The rise of unstructured data (UD), propelled by novel technologies, is reshaping markets and the management of marketing activities. Yet these increased data remain mostly untapped by many firms, suggesting the potential for further research developments. The integrative framework proposed in this study addresses the nature of UD and pursues theoretical richness and computational advancements by integrating insights from other disciplines. This article makes three main contributions to the literature by (1) offering a unifying definition and conceptualization of UD in marketing; (2) bridging disjoint literature with an organizing framework that synthesizes various subsets of UD relevant for marketing management through an integrative review; and (3) identifying substantive, computational, and theoretical gaps in extant literature and ways to leverage interdisciplinary knowledge to advance marketing research by applying UD analyses to underdeveloped areas.

The contemporary world is characterized by rapid advances in technology that are pervasive in everyday life (Huang and Rust 2017). This swift development has also spurred an unprecedented influx of unstructured data (UD). UD is commonly understood as Binformation that either does not have a predefined data model or is not organized in a pre-defined manner^ (Wikipedia 2017). An estimated 80% of data held by firms today are unstructured (Rizkallah 2017), and they are growing 15 times faster than structured data (SD) (Nair and Narayanan 2012). This global expansion has not gone unnoticed; 87% of marketers cite data as their most underutilized resource but note that deriving value from various sources of UD remains a key challenge (Howatson 2016). The obstacles faced in extracting knowledge from UD mean that firms often sit idly on expansive troves of it, earning UD the designation Bdark analytics^ (Briggs and Hodgetts 2017). Yet, unlocking the insights embedded in this burgeoning resource has the potential to be particularly valuable in marketing, sales and service settings where UD volumes are an estimated five times greater than SD (Davies 2015). A wealth of unique information can be derived from analyses of UD for managerially relevant domains of interest such as competitive advantages (Coughlin 2017), social networks (Lohr 2012), and data privacy (Rizkallah 2017). The rapid emergence and growth of technologies capable of analyzing vast amounts of UD through machine learning and other artificial intelligence methods (Marr 2017) has also made UD increasingly prominent in marketing literature. However, current applications of UD in marketing are fragmented, reflecting the scattered domains that house requisite theories (communication, linguistics, or psychology), novel methods (computer science), and untapped data owned by organizations. A critical review of this new and thriving field, to create an organizing framework, thus is essential to identify opportunities for future research. This article makes three main contributions to the literature. First, it contributes to the growing body of research analyzing UD by offering a unifying definition and conceptualization of UD in marketing. This effort can assist scholars pursuing research with UD by shedding light on its characteristics, which can be used to leverage unique insights compared with traditional, SD analyses. Recent research gives examples of data that are unstructured (e.g., Hewett et al. 2016; Wedel and Kannan 2016), but to the best of our knowledge no formal definition or conceptualization of UD appears in the marketing literature. Second, this article unifies disjoint literature within an organizing framework that synthesizes numerous subsets of UD relevant for marketing management through a review of marketing and other relevant literature. This contribution reflects the conceptual goals of explicating and relating (MacInnis 2011), in that we detail the unique characteristics of UD, Btaking stock or reducing what is known^ about the use and unique contribution of UD to a manageable set of key takeaways (MacInnis 2011, p. 144) and we draw connections between scattered research that uses UD across substantive domains. This integrative framework also provides insights on the dynamic nature of UD and reveals the theoretical richness and computational advancements that can be gained from other disciplines.