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Creating customer value from data: foundations and archetypes of analytics-based services

Author

Listed:
  • Fabian Hunke

    (Institute of Information Systems and Marketing (IISM) and Karlsruhe Service Research Institute (KSRI), Karlsruhe Institute of Technology (KIT))

  • Daniel Heinz

    (Institute of Information Systems and Marketing (IISM) and Karlsruhe Service Research Institute (KSRI), Karlsruhe Institute of Technology (KIT))

  • Gerhard Satzger

    (Institute of Information Systems and Marketing (IISM) and Karlsruhe Service Research Institute (KSRI), Karlsruhe Institute of Technology (KIT))

Abstract

The digital transformation offers new opportunities for organizations to expand their existing service portfolio in order to achieve competitive advantages. A popular way to create new customer value is the offer of analytics-based services (ABS)—services that apply analytical methods to data to empower customers to make better decisions and to solve complex problems. However, research still lacks to provide a profound conceptualization of this novel service type. Similarly, actionable insights on how to purposefully establish ABS in the market to enrich the service portfolio remain scarce. We perform a cluster analysis of 105 ABS and triangulate it with a revelatory case study to identify four generic ABS archetypes and to unveil their specific service objectives and characteristics. We also isolate essential factors that shape decision-making regarding the choice of adequate archetypes and subsequent transitions between them. The detailed characterization of different ABS types contributes to a more profound theorizing process on ABS as well as provides a systematization for strategic opportunities to enrich service portfolios in practice.

Suggested Citation

  • Fabian Hunke & Daniel Heinz & Gerhard Satzger, 2022. "Creating customer value from data: foundations and archetypes of analytics-based services," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(2), pages 503-521, June.
    • Handle: RePEc:spr:elmark:v:32:y:2022:i:2:d:10.1007_s12525-021-00506-y
    DOI: 10.1007/s12525-021-00506-y
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    Cited by:

    1. Daniel Beverungen & Thomas Hess & Antonia Köster & Christiane Lehrer, 2022. "From private digital platforms to public data spaces: implications for the digital transformation," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(2), pages 493-501, June.
    2. Estelle Duparc & Frederik Möller & Ilka Jussen & Maleen Stachon & Sükran Algac & Boris Otto, 2022. "Archetypes of open-source business models," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(2), pages 727-745, June.
    3. Niklas Kühl & Max Schemmer & Marc Goutier & Gerhard Satzger, 2022. "Artificial intelligence and machine learning," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(4), pages 2235-2244, December.
    4. Nora Nahr & Marikka Heikkilä, 2022. "Uncovering the identity of Electronic Markets research through text mining techniques," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(3), pages 1257-1277, September.
    5. Daniela Ioniță & Andreea Orîndaru & Marian Bratu, 2022. "CRM Software Adoption by Small Enterprises," Journal of Emerging Trends in Marketing and Management, The Bucharest University of Economic Studies, vol. 1(1), pages 189-199, November.

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