Growth hacking: Insights on data-driven decision-making from three firms

Theoretical background: The work explores how Big Data analysis can reshape marketing decision-making in B2B sector. Deriving from Data-Driven Decision-Making (DDDM) approach, the Growth Hacking model is employed to investigate the role of cognitive computing and big data analytics in redefining business processes. Purpose: The main objectives of the study are: 1) to assess how a data-driven orientation to the use of big data analytics and cognitive computing can reframe marketing decisions in B2B segment; 2) to explore whether the adoption Growth Hacking can be helpful in exploiting the opportunities offered by big data analytics and cognitive computing in B2B marketing. Methodology: The paper is based on Action Research (AR) methodology that permits researchers to participate actively in the observation of businesses and to examine how decisions are undertaken and managed over time. Results: The main findings allow identifying the most common strategies and tactics employed in three companies operating in different B2B sectors to exploit the opportunities offered by cognitive computing and big data analytics according to a data-driven marketing approach. Based on the application of the Growth Hacking model, the tools of analytics and the main objectives, outcomes and implications on marketing decision-making are revealed. Originality: The identification of the main objectives and outcomes produced across the three dimensions of the Growth Hacking model (data analysis, marketing and programming) can help academics and practitioners to understand the main levers to attain marketing goals, such as the enhancement of relationship with customers (CRM), continuous learning and development of new products and potential innovation.