it’s not just about technology anymore. Strateg Leadersh 35(6):12–17CrossRefGoogle Scholar
  14. Christensen C (1997) The innovator’s dilemma. Harvard Business School Press, CambridgeGoogle Scholar
  15. Committee on Technology National Science and Technology Council (2016) Preparing for the future of artificial intelligence. CreateSpace Independent Publishing Platform. https://obamawhitehouse.archives.gov/sites/default/files/whitehouse_files/microsites/ostp/NSTC/preparing_for_the_future_of_ai.pdf. Zugegriffen am 15.10.2017
  16. Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273–297zbMATHGoogle Scholar
  17. Coussement K, Van den Poel D (2008) Churn prediction in subscription services: an application of support vector machines while comparing two parameter-selection techniques. Expert Syst Appl 34(1):313–327CrossRefGoogle Scholar
  18. Coussement K, Van den Poel D (2009) Improving customer attrition prediction by integrating emotions from client/company interaction emails and evaluating multiple classifiers. Expert Syst Appl 36(3):6127–6134CrossRefGoogle Scholar
  19. D’Haen J, Van den Poel D (2013) Model-supported business-to-business prospect prediction based on an iterative customer acquisition framework. Ind Mark Manag 42(4):544–551CrossRefGoogle Scholar
  20. Dahlmeier D (2017) Machine learning: making customer service operations smarter and more strategic. https://news.sap.com/machine-learning-smart-customer-service. Zugegriffen am 06.11.2017
  21. Damm W, Kalmar R (2017) Autonome Systeme. Informatik-Spektrum 40(5):400–408CrossRefGoogle Scholar
  22. Davenport T, Kirby J (2016) Just how smart are smart machines? MIT Sloan Manag Rev 57(3):20–25Google Scholar
  23. Dorogush AV, Ershov V, Gulin A (2017) CatBoost: gradient boosting with categorical features support. In: Conference on Neural Information Processing Systems, MontréalGoogle Scholar
  24. Egle U, Keimer I, Hafner N (2014) KPIs zur Steuerung von Customer Contact Centern. In: Möller K, Schultze W (Hrsg) Produktivität von Dienstleistungen. Springer Fachmedien, Wiesbaden, S 505–543Google Scholar
  25. Erichsen J (2007) Benchmarking – von den Besten lernen. WissenHeute (Deutsche Telekom) 60(2):21–31Google Scholar
  26. Europäisches Parlament (2017) Bericht mit Empfehlungen an die Kommission zu zivilrechtlichen Regelungen im Bereich Robotik (2015/2103(INL)). http://www.europarl.europa.eu/sides/getDoc.do?type=REPORT&reference=A8-2017-0005&language=DE. Zugegriffen am 26.10.2017
  27. Fachforum Autonome Systeme im Hightech-Forum (2017) Autonome Systeme – Chancen und Risiken für Wirtschaft, Wissenschaft und Gesellschaft. Kurzversion, Abschlussbericht, BerlinGoogle Scholar
  28. Farquad H, Ravi V, Bapi R S (2009) Data Mining Using Rules Extracted from SVM: An Application to Churn Prediction in Bank Credit Cards. Rough Sets, Fuzzy Sets, Data Mining and Granular Computing: 390–397Google Scholar
  29. Google (2017) Google now. https://www.google.com/landing/now. Zugegriffen am 29.10.2017
  30. Grace K, Salvatier J, Dafoe A, Zhang B, Evans O (2017) When will AI exceed human performance? Evidence from AI experts. arXiv:1705.08807:1–21Google Scholar
  31. Guzella TS, Caminhas WM (2009) A review of machine learning approaches to spam filtering. Expert Syst Appl 36(7):10206–10222CrossRefGoogle Scholar
  32. Hsu CW, Chang CC, Lin CJ (2004) A practical guide to support vector classification. Technical report, Department of Computer Science and Information Engineering, National Taiwan UniversityGoogle Scholar
  33. Huber AS (2016) Das Digital Enterprise nimmt Gestalt an. In: Sendler U (Hrsg) Industrie 4.0 grenzenlos. Springer-Verlag, Berlin/Heidelberg, S 229–243CrossRefGoogle Scholar
  34. Hung S, Yen DC, Wang H (2006) Applying data mining to telecom churn management. Expert Syst Appl 31(3):515–524CrossRefGoogle Scholar
  35. Hurwitz J, Kaufman M, Bowles A (2015) Cognitive computing and big data analytics. John Wiley & Sons Inc, HobokenGoogle Scholar
  36. IBM (2017) IBM Watson. https://www.ibm.com/watson. Zugegriffen am 28.10.2017
  37. Jahromi AT, Stakhovych S, Ewing M (2014) Managing B2B customer churn, retention and profitability. Ind Mark Manag 43(7):1258–1268CrossRefGoogle Scholar
  38. Kelly K (2014) The three breakthroughs that have finally unleashed AI on the world. http://www.wired.com/2014/10/future-of-artificial-intelligence. Zugegriffen am 14.10.2017
  39. Kelly JE, Hamm S (2013) Smart machines – IBM’s Watson and the era of cognitive computing. Columbia University Press, New YorkCrossRefGoogle Scholar
  40. Kirui C, Li Hong L, Cheruiyot W, Kirui H (2013) Predicting customer churn in mobile telephony industry using probabilistic classifiers in data mining. Int J Comput Sci Issues 10(2):165–172Google Scholar
  41. Kohavi R (1995) A study of cross-validation and bootstrap for accuracy estimation and model selection. In: Proceedings of the 14th international joint conference of artificial intelligence 2:1137–1143Google Scholar
  42. Kruppa J, Schwarz A, Arminger G, Ziegler A (2013) Consumer credit risk: individual probability estimates using machine learning. Expert Syst Appl 40(13):5125–5131CrossRefGoogle Scholar
  43. Lippold D (2016) Akquisitionszyklen und -prozesse im B2B-Bereich. Springer Gabler, WiesbadenCrossRefGoogle Scholar
  44. Litjens G, Kooi T, Bejnordi EB, Setio AAA, Ciompi F, Ghafoorian M, van der Laak JAWM, van Ginneken B, Sánchez CI (2017) A survey on deep learning in medical image analysis. Med Image Anal 42:60–88CrossRefGoogle Scholar
  45. Mainzer K (2016) Künstliche Intelligenz – Wann übernehmen die Maschinen? Springer, MünchenCrossRefGoogle Scholar
  46. Marsland S (2015) Machine learning: an algorithmic perspective. Taylor & Francis Group, FloridaGoogle Scholar
  47. McCarthy J, Minsky ML, Rochester N, Shannon CE (1955) A proposal for the Dartmouth summer research project on artificial intelligence. http://jmc.stanford.edu/articles/dartmouth.html. Zugegriffen am 30.09.2017
  48. Megahed A, Yin P, Nezhad HRM (2016) An optimization approach to services sales forecasting in a multi-staged sales pipeline. In: IEEE international conference on services computing, San Francisco 713–719Google Scholar
  49. Mitchell TM (1997) Machine learning. McGraw-Hill Inc, New YorkzbMATHGoogle Scholar
  50. Murphy KP (2012) Machine learning: a probabilistic perspective. The MIT Press, CambridgezbMATHGoogle Scholar
  51. Murthy SK (1998) Automatic construction of decision trees from data: a multi-disciplinary survey. Data Min Knowl Disc 2(4):345–389CrossRefGoogle Scholar
  52. Ngai EWT, Xiu L, Chau DCK (2009) Application of data mining techniques in customer relationship management: a literature review and classification. Expert Syst Appl 36(2):2592–2602CrossRefGoogle Scholar
  53. Niefind F, Wiegran A (2010) Was sind Beschwerden? In: Ratajczak O (Hrsg) Erfolgreiches Beschwerdemanagement. Springer Gabler, Wiesbaden, S 19–32CrossRefGoogle Scholar
  54. Nilsson NJ (2014) Principles of artificial intelligence. Morgan Kaufmann, BurlingtonzbMATHGoogle Scholar
  55. Osterwalder A, Pigneur Y (2010) Business model generation: a handbook for visionaries, game changers, and challengers. Wiley, HobokenGoogle Scholar
  56. Patel J, Shah S, Thakkar P, Kotecha K (2015) Predicting stock and stock price index movement using Trend Deterministic Data Preparation and machine learning techniques. Expert Syst Appl 42(1):259–268CrossRefGoogle Scholar
  57. Pennachin C, Goertzel B (2007) Artificial general intelligence. Springer, Berlin/HeidelbergzbMATHGoogle Scholar
  58. Quinlan JR (1986) Induction of decision trees. Mach Learn 1:81–106Google Scholar
  59. Reeves M, Ueda D (2016) Designing the machines that will design strategy. https://hbr.org/2016/04/welcoming-the-chief-strategy-robot. Zugegriffen am 30.09.2017
  60. Reinartz W, Kumar V (2003) The impact of customer relationship characteristics on profitable lifetime duration. J Mark 67(1):77–99CrossRefGoogle Scholar
  61. Reinartz W, Krafft M, Hoyer WD (2004) The customer relationship management process: its measurement and impact on performance. J Mark Res 41(3):293–305CrossRefGoogle Scholar
  62. Ribeiro MT, Singh S, Guestrin C (2016) „Why should I trust you?“ Explaining the predictions of any classifier. arXiv:1602.04938Google Scholar
  63. Russell SJ, Norvig P (2010) Artificial intelligence – a modern approach. Pearson Education Inc., Upper Saddle RiverzbMATHGoogle Scholar
  64. SAE International (2016): Taxonomy and definitions for terms related to on-road motor vehicle automated driving systems. J3016:1–16Google Scholar
  65. Salesforce (2017) Salesforce Einstein is artificial intelligence in business technology – Salesforce EMEA. https://www.salesforce.com/eu/products/einstein/overview. Zugegriffen am 27.10.2017
  66. SAP (2017a) Build your intelligent enterprise with machine learning, a case study with BASF. http://events.sap.com/sapandasug/en/session/32268. Zugegriffen am 30.10.2017
  67. SAP (2017b) Machine learning applications and platform. https://www.sap.com/products/leonardo/machine-learning.html. Zugegriffen am 24.10.2017
  68. SAP (2017c) SAP API business hub: SAP Leonardo machine learning – business services. https://api.sap.com/shell/discover/contentpackage/SAPLeonardoMLBusinessServices. Zugegriffen am 10.11.2017
  69. SAP (2017d) SAP service ticket intelligence. https://help.sap.com/viewer/934ccff77ddb4fa2bf268a0085984db0/1708/en-US. Zugegriffen am 06.11.2017
  70. Schapire RE, Freund Y (2012) Boosting: foundations and algorithms. The MIT Press, CambridgezbMATHGoogle Scholar
  71. Schroeck M, Shockley R, Smart J, Romero-Morales D, Tufano P (2012) Analytics: the real-world use of big data. IBM Global Business Services 12:1–20Google Scholar
  72. Silver D, Huang A, Maddison CJ, Guez A, Sifre L, van den Driessche G, Schrittwieser J, Antonoglou I, Panneershelvam V, Lanctot M, Dieleman S, Grewe D, Nham J, Kalchbrenner N, Sutskever I, Lillicrap T, Leach M, Kavukcuoglu K, Graepel T, Demis Hassabis D (2016) Mastering the game of go with deep neural networks and tree search. Nature 529:484–489CrossRefGoogle Scholar
  73. Silver D, Schrittwieser J, Simonyan K, Antonoglou I, Huang A, Guez A, Hubert T, Baker L, Lai M, Bolton A, Chen Y, Lillicrap T, Hui F, Sifre L, van den Driessche G, Graepel T, Hassabis D (2017) Mastering the game of Go without human knowledge. Nature 550:354–359CrossRefGoogle Scholar
  74. Smith TM, Gopalakrishna S, Chatterjee R (2006) A three-stage model of integrated marketing communications at the marketing-sales interface. J Mark Res 43(4):564–579CrossRefGoogle Scholar
  75. Stanford University (2017) One hundred year study on artificial intelligence (AI100). https://ai100.stanford.edu. Zugegriffen am 17.11.2017
  76. Susto GA, Schirru A, Pampuri S, McLoone S, Beghi A (2015) Machine learning for predictive maintenance: a multiple classifier approach. IEEE Trans Ind Inf 11(3):812–819CrossRefGoogle Scholar
  77. Tamaddoni A, Stakhovych S, Ewing M (2016) Comparing churn prediction techniques and assessing their performance a contingent perspective. J Serv Res 19(2):123–141CrossRefGoogle Scholar
  78. Vafeiadis T, Diamantaras KI, Sarigiannidis G, Chatzisavvas KC (2015) A comparison of machine learning techniques for customer churn prediction. Simul Model Pract Theory 55:1–9CrossRefGoogle Scholar
  79. Vallis H (2017) The art and science of reducing involuntary subscriber churn. Forrester consulting thought leadership paper [1–13MQMED]:1–11Google Scholar
  80. Verbeke W, Martens D, Baesens B (2014) Social network analysis for customer churn prediction. Appl Soft Comput 14:431–446CrossRefGoogle Scholar
  81. Wahlster W (2017) Künstliche Intelligenz als Grundlage autonomer Systeme. Informatik-Spektrum 40(5):409–418CrossRefGoogle Scholar
  82. Wang YF, Chiang DA, Hsu MH, Lin CJ, Lin IL (2009) A recommender system to avoid customer churn: a case study. Expert Syst Appl 36(4):8071–8075CrossRefGoogle Scholar
  83. Watson HJ (2017) Preparing for the cognitive generation of decision support. MIS Q Exec 16(13):153–169Google Scholar
  84. Witten IH, Frank E, Hall MA, Pal CJ (2017) Data mining: practical machine learning tools and techniques. Elsevier Inc, CambridgeGoogle Scholar
  85. World Economic Forum (2016) Digital transformation of industries: digital enterprise. http://reports.weforum.org/digital-transformation/wp-content/blogs.dir/94/mp/files/pages/files/digital-enterprise-narrative-final-january-2016.pdf. Zugegriffen am 11.11.2017
  86. Yan J, Zhang C, Zha H, Gong M, Sun C, Huang J, Chu S, Yang X (2015) On machine learning towards predictive sales pipeline analytics. In: Proceedings of the 29th AAAI conference on artificial intelligence 1945–1951Google Scholar
  87. Yin S, Zhu X, Jing C (2014) Fault detection based on a robust one class support vector machine. Neurocomputing 145:263–268CrossRefGoogle Scholar
  88. Yu L, Liu H (2004) Efficient feature selection via analysis of relevance and redundancy. J Mach Learn Res 5:1205–1224MathSciNetzbMATHGoogle Scholar
  89. Zorn S, Jarvis W, Bellman S (2010) Attitudinal perspectives for predicting churn. J Res Interact Mark 4(2):157–169CrossRefGoogle Scholar

Copyright information

© Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature 2019

Authors and Affiliations

  1. 1.Software & Digital BusinessTU DarmstadtDarmstadtDeutschland

Personalised recommendations