Determinants of Customer Analytics Capabilities: A Model to Achieve Sustainable Firm Performance

Determinants of Customer Analytics Capabilities: A Model to Achieve Sustainable Firm Performance

Meenal Arora, Amit Mittal, Anshika Prakash, Vishal Jain
Copyright: © 2024 |Pages: 14
DOI: 10.4018/979-8-3693-3253-5.ch014
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Abstract

Customer analytics is essential for creating insights from massive data that can be used to enhance management decision-making at various consumer levels, product creation, and service innovation. However, no studies have examined the potential of consumer analytics for achieving long-term corporate success. This research examines the structures of customer analytics capabilities in order to fill this gap by drawing upon a rigorous assessment of the big data literature. The interpretative framework for this study shows the concept of customer analytics, its significance, and the building blocks for consumer analytics capabilities. The research suggests a model of consumer analytics capabilities made up of four main constructs and some significant supporting sub-constructs. The study elaborates on developing a model to analyze sustainable firm performance through dimensions of customer analytics capabilities.
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I. Introduction

Apart from providing goods and services to developed markets, low per capita income, unpredictable demand, an abundance of options, inadequate infrastructure, and, most importantly, fragmentation are characteristics of emerging markets. For firms to create their business strategy, it is crucial to recognize these traits (Aguiar & Gopinath, 2007). Customer engagement is a key element under these circumstances that determines the organization's longevity. A firm’s business communications with a client or consumer across various channels are the core of customer engagement, ultimately generating sales opportunities (Ference, 2017). It also includes provoking customers to interact and share their brand-related experiences by offering encouragement to them (Maslowska et al., 2016). The strongest driver of corporate success and market expansion in the connected world today is client engagement, which eventually results in profitability for the business (Brodie et al., 2011). The goal should be to create value whenever the company has a chance to interact with a consumer. The customer interaction strategy must be an essential component of the business strategy in order to facilitate the same. The goal of customer-centric enterprises is to accomplish customer engagement to foster a closer, more solidified relationship between the client and the business.

The strategic problem of comprehending and maintaining client relationships has grown more challenging and crucial as businesses are vying for consumers' attention in an increasingly cutthroat manner, driving up acquisition costs and making client retention harder (Gupta et al., 2006). Simultaneously, it has been simpler to gather enormous amounts of client data that, when collaborated with ever more complicated analysis, may produce significant and helpful inputs (McAfee & Brynjofsson, 2012), enabling businesses to innovate and stand out from rivals. However, because of the widespread use of analytics, many of the opportunities are already taken. As a result, isolated, ad hoc analytics projects are unable to establish or maintain a competitive advantage. Companies that want to use big data to produce strategic value must develop targeted analytics capabilities that allow for quick adaptation to a dynamic environment.

Analytics in Consumer aspect have typically focused on compartmentalized information which captures a particular component of consumer activity at single time, with the possible exception of a limited number of start-ups focused on technology and built exclusively on analytic capabilities (e.g. Google, Amazon, Capital One) (Davenport, 2013). The data diversity and velocity with businesses have rapidly increased (Lu et al., 2021, Goyal et al., 2019) over the past ten years as a result of the growth of customer relationship management (CRM) software, social networking, online reviews, web traffic statistics, and other information technology-enabled technologies (Agarwal & Dhar, 2014; Chen & Storey, 2012). The businesses that advance from typical walled consumer analytics refer to “advanced customer analytics” will benefit the most from this expansion which enables comprehensive actionable inputs and outputs for customer equity, acquisition, retention, and growth come from an understanding of customers.

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