The Path to Happiness? How Customer
Journeys Affect the Customer Experience
Master Thesis, MSc Marketing Intelligence June 18, 2018 MIRA BLAGOJEVIC Student number: 3500543 Pottebakkersrijge 5a 9718 AE Groningen Mobile: +49 17632566110 E-mail: mirablagojevic@arcor.de First supervisor: Prof. Dr. Peter C. Verhoef
Second supervisor: E.N.M. Lesscher Third supervisor: Dr. Hans Risselada
University of Groningen Faculty of Economics & Business
Management Summary
By creating excellent customer experiences, firms can increase repurchase intention, customer satisfaction, and customer loyalty, and thus, they can increase the firm profitability. But what are excellent customer experiences? Firms assume that providing more channels can simply create a stronger customer experience, however, more channels may also create more complexity. Research by Bain & Company highlighted that 8% of the surveyed customers reported their experience as ‘superior’, while 80% of the companies surveyed believe the experience that they have been providing is indeed superior. With such a gap in perceptions, prospects for improvement are small. Consequently, firms are urged to understand both the relevance of customer experience management and what its drivers are.
Due to the rise of digital technologies, firms have more and more opportunities to create outstanding customer experiences. For instance, RFID technologies and interactive media applied in fashion stores can create a highly choreographed shopping experience, which subsequently leads to higher product engagements. Moreover, the rise of the Internet of Things created new touchpoints, which is why consumers can now have connectivity anywhere and with almost everyone. The notion of the ‘connected consumer’ has spawned new consumer behaviors such as ‘showrooming’ – that is, consumers search for information in store while using their mobile devices to get more information about offers and more attractive prices. Consequently, the rise of new technologies involves various opportunities for firms to create enriched customer experiences and more importantly, to increase customer satisfaction. Therefore, firms are challenged to manage touchpoints that are present in a world that is increasingly omnipresent, multifaceted and multidimensional.
Given the discussion above, this study examines the effects of different customer journey characteristics on customer satisfaction. 3,105 customers from Austria, Germany, and Switzerland were asked to describe their last purchase at a multichannel retailer. The respondents were asked to indicate what product(s) they bought, which channels they used, and which touchpoints they were exposed to. After they indicated all visited touchpoints for their last purchase, participants were again confronted with the touchpoints to rank them chronologically. Additionally, respondents were asked to indicate how satisfied they were with certain touchpoints, with the overall product experience, and with the buying process itself. As customer satisfaction is a key driver of customer experience, the overall journey experience was measured and subsequently used to identify determinants of customer experience.
Abstract
Today, firms have more and more opportunities to create (excellent) customer experiences due to the rise of digital and smart technologies. Moreover, the ongoing digitalization leads to inter-connected consumers as consumers can now have connectivity anywhere and with almost everyone. This results in larger growing, more diverse, and more mobile networks, and therefore, in more complex customer journeys. Consequently, firms are challenged to manage touchpoints that are present in a world that is increasingly omnipresent, multifaceted, and multidimensional.
Since consumers move through multiple stages and interact with many different touchpoints, this study was examined to analyze which customer journey characteristics lead to greater customer experiences. It was found that customer experience was significantly driven by independent touchpoints. Independent touchpoints encompassed touchpoints such as search engines, social media, video/image portals, newspaper/news portals, price comparison portals, product rating portals, blogs, and offline WOM.
Table of Content
1
Introduction ... 1
2
Theoretical Background ... 3
2.1
Why Everyone Talks About Customer Experiences ... 3
2.2
Stages of the Total Customer Experience: The Customer Journey ... 5
2.3
Types of Touchpoints in the Customer Journey ... 6
3
Conceptual Framework and Hypotheses ... 8
3.1
The Journey to (Customer) Happiness? ... 8
3.2
More Touchpoints, More Complexity? ... 14
3.3
Moderating Effects ... 15
4
Method ... 21
4.1
Data Collection ... 21
4.2
Measures ... 23
4.3
Analysis ... 25
5
Results ... 26
5.1
Multivariate Regression Analysis (Model 1)... 29
5.2
Multivariate Regression Analysis (Model 2)... 32
5.3
Multivariate Regression Analysis with Interaction Effects ... 33
6
Discussion and Implications ... 40
7
References... 47
8
Appendices ... 60
Appendix A: Measurement and Operationalization ... 60
Appendix B: Correlations and PCA of Journey eExperience ... 61
Appendix C: Measurements and Results of the Additionally Conducted Survey (Experience
Goods, Search Goods, Product Related Risk) ... 63
Appendix D: Multivariate Regression SPSS Output (Model 1) ... 64
Appendix E: Multivariate Regression SPSS Output (Model 2) ... 66
1 Introduction
Managing customer journeys was never more important. Due to fragmented media and the rise of digital and smart technologies, customers have more opportunities to contact and interact with firms, resulting in more complex customer journeys (Lemon & Verhoef 2016; Rawson, Duncan, Jones 2013). Moreover, customers progressively hold control in the fast-changing digital environment (Foroudi, Gupta, Sivarajah, Broderick 2018; Marketing Science Institute (MSI) 2016). In particular, customers are increasingly in control of the information flow and they have formed expectations towards seamless experiences (Barthel, Hudson-Smith, de Jode 2015; Foroudi, Gupta, Sivarajah, Broderick 2018). Consequently, it is even more important for firms to identify the ‘moments that really matter’ (or ‘moments of truth’) to customize experiences, offers, and content (Edelman & Singer 2015; Lemon & Verhoef 2016; MSI 2016; MSI 2018; Verhoef, Kannan, Inman 2015).
Several issues exist that firms should focus on to deliver outstanding or perfect customer experiences. For instance, firms could put effort into identifying perceived customer experiences to find room for improvements. (Frow & Payne 2007) Although the whole customer journey as such is highly valuable to customers, managing each touchpoint individually is key (Frow & Payne 2007; Maechler, Neher, Park 2016; Rawson, Duncan, Jones 2013). To ensure consistencies within and across multiple touchpoints, companies need to manage them carefully (Frow & Payne 2007). Consistent customer experiences can be considered being even more important nowadays, since disruptive technologies change the way consumers search for information and how they take their purchase decisions, resulting in more complex customer journeys (Verhoef, Kannan, Inman 2015). In particular, consumers search for information in store while using their mobile device to get more information about offers and more attractive prices, which is referred to ‘showrooming’ (Gensler, Neslin, Verhoef 2017). Also, the rise of the Internet of Things (IoT) created new touchpoints which is why consumers can now have connectivity anywhere and with almost everyone. Therefore, firms are challenged to manage touchpoints that are present in a world that is increasingly omnipresent, multifaceted, and multidimensional. (Verhoef et al. 2017)
2 Theoretical Background
The following chapter addresses the research question: Which customer journey characteristics make
customers happier? Since ‘customer experience’ has recently become one of the major buzzwords in
marketing (Lemon & Verhoef 2016) and was again identified as one of the most important research topics of the MSI (2016; 2018), this chapter aims to combine existing knowledge of customer journey characteristics, customer experience and customer experience measurement.
2.1
Why Everyone Talks About Customer Experiences
Multiple definitions of customer experience exist in the literature. Lemon and Verhoef (2016; p. 71) summarize that “customer experience is a multidimensional construct focusing on a customer’s cognitive, emotional, behavioral, sensorial, and social responses to a firm’s offerings during the customer’s entire purchase journey.” In another study, they state that the “customer experience construct is holistic in nature and involves the customer’s cognitive, affective, emotional, social, and physical responses to a firm” (Verhoef & Lemon 2016, p. 12). Stein and Ramaseshan (2016, p. 8) recap that “customer experience is widely recognized as the internal and subjective response customers have to any interaction with a company”. In sum, customers are exposed to many different ‘touchpoints’ of a firm forming cognitive, emotional, behavioral, sensorial, and social responses every time they encounter any part of the product, service, brand, or organization across multiple channels and at various points in time (Stein & Ramaseshan 2016).
Due to the rise of cloud, cognitive, and other digital disruptors (Amazon Web Services, IBM Watson, AirBnB), companies have not only the ability to support product innovations and rewired industry ecosystems but also new customer experiences (Deloitte 2017; Verhoef et al. 2017). 67 out of 100 Fortune companies invest in at least one innovation center, which means that even more disruptive technologies are on the rise. Vodafone Germany, one of the country’s leading telecom operators, shows great customer experiences already. Vodafone decided to transform its infrastructure and ready its IT stack for digital and is, therefore, able to support end-to-end customer experiences. So far, this initiative resulted in increased efficiency and significant cost savings. (Deloitte 2017)
really matter’ (Lemon & Verhoef 2016; MSI 2016), or as Albrecht said in 1992 “the only thing that matters in the new world of quality is delivering customer value” (Albrecht 1992, p. 7).
The ongoing digitalization changed retailing dramatically. The online channel has become very dominant, most presumably because of its disruptive development. (Verhoef, Kannan, Inman 2015) An example is the shift from in-store media purchases to streaming media on demand, such as NETFLIX.COM. In other industries such as food-retailing, the disruptive development had a smaller impact (Lemon & Verhoef 2016). However, the continuously growing innovations offer new opportunities for retailers to target consumers more accurately, to generate customer values (Kumar & Reinartz 2016), and to create effective (price) promotions. For example, RFID technologies applied in stores can offer broader implications and advantages for retailers to implement dynamic pricing. Furthermore, technologies such as in-store digital messaging affect consumer shopping behavior but the authors also state that further exploration in this field is needed. (Grewal et al. 2011) Retailers have also increasingly started to take advantage of the mass of data they collected on a daily basis. The collection and interpretation of big data help them to understand consumer behavior, and to target consumers more accurately. Moreover, prospects for improvements are promising as new technologies such as augmented/virtual reality or artificial intelligence are on the rise. (Grewal, Roggeveen, Nordfält 2017)
Disruptive technologies and the presence of the online channel resulted not only in increasingly connected consumers and enriched shopping experiences but also in the need of firms have to manage customers and to integrate the retail mix across channels, as customer journeys are getting more complex. As a result, multi-channel retailing is shifting to omni-channel retailing. (Verhoef, Kannan, Inman 2015; Verhoef et al. 2017) Verhoef, Kannan, and Inman (2015, p. 3) define omni-channel management as “the synergetic management of the numerous available channels and customer touchpoints, in such a way that the customer experience across channels and the performance over channels is optimized.” Accordingly, ‘showrooming’ is becoming an important issue, meaning that shoppers search for information in the store while using their mobile device to gain more information about offers and to maybe find more attractive prices (Verhoef, Kannan, Inman 2015). Another elaborated consumer behavior is the ‘research shopper’ phenomenon – consumers search for information in one channel (e.g., the Internet) and then purchase in another channel (e.g., the store) (Verhoef, Neslin, Vroomen 2007). Today, this behavior is also known as ‘webrooming’ since the online channel is very dominant. To tackle these new evolved shopping behaviors, firms can implement seamless experiences by having mobile devices in the store or by providing in-store WiFi-networks. (Verhoef, Kannan, Inman 2015)
Therefore, firms are becoming more customer-centric and are increasingly making use of advanced analytics and big data to create modern CRM tools. (Haenlein 2017) Several authors aim to develop a stronger understanding of customer experience management and they stress the value creation for customers and firms (Edelman & Singer 2015; Haenlein 2017; Kumar & Reinartz 2016; Verhoef & Lemon 2013). Many firms have already adopted customer experience management to their business, resulting in new journey management organizations and subsequently, new job opportunities such as chief experience officer, journey strategists, or customer experience mangers (Edelman & Singer 2015; Lemon & Verhoef 2016).
Given the aforementioned discussion, the ongoing digitalization and the presence/dominance of the online channel enable firms to target consumers in new ways, to create richer customer experiences and to strengthen customer relationships. Firms are challenged to generate ‘Integrated Customer Experiences’ which was announced to be one of the five ‘Research Priorities’ of the MSI (2016), emphasizing the importance of ‘right’ marketing in real time, whether B2B or B2C. Since consumers are increasingly in control of the information flow and decide when and where they make their purchase decisions, marketers are challenged to customize experiences, offers and content at the right point of the customer journey. (Lemon & Verhoef 2016; MSI 2016)
2.2
Stages of the Total Customer Experience: The Customer Journey
On their way to purchase, customers move through different stages: the prepurchase stage, the purchase stage, and the postpurchase stage, while interrelationships between the different stages can exist (Lemon & Verhoef 2016). For instance, past experiences can affect current experiences, since consumers form expectations and they tend to stick to their experience evaluations (Lervik-Olsen, Van Oest, Verhoef 2015). Additionally, prior experience influences current satisfaction, which in turn influences future usage (Bolton & Lemon 1999). This makes the total customer experience a dynamic and iterative process. Moreover, in each stage, customers are exposed to different touchpoints which are mostly under the firm’s control. (Lemon & Verhoef 2016) Therefore, Lemon and Verhoef (2016, p. 74) conceptualize customer experience as the “customer’s journey with a firm over time during the purchase cycle across multiple touch points”. The customer journey or path to purchase also includes external factors, for example, the weather, political events, peer influences, that can influence a consumer’s decision-making (Anderl, Schumann, Kunz 2015; Lemon & Verhoef 2016).
journey is equal to the consumer decision process which encounters customers’ need recognition, information search, evaluation, purchase, and postpurchase (Puccinelli et al. 2009). I am dividing the overall customer experience into three stages, as suggested by Lemon and Verhoef (2016): prepurchase, purchase, and postpurchase stage.
Prepurchase Stage. This stage refers to all aspects of the customer’s interaction with the brand,
category, and environment before a purchase transaction. The prepurchase stage encounters a customers’ need/problem recognition (i.e., timing, causes, intensity), information search (i.e., media usage, shopping patterns and frequency), and evaluation (i.e., attitudes, affective processing) (Puccinelli et al. 2009; Titus & Petroshius 1993).
Purchase Stage. The purchase stage encompasses all customer interactions with the brand and
its environment during the purchase event itself. In retailing and consumer products research, a strong emphasis has been placed on the shopping experience. (Lemon & Verhoef 2016) However, multi-attribute choice models, inference-making, and choice heuristics are also interesting to study in this stage (Titus & Petroshius 1993).
Postpurchase Stage. The postpurchase stage encounters satisfaction or dissatisfaction,
cognitive dissonance, complaint behavior, consumption amount/frequency, and word-of-mouth (Titus & Petroshius 1993). Therefore, this stage comprises customer interactions with the brand and its environment as well as customer reactions following the actual purchase. The product itself becomes a critical touchpoint in this stage. (Lemon & Verhoef 2016)
Given the perspective of the customer moving through a purchase journey, firms need to understand both the firm and customer perspective of the journey, identifying key aspects in each stage. They also need to identify the specific elements or touchpoints that occur throughout the journey. Finally, firms should aim at identifying specific trigger points (‘moments of truth’) that lead customers to continue or discontinue in their purchase journey. (Verhoef & Lemon 2016) Overall, the customer journey is a powerful as well as a simple tool to put the customer experience into action (Addis 2016).
2.3
Types of Touchpoints in the Customer Journey
or importance of each touchpoint category may differ in each customer journey stage. (Lemon & Verhoef 2016)
Brand-owned Touchpoints. These touchpoints are designed and managed by the firm and are,
therefore, under the firm’s control. Brand-owned touchpoints include all brand-owned media (e.g., advertising, websites, loyalty programs) and any brand-controlled elements of the marketing mix (e.g., attributes of product, packaging, service, price, convenience, sales force). (Lemon & Verhoef 2016)
Partner-owned Touchpoints. These touchpoints are designed and managed by the firm and by
one or more of its partners, such as marketing agencies, multichannel distribution partners, multivendor loyalty program partners, and communication channel partners. (Lemon & Verhoef 2016) However, the distinction between brand-owned and partner-owned touchpoints is not always clear, since partners may also benefit from a focal brand’s product or service (Lemon & Verhoef 2016).
Customer-owned Touchpoints. As part of the overall customer satisfaction, these touchpoints
are customer actions that are not influenced by the firm, its partners, or others. A prepurchase example would be customers thinking about their own needs or desires. While purchasing, the customer’s choice of payment is particularly a customer-owned touchpoint, whereas partners may also play a role. (Lemon & Verhoef 2016) Most customer-owned touchpoints are critical postpurchase, as individual’s consumption and usage matters (Lemon & Verhoef 2016). Moreover, customers can act as co-creators of values when firms allow them to participate in value-creating activities, such as brainstorming product ideas. Customer co-creation can be independently or jointly with firms. (Gill, Sridhar, Grewal 2017; Verhoef, van Doorn, Beckers 2013)
Social/External/Independent Touchpoints. Throughout the customer journey, customers are
3 Conceptual Framework and Hypotheses
The following chapter proposes a conceptual framework and develops hypotheses about the effects of customer journey characteristics on customer satisfaction. Figure 1 presents the hypothesized model of the relationships among journey characteristics and customer satisfaction, while controlling for customer characteristics and characteristics of the purchased good.
Fig. 1: Conceptual model hypothesizing the relationships among customer journey characteristics and customer satisfaction.
3.1
The Journey to (Customer) Happiness?
Several studies have been conducted to analyze the effects of different environments and certain touchpoints on consumer behavior, such as brand consideration or purchase intention (e.g., Anderl, Schumann, Kunz 2016; Baxendale, Macdonald, Wilson 2015). Although customer satisfaction is an established metric to determine customer reactions, the effects of touchpoints on customer satisfaction are still unclear (Cardozo 1965; Churchill & Surprenant 1982; Lemon & Verhoef 2016). Customer satisfaction is a major outcome of marketing activity and according to the expectation confirmation theory, it builds upon an individual’s expectations. Accordingly, an individual’s expectations can be confirmed when a product performs as expected, they can be negative disconfirmed when the product performs more poorly than expected, and they can be positively disconfirmed when the product performs better than expected. (Churchill & Surprenant 1982) However, it was found that effort and expectation have an influence on both product and shopping experience. Therefore, customer satisfaction does not only depend on the product itself. The experience surrounding the purchase may also play a role that is why customer satisfaction is more than a simple product evaluation. (Cardozo 1965)
want to monitor a firm’s performance and guide improvements efforts with regard to customer loyalty (Kleiningham et al. 2007). That is probably the case “because it is generic and can be universally gauged for all products and services (including nonprofit and public services). Even without a precise definition of the term, customer satisfaction is clearly understood by respondents, and its meaning is easy to communicate to managers.” (Zeithaml et al. 2006, p. 170). Another widely used customer feedback metric is the net promoter score (NPS) that is referred to ‘the ultimate question’, asking customers: “on a 0-to-10 scale, how likely is it that you would recommend [company x] to a friend or colleague?” (Grisaffe 2007; Reichheld 2003). Both customer satisfaction and the NPS have been found to perform equally well in predicting firm performance and customer behavior (Lemon & Verhoef 2016). The NPS quantifies customer behavior, such as repeat buying, based on assessing the likelihood that the customer will recommend that product/service to others. Therefore, the NPS is a forward-looking metric (Reichheld 2003), while customer satisfaction is a backward-looking metric (Lemon & Verhoef 2016).
Lemon and Verhoef (2016) summarize that recent studies provide a nuanced view, meaning that the predicting power between these two metrics is very small. Furthermore, combining several metrics may improve the predicting performance (Lemon & Verhoef 2016). However, the authors further conclude that there is not yet agreement on how to robustly measure all aspects of the customer experience across the customer journey (Lemon & Verhoef 2016). As discussed earlier, more research and elaboration on this topic is needed, since more and more firms concentrate on customer experience, primarily to create enduring customer values (Kumar & Reinartz 2016). Nevertheless, customer satisfaction can be considered as a component of customer experience since it is a customer’s cognitive evaluation of the experience. Consequently, Lemon and Verhoef (2016) conclude that “customer experience is broadening the concept of customer satisfaction, leading to a richer view.”
Start and End of Journey
“There was a time when the online and offline business were viewed as being different. Now we are realizing that we actually have a physical advantage thanks to our thousands of stores, and we can use it to become Number 1 online.”
Raul Vasquez, Chief Executive at Walmort.com (Herhausen et al. 2015).
consumers with information that enable them to find the right product. However, good in-store information provided by salespeople might increase consumers’ satisfaction with the store, or increase affect toward the store, leading to more in-store purchases and hence less showrooming. (Gensler, Neslin, Verhoef 2017). To maintain good communication in the online channel, e-vendors are challenged to focus on relationship marketing to steadily improve the communication between them and their customers. This will not only result in higher quality of products/services but also better customer services. Nowadays e-vendors have more opportunities to offer interactive and personalized marketing in the online environment (compared to the offline environment), leading to higher customer satisfaction and loyalty. (Shankar, Smith, Rangaswamy 2002) Thus, firms are challenged to provide user-friendly web surfaces that elicit greater pleasure accordingly (Puccinelli et al. 2009). The online environment is associated with greater ease of obtaining information and therefore lower search costs. When the information search online is easier, consumers experience learning and are more likely to remain loyal. Yet, customers need to have a certain understanding and technology-based acceptance. (Shankar, Smith, Rangaswamy 2003; Wu 2013) Moreover, when integrating offline channel information into the online channel, multichannel retailers can enhance search intention, purchase intention, and WTP in the Internet store, which subsequently generates a competitive advantage (Herhausen et al. 2015). Consequently, I assume that the online environment is associated with higher customer satisfaction because e-vendors have more opportunities to create interactive and enriched customer experienced.
Hypothesis 1a. Customer journeys that started in the online channel are associated with higher customer satisfaction compared to journeys that started in the offline channel.
Hypothesis 1b. Customer journeys that ended in the offline channel are associated with higher customer satisfaction compared to journeys that ended in the online channel.
Touchpoint Usage
Since different types of touchpoints exist, it is necessary to distinguish their individual effects on customer satisfaction. As mentioned earlier, there are firm-owned touchpoints, partner-owned touchpoints, competitor-owned touchpoints, and independent touchpoints (see Figure 1).
Firm-owned Touchpoints. Retailers are able to use several interventions to affect consumer mood. For
instance, supermarkets play joyful music to trigger happy memories or banks give away free pens or offer cookies to children as a mood lever in order to achieve transactions. Retail experiences other than based on atmospheric elements can also influence customer emotions. Since search regret
which is why retailers such as Coach and Brighton tell their customers that the purchased items suit them well. (Puccinelli et al. 2009) Therefore, customer satisfaction is a postpurchase and postuse evaluation, whereas value perceptions can be created in earlier stages of the customer journey. As a result, satisfaction depends on product and service experiences. (Sweeney & Soutar 2001) Since employee attributes affect customer experiences, the interpersonal nature of the interaction between the customer and employee is the key to customer satisfaction (Lemon & Verhoef 2016; Puccinelli et al. 2009). In order to create a more satisfying experience, retailers also try to encourage customers to contact its employees, as programmatic conversation starters were found to increase customer satisfaction (Puccinelli et al. 2009). By engaging customers, firms can increase customer satisfaction, which subsequently motivates them to make formal referrals (Kumar et al. 2010). Another touchpoint, which can be controlled by the firm, is the availability of loyalty programs which are described as relationship-building instruments that enhance customers’ affective commitment or attitudinal loyalty to produce lasting effects. Firms can even further increase customer satisfaction and loyalty by enhancing the perceived attractiveness of the loyalty program and its rewards. (Dorotic, Bijmolt, Verhoef 2012) However, customers are increasingly in control of their journeys, since they are informed, connected, empowered, and active (Lemon & Verhoef 2016; Prahalad & Ramaswamy 2004). Therefore, firms are challenged to integrate multiple business functions in order to create and deliver positive customer experiences (Lemon & Verhoef 2016). Moreover, consumers increasingly seek to exercise their influence in every part of the business systems. They want to interact with firms and thereby ‘co-create’ value, which in turn creates individualized interactions and experiences, resulting in higher customer satisfaction (Prahalad & Ramaswamy 2004; Vega-Vazquez, Revilla-Camacho, Cossío-Silva 2013).
Hypothesis 2a. Firm-owned touchpoints are associated with high customer satisfaction.
Partner-owned Touchpoints. As indicated earlier, firms can establish long-lasting relationships with
promotions that feature individual vendors. (Dorotic et al. 2011) Vendor’s sales promotions in a multi-vendor loyalty program were not found to change the aggregate patterns of cardholders’ purchase behavior, as consumers are more likely to spend points when they can easily anticipate the benefits they can enjoy with the points (Kwong, Soman, Ho 2011). Moreover, the cross-vendor effects of promotions of coalition partners on the performance of the focal vendor were also found to be nonsignificant. Next to considering the sales effects of multi-vendor loyalty programs, it might be interesting to discuss their experience effects.
Hypothesis 2b. Partner-owned touchpoints are associated with low customer satisfaction. Competitor-owned Touchpoints. Especially in the online service industry, the number of online vendors
is growing tremendously which subsequently means that the number of competitors is growing as well. Therefore, it is necessary to consider consumer churn and switching behavior. (Keaveney & Parthasarathy 2001) Perceived service performance is associated with unfavorable or favorable behavioral intentions and therefore, may or may not induce switching behavior (Zeithaml, Berry, Parasuraman 1996). Moreover, consumers may be affected by marketing mix variables that consequently lead them to switch to another brand (Deighton, Henderson, Neslin 1994). More importantly, consumers show additional behaviors such as impulse buying and variety seeking (Sharma, Sivakumaran, Marshall 2010), which is why customers may switch between firms in the journey. Also, consumers increasingly show competitive showrooming behavior (Gensler, Neslin, Verhoef 2017). Therefore, the effects of competitor-owned touchpoints need to be considered. By assessing several costs and benefits, consumers aim to anticipate the utility they will gain when they purchase. (Konus, Verhoef, Neslin 2008) Since consumers tend to maximize their utility, I assume that consumers might end up having high expectations when they are exposed to competitor-owned touchpoints. This might also be due to the fact that multi-channel firms aim to overcome switching behavior by striving for channel integration and by providing a seamless channel experience (Herhausen et al. 2015; Lemon & Verhoef 2016).
Hypothesis 2c. Competitor-owned touchpoints are associated with low customer satisfaction. Independent Touchpoints. In order to create superior customer experiences, other elements such as
can positively affect the customer experience due to their relationships. However, relationships among customers can also be negative and can even create anxiety, for instance when crowding occurs or customers stand too close to others. Furthermore, eye contact between strangers may be negatively perceived and someone’s appearance may be perceived as threatening. Some customers may be disruptive when talking too loudly while other customers may assist fellow customers by playing the role of an advisor. Firms are therefore challenged to manage the service environment to foster positive interactions between customers to enhance customer satisfaction. Additionally, by enabling customers to form virtual communities, customer experiences are getting more enriched which can help build customer loyalty. (Verhoef et al. 2009) Third-party information sources, such as review sites and social media, may also exert influence on customers, however, the effects of customer experience have not been widely reported (Lemon & Verhoef 2016). I conclude that the effects of independent touchpoints on customer satisfaction are rather unclear, which is why I include them in my model to conduct an exploratory analysis. I assume that independent touchpoints are associated with low customer satisfaction because they enrich a consumer’s knowledge and therefore, they raise their expectations.
Hypothesis 2d. Independent touchpoints are associated with low customer satisfaction. Mobile Device Usage
anticipated satisfaction, are more likely to mention tangible attributes as driving their decisions and see instinct as more important in the choice process compared to their mouse-using counterparts (Brasel & Gips 2015).
Hypothesis 3. Mobile device usage is associated with greater customer satisfaction compared to desktop usage.
3.2
More Touchpoints, More Complexity?
As mentioned in chapter 2, customers go through different stages on their path to purchase, they are exposed to a myriad of touchpoints, and the ongoing digitalization offer even more opportunities for firms to interact with customers. Consequently, firms are increasingly focusing on creating enriched customer journeys, assuming that providing more channels is the key to create greater customer experiences. (Verhoef & Lemon 2013) Indeed, providing more channels can create stronger customer experiences, however, more channels may also result in more complexity (Clatworthy 2011). This is why firms are challenged to move away from its siloed nature of service delivery to cross-functional processes to redesign and support customer-oriented journeys across different channels (Rawson, Duncan, Jones 2013). Although much emphasis has been placed on the shopping experience as such so far, concepts such as choice overload, purchase confidence, and decision satisfaction might also be relevant to study (Lemon & Verhoef 2016). In fact, choice overload may play a role, though, ‘experience-centric’ firms that provide a value proposition with some variety can make it easier for customers to find the right product or service for them; hence reducing customer effort. More importantly, a firm’s value proposition should have some degree of uniqueness to stimulate pleasure, while not deviating too far from standards in the industry. Moreover, user-friendly interfaces of products, services and channels (value delivery) can reduce customer effort. (Saebi et al. 2017) Varying service levels across channels attract customers, which is why cross-channel coordination is even more important to assign customers to the channel that best satisfies their needs and to drive overall customer satisfaction (Montoya-Weiss, Voss, Grewal 2003). Firms that provide interchangeable service levels appear increasingly appealing to customers. However, multi-channel servicing may also result in diverging customer satisfaction levels across various channels. (De Birgelen, de Jong, de Ruyter 2006)
and invariable customer experiences to “make the journey a compelling, customized, and open-ended experience” (Addis 2016, p. 22). This requires both top-down, judgment-driven evaluations and bottom-up, data-driven analysis. By looking at the entire customer experience end-to-end, firms are able to transform the overall customer experience and therefore, they can meaningfully improve their business performance (Maechler, Neher, Park 2016; Rawson, Duncan, Jones 2013). Subsequently, firms can use regression models to understand which journeys have the greatest impact on both overall customer satisfaction and business outcomes (Rawson, Duncan, Jones 2013). Thus, higher satisfaction is correlated with higher revenue growth. More importantly, performance on journeys is substantially more strongly correlated with customer satisfaction than performance on touchpoints. As a result, delivering a distinctive journey experience leads to customer repeat buying, increased sales, recommendations and customer retention. (Maechler, Neher, Park 2016) In B2B settings, multichannel shoppers were found to have deeper relationships with the supplier and have greater trust and lower perceived risk in their transactions that could motivate them to spend more with the supplier (Kumar and Venkatesan 2005). To sum up, multiple touchpoints are more enjoyable to customers as long as they are consistent across the journey. Depending on the type of product a consumer is looking for, the choice overload theorem may play a distinct role when the customer journey is getting too long.
Hypothesis 4a. Multiple touchpoints contribute more to the overall customer satisfaction than just one single touchpoint.
Hypothesis 4b. An increasing number of touchpoints is associated with greater customer satisfaction, however, customer satisfaction decreases after a certain number of touchpoints.
3.3
Moderating Effects
Product Characteristics
Experience and search goods. Based on the search/experience classification paradigm (e.g., Rosa &
extent to which consumers feel they need to directly experience a good to evaluate its quality. A good possesses more experience qualities, the greater someone feels the need to use his/her senses to evaluate that good. And the more one feels that additional information is necessary for an adequate evaluation of the good, the more search qualities the good has. Almost all goods possess search and experience attributes (e.g., Sheffet 1983) and it is therefore useful to consider the differences between perceived experience and search qualities. The larger or smaller this difference, the more or less experience qualities a good possesses relative to search qualities (Weathers, Sharma, Wood 2007). Huang et al. (2009) provide an alternative perspective on information search: Since the Internet can be considered as an important information source for both experience and search goods, they outline that one can distinguish the type of information that consumers seek, and therefore the way they search and make choices. Consequently, the amount of time spent per page, the number of pages searched, the likelihood of free riding (purchasing from a vendor that is not the primary source of product information), and the relative importance of interactive mechanisms (e.g., consumer recommendations) are affected by the difference between experience and search goods. In particular, experience goods involve greater depth of search (more time spent per product page), whereas search goods involve greater breadth of search (more product pages viewed). Additionally, free riding was found to occur more frequently for search than for experience goods. Finally, mechanisms used by Internet vendors to enable consumers to learn from the experience of others or to experience product attributes before purchase increase the time spent on a website and the likelihood of purchase from that website to a greater extent for experience than for search goods. Huang et al. (2009) conclude that these findings do not occur because of differences in the ability to assess product quality before and after a purchase, as originally outlined by Nelson (1970, 1974); rather it is because of fundamental differences in the type of products, and therefore, in their search and purchase behavior. Consequently, my aim is to explore the moderating effects of experience and search goods on my independent variables1. For instance, search goods involve greater breadth of search and therefore,
greater consumer involvement, which is why I assume that consumer expectations may be greater and search goods may be associated with lower customer satisfaction.
Product Related Perceived Risks. One could further distinguish between non-risky and risky products.
For instance, the mere presence of the representation of a risky product (either pictorial or verbal) elicits arousal in views, more importantly, there is little evidence to suggest that risky products elicit more attention than non-risky products. Therefore, these findings have some practical implications for designing effective messages. (Lang et al. 2005) Moreover, some business-to-business goods and
services are inexpensive and relatively less risky such as purchasing minor stationary, e.g., pens or paper (goods). However, many business-to-business goods and services are of rather high transaction value and therefore, they are driven by an accountable purchasing process. Consequently, it is likely that careful consideration is given to the selection of a brand and it is unlikely to be an impulse purchase with little or no decision-making. More importantly, high-risk purchases are associated with high involvement, thus central route processing is likely to occur, and motivation is likely to be high. Since services are associated with higher risk compared to products, involvement with the service category will be more dominant in its influence on brand loyalty than satisfaction with the preferred brand. (Bennett, Härtel, McColl-Kennedy 2005) Moreover, the post-evaluation of an actual experience is essential for consecutive behavior, which is why customers will return to the retailer for future transactions and will not take the risk associated with another service offering (Van Birgelen, de Jong, de Ruyter 2006). Since more risky services are typically more complex, attempt to meet unique customer requirements, require more cognitive effort, and involve greater exchange of information and higher levels of customization (Van Birgelen et al. 2006), they may be associated with lower customer satisfaction. This might be because consumers are highly involved and form expectations respectively. Furthermore, the Internet is often considered convenient for gathering information, while it is also considered to be risky to purchase because of security factors or the inability to physically touch and test the product. On the other hand, consumers may find it difficult to search for information in retail stores, but not risky to make the final purchase there, and therefore consumers might search on the Internet and purchase the product in the store. Risk and privacy were found to be particularly important deterrents from using the Internet for purchase. (Verhoef, Neslin, Vroomen 2007) Consequently, the perceived risk of a certain product category may differ in the online and offline environment, which is why I want to explore their moderating effects on customer journey characteristics.
Customer Characteristics
Sociodemographic Characteristics. Not all consumers respond equally to increases in
segmentation studies focus on the utility that consumers derive from using different channels which subsequently influences their channel usage (e.g., Neslin et al. 2006). One can further distinguish between customers based on what products they prefer to buy online and offline (Bhatnagar & Ghose 2004), the importance they put on attributes such as price for their purchase decision (Keen et al. 2004), their responses to channel introduction (Pauwels et al. 2011), their channel preferences (Konus, Verhoef, Neslin 2008), and their channel choice (Thomas & Sullivan 2005; Kushwaha & Shankar 2013). These studies also consider how segments differ in terms of psychographic and socio-demographic characteristics. For instance, Konus, Verhoef, Neslin (2008) suggest that multi-channel users are more innovative and more price-conscious. Based on the anticipated utility theory (Quiggin 1982), one can expect that using touchpoints during the customer journey depends on the marginal utility the customer derives from different touchpoints. The overall utility depends on the benefits and costs of using multiple touchpoints, which in turn can be explained by psychographic, demographic, and other individual characteristics of customers. Customers maximize their utility by assessing several costs and benefits, such as monetary savings, time savings, search costs, and entertainment. However, these assessments are determined by customer’s individual shopping goals and shopping context. (De Keyser, Schepers, Konus 2015; Konus, Verhoef, Neslin 2008) Next to analyzing shopping motives and product categories, Heitz-Spahn (2013) analyzes sociodemographic details to show that the likelihood of cross-channel free-riding does not differ across sociodemographic covariates. On the other hand, Mittal et al. (2001) find that consumers with different characteristics have different thresholds (i.e., are more loyal) and consequently different repurchase probabilities. Moreover, the nature and extent of response bias in satisfaction ratings varies by customer characteristics (Mittal & Kamakura 2001). Consequently, sociodemographic variables drive customer behaviors. Since ‘age’ was found to be an important moderator of the satisfaction-loyalty relationship (Homburg & Giering 2000) and due to the reasons mentioned above, I include ‘age’ in my analysis in order to explore its moderating effects on the relationship between customer journey characteristics and customer journey experience. In particular, I assume that age does influence journey experience, i.e., older people are generally less satisfied. I also hypothesize that the online environment does not add value to older people and that they are more satisfied when using the desktop device compared to using their mobile device.
Purchase Reasons
Shopping Goals. Generally, goals provide a sense of direction and clarity for actions, and they
are more malleable. (Lee & Ariely 2006) More importantly, consumers shop differently depending on whether their motivations for search are primarily experiential (for fun) or goal-oriented (for efficiency). Also, the degree to which online or offline shopping fulfills goal-oriented and/or experiential consumer needs will affect how much consumers will spend in each environment. Experiential shoppers are likely to have an ongoing, hobby-type interest, which is why they enjoy the ‘thrill of the hunt’. Generally, experiential behavior is associated with enjoyment, thus, it results in a more positive mood, greater shopping satisfaction, and is more likely to result in impulse purchasing compared to goal-oriented shopping. On the other hand, goal-oriented (or utilitarian) shopping can be described as task-oriented, efficient, rational, and deliberate. Therefore, goal-oriented shoppers are transaction-oriented, and they desire to purchase goods quickly and without distraction. Surprisingly, they are more impulsive and more committed offline, and they feel disappointed if they come home empty-handed. (Wolfinbarger & Gilly 2001) In general, a shopper can be simply motivated to find the product he or she is looking for. Moreover, attracting others’ attention, finding time to be with peers, or simply passing time are other motivations propelling people to shop. Therefore, people do not only shop for the utilitarian value of the products but also for the satisfaction obtained during the shopping process. (To, Liao, Lin 2007) Consequently, I want to explore the moderating effects of shopping goals. In particular, I assume that experiential shopping is associated with higher customer satisfaction because consumers enjoy, for instance, a higher number of touchpoints. On the other hand, I assume that goal-driven shopping is associated with lower customer satisfaction due to the rising complexity with a higher number of touchpoints. Nevertheless, goal-driven shopping may be associated with higher customer satisfaction in the online environment because shoppers are distracted by staff personal or the external environment. I also assume that both experiential and goal-driven shopping are associated with higher customer satisfaction when shoppers use a desktop device instead of their mobile device, since they able to explore more or to find information easier.
Important Criteria When Making the Purchase. Nowadays, retailers have more and more
(Morganosky & Cude 2000) Nevertheless, consumers also exchange information by using the Internet as a communication medium, which is why they can be considered as purchasers of goods and as users of information technology (Cho & Park 2001). Therefore, the influence of utilitarian and hedonic value on shopping motivations were studied and also whether different shopping motivations would influence search intention and purchase intention. Consumers of Internet shopping have both utilitarian and hedonic shopping motivations, not unlike shoppers in physical stores. Consumers experience not only product value, but also some pleasure and enjoyment during the process of Internet shopping. Some people shop on the Internet for the utilitarian value made available by Internet shopping, such as convenience, cost saving, information availability and selection while others do so for the hedonic values such as adventure, authority, and status. Moreover, utilitarian motivation has more influence on search intention and purchase intention than hedonic motivation. The motivation of Internet shoppers is different to the motivation of physical storefront shoppers. Hedonic motivation is important in a physical distribution channel, which is why vendors could emphasize strengthening consumer experience via visual, audio and physical stimulation in the physical shopping environment. (To, Liao, Lin 2007)
4 Method
4.1
Data Collection
For my study, I was allowed to use specific multilevel and multisource data that was collected in 2016 (Kleinlercher et al. 2018). In this data collection, 3,105 customers from Austria, Germany, and Switzerland were asked to describe their last purchase at a multichannel retailer. Additionally, website data from the respective retailers were obtained. The quotes chosen for age and gender distribution were aligned with the population of Internet users according to the federal statistical offices in those three countries. At the beginning of the survey, some screening criteria were applied, and unfinished surveys were filtered out. For instance, customers were filtered out when they were not able to recall a multichannel retailer of which they knew the online and the offline store. Moreover, if more than 12 months passed since they bought the item(s), respondents were filtered out to avoid mental blackouts that might affect the study results. After all these screening and filtering out criteria were applied, the final data set contained 2,941 respondents. The respondents were asked to indicate at which multichannel retailer they had made the purchase, what they had bought, which channel did they use to make the purchase, and what were their primary motivations to make the purchase. In order to reconstruct their path to purchase, customers were provided with a list of touchpoints offered by the respective multichannel retailer, its competitors, and independent providers. After they indicated all visited touchpoints for their last purchase, participants were again confronted with the touchpoints to rank them chronologically. In particular, the last touchpoint represented the purchase channel. Additionally, participants were asked to indicate how satisfied they were with certain touchpoints, with the overall product experience, and with the buying process itself.
‘apparel’ is higher than its search level, which is why I assigned this category as being an experience good. Finally, seven product categories were assigned to experience goods (apparel, sports, toys, beauty, household goods, food, drink and tobacco) while ten product categories were assigned to search goods (electronics, entertainment, jewelry, household goods, home furnishings, tools, mobility offerings, telecommunication services, travel services, and insurance and banking services). Household goods were assigned to both, experience and search goods.
Another variable that was included in my additional survey measured consumer perceptions of product related risks. Based on Kushwaha and Shankar (2013) and adopted by De Haan et al. (2018), my survey respondents were further asked to rate each of the 16 categories on the five components of risk – financial, functional, safety, psychological, and social risk (Jacoby & Kaplan 1971). Furthermore, they were asked to indicate the overall risk perception of that particular product category. The risk perceptions were measured on a nine-point scale (see Appendix C). As stated above, 22 participants have filled in the survey, resulting in a total of (22*5) 110 product category evaluations. The results showed some moderate to high correlations (see green highlighted spots in Table 1).
Given the high correlations between functional and financial risk (r = ,678**) and between psychological and social risk (r = ,718**), they were combined into one component to reduce multicollinearity. However, financial risk cross-loaded to social risk, which needed to be considered. Therefore, I conducted an exploratory Principal Component Analysis (PCA) to find the optimal factor solution. Firstly, most of the criteria (Eigenvalues greater than 1, total amount of variance explained, scree plot) suggested a one-factor solution. Next, I conducted two additional PCA while forcing a two-factor and a three-two-factor solution. Both analyses still showed high cross-loadings, therefore I decided to proceed with the overall risk as my main variable to measure the related risk with a certain product category. The risk scores for the ‘overall risk’ component can be seen in Table 2, indicating that search goods like ‘electronics’ or ‘insurance and banking services’ (highlighted in red) were perceived to be most risky, while ‘food’ and ‘entertainment’ were perceived as the least risky (highlighted in green). Subsequently, the overall risk scores were added to the original data set and finally assigned to the respective product category.
Functional Risk Financial Risk Safety Risk Psychological Risk Social Risk Functional Risk
Financial Risk .678**
Safety Risk .223 .550**
Psychological Risk .486* .740** .609**
Social Risk 465* .698** .242 .718**
Functional
Risk Financial Risk Safety Risk Psychological Risk Social Risk Overall Risk
Apparel EG 4,33 5,00 2,86 6,10 6,38 4,33 Electronics SG 6,67 6,95 4,76 4,86 5,33 6,29 Entertainment SG 3,90 4,24 2,57 4,76 4,33 3,57 Sports EG 4,57 4,00 4,62 4,86 4,86 4,24 Toys EG 4,52 4,62 4,48 3,67 3,29 4,05 Jewelry SG 5,00 5,52 2,71 5,62 5,67 4,86 Beauty EG 5,67 5,14 5,48 5,81 5,52 5,67
Household Goods EG/SG 4,62 4,43 3,38 3,57 3,05 3,81
Food EG 4,29 3,95 5,33 4,00 3,95 3,71
Drink & Tobacco EG 3,86 3,81 5,75 4,57 4,29 4,10
Home Furnishings SG 5,29 6,00 3,33 5,29 4,10 4,48
Tools SG 6,19 6,14 6,05 3,24 2,48 5,57
Mobility Offerings SG 6,19 5,33 5,33 4,67 4,62 4,90
Telecommunication Services SG 5,86 5,52 3,24 3,90 3,76 5,24
Travel Services SG 6,00 6,83 4,81 4,48 4,33 5,67
Insurance & Banking Services SG 6,19 6,52 4,38 4,05 3,67 6,14
Mean 4,88 4,92 4,06 4,32 4,10 4,51
Table 2: Perceived risk scores for all product categories (EG = Experience good; SG = Search good); Note: All items were measured on a scale from 1 (low risk) to 9 (high risk).
4.2
Measures
The goal was to explore all 23 touchpoints, which were used by the study respondents, to find insights into how they influenced customer satisfaction: nine firm-owned touchpoints (online-shop, physical store, catalog, newsletter, online community, mobile app, call center, radio/TV spot, live chat), four partner-owned touchpoints (search engine, brand website, comparison portal, review portal), four competitor-owned touchpoints (online-shop, physical store, catalog), and six independent touchpoints (blogs, social media, video portals, image portals, news portals and newspapers, offline Word-of-Mouth (WOM)). Additionally, new variables were created to specify whether customers started their journey in the online or offline environment and whether they ended their journey in the online or offline environment. The number of used touchpoints were counted and captured by another created variable. Moreover, it was captured which device was used by customers on their path to purchase. As mentioned earlier, I included several moderating effects. In particular, one sociodemographic variable (age), important criteria when making the purchase, the purchase goal as well as multiple product characteristics (experience good, search good, perceived product related risk), which may be related to the touchpoint usage, were assessed. All measured variables can be seen below (Table 3).
Journey characteristics
Journey start online Journey start offline Journey end online Journey end offline
Physical store Catalog Newsletter
Online community (e.g. Facebook fan page) Mobile app
Call center Radio/ tv spot Live chat
Partner-owned touchpoints Search engine (e.g. Google etc.) Brand website
Comparison portal (e.g. Geizhals.com etc.) Review portal (e.g. Ciao.de etc.)
Competitor-owned touchpoints Competitor physical store Competitor online store Competitor catalog Competitor newsletter Independent touchpoints Blog(s), forum
Social media
Video portals (e.g. YouTube etc.) Image portals (e.g. Pinterest etc.) News portals and newspaper Offline Word-of-Mouth (WOM) Other touchpoints
Mobile device usage Desktop device usage Number of touchpoints used
Moderating variables Sociodemographic variable Age
Important criteria while shopping Having a vast assortment
Being able to shop at any time and everywhere Saving costs
Receiving personal advice
Receiving cross-channel service offerings Receiving promotional mailings
Primary shopping reason Having fun
To avoid boredom To finish errands quickly Being goal-oriented Other moderators Experience goods Search goods
Perceived product related risk
Outcomes
Journey experience Product experience
Table 3: Overview of all independent and dependent variables.
the assessment of journey and product experience (Keiningham et al. 2017). The customers’ journey experience was therefore measured with a multi-item scale based on Fitzsimons (2000) and Heitmann et al. (2007), and product experience was measured with a multi-item scale based on Crosby and Stephens (1987). Kleinlercher et al. (2018, p. 25) highlight that the “journey experience encompasses all the experiences a customer makes while searching for a product (i.e., during the path to purchase), whereas product experience encompasses customers’ experiences with the choice outcome (i.e., with the purchased product)”. Consequently, I was focusing on ‘journey experience’ as being my main outcome variable because my goal was to measure the effects of customer journey characteristics on customer satisfaction and therefore, to capture the customer experience itself. The four items that were used to assess the journey experience were (1) ‘several good options were available for me to choose between’, (2) ‘I thought the choice selection was good’, (3) ‘I would be happy to have the same options on my next purchase occasion’, and (4) ‘I found the process of deciding which product to buy interesting’. As all four items were highly correlated and measured therefore the same underlying phenomenon, a PCA was conducted to get parsimony and to reduce multicollinearity. The results of the PCA showed that a one-factor solution explained 69% of the variance (KMO = ,767; p = ,000). Combining all four parameters into one factor appeared to be a reliable solution (Cronbach’s alpha = ,835). Since negative factor scores were computed, I decided to create a new variable by taking the means of all four questions. This newly computed variable was the final outcome variable to measure journey experience. The measurement and operationalization of all variables is detailed in Appendix A and the results of the PCA can be found in Appendix B.
4.3
Analysis
5 Results
Descriptive Statistics
First of all, I examined my data set to find potential outliers or unusual observations. By looking at scatterplots for all independent variables, I identified an outlier for age (x2811 = 689). Therefore, I
indicated for age that value ‘689’ is a missing value to make sure that this value does not affect the following analyses. Next, I dived deeper into my data set. The sample consisted of 1,468 (49.9%) women and of 1,473 (50.1%) men, with an average age of 43 (Mage = 42.81, SD = 16.153). The sample
contained 1,337 (45.5%) respondents from Germany, 799 (27.2%) respondents from Austria and 805 (27.4%) respondents from Switzerland. The respondents were asked to indicate how frequently they buy products for their personal purpose in the online
environment. As depicted in Fig. 2, the online purchase frequency was approximately normally distributed, ranging from a frequency of ‘maximum once a year’ to ‘daily and more frequently’. However, there was a slight right skewed distribution because most people tended to buy online 1-2 times per month (Monline_purchase_frequency = 3.77, SD = .725). Moreover, the
respondents spent on average 139 euro (Missuing_price =
138.92, SD = 464.33). The most frequently used purchase channel was the offline store (55.2%), followed by the online store (43.6%). Out of the offline channel users, i.e. physical store visitors, 37.9% indicated they would switch from the offline to the online environment to take advantage of a price promotion, while 17.3% indicated they would stick to the physical store. The respondents were also asked to indicate which actions would convince them to make the purchase in the online store and not in the offline store. Several answers were given, and the participants had to indicate to what extent they agreed or not (1=strongly disagree, 7=strongly agree). On average, next day delivery was the highest ranked indicator which would convince them to shop in the online environment, followed by a bigger online assortment and online customer reviews (see Table 4).
…next day
delivery. …a bigger online assortment. …different assortment online. …better product information online. …showing customer reviews online. I do not want to buy in the online store at all. N Valid 1,624 1,624 1,624 1,624 1,624 1,624 Invalid 1,317 1,317 1,317 1,317 1,317 1,317 Mean 4.59 4.30 3.58 3.96 4.07 2.27 Median 5.00 5.00 4.00 4.00 4.00 1.00 Standard deviation 2.09 2.02 1.94 2.00 2.03 1.80
Reversely, 34.8% of the online channel users indicated they would switch from the online to the offline channel to derive benefit from a price promotion. 8.8% indicated they would not take advantage of a price promotion and stick to the online environment. The participants were asked what would need to be necessary to persuade them to buy in the offline and not in the online store. Most persuading was the product availability, followed by multiple options to touch and feel the product, and the product availability of products that are not available online but that are available offline (see Table 5). …Click & Collect (order online & pick up your order offline) …personal
advice. …my desired product is available. …products that are not available online are available offline …multiple options to touch and feel the product. I do not want to buy in the online store at all. N Valid 1,281 1,281 1,281 1,281 1,281 1,281 Invalid 1,660 1,660 1,660 1,660 1,660 1,660 Mean 3.72 3.44 5.57 4.89 4.91 2.07 Median 4.00 3.00 6.00 6.00 5.00 1.00 Standard deviation 2.24 2.08 1.84 2.10 2.02 1.61
Table 5: Potential reasons to purchase in the offline and not in the online store (measured on a 7-point Likert scale).
In order to make their purchase, most of the respondents used a laptop/desktop (35%), while 4.2% used their smartphone, 4% used their tablet, 0.4% used a different device and 56.4% shopped offline (see Table 6). More interestingly, 59% of the respondents started their customer journey online and 41% started their journey offline, however, 80% ended their journey online and offline. Moreover, the customers always used at least one firm-owned touchpoint (Mfirm_owned = .9986, SD = .03686). On
average, 50% of the respondents were exposed to partner-owned touchpoints (Mpartner_owned = .4978,
SD = .50008), 61% were exposed to competitor-owned touchpoints (Mcompetitor_owned = .6073, SD =
.48844) and 31% were exposed to independent touchpoints (Mindependent = .3128, SD = .46372). The
most often used touchpoint was the physical store (Mphysical_store = .65, SD = .478), while the least often
used touchpoint was the live chat (Mlive_chat = .00, SD = .041). Furthermore, the customers used on
average 4 touchpoints (Mtouchpoints = 3.7042, SD = 2.37738). In particular, customers used a minimum
Mean SD
Journey characteristics
Journey start online Journey start offline Journey end online Journey end offline
5% 41% 80% 80% .49199 .49199 .39899 .39899 Touchpoint usage Firm-owned touchpoints Online store Physical store Catalog Newsletter Online community Mobile app Call center Radio/ tv spot Live chat 100% 58% 65% 18% 17% 2% 7% 1% 3% 0% .03686 494 .478 .388 .376 .125 .248 .116 .179 .041 Partner-owned touchpoints Search engine Brand website Comparison portal Review portal 50% 39% 17% 13% 4% .50008 .487 .373 .338 .203 Competitor-owned touchpoints
Competitor physical store Competitor online store Competitor catalog Competitor newsletter 61% 28% 39% 11% 7% .48844 .447 .487 .308 .251 Independent touchpoints Blog(s), forum Social media
Video portals (e.g. YouTube etc.) Image portals (e.g. Pinterest etc.) News portals and newspaper Offline WOM 31% 2% 6% 4% 1% 10% 20% .46372 .146 .245 .188 .117 .297 .397 Other touchpoints
Mobile device usage Desktop device usage Number of touchpoints used
8.2% 35% 3.7067 .669 .669 2.37622
Table 6: Summary of customer journey characteristics (means and standard deviations).
T-Test Independent Samples
competitor-owned touchpoint users and non-competitor-owned touchpoint users, and finally, between independent touchpoint users and non-independent touchpoint users (see Table 7).
Mean SD
Offline-to-online switchers vs. offline channel users § Offline-to-online switchers
§ Offline channel users -.11 .01 1.03 1.00
Online-to-offline switchers vs. online channel users § Online-to-offline switchers
§ Online channel users
.06 .21
.96 1.00 Partner-owned TP users vs. non-firm-owned TP users
§ Partner-owned TP users
§ Non-partner-owned TP users -.05 .05 1.01 .99
Competitor-owned TP users vs. non-firm-owned TP users § Competitor-owned TP users
§ Non-competitor-owned TP users -.05 .03 1.01 .99
Independent TP users vs. non-independent TP users § Independent TP users
§ Non-independent TP users -.06 .14 1.03 .91
Table 7: Significant independent samples t-tests, p < .05.
5.1
Multivariate Regression Analysis (Model 1)
Testing the Assumptions
In order to perform a multivariate regression, several assumptions must hold. After I confirmed that I have one dependent variable that is measured at the continuous level (journey experience), and more than two independent variables that are measured either at the continuous or nominal level, I could proceed with testing the other six assumptions, such as (a) there should be independence of errors (residuals); (b) there should be a linear relationship between the predictor variables and the dependent variable, (c) there should be homoscedasticity of residuals (equal error variances), (d) there should be no multicollinearity; (e) there should be no significant outliers; and (f) the errors (residuals) should be approximately normally distributed.
