Towards a Conceptual Framework for Customer Intelligence in the Era of Big Data

Introduction

The dominance of the service sector in today’s economy gives prominence to customer intelligence as a means for enterprises to provide optimal services and service-based products (France & Ghose, 2019; Ramaswamy & Ozcan, 2019). The age of big data defines customer intelligence as knowledge or insights on customers extracted from the data mining process by integrating, analyzing, and interpreting various sources of customer data (Dam et al., 2020b; Davenport & Spanyi, 2019). Accordingly, customer intelligence enables enterprises to develop service offerings, understanding customer behaviors, and improving marketing strategies in the service-based economy (Anshari et al., 2019; Yan et al., 2020; Zerbino et al., 2018). Nowadays, the service sector occupies the majority of the Gross Domestic Product (GDP), particularly in developed countries with over two-thirds (Szirmai & Verspagen, 2015). The proliferation of the service sector in the age of big data highlights the role of customers as co-creators for business value. Customer interactions on various digital platforms have created a vast amount of customer data, which is considered as a great source for customer intelligence (Cooke & Zubcsek, 2017; Crandell, 2016).

However, most enterprises find it challenging to overcome the three major obstacles related to customer intelligence. The first challenge arises from customer co-creation concerning engagement forms (Crandell, 2016; Frow et al., 2016; Xie et al., 2016), which leads to a burdensome task to figure out the roles of customers in the process of co-creating with enterprises for customer intelligence. The second challenge happens within the organizational, management, and technological dimensions of enterprises (Davenport & Spanyi, 2019; Tabrizi et al., 2019). As a matter of fact, the majority of enterprises tend to overemphasize the importance of technological changes while lacking the mindset of restructuring the organizational and management viewpoints to better offer service for customers (Anshari et al., 2019; Tabrizi et al., 2019; Yohn, 2018). The last challenge involves how enterprises apply customer intelligence for service development and innovation (Amado et al., 2018; Anshari et al., 2019). Accordingly, optimal service offerings require the congruence in the dimensions of organization, management, and technology which enables enterprises to acquire customer intelligence and turns it into optimal applications for service (Lafrenière, 2020; McGrath & McManus, 2020).

To address this challenge, the objective of the paper is to propose a service-based framework for customer intelligence in the age of big data - hereafter called the SBCI framework. Considering the significance of service in the era of big data, the framework is developed through the lens of design science and service science. On the one hand, the design science approach deals with the development and validation of designed artefacts or concepts related to customer intelligence (Hevner et al., 2004; Peffers et al., 2007). On the other hand, the service science approach focuses on science, management, and engineering dimensions in the transformation to the service-based economy (Maglio & Spohrer, 2013; Spohrer et al., 2007). The science dimension focuses on the organizational viewpoint, the management dimension covers the strategic viewpoint, and the engineering dimension touches upon the technological viewpoint.