Customers are the lifeblood of organisations. In seeking to capture the intricacies of intangible customer value, marketing has become increasingly quantitative, spawning a gamut of customer-based metrics to estimate customer financial worth. Most prevalent is customer lifetime value (CLV), widely accepted as the net present value of a customer’s ‘purchase potential’ (Rust, Lemon, and Zeithaml 2004). Seminal literatures document positive relationships between CLV and customer equity, sales (Vogel, Evanschitzky, and Ramaseshan 2008), market capitalisation (Kumar and Shah 2009) and shareholder value (Berger, Eechambadi, Lehmann, Rizley, and Venkatesan 2006). Drawing empirical attention from finance and marketing, CLV is cross-disciplinary and relevant for academicians and practitioners alike. Both have vested interests in understanding how to improve firm performance via customer-based metrics management.
However, extant literature remains relatively silent on CLV’s industry-based applications in services marketing, particularly in service failure-recovery contexts. Firms experience service failures every day. The cost of failure is often measured in terms of financial impact to firms, or by the costs of service recoveries required to compensate for failures. However, these are firm-centric metrics and with customers remaining at the heart of service failure impacts, attention must shift towards customer-based metrics. This thesis considers an alternative viewpoint to firm-centric measures, by examining the cost of human emotion when service failures occur. A central question guiding this research is: does putting a price on the costs of human emotion provide a better proxy than traditional CLV for the costs of service failures and recoveries? To capture human emotion, a new conceptualisation is proposed as customer network power. Customer network power is a mathematical revision to CLV, capturing individual customers’ online positive or negative word-of-mouth potential as a function of social network size. Online word-of-mouth is a form of customer network power because firms are at the mercy of customers online. Quantifying customer propensity to share information online via customer network power, post-failure and post-recovery, places a proxy price on human emotional responses. Employing a national online consumer panel, original and revised CLV is applied in a dyadic cross-comparison of the top two Australian Securities Exchange (ASX) companies by market capitalisation for the airlines and mobile telecommunications industries. Simulating data for the 2011 financial year, CLV is explored in a quasi-experimental 2 (industry type: airlines, mobile telecommunications) × 3 (service failure: low, medium, high) × 3 (service recovery: low, medium, high) design employing structural equations modeling. Repeated measures analysis of variance assesses mean differences of within-group changes post-failure and post-recovery for dissatisfaction, negative word-of-mouth, switching intention and repurchase intention.
This thesis reveals important findings. Dissatisfaction fully mediated the relationship between recovery and CLV for the mobile industry. Repurchase intention fully mediated this same relationship for the airline industry. Comparing mean differences post-failure and post-recovery revealed interactions across differing levels of CLV, dissatisfaction, negative word-of-mouth, switching intention and repurchase intention. This thesis substantively contributes to services marketing literature as the first empirical examination of service failure and recovery on CLV, quantifying customer emotion and behaviour. Moreover, the metrics debate is extended, providing an alternative way to capture customer value. This is posited through the newly conceptualised customer network power, which assigns a proxy price to customer online word-of-mouth diffusion in social networks. Estimating costs of negative (positive) word-of-mouth potential to firms when failures (recoveries) occur has implications for managers tasked with reparation in the wake of service failure. This thesis also impacts scholars developing improved customer-based metrics as an enduring research priority for both the American Marketing Association and the Marketing Science Institute.