Applying big data to guide firms’ future industrial marketing strategies

Abstract

Purpose

While big data (BD), a transformative emerging phenomenon on its youth, plays a growing role in organizations in improving marketing decision-making, few academic works examine the mechanism through which BD can be applied to guide future competitive advantage strategies. The purpose of this paper is to examine if BD’s predictive power helps business to business (B2B) firms selecting their intended generic (differentiation, focus, and cost leadership) strategies.

Design/methodology/approach

Drawing on the learning theory, the study proposes the use of BD as a key driver of intended strategies. Based on data from a cross-industry sample of executives, a conceptual model is tested using path and robustness analyses.

Findings

The use of BD plays a prominent role in the selection of intended future strategies in industrial markets. Additional tests demonstrate conditions of competitive intensity and strategic flexibility where BD is more and less beneficial.

Research limitations/implications

The study furthers the understanding of traditional learning and intelligence use frameworks and of contemporary future strategies drivers.

Practical implications

BD availability enables managers leveraging knowledge embedded in data-rich systems to gain predictive insights that help in guiding new strategic directions to maintain competitive advantage.

Originality/value

The study reinforces the continued applicability of Porter’s generic positioning strategies in the digital era. It addresses the paucity of research on BD in B2B context and is the first to provide theoretical and practical reflections on how BD utilization influences industrial intended strategies. The study strengthens contemporary managerial views defending that data drive strategies rather than the opposite.

Citation

Gnizy, I. (2020), "Applying big data to guide firms’ future industrial marketing strategies", Journal of Business & Industrial Marketing, Vol. 35 No. 7, pp. 1221-1235. https://doi.org/10.1108/JBIM-06-2019-0318