Moving Toward Economic and Digital Sustainability in Marketing Analytics

Moving Toward Economic And Digital Sustainability In Marketing Analytics

Big data analytics (BDA) is a new field, yet at the same time, a powerful source of competitive advantage in which firms continuously increase their investments (Grover et al., 2018; Rialti et al., 2019). According to the 2021 International Data Corporation (IDC) report, global spending on big data (BD) and analytics is increasing at about 10 percent annually and reached $215.8 billion in 2021 (Shirer & Goepfert, 2021). Since markets become more volatile and consumer behaviours seem unpredictable, firms heavily depend on massive data to get insights about their consumers and market trends (Cao & Tian, 2020). Moreover, the ever-increasing competition forces firms to be more customer-centric in delivering goods, services, and marketing campaigns sustainably (Corrigan et al., 2014; United Nations, 2023). BDA enables firms and organizations to know their customers better by monitoring the real-time state of internal operations, business processes, and market conditions (Rialti et al., 2019). That is why recent literature has referred to BD as the “next frontier for competition and productivity” and the “next management and data revolution” (United Nations, 2023; Wamba et al., 2017).

Although there have been considerable investments in BDA, very few firms have made a meaningful impact using BD marketing analytics (BDMA) (Akter, et al., 2017). Many businesses struggle to gain good value and firm performance from marketing analytics and analytics (Ermakova et al., 2021), posing an economic and digital sustainability threat for the firms. Economic and digital sustainability relates to a range of issues and concerns related to the longevity of digital information, processes, and platforms, as well as the firm's economic viability (Bradley, 2007).

Gartner reported a high failure rate in implementing BDA projects, with close to 85% of them failing (Waid, 2019). The fundamental value of this research is related to environmental uncertainty (as measured technological and market uncertainty) and, subsequently, poor analytics quality, which restricts data analysts from getting real consumer insights and predicting market trends (et al., 2023). Therefore, developing internal practices and processes in terms of data quality that enable and facilitate successful economic and digital sustainability transformation is essential. Quality analytics largely depends on the data quality, overall data management, understanding of organizational needs, choice of the analytic tools, design of the data model, and predictability of unforeseen events (Aljumah et al., 2021; Côrte-Real et al., 2020; Osaysa, 2022). Without proper knowledge and information, data analysts might have poor-quality analytical results (Wamba et al., 2017). These poor-quality results may lead to misreading of data and inapt business decisions, costing the firms heavily and adversely affecting overall performance (Ji-fan Ren et al., 2017; Torres & Sidorova, 2019), thereby threatening the economic and digital sustainability of the firms. Therefore, firms should prioritize quality to realize the full operational and strategic potential of BDMA, even under severe uncertainty.