Examining the Role of Social Media Analytics in Providing Competitive Intelligence: The Impacts and Limitations

Introduction

The use of social media (SM) in business and everyday life is increasing exponentially. A recent report shows that there are 3.196 billion SM users globally (representing an annual growth of 13%), with 79% of the global internet population (estimated at 4.021 billion in 2018) and 90% of brands using SM to build brand awareness (Chaffey, 2018). These staggering figures point to the potential value of SM for businesses. In particular, given the pervasiveness of SM use among multiple tiers of society, SM data can provide valuable business intelligence about customers, including their demographics and psychographics, purchasing habits, preferences, and behavioral intentions (He et al., 2017). User-generated data from SM, including users’ geolocations, opinions, and preferences, can reveal valuable information about customers’ tastes, thoughts, and behaviors, constituting an important source of analytics to obtain competitive intelligence (CI) and other types of business intelligence for decision-makers (Shollo & Galliers, 2016).

Pellissier and Nenzhelele (2013) define CI as:

Given the increased competition and associated technological developments (e.g., big data analytics) that enable the collection and processing of large volumes of multicontextual data, CI has assumed center stage, arguably becoming a core business strategy to enhance market position and profitability (Lee, 2018). Moreover, as the world is becoming interconnected enabling formation of new organizational structures and alliances internationally, leveraging upon the insights developed from global information resources (e.g. SM), and management of such information resources is pivotal to survive and succeed in the global marketplace (Rialp-Criado & Rialp, 2020).

Social media–driven big data (hereafter ‘SM data’) are an important source of unstructured and external data that are suboptimally utilized for CI purposes (Xu et al., 2017). Facebook alone has more than 2 billion active users (including businesses), 1.5 billion of whom are active on a daily basis (Marr, 2018). Other SM platforms such as Twitter, Instagram, WeChat, and Snapchat contribute to the creation of staggering volumes of data comprising transactions, communications, and information. The SM platforms being used globally are becoming potent IT infrastructure for the companies to expand internationally (Rialp-Criado & Rialp, 2020). This is seen from the impact of SM, which is visible at, both, global (e.g., Rialp-Criado & Rialp, 2020) as well as national cultural level (e.g. Halawani et al., 2020’s study conducted in a Lebanese context). The insights derived from SM data are helping companies understand local and global market sentiments and strategize accordingly (Iftikhar & Khan, 2020). Therefore, SM analytics (SMA), or the collection and analysis of SM data to create business value, provides a significant opportunity to acquire CI to improve business operations and competitive positioning (Halawani et al., 2020). This position is in line Chen and Ching’s (2004) study as they suggest that organizations should use technology infrastructure to improve their CI capabilities for customer relationship management and product innovations.

However, the academic understanding of the role of SMA in obtaining CI is fragmentary and limited. This study addresses this gap by posing the following research question: What role does SM analytics play in providing CI beyond traditional competitor-based insights? The motivation for this research question stems from a number of issues that need attention. First, current SM-driven CI research (e.g., He et al., 2015; Xiang et al., 2017) has mainly focused on customer sentiment text analysis or, to some extent, competitor analytics, CI, however, is much broader in scope, going beyond the traditional customer–competitor orientation (Porter, 1980). Köseoglu et al. (2019) explain this important consideration: