Streaming Analytics—Real-Time Customer Satisfaction in Brick-and-Mortar Retailing

  • Felix WeberEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 986)


The manifold changes in retailing in recent years has led to the scenario where competition is mainly driven by price. However, this one-sided focus on price as the only competitive instrument has become a significant problem for many retailers due to the increase in competition and reduction in scope for price differentiation. For brick-and-mortar stores in particular, however, customer satisfaction within the store is also a decisive factor, although this can currently only be assessed manually by employees as there are no analytical processes in place. Active evaluation and control of overarching measures is technically and economically not yet feasible. The aim of this research is to sketch a sentiment analytics model to analyze customer satisfaction for brick-and-mortar retailing. Using the presented Customer Satisfaction Streaming Index (CSSI), a mathematical model is developed that is tailored to the characteristics of the available data sources. In a second step, a framework for conducting big data analyses based on a standard retail system architecture is demonstrated, and a prototypical implementation is demonstrated. The preliminary results show that this is a suitable method for brick-and-mortar retailers. As the quality of social media sources might not be fully sufficient, alternate resources are discussed.


Streaming analytics Sentiment analysis Retail Customer satisfaction 


  1. 1.
    Hallowell, R.: The relationships of customer satisfaction, customer loyalty, and profitability: an empirical study. Int. J. Serv. Ind. Manag. 7(4), 27–42 (1996)CrossRefGoogle Scholar
  2. 2.
    Homburg, C., Koschate, N., Hoyer, W.D.: Do satisfied customers really pay more? A study of the relationship between customer satisfaction and willingness to pay. J. Mark. 69(2), 84–96 (2005)CrossRefGoogle Scholar
  3. 3.
    Francioni, B., Savelli, E., Cioppi, M.: Store satisfaction and store loyalty: the moderating role of store atmosphere. J. Retail. Consum. Serv. 43, 333–341 (2018)CrossRefGoogle Scholar
  4. 4.
    Kumar, V., Anand, A., Song, H.: Future of retailer profitability: an organizing framework. J. Retail. 93(1), 96–119 (2017)CrossRefGoogle Scholar
  5. 5.
    Anderson, E.W.: Customer satisfaction and price tolerance. Mark. Lett. 7(3), 265–274 (1996)CrossRefGoogle Scholar
  6. 6.
    Renker, C., Maiwald, F.: Vorteilsstrategien des stationären Einzelhandels im Wettbewerb mit dem Online-Handel. In: Binckebanck, L., Elste, R. (eds.) Digitalisierung im Vertrieb: Strategien zum Einsatz neuer Technologien in Vertriebsorganisationen, pp. 85–104, Springer Fachmedien Wiesbaden, Wiesbaden (2016)Google Scholar
  7. 7.
    Fleer, J.: Kundenzufriedenheit und Kundenloyalität in Multikanalsystemen des Einzelhandels: Eine kaufprozessphasenübergreifende Untersuchung. Springer, Wiesbaden (2016)CrossRefGoogle Scholar
  8. 8.
    IFH. Catch me if you can - Wie der stationäre Handel seine Kunden einfangen kann (2017). Accessed 23 July 2018
  9. 9.
    Töpfer, A.: Konzeptionelle Grundlagen und Messkonzepte für den Kundenzufriedenheitsindex (KZI/ CSI) und den Kundenbindungsindex (KBI/ CRI). In: Töpfer, A (ed.) Handbuch Kundenmanagement: Anforderungen Prozesse, Zufriedenheit, Bindung und Wert von Kunden, pp. 309–382. Springer, Berlin (2008)Google Scholar
  10. 10.
    Anders, G.: Inside Amazon’s Idea Machine: How Bezos Decodes Customers (2012). Accessed 20 May 2018
  11. 11.
    Constantinides, E., Romero, C.L., Boria, M.A.G.: Social Media: A New Frontier for Retailers?. In: European Retail Research, pp. 1–28 (2008)Google Scholar
  12. 12.
    Piotrowicz, W., Cuthbertson, R.: Introduction to the special issue information technology in retail: toward omnichannel retailing. Int. J. Electron. Commer. 18(4), 5–16 (2014)CrossRefGoogle Scholar
  13. 13.
    Evanschitzky, H., et al.: Consumer trial, continuous use, and economic benefits of a retail service innovation: the case of the personal shopping assistant. J. Prod. Innov. Manag. 32(3), 459–475 (2015)CrossRefGoogle Scholar
  14. 14.
    HappyOrNot. Case Study: Elkjøp Leading Consumer Electronics Industry with Excellent Customer Service (2013). Accessed 22 Nov 2017
  15. 15.
    Oliver, R.L.: Effect of expectation and disconfirmation on postexposure product evaluations: an alternative interpretation. J. Appl. Psychol. 62(4), 480 (1977)CrossRefGoogle Scholar
  16. 16.
    Bösener, K.: Kundenzufriedenheit, Kundenbegeisterung und Kundenpreisverhalten: Empirische Studien zur Untersuchung der Wirkungszusammenhänge. Springer, Berlin (2014)Google Scholar
  17. 17.
    Simon, A., et al.: Safety and usability evaluation of a web-based insulin self-titration system for patients with type 2 diabetes mellitus. Artif. Intell. Med. 59(1), 23–31 (2013)MathSciNetCrossRefGoogle Scholar
  18. 18.
    Fornell, C., et al.: The American customer satisfaction index: nature, purpose, and findings. J. Mark. 60(4), 7–18 (1996)CrossRefGoogle Scholar
  19. 19.
    Becker, J., Schütte, R.: Handelsinformationssysteme domänenorientierte Einführung in die Wirtschaftsinformatik, 2nd edn. Redline-Wirtschaft, Frankfurt/M (2004)Google Scholar
  20. 20.
    Schütte, R.: Analyse des Einsatzpotenzials von In-Memory-Technologien in Handelsinformationssystemen. In: IMDM (2011)Google Scholar
  21. 21.
    Woesner, I.: Retail Omnichannel Commerce – Model Company (2016). Accessed 01 July 2017
  22. 22.
    Plattner, H., Leukert, B.: The In-Memory Revolution: How SAP HANA Enables Business of the Future. Springer, Berlin (2015)Google Scholar
  23. 23.
    Schütte, R., Vetter, T.: Analyse des Digitalisierungspotentials von Handelsunternehmen. In: Handel 4.0. Springer, Berlin, pp. 75–113 (2017)Google Scholar
  24. 24.
    Meffert, H., Burmann, C., Kirchgeorg, M.: Marketing: Grundlagen marktorientierter Unternehmensführung Konzepte - Instrumente - Praxisbeispiele, 12th edn, pp. 357–768. Springer Fachmedien Wiesbaden, Wiesbaden (2015)Google Scholar
  25. 25.
    Daurer, S., Molitor, D., Spann, M.: Digitalisierung und Konvergenz von Online-und Offline-Welt. Zeitschrift für Betriebswirtschaft 82(4), 3–23 (2012)CrossRefGoogle Scholar
  26. 26.
    Weber, F., Schütte, R.: A domain-oriented analysis of the impact of machine learning—the case of retailing. Big Data Cogn. Comput. 3(1), 11 (2019)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.University of Duisburg-EssenEssenGermany

Personalised recommendations