Big data analysis has become a source of competitive advantage for businesses and even whole economies. Big data is indeed a critical issue for the digital economy, innovation and services that are expected to drive growth and job creation in the EU. Compared to the USA, however, the EU has been slow in the adoption of big data analysis. Fortunately, big data opportunities still exist in many business sectors to improve the competitiveness of European firms. In particular, performance may be improved by investing in and making use of big customer data to offer goods and services that better meet customer needs, i.e. “big data-driven marketing (BDM)”. However, adopting BDM has a steep learning curve due to organizations’ lack of understanding regarding the diverse factors required to succeed.
Against this backdrop, the BIDAMARK project aims to assess (1) investment into Big Data, i.e. how the acquisition and/or development of strategic Big Data –related resources improve firm marketing strategy, and ultimately, firm performance; and (2) utilization of Big Data, i.e. how the organizational use of customer insights in marketing decision-making helps firms offer goods and services that better meet customer needs, and ultimately, improve firm profitability. To address these issues, a multivariate analysis different types of firms and industries is implemented. A quantitative survey methodology will be applied with structural equation modelling (PLS) as the primary analysis method.
Three important results were found in relation to Big Data investment. First, the results show that despite the challenges associated with big data, firm performance increases 11-12% as a result of big data resources, as opposed to previously reported 5-6% increases with traditional business intelligence (BI) and marketing analytics. Second, this study identifies and assesses the relative importance of three critical IT resources: (1) big data-related technology; (2) analytics skills; and (3) organizational resources, all of which are confirmed as necessary and complementary dimensions for superior performance. Third, this study highlights the critical role of the firm’s adopted business strategy in driving big data success: Differentiating firms are far more likely to benefit from big data than cost leadership firms.
Three important results were also found in relation to Big Data utilization. First, this study highlights the role of information quality (IQ) in predicting big data customer analytics use. In particular, the format (visualization) of customer information is the most critical aspect of IQ. Second, big data customer analytics use, and personalization as its most crucial dimension, is found to be a key predictor of firm performance. Third, the performance impacts of big data customer analytics use are highly contingent on its prevalence within an industry, i.e. competitive advantage can be imitated away by rivals. However, the hyper-personalization afforded by big data makes the firm’s customers less vulnerable to competitor moves when strong customer relationships are built.
The results of BIDAMARK can be exploited in guiding Big Data –related policy-making in public bodies related to society and citizens as consumers, and strategic decision-making in firms investing and utilizing big data for better competitiveness.
