Exploring how to develop data-driven innovation capability of marketing within B2B firms: Toward a capability model and process-oriented approach

Ludivine Ravat (), Aurélie Hemonnet-Goujot () and Sandrine Hollet-Haudebert ()
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Ludivine Ravat: CERGAM - Centre d'Études et de Recherche en Gestion d'Aix-Marseille - AMU - Aix Marseille Université - UTLN - Université de Toulon, AMU IAE - Institut d'Administration des Entreprises (IAE) - Aix-en-Provence - AMU - Aix Marseille Université
Aurélie Hemonnet-Goujot: CERGAM - Centre d'Études et de Recherche en Gestion d'Aix-Marseille - AMU - Aix Marseille Université - UTLN - Université de Toulon, AMU IAE - Institut d'Administration des Entreprises (IAE) - Aix-en-Provence - AMU - Aix Marseille Université
Sandrine Hollet-Haudebert: CERGAM - Centre d'Études et de Recherche en Gestion d'Aix-Marseille - AMU - Aix Marseille Université - UTLN - Université de Toulon, IAE Toulon - Institut d'Administration des Entreprises (IAE) - Toulon

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Abstract: The increasing digitalization of innovation activities is reshaping how marketing organizations practice innovation, and, to date, little research has studied the data-driven innovation capability of marketing. Given the essential role played by B2B marketing departments in new product and service development, this paper takes an abductive approach to elucidate how the data-driven innovation capability of marketing can be constructed by identifying its constituent elements and underlying processes. It draws upon the existing literature on digital marketing capabilities applied to innovation and 17 in-depth interviews with B2B managers. The paper proposes a capability model for the data-driven innovation capability of marketing that articulates building blocks according to three major phases: (1) ideation, (2) analysis, and (3) deployment. It reveals the resources and competencies for operationalizing three successive routines to: (1) generate a knowledge ecosystem, (2) disseminate data-driven insights, and ( 3) design an answer. Each routine is facilitated by three distinct learning mechanisms that (1) capture, (2) articulate, and (3) codify, throughout the innovation phases. Furthermore, potential enablers and consequences are identified to offer an overall process-oriented framework. This study provides valuable guidance to marketing managers by determining the critical resources, routines, and learning mechanisms for engaging in data-driven innovation.

Keywords: Data-driven innovation capability; Digital marketing capabilities; Marketing strategy; Dynamic capabilities; Business-to-business; Capability model; Business-to-business firms (search for similar items in EconPapers)
Date: 2024-04
Note: View the original document on HAL open archive server: https://hal.science/hal-04893429v1
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Published in Industrial Marketing Management, 2024, 118, pp.110-125. ⟨10.1016/j.indmarman.2023.12.015⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-04893429

DOI: 10.1016/j.indmarman.2023.12.015

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