Introduction

The term customer experience was first coined by Holbrook and Hirschman (1982) and refers to a “subjective state of consciousness” that takes customers’ cognitive and behavioral processes into account. Later, Gronroos (1988) simplified the concept of customer experience (CX) by stating that customer experience is a phenomenon that forms in them depending on their perception of service quality when they interact with sellers at different stages of the buying process. From the marketing perspective, CX considers customers’ every encounter with an organization that includes the prepurchase, consumption, and post-purchase phases (Codeluppi, 2001; LaSalle & Britton, 2003). In order to provide the desired CX or improve CX, organizations usually focus on multiple touchpoints, rather than the whole customer journey, for direct or indirect interactions with their potential and existing customers (Kuehnl et al., 2019). It has been established in previous studies that CX notably influences customers’ cognitive and affective buying behavior (Berry et al., 2002; Cetin and Dincer, 2014). In addition, the concept of CX has been studied from a variety of perspectives, namely, extraordinary experience (Arnould and Price, 1993), relationship experience (Payne et al., 2008) and prepurchase and actual service experience (Edvardsson et al., 2005; Winsted, 1997). Effective CX has been found to be a source of competitive advantage over an organization’s competitors (Lemon & Verhoef, 2016).

Big data refers to large sets of data that could be structured, unstructured or semi-structured (Oussous et al., 2018). BDA requires sophisticated technologies and complex data analytical skills to produce meaningful information that traditional business analysis tools can’t deliver (Kietzmann et al., 2018; Sivarajah et al., 2017). BDA allows organizations to extract meaningful insights from a huge amount of data that further assist them to adopt better and more effective customer-centric strategies (Gupta & George, 2016), by utilizing a wide range of industry, competitor and market trend-specific data to make better customer-centric managerial decisions (O’Brien & Marakas, 2005; Said et al., 2015). BDA scholars have characterized big data into seven main categories, namely, volume, velocity, variety, veracity, variability, visualization, and value (Kietzmann et al., 2018; Sivarajah et al., 2017; Wedel & Kannan, 2016). Previous studies on CX and BDA reported somewhat mixed findings. While Wedel and Kannan (2016) and McColl-Kennedy et al. (2019) found BDA benefitted organizations to a great extent in enriching their CX, Said et al. (2015) and Villarroel Ordenes & Zhang (2019) reported that organizations still found it challenging to utilize BDA to generate meaningful customer insights to better handle their CX.

In an effort to further enrich the literature on CX and BDA, we have summarized some key existing literature to give marketers and practitioners a clear indication about how to manage and deal with BDA effectively to extract the most meaningful customer insights to improve or update their CX. At the end, we have put together a few exciting research areas for future CX and BDA research.

Brief Theoretical Background

Effective BDA Strategies for Enhanced CX

It has emerged from the literature that BDA provides many possibilities to practitioners to unlock clear customer insights for enhanced CX, but, at the same time, it imposes challenges on practitioners (McColl-Kennedy et al., 2019). Therefore, it is imperative to have some clear strategies about how to use BDA effectively to enhance CX to increase organizational capabilities (Homburg et al., 2017). Holmlund et al. (2020) came up with a framework of six stages, namely, strategize, assess, examine, decide, implement, and learn, to make effective use of BDA for enhanced CX, which is congruent to Alharthi et al.’s (2017) study in which they stated the importance of BDA for offering better or improved customer experience to ultimately improve an organization’s profit margin and competitiveness over others. These stages have been briefly discussed below, along with our propositions that have been further supported by the existing literature:

• Whether an organization wants a short-term or long-term strategy for their enhanced CX through effective BDA, and whether the entire customer journey is going to be dealt with or just a few touchpoints, should be carefully decided. We propose that organizations identify the key touchpoints first that need immediate attention, rather than dealing with the entire customer journey in one go, as this might make the entire process slower and somewhat inefficient (Homburg et al., 2017; Ransbotham et al., 2015).

• What kind of CX insights (e.g., attitudinal or behavioral) and BDA (e.g., descriptive or inquisitive) would be required to improve an organization’s CX, needs careful consideration. It should also be considered whether an organization has the in-house expertise to extract insights through BDA and whether the management is skilled enough to interpret the big data insights for the desired CX improvement. We propose a benchmarking audit in the relevant industry for organizations to identify the best practice regarding the type of CX insights, BDA, and the required management skill sets (Ransbotham et al., 2015).

• It must be decided what kind of CX data (e.g., structured or unstructured) would be required for the BDA for an organization. Ownership of the CX data must be carefully determined, as well, at this stage. Importantly, any privacy, ethical, or legal concerns also must be addressed here in relation to the data acquisition and data uses. We propose that organizations monitor these areas on a regular basis to ensure regularity compliance and avoid any ethical or legal consequences (Martin 2015; Martin 2018).

• Whether the captured CX data is sufficient to extract the aimed CX insights is an important area of focus. If not, will the organization require external expertise? Or can the in-house expertise still be used? Regardless of the decision, the associated cost and benefits need to be measured before moving further. When seeking assistance from an external expert entity that is instrumental and cost-efficient, we propose that an organization chooses the most suitable one from the available options (Popovic et al., 2018).

• The people necessary to be involved in implementing enhanced CX for an organization need careful scrutiny. It is crucial to decide here how to measure the success or failure of the enhanced CX of an organization once the solution has been implemented and how to troubleshoot any technical issues if they arise. We propose that to measure the performance in this regard, an organization should take data related to total sales, total revenue, market share and customer satisfaction into account and compare them with the same data of the past couple of years (Mela & Moorman, 2018; Mikalef et al., 2019).

• Reflection connected with a feedback loop is critical here on the positive or negative outcomes of the BDA-actuated CX insights. All the relevant organizational stakeholders should be informed about the insights of the reflection and the relevant challenges, thereby enabling challenges to be better handled or avoided in future. We propose that intra-team involvement is required in the learning process to further improve the CX through BDA (Jacobs & Moore, 2017).

While the above strategies could enhance organizations’ CX, organizations still need to deal with some BDA-related challenges to draw out the desired CX insights from the data sets available to them. Some of those key challenges and the relevant possible remedies have been briefly discussed below:

Overcoming the Challenges of BDA For Enhanced Cx

To fully embrace BDA to deliver the desired CX to customers, Alharthi et al. (2017) identified some relevant challenges, namely, infrastructure readiness, the complexity of data, lack of skills, privacy and cultural barriers that hinder organizations’ ability to do so. In other studies, as well, BDA’s significance in enhancing customer experience has been implied along with the relevant challenges, namely, quality of data, availability of data and systematic challenges (Ghani et al., 2019; Gupta & George, 2016; Wang et al., 2016). To address the challenges, some proposed strategies are outlined below (Alharthi et al., 2017; Ghani et al., 2019; Gupta & George, 2016; Wang et al., 2016):

• Widely available hardware devices that are competitively priced and somewhat interchangeable with other devices should be used by organizations (Trelles et al., 2011).

• Organizations should use established software tools (e.g., Hadoop) that are reliable, efficient, and price-competitive to extract CX insights from the complex data sets (Douglas, 2013).

• Hands-on training of employees who will be dealing with BDA to extract CX insights needs to be ensured. In this regard, organizations can collaborate with external training providers who are familiar with the industry requirements (Miller, 2014).

• The protection of the sensitive data of customers should be carefully handled by organizations; therefore, they need to incorporate the relevant legislation into their policies and practices (Schadt, 2012).

• The incorporation of BDA and CX into an organization’s vision is critical to ensure a smooth cultural change (McAfee & Brynjolfsson, 2012).

• Data quality-related challenges often make an organization’s BDA process somewhat inefficient when extracting the desired information mainly from user-generated data as they are commonly unstructured and qualitative in nature (Ghani et al., 2019). Organizations should spend more time cleaning and structuring the gathered data, using a relevant machine language, before conducting the analysis (Wang et al., 2016).

• To deal with the issue of data availability, organizations should gather an entire dataset before the BDA starts (Ghani et al., 2019). It is worth noting that adding new data during or after the BDA process is acceptable on a small scale. To better manage the availability of data, organizations should proactively integrate internal and external data (Gupta & George, 2016).

• To deal with systematic challenges of BDA that involve system architecture, data processing platforms and energy efficiency, organizations can take cluster computing into consideration (Wang et al., 2016).

While dealing with BDA-related challenges in extracting the desired insights into customer experience or expectation, we need to consider that there is no flawless solution and there is always room for improvement. We propose that organizations consider information from other sources as well to crosscheck the CX insights that have been extracted through BDA for better and effective customer CX.

Future Research Directions

To further strengthen the concepts of CX and BDA in the literature, as well as for practitioners, we have put together some key future research directions based on the explicit and implied insights from current literature.

• The concept of CX should be redefined, considering it can be viewed from a variety of perspectives (Holmlund et al., 2020). Ideally, the ecosystem of customers should be considered, which will provide organizations with extended scope to gather more meaningful CX insights (Jain et al., 2017).

• It is also important to come up with more ways to generate CX insights (Holmlund et al., 2020). Especially in the context of BDA, it is not yet clearly stated how to generate a variety of CX insights from the relevant data sets. Further attention should also be paid to explaining how CX insights could be meaningfully interpreted.

• It is known that customer experience varies from channel to channel when dealing with a particular organization (Verhoef et al., 2009). A comparative study for a variety of channels in the context of measuring the effectiveness of CX through BDA would be useful (Brun et al., 2017; Fernandes & Pinto, 2019; Shi et al., 2020).

• Customers from different countries could be somewhat different, based on a variety of social dimensions. Therefore, it warrants further studies in the context of CX and BDA to see how people define and perceive CX in different countries (Brun et al., 2017; Rather, 2019; Roy, 2018).

• In the context of BDA, across industries, there is a need for further research to develop a framework to manage organizations’ total CX (Holmlund et al., 2020; Jain et al., 2017). In doing so, resources available to organizations, such as skills, work experience and technologies, should also be taken into account.

• In line with the concern for global sustainable development, dimensions of social causes and societal well-being could be considered in future when developing a framework to deal with organizations’ CX through BDA (Jain et al., 2017).

• While a few studies have been conducted on the concepts of CX and BDA in the B2C setting, the same for the B2B setting is yet to be explored, especially as an individual customer could be somewhat different from a business customer (Mclean, 2017; Zolkiewski et al., 2017).

Conclusion

In this chapter, we focused on putting together all the relevant strategies in the context of CX and BDA based on the available literature. In addition, we also discussed how to overcome the relevant challenges while implementing those strategies. Finally, we discussed some interesting future research directions for CX and BDA research. It is believed that the discussed information will enrich the current literature on CX and BDA and greatly benefit practitioners from a variety of industries.

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