The Past, Present and Future of Service Marketing:
From Understanding Quality to Understanding Customers
Service marketing emerged in response to the shortfalls of product marketing. Although earlier traces exist, it gained traction in the mid 70s. The field’s evolution can be divided into phases in which critical incidents can be identified that have led the service field in new directions. Central to the discipline is the service encounter. Research referred to here consists of ways of understanding customer experiences with the service encounter and consequences of the encounter. We summarize various methods or approaches that have been and are applied to understand the service encounter.Keywords:: service encounter, customer experience, customer journey, Norwegian Customer Satisfaction Barometer
The service sector is likely to be the most important sector in any developed economy. In developed countries the service sector now generates more than 70% of a country’s GDP (Ostrom et al., 2010). The importance of the service economy is still increasing; the largest growth in the number of firms is in the service sector, which in turn means that the number of employees is also increasing in the sector. New, more service-oriented business models are being implemented, seen in manufacturing companies that are turning into service suppliers. For instance, IBM has totally shifted its business such that it no longer produces goods, but produces and delivers services only. We also see a rise in the collaborative economy and companies such as Airbnb, Uber, and Craigslist are shifting the competitive rules in service industries (Benoit, Baker, Bolton, Gruber, & Kandampully, 2017). Furthermore, business models are shifting in the retail industry, where companies such as AliExpress and Amazon are forcing changes in competition. To survive, companies must be able to deliver good service with a perception of high quality that is adapted to customers’ needs. But this is just a necessary condition; to be competitive, they must go even further. Companies must understand and enhance customer engagement and experience in all parts of any service encounter. Consequently, there is no way around the fact that the service sector is increasing in importance and will continue to be relevant. To be relevant, researchers and organizations must develop new and better tools built to get a deeper understanding of the customer perspective. Companies must balance how much their customers want an organization to know about them with what they should find out. It is a fact that, with still emerging technology, organizations can acquire very deep knowledge about their customers.
Service researchers typically build on Shostack’s (1985, p. 243) definition of a service encounter as “a period of time during which a consumer directly interacts with a service.” It is during these encounters that customers form their perceptions of service quality. As customers experience multiple encounters, not only directly, but also indirectly through commercials or social media, various encounters may result in loyalty and relationship formations. The focus in a service encounter is usually on the core encounter, but what happens prior to and after the core will also influence the customers’ perception of the core service encounter and the overall relationship with an organization (Voorhees et al., 2017).
Service researchers have focused on trying to measure and understand the nuances in service encounters. Parasuraman, Zeithaml, and Berry (1988) developed their SERVQUAL model as an effort to understand the transaction-specific perception of service quality in a service encounter. This was followed by Fornell, Johnson, Anderson, Cha, and Bryant (1996), who recognized that relationships are formed over a series of transactions, a phenomenon known as the cumulative perspective of customer satisfaction. The next step is a realization that not all relationships are created equal and that companies must study portfolios of relationships (Johnson & Selnes, 2004) and understand where in a relationship formation a customer is (e.g., acquaintances, friends, and partners). These ideas were the start of a development to understand how to invest in customers to maximize customer lifetime value, that is, the accumulated cash flow a customer accrues during his or her lifetime (Kumar & Pansari, 2016). Companies need all of these in order to be profitable in the long term. The state-of-the-art thinking of service encounters from a cumulative perspective is to understand customer experiences over a customer journey across different channels or touchpoints (e.g., online, in-store, and customer service). These customer experiences can cut across a multitude of service providers.
The purpose of this chapter is to give an overview of the above-described development with a focus on understanding the service encounter. We will go back to the start of the field, which is usually stated to be 1977 with a seminal paper by Shostack (1977), and we end by giving one perspective on where the field may be going in the future.
15.2 Early stages of research in service—the importance of service quality
Even if there are earlier traces, Shostack’s 1977 article is generally stated as somewhat of a starting point of service as a field of research. She stated that product marketing fell short when marketing services and that “new concepts are necessary if service marketing is to succeed” (Shostack, 1977, p. 73). It was around the same time as the quality movement had started to gain momentum, with important figures such as Crosby, Deming, and Juran. In this school of thought, the quality of products was defined in terms of consistency and low variation in the production. This quality management perspective received extensive attention and enthusiasm, some of which was carried over to service research. For this quality perspective to be properly implemented, service as a phenomenon had to be understood, which was followed by ways to measure and analyze the customer’s perspective of a service encounter.
The initial focus in service research was on defining major concepts of what the differences are between services and products. Zeithaml, Parasuraman, and Berry (1985) summarized the discussion on the nature of services to consist of four characteristics, services are intangible, heterogeneous, inseparable, and perishable (the so-called IHIP) and that the focus in service was on the process components rather than on the final product or the outcome of a process (Grönroos, 1998). A joke sometimes told to better describe what a service was, was that a service, as opposed to a tangible good, did not hurt if it was dropped on your foot. Another very important and highly cited concept was the gap model (Parasuraman, Zeithaml, & Berry, 1985). The gap model identifies five gaps—“these gaps can be major hurdles in attempting to deliver a service which consumers would perceive as being of high quality” (Parasuraman et al., 1985, p. 44). The natural conclusion from these concepts was that service quality is seen as something different from product quality and it must be measured and managed differently. For instance, quality attributes can be categorized as being search, experience, and credence attributes (Zeithaml, 1981). For products, search attributes (attributes that can be determined prior to purchase) are usually more important, while for service, experience and credence attributes are more important. It is from these perspectives that the development of SERVQUAL should be seen. SERVQUAL is designed to measure and understand the service quality in one service encounter; in other words, SERVQUAL takes a transaction-specific view because it focuses one transaction. In the SERVQUAL model, service quality is said to be constituted of five dimensions (Parasuraman et al., 1988): tangibles (physical facilities, equipment, and appearance of personnel), reliability (ability to perform the promised service dependably and accurately), responsiveness (willingness to help customers and provide prompt service), assurance (knowledge and courtesy of employees and their ability to inspire trust and confidence), and empathy (caring, individualized attention the firm provides its customers). The general idea in SERVQUAL is to focus on the dimension that has the largest gap between expectation and performance and close this gap. The SERVQUAL has been very influential and heavily cited, and it does capture the low-hanging fruit when it comes to quality problems. It has, however, been criticized because there is no way of knowing that the identified gaps actually will affect customer satisfaction. In other words, even if there is a large gap between expectation and performance, there is no way of knowing that closing this gap will influence customer satisfaction, because we do not estimate the statistical effect from the gap on customer satisfaction. Also, since the service sector today covers more than 70% of everything that is produced in a country, it includes multiple contexts with multiple understandings of what constitutes quality. In light of this development, SERVQUAL having predefined dimensions and questions might not be sufficient to diagnose details of how to improve business. This paved the way for the next phase of the evolution; instead of focusing entirely on understanding quality, firms started focusing on what drives customer satisfaction and loyalty.
15.3 From service quality to customer satisfaction and loyalty
The logic behind the next phase of service research is that service quality is not the only aspect that is important for a company to gain market share. The focus should be on how to change or improve attributes in order to make their customers even more satisfied and in the process make existing customers more loyal and attract new customers (Anderson, Fornell, & Lehmann, 1994). Also, no company has endless resources and will therefore have to carefully consider where an investment in quality should be made. Resource limitation makes improved quality an optimization process rather than a maximization process; in other words, where should a company invest to get the largest return on investment (Rust, Moorman, & Dickson, 2002). As previously noted, the original interest in marketing and consumer research was on transaction-specific satisfaction, or a customer’s experience with a product episode or service encounter. Although the transaction-specific approach had its merits, it does not perform well when predicting subsequent consumer behaviors and economic performance of firms (Fornell et al., 1996; Johnson, Anderson, & Fornell, 1995). In response to the low predictive ability of the transaction-specific approach, researchers started to focus more on what is called cumulative satisfaction (Johnson et al., 1995). The cumulative approach defines satisfaction as a customer’s overall experience to date with a product or a service provider; this includes the experience of all service encounters (Johnson & Fornell, 1991). At this stage we would like to point out that we cannot forget about the transaction; it is still important, but organizations need more tools to understand the holistic customer perspective. One of the most well-known approaches in this research is the national customer satisfaction models. The Swedish Customer Satisfaction Barometer (SCSB) model contains two primary drivers of customer satisfaction: expectations about how well the firm would perform when delivering quality and an assessment of how well the firm actually performed (Fornell, 1992). The model contained two consequences of customer satisfaction: customer complaints and customer loyalty. Later, perceived value was added as an antecedent of satisfaction.
The SCSB model is likely to be one of the most well-known models for measuring the causes and consequences of customer satisfaction. Over the years, the SCSB has been used an indicator of various important performance metrics such as market share (Rego, Morgan, & Fornell, 2013), stock market (Fornell, Mithas, Morgeson III, & Krishnan, 2006), and profitability (Anderson, Fornell, & Lehmann, 1994). The national customer satisfaction indexes are useful for the purpose of comparing an organization’s performance across industries to get a sense of how well it performs in its own industry and compared to companies in other industries. We will use the Norwegian Customer Satisfaction Barometer (NCSB) to exemplify this comparison. The same approach can, however, be used to measure and manage any organization’s more detailed understanding of the customer perspective (Johnson & Gustafsson, 2000). One of the most important aspects therefore is to create a good lens on the customers’ perception of the benefits an organization delivers to its customers. These benefits are not measurable using single indicators but instead must be measured using a latent variable. For instance, easy access as a latent variable may be measured by ease of parking, opening hours, and ease of finding. The benefits will lead to attitudes or a perception of satisfaction which in turn leads to a behavior.
15.3.1 The Norwegian Customer Satisfaction Barometer
A change from focusing on service quality to focusing on customer satisfaction as the crucial variable of interest took place throughout the 1990s and in the beginning of the new millennium. It all started in Sweden. Claes Fornell, professor at the University of Michigan, first launched the SCSB already in 1989 (Fornell, 1992). This model served as the prototype when developing the American Customer Satisfaction Index (ACSI) introduced in 1994 and the NCSB introduced in 1996 (Fornell, 1992). In the early days, the NCSB reported results from more than 42 companies across 12 industries; today NCSB reports results from 169 companies across 30 industries annually. NCSB has gained significant influence over the years and represents an important performance metric for companies as well as a benchmark toward competitors. The best performers in each industry, that is, the companies with the highest customer satisfaction scores, typically use their achievements for marketing purposes. The very first NCSB model was identical to the ACSI model, which was an evolution of the SCSB model. The only difference from the ACSI model was that the NCSB model included the variable “corporate image” and its relationship to customer satisfaction and customer loyalty (Johnson et al., 2001). However, at the core of the model we find the relationship between quality, cumulative customer satisfaction, and customer loyalty, which has been measured since the very beginning of NCSB’s existence, thus providing great insights for both the companies and researchers on how the level of customer satisfaction has developed over the years. Due to the shortcomings of the transaction-specific approach, the first version of the NCSB model was later expanded to include two relational dimensions, that is, calculative and affective commitment (ibid.). While the calculative commitment reflects customers’ economic and rational reasons to continue the relationship, affective commitment reflects customers’ warmer and more emotional motivations. Including commitment in the model led to a significant improvement in explaining customer loyalty. While the previous transaction-specific model explained only 20–30 percent of customer loyalty, the new model explained between 50 and 70 percent, depending on the type of industry (Johnson et al., 2001). In addition to including the relationship dimensions, value was replaced by a purely price construct to avoid methodological problems between the earlier modeled quality and value variables. Recently, the NCSB model has been updated and extended by the addition of two variables (i.e., a company’s digital solutions, and its sustainability and corporate social responsibility (CSR), reflecting priorities across industries). The current NCSB model can be seen in Figure 15.1.
The current NCSB model includes six dimensions that customers evaluate in a service encounter (i.e., the price/value, tangibles, information, digital solutions, product quality, and service quality) in determining their satisfaction. Satisfaction in turn is related to both types of commitment as well as loyalty. Furthermore, satisfaction helps building the company’s reputation. The company’s sustainability and CSR efforts play a central role in customers’ evaluations of the company and affect satisfaction, affective commitment, reputation, and customer loyalty.
The evolution of the customer satisfaction barometer models does to a great extent reflect the evolution of three decades of service marketing research. First, applying the total quality management perspective to service marketing led to a focus on understanding quality in the 1980s. The focus shifted, however, to customer satisfaction in the 1990s as a response to the legitimate question, Why is quality so important? asked by both managers and academics. The answer of course being, We want satisfied customers. From around 1995 to 1999 the focus shifted to customer loyalty as a response to yet another legitimate question, that is, Why do we need to have satisfied customers? The answer was, Because we want loyal customers that come back, spread positive word of mouth and recommend the company to family and friends (Zeithaml, Berry, & Parasuraman, 1996). At that time, it was established that it is less expensive to maintain existing customers than to constantly hunt for new ones. However, applying both strategies would be necessary, because the customer base is a leaky bucket.
While the customer satisfaction barometer models grasp the quintessence of the service encounter, they do not really include what is going on inside the company and how the inside affects the outside in terms of customer loyalty and profitability. The service-profit chain established the relationship between employee satisfaction on the inside and growth and profitability on the outside (Heskett, Jones, Loveman, Sasser, & Schlesinger,1994). More specifically, the logic of this model is that the internal service quality (i.e., the work environment) influences employees’ satisfaction, productivity, and loyalty. Satisfied employees will be more productive and loyal, they will create greater value for customers, thus leading to customer satisfaction. Customer satisfaction drives loyalty, which ultimately affects both the company’s growth and its profitability. This model has had a tremendous influence both in academia and for practitioners. For example, educational programs have been developed in line with the logic, while leaders across industries have changed their practices accordingly.
15.4 The relationship perspective
The next phase in the evolution of the field is to gain a better understanding of what constitutes a relationship between organizations and their customers at an individual level. Customer satisfaction is without a question very important. Not all relationships, however, are created equal and organizations must differentiate how a customer is treated according to how value is created. Furthermore, an organization must link value creation within individual relationships to the overall value creation of the organization (Johnson & Selnes, 2004). It can be argued that an organization cannot survive in the long term having only customers that are extremely satisfied and loyal, as this base will slowly shrink and become smaller. It has been said that an organization’s customer base is a leaky bucket and new customers constantly need to be added to cover the fixed costs. These ideas are closely related to the notion that all markets are created; no market is predefined. What companies must do is to nourish their market, or ecosystem, to make it grow and evolve continuously.
Also, although customer relationships with organizations are primarily economic relationships, they do have social meaning. Johnson and Selnes (2004) argue that customers start by being acquaintances and then are moved to being friends and then on to being partners. In this process, customers’ perceptions of commitment and trust are altered. Another premise is that all customer relationships are important for an organization because they need them in the short term to cover the fixed cost and in the long term they need loyal customers. The underlying logic also dictates that organizations can differentiate their investments in the customer base according to the value of a customer. One example of a study that is built on this approach can be found in Tarasi, Bolton, Gustafsson, and Walker (2013). They show that customers having higher cash-flow levels (e.g., younger, purchasing more products in more categories) have higher variability in their cash flow. When variability is higher purchase patterns are more difficult to predict. Younger customers are also easier to acquire and lose, and therefore more unpredictable, while older customers could be more reliable and predictable, and therefore easier to serve and plan for. These characteristics help managers decide how to allocate sales and service efforts to segments.
As readers of this chapter will see later on, this evolutionary track does continue and is likely to arrive at with what is called the Internet of Things (we will return to this). However, before we do so, we need to cover another phase of the development: the customer experience perspective. This shift in focus was in part driven by Vargo and Lusch (2004), who suggested a revised logic focusing where services (rather than goods) are fundamental to economic exchange, and where the cocreation of value is the objective, thus emphasizing the role of the customer in any relationship. The experience is considered essential to value determination and the ecosystem is considered an active party in service provision (Lusch, Vargo, & O’Brien, 2007).
15.5 The experience perspective
As physical store environments have started to have increased competition from online shopping, there has been an increased interest in the service encounter but from a slightly different perspective. Physical stores cannot compete head on with online stores on price therefore they must compete on something else—which happens to be the customer experience. The customer experience is defined as the period during which all service encounters relevant to a core service offering may occur (Voorhees et al., 2017). The service experience can be seen as a process while the encounter is a specific occurrence. The service experience is holistic in nature and includes cognitive, affective, emotional, social, and physical responses to interactions with a service provider (Berry, Carbone, & Haeckel, 2002; Lemke, Clark, & Wilson, 2011).
There are at least three aspects of importance that we would like to point out. The first aspect is the process perspective; all service encounters imply a series of encounters that do not have to be with only one service provider (Lemon & Verhoef, 2016). What happens prior to and after the core encounter will affect the service experience. For instance, if a customer is given a coupon to use or even a recommendation from a friend, this action will influence downstream customer behavior and purchase decisions. Furthermore, customer processes may involve the use of different channels or touchpoints (i.e., Internet, catalogue, customer service, and a physical environment). It is very likely that customers will use different channels or touchpoints to achieve different goals. Because customers behave differently in different channels, companies would like to influence customers’ choice of channels. For instance, if customers buy products online, they are less likely to make spontaneous purchases. To capture this process perspective, companies need to understand the customer journey. We will explore this question in more detail below.
The second aspect we want to highlight based on the definition of “service experience” is that it is a multi-dimensional, or holistic, perspective. As pointed out by Bitner (1992) in her conceptualization of servicescapes, there are a multitude of attributes that will influence customer perceptions of an experience both in an online environment and in a physical environment. The layout, colors, scents, symbols, or sounds as well as employee behavior and the possibility for customer interaction (with all aspects of a company) are important components that all influence the perception of service quality. For instance, the color of a room will influence how a wine tastes (Spence, Velasco, & Knoeferle, 2014); scent influences the perception of quality (Baker, Grewal, & Parasuraman, 1994). As seen from the definition, customer experience cannot be measured as an outcome of one dimension (e.g., satisfaction). The definition implies that aspects such as emotions or even physical responses may be important. Technological developments such as facial recognition make it possible to capture these types of outcome variables.
The third aspect which is difficult to capture in order to evaluate customer experience is the influence of a context. No company acts in a vacuum; customer experiences are formed by previous encounters and existing offerings from competitors. The experience is thus formed as a relative measure according to previous experiences of what competition is doing. Furthermore, what customers value varies according to both demographics and culture. If a company is active in different markets, it has to take this variation into consideration. Companies such as McDonald’s are well aware of this variation as shown by the fact that they adapt their offerings for different contexts.
15.5.1 The customer journey perspective
An experience can be thought of as the result of a process or as a customer journey that builds on multiple encounters at different touchpoints. Touchpoints can be seen as various ways a service provider has set up to interact with their customers. One of the ways to understand customer experiences is to follow in the footsteps of a customer across the touchpoints. On the Internet this is regularly done when customers leave traces on webpages they visit and by what they click on before making a purchase. This means companies can predict how customers will act in different situations. In a physical environment, predicting is slightly more difficult. Traditionally, this has been done by following customers around in stores and making notes on what they do. It looks as though new technology will make it possible to follow customers online just as easy as following them in a physical environment. If this happens, we will see predictions in real time in physical environments too. Personalized advertising can be used to nudge customers in making decisions just like in the movie the Minority Report where advertising is asking the main character John Anderton whether what he purchased the last time fits.
What is possible today is that a physical store can follow a customer throughout the store by communicating with the customer’s cell phone without the customer even knowing (Henry, 2013). How this happens is that a cell phone is constantly looking for free Wi-Fi and a receiver can pick up that signal and follow the customer (cell phone) around a store. In relation to this, stores especially in Asia have started to implement facial recognition to understand who (e.g., age and gender) is doing the shopping in a store. Digital advertising may also include eye trackers to detect interest in the transmitted advertising; if a customer is not interested, a new advertisement will be displayed. In addition, technology is also being developed that in essence involves transmitters (e.g., Ibeacons) that are designed to send messages to customers who are using smartphones.
From a research perspective, these are very interesting technical solutions that will help us understand what a customer is doing in different settings. Customer engagement and connecting with a service provider emotionally are ways that service providers are trying to insulate customers from doing business on a transactional basis and/or just picking the best option according to price. With these devices, we may understand what triggers arousal in different situations using, for instance, ways to measure electrodermal activity (EDA). We will also be able to understand what really happens with customer attention and behavior when scent or music is being used in physical stores.
This development is of course also slightly scary to think about but the technological solutions to accomplish this already exist and are already being tested or being used and we will have to get used to them in the future. What organizations are doing to counterbalance these opinions is to somehow pay customers to give away information.
15.6 Internet of things (IoT)
Technology is a game changer in the service context and it is changing how people behave (Larivière et al., 2017). For instance, new technologies such as smart grids, home management systems, electric cars, solar voltaic panels, and home batteries are changing the way customers perceive and manage energy consumption. Prerequisites of this technology are that Internet connectivity can be collected from all of these devices. This is the notion of the Internet of Things (IoT); everything is connected and information can be harvested. Systems may react autonomously. The IoT is a game changer in itself with the potential to affect consumers, businesses, and societies in unforeseen ways. The IoT is a network of entities that are connected through any form of sensor, enabling these entities, which we term as “Internet-connected constituents,” to be located, identified, and even operated upon (Ng & Wakenshaw, 2017). The IoT represents a new context of service, characterized by a many-to-many, interconnected world, where people and devices are empowered by a constant flow of information and by the results of data analytics. The IoT is likely to disrupt many different markets such as healthcare, transportation, and retail.
As has recently been shown in the Cambridge Analytica scandal, data from the Internet, and in the future from the IoT, can potentially be linked to customers and used to predict when and where customers will want to have something, or will want to be connected to customer relationships. Here lies a potential danger but also a tempting future for the field—and this use of data is already being implemented. It was revealed recently that Amazon.com has obtained a patent for what it calls “anticipatory shipping”—a system of delivering products to customers before they place an order.
15.7 Transformative service research
Since 2010, researchers have started to recognize that because we are surrounded by all kinds of services every day, services actually have a much greater effect on our lives and well-being than traditional service dependent measures such as service quality, customer satisfaction, and loyalty reflect (Anderson & Ostrom, 2015). We go to school, to the doctor, we use financial services, governmental services, health care services, and more. The quality of these services affects our lives way beyond satisfaction and loyalty, they actually affect the quality of our lives and general well-being. This field of research is referred to as “transformative service research” and can be defined as research that focuses on “creating uplifting changes aimed at improving the lives of individuals, both consumers and employees, families, communities, society and the ecosystem more broadly” (Anderson & Ostrom, 2015, p. 243). In this area, both detractors and enhancers of well-being are studied in a variety of contexts, from financial services to health care. So far, topics such as cocreation, employee well-being, vulnerable consumers, social support, access, service literacy, service design, and service systems have been studied, while new themes are added as we speak (Anderson & Ostrom, 2015, p. 243).
15.8 Future research avenues
The service sector continues to grow in size and importance for any developed country. With the growth comes an endless interest in knowledge about the customer perspective of things, which is at the heart of service research. We want to generate information that helps customers receive a better experience or improve their way of life. This thirst for customer information seems to be endless at the moment. Companies such as Facebook, Skype, and Google have knowledge about pretty much every aspect of what happens in their customers’ lives. The reason is that companies want to draw inferences from customer information in order to predict future behavior. Amazon, for instance, is predicting where a customer will make a purchase decision and send products ahead of time to ensure a quick delivery and a good customer experience. The IoT also makes it possible to localize and communicate with any product, and it will generate even more user information. Organizations will communicate with customers and employees in any physical environment, not just online. The communication is likely to occur with transmitters to communicate with various devices for the purpose of making customers aware of offerings in the vicinity. We have already started to see interactive advertising that reads faces and tries to figure out what generates customer interest. The first self-checkout story has already been opened, meaning that every interaction a customer has with any product will be supervised. All of these developments are a bit scary to think about: so much information will be easily available and we must consider the implications. There is not much we can do to prevent such developments; it would be like trying to prevent the rain. What we can do is develop awareness and build on the benefits of this evolution.