Customer experience – The relevance of touchpoints and stages within the customer journey

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Customer experience

The relevance of touchpoints and stages within the customer



University of Groningen and

Westfälische Wilhelms-Universität Münster

Master Thesis

Customer experience

The relevance of touchpoints and stages within the customer journey

Faculty: Faculty of Economics and Business Supervisors: Prof. Dr. Sonja Gensler

Prof. Dr. Peter Verhoef Dr. Arnd Vomberg

Issuing Date: 8th February 2019 Due Date: 17st June 2019



Management Summary

The retail industry is one of the most dynamic industries and requires managers to keep their strategies up to date with new scenarios and technologies. Digitalization is changing everyday lives of consumers and within firms. However, digitalization only seems to have partially arrived within the industry of groceries. Consequently, knowledge from other industries about targeting customers and shaping the customers’ journeys are not directly transferrable to products like coffee, snacks, beer or wine.

Thus, the aim of this thesis is to help brand managers to allocate their budgets appropriately by (1) identifying the touchpoints and their sequence, which customers perceive as relevant in the digitalized retail industry for groceries and by (2) determining the impact of each stage of the customer journey on the overall customer experience. Additionally, I explore the impact of product involvement and prior experience on both the relevance of touchpoints and purchase stages.

This thesis confirms that offline touchpoints still occur more often than their online counterparts. This holds true across all three stages of the customer journey: awareness creation and information gathering pre-purchase, the actual purchase of the product and the post-purchase engagement afterwards. Consequently, managers must analyze their customers’ needs carefully, when aiming to digitalize their business in one of these industries. Additionally, customers consciously perceive online touchpoints pre-purchase at a later point in time than offline touchpoints, suggesting that offline touchpoints dominate in creating awareness and online touchpoints help customers to deepen their knowledge about product attributes.

From the customers’ perspective the most relevant touchpoints, i.e. the moments of truth, are pre-purchase as well as post-purchase customer-owned followed by social/independent/ external touchpoints. Thus, the direct influence of a brand and its partners through touchpoints seems to be less effective for snacks, coffee, beer, and wine. However, marketing campaigns aiming to stimulate positive Word of Mouth or the consumption of products in public are worthwhile. This is further supported by the finding that a satisfying post-purchase experience has the largest direct impact on the overall customer satisfaction.




Being grown up in a digitalized world, I can hardly imagine an everyday life without the Internet. For instance, it is a matter of course for me to shop online. However, I noticed that my shopping habits for groceries differ from other products. I started thinking about the relevant touchpoints when buying food or drinks. Which are they and who influences them? What determines whether we are satisfied with our customer journey and the experiences we made or not?

I thank Professor Doctor Sonja Gensler and Professor Doctor Peter Verhoef for giving me the chance to explore these questions by writing my master’s thesis. I further thank Jan Moritz Karthaus and Nerina Dombrink for their valuable recommendations.


IV Table of Contents Page Management Summary I Preface III Table of Contents IV List of Figures VI

List of Tables VII

List of Abbreviations VIII

1 Introduction 1

2 Theoretical background and previous research 3

2.1 Effectiveness of marketing 3

2.2 Customer journey 6

2.3 Customer touchpoints 8

2.4 Customer experience 9

2.5 Approaches for measuring customer experience 11 2.6 Marketing and customer experience across the customer journey 13

3 Conceptualization 17

3.1 Conceptual framework 17

3.2 Hypothesis development 18

3.2.1 Touchpoint usage, order and relevance 18 3.2.2 Development of customer satisfaction across the stages 19 3.2.3 The role of involvement and prior experiences 22

4 Methodology 22

4.1 Data collection 22

4.2 Measurement of customer touchpoints 23

4.2.1 Identification of relevant touchpoints 23



4.3 Measurement of customer experience 26

4.3.1 The current overall customer experience 26 4.3.2 The purchase stages of the current and prior experiences 27

4.3.3 Involvement and control variables 28

5 Data analysis and results 29

5.1 Results of data collection and sample reliability 29

5.2 Analysis and results of research focus 1 31

5.2.1 Touchpoint usage and order 31

5.2.2 Touchpoint relevance 34

5.2.3 Involvement and prior experience 35

5.3 Analysis and results of research focus 2 38

5.3.1 Two separate models 38

5.3.2 Testing assumptions for model 1 38

5.3.3 Final model 1 and testing hypotheses 42

5.3.4 Testing assumptions for model 2 44

5.3.5 Final model 2 and testing hypotheses 46

6 Conclusions 49

6.1 Final Discussion 49

6.2 Recommendations for praxis 51

6.3 Generalizability, limitations and recommendations for future research 52

References 55

Appendices 68

Appendix A – Initial list of touchpoints 68

Appendix B – Questionnaire of qualitative pre-study 69

Appendix C – Variable specifications 71

Appendix D – Correlations 74

Appendix E – Model comparisons 75

Appendix F – Plots 80

Appendix G – Model estimation 86

Appendix H – Transcribed protocols of pre-study 93



List of Figures

Page Figure 1: The evolving customer experience along a customer’s journey

with a firm. 5

Figure 2: Conceptual framework. 17



List of Tables

Page Table 1: Previous measurement of marketing across the customer journey

in the digital world. 16

Table 2: Description of sample (n = 262). 30

Table 3: Descriptive statistics of satisfaction variables, NPS and product involvement. 30 Table 4: Pearson Correlation coefficient of IVs and DVs. 31

Table 5: Final list of touchpoints. 33

Table 6: Average relevance of touchpoints per ownership group. 35 Table 7: Average relevance of touchpoints which varies significantly across product

involvement and prior experience. 37



List of Abbreviations

AI Augmented Reality

AIDA attention–interest–desire–action

CLV customer lifetime value

DV dependent variable

EXQ scale scale for measuring customers’ service experience

i.e. id est

IV independent variable

n.a. not applicable

NPS net promoter score

OLS ordinary least squared

ROI return on investment

SEA search engine advertising

SERVQUAL service quality



1 Introduction

Digitalization is changing everyday lives of consumers and within firms. Starting with the advent of the Internet and the introduction of mobile devices such as smartphones, tablets and recently smartwatches, there have been many technological advancements. Thus, consumer behavior and particularly the consumers’ behavior across their entire journey with a firm is evolving (Batra and Keller 2016). As the retail industry is one of the most dynamic industries market operators have to keep their strategies up to date with changing scenarios and new technologies (Kumar, Anand and Song 2017). From a firm’s perspective digitalization provides many challenges as well as opportunities (Lamberton and Stephen 2016; Leeflang et al. 2014).

On the one hand, especially firms operating in the retail industry have to transform their business practices in order to still meet their customers’ needs. During the first years of digitalization marketing literature described the retailing landscape as a multi-channel environment (Neslin et al. 2006). The continuous development and emergence of new channels, specifically mobile channels, caused another disruption of the retail environment and thus, a shift to omni-channel strategies (Rigby 2011; Verhoef, Kannan and Inman 2015). Following Neslin et al. (2006) channels are the points of contact and interaction of firms with their customers. Omni-channel does not only involve more channels than multi-channel but also blurres their borders and the traditional division between two-way and one-way communication channels (Verhoef, Kannan and Inman 2015). Additionally, the importance of customer touchpoints is increasing in the omni-channel context (Baxendale, Mcdonald and Wilson 2015; De Haan, Wiesel, and Pauwels 2016). A touchpoint is closely related to the concept of channels as it represents each individual, verbal or nonverbal, point of contact a consumer has with a firm (Duncan and Moriarty 2006; Homburg et al. 2015). For firms it is important to provide a seamless shopping experience across channels and touchpoints since customers use them interchangeably, as Gensler, Neslin and Verhoef (2017) and Herhausen et al. (2019) show by providing evidence for consumer behaviors as showrooming and webrooming, respectively.


2 Within the retail industry, the market of groceries is not easily comparable to other product categories as the offline retail network is still very dominant (Herhausen et al. 2019). Digitalization is only beginning to become an influencing factor. For instance, already in 1995 Amazon, the pioneering company in online distribution, started with books. Today, Amazon is selling a vast number of different products and services to its clients. However, it was not until 2017 that Amazon entered the German food retail industry by introducing Amazon prime now and Amazon fresh (Amazon 2019). Yet, it is not clear whether they will succeed in disrupting the food retailing as they did in so many other industries. In this dynamic digital environment brand managers must decide how to market and distribute their products. The existence of plenty different national brands as well as private labels brands offering good quality for lower prices (Steenkamp and Sloot 2019) and the fact that switching costs for customers are low (Meyer‐Waarden and Benavent 2009), make the market of groceries highly competitive. Thus, firms’ strategies to differentiate themselves as innovators in marketing and sales pays off (Buck et al. 2019). Identifying the best performing channels and touchpoints and allocating the budgets appropriately is crucial for managers to market their brands successfully.

The concept of dual value creation implies that firms have to deliver value to the customer first in order to extract value for the firm (Kumar and Reinartz 2016). Therefore, taking over the customer perspective (Shah et al. 2006) and analyzing their experience along the customer journey (Schmitt 2011; Lemon and Verhoef 2016) is neccessary. Lemon and Verhoef (2016) divide the customer journey in three stages: pre-purchase, purchase and post-purchase, and differentiate touchpoints in brand-owned, partner-owned, customer-owned, and social/independent/external. Many studies analyze the impact of marketing channels and touchpoints on financial and behavioral metrics, like sales or conversion rates (Anderl et al. 2016; Kumar et al. 2016). These are often short-term focused, capture the customer journey only partially and do not consider the customers’ perspective. A common practice to elaborate on customer perceptions are customer feedback metrics, i.e. customer satisfaction, the net promoter score and the customer effort score (Lemon and Verhoef 2016). However, studies which follow these approaches to determine the effectiveness in the current digital environment, again do not capture all stages of the journey (Herhausen et al. 2019) or do not consider online as well as offline touchpoints (McColl-Kennedy et al. 2019).


3 impact of each stage of the customer journey on the overall customer experience. Additionally, I will explore the impact of product involvement and prior experience on both the relevance of touchpoints and purchase stages.

The study is structured as it follows: Chapter 2 provides an overview of the theoretical background and existing research. The relevant content was identified by reviewing recent studies published in top marketing journals1, online and offline databases, as well as cross-reference searches. More specifically, the chapter introduces the topics of determining the effectiveness of marketing, the concept of the customer journey, customer touchpoints and customer experience. It further summarizes how previous researchers measured customer experience and the effectiveness of marketing across the customer journey and ends by identifying a research gap. Chapter 3 presents my conceptual model and the hypothesis development, followed by the variable measurement in chapter 4 and the analysis of the gathered data as well as a presentation of the results in chapter 5. The study finishes with a final discussion, recommendations for praxis, limitations and future research opportunities.

2 Theoretical background and previous research

2.1 Effectiveness of marketing

Our increasingly digitalized world offers many new opportunities for marketers. These various options make the accountability of marketing investments especially important. However, not only in the past, top management has often treated marketing expenditures as short-term costs rather than long-term investments (Rust, Lemon and Zeithaml 2004). Today, there is still a trend to ignore the long-term focus by only considering short-term performance metrics, such as conversion rates in click stream data (Kannan, Reinartz and Verhoef 2016).

However, “Customers are the lifeblood of any organization.” (Gupta and Zeithaml 2006, p. 718) and firms need customers to create revenues, profits and finally market value. According to the dual concept of customer value, firms first need to provide value to their customers to be able to extract in a second step value for themselves and in order to develop a sustainable business (Kumar and Reinartz, 2016). Kumar et al. (2010) emphasize that customers provide value to firm in many ways. Next to creating financial value, i.e. the customer lifetime value (CLV), they also provide referral, influencer and knowledge value. Kumar and Reinartz (2016) undermine the difference between the value which firms believe to deliver to their customers

1 The term “top marketing journals” refers to the VHB-JOURQUAL3 ranking of scientific journals:


4 and the value which their customers perceive. Accordingly, customers form their judgements only based on the benefits created by the product attributes which they notice. The form and number of these attributes may deviate from what the firm is objectively offering. Consequently, in order to extract value from customers, firms first must understand their customers’ perceptions.

In line with these insights, researchers identified a shift among marketing practitioners from product centricity to customer centricity, meaning that firms move from focusing on internal aspects, transactions and simply selling their products to focusing on external aspects, relationships and serving their customers’ needs (Kumar, Anand and Song 2017; Shah et al. 2006). Additionally, the usage of performance metrics evolves and customer satisfaction and the CLV become highly relevant. Many studies confirm the positive effect of customer centricity on the firm’s financial performance (Kumar and Shah 2009; Rust, Lemon and Zeithaml 2004; Venkatesan and Kumar 2004).


5 Figure 1: The evolving customer experience along a customer’s journey with a firm.

Legend: The circles are representing the touchpoints a customer has with the firm. Their size indicates their perceived relevance for the customer. The capital letters inside the circles stand for B = brand-owned, P = partner-owned, S = social/independent/external, C = customer-owned. 1 The process of value delivery exists for all touchpoints but is shown

here only once for simplification purposes. Source: author’s own illustration adapted from Lemon and Verhoef (2016), p. 77. S C




B B P C S S S C C P P B B P V a lu e d e li v e ry 1

Pre-purchase Purchase Post-purchase

Previous experience

Future experience


6 2.2 Customer journey

A first attempt to model the customer’s way to purchase, the purchase itself and its evaluation afterwards was suggested by Horward and Sheth (1969). The attention–interest–desire–action (AIDA) model by Lavidge and Steiner (1961) describes the role of advertising during this journey, from attention, over interest and desire to action. With the advancements in technology and the increasingly digitalized world, the amount of new media and opportunities for advertising is exploding. Customers change the kind of media sources they use and their habits in doing so (Batra and Keller 2016). Thus, the contact points between firms and their customers are changing along the entire customer journey. During the first decade of the 21st century, the concept of multi-channel retailing was the dominant approach. Neslin et al. (2006, p. 96) define the multi-channel customer management as the “design, deployment, coordination, and evaluation of channels to enhance customer value through effective customer acquisition, retention, and development.” These authors, as well as Neslin and Shankar (2009), provide detailed overviews on this topic. Channels are the contact points through which a customer interacts with the firm (Neslin et al. 2006). It was mainly the emergence of online channels which led to significant changes in the retail industry (Verhoef, Kannan and Inman 2015). Retailers thought about whether to invest in the new Internet-based channels (Geyskens, Gielens, and Dekimpe 2002) and how to manage their customers as well as their retail mix in this multi-channel environment (Neslin et al. 2006).


7 inform themselves online but shop offline, are highly relevant (Gensler, Neslin and Verhoef 2017; Jing 2018). Since customers also do both simultaneously, i.e. searching online for information while being in a store, Verhoef, Kannan and Inman (2015) conclude that it is crucial for firms to offer their own channels which can be used interchangeably, e.g. by having tablets in their store. Furthermore, they call for broadening the scope of channels by including customer touchpoints. Their concept is explained in detail in the following chapter 2.3.

In accordance with prior literature (Howard and Sheth 1969; Neslin et al. 2006; Pucinelli et al. 2009), Lemon and Verhoef (2016) divide the customer’s journey with a firm in three stages: pre-purchase, purchase and post-purchase (figure 1). Theoretically, the entire customer experience before the purchase would belong to the pre-purchase stage. The authors emphasize that, in practice, this stage begins when the customer2 recognizes his need and starts searching. The first stage ends immediately before the purchase event itself takes place. This is the second stage of the customer’s journey which comprises actions such as choice, ordering, and payment. Although the purchase stage is the shortest in time, there is a significant amount of research about it. For instance, Chernev, Böckenholt and Goodman (2015) provide a conceptual review and meta-analysis about choice overload. Consumers are facing a vast amount of options which can be overwhelming. Consequently, some feel less satisfied with their choice or even to not purchase at all (Iyengar and Lepper 2000). Third, there is the post-purchase stage in the customer journey. Costumers consume or use the acquired products. Depending on their experience, customers may become loyal and intent to repurchase from the firm (Bolton 1998; Heitmann, Lehmann and Herrmann 2007; Herhausen et al. 2019). There are different forms of customer engagement, such as positive or negative Word of Mouth (WoM) (Heitmann, Lehmann and Herrmann 2007; Van Doorn et al. 2010). Taken together, the three stages form the entire current customer experience during his journey with the firm, preceded by previous experiences and followed by future experiences with the same or different product or brand (figure 1).

The emergence of digital technologies has empowered costumers. They can easily search for information on the Internet and compare products. Furthermore, they can immediately share their (dis)satisfaction with others (Foroudi et al. 2018; Jing 2018). Firms can also profit by using the new technologies to adapt their processes and create competitive advantages. Instead of reactively following their customers throughout their journeys, firms can actively shape the

2 Throughout this thesis the customer is considered to be male to simplify the explanations. This should not present


8 journeys by treating them like products which provide real value to customers (Edelman and Singer 2015). Therefore, it is crucial to know at which stage of the journey the customers are and which contact points with the firm, i.e. touchpoints, are the most relevant within each stage.

2.3 Customer touchpoints

Within each stage, customers get in contact with the firm via various touchpoints (Lemon and Verhoef 2016). A touchpoint is closely related to the concept of channels as it represents each individual, verbal or nonverbal, contact point a consumer has with a firm (Duncan and Moriarty 2006; Homburg et al. 2015). Touchpoints may include channels but are defined as a broader construct (Baxendale, Mcdonald and Wilson 2015; Herhausen et al. 2019). In contrast to channels in the multi-channel phase, touchpoints cannot always be clearly differentiated as one-way or two-one-way communication (Verhoef, Kannan and Inman 2015). Instead, they are categorized by their owner or initiator. Among others, there are the paid-owned-earned classification used by Srinivasan, Rutz and Pauwels (2016) and the customer-, firm- or other-initiated classification (Li and Kannan, 2014; Anderl et al. 2016). This thesis follows the approach of Lemon and Verhoef (2016) and later Herhausen et al. (2019), because they do not only consider online touchpoints as the previously mentioned authors but consider online and offline touchpoints. Both are relevant to capture the customer journey entirely and are, thus, part of this thesis. The authors categorize online as well as online touchpoints by their owners as follows: brand-owned, partner-owned, customer-owned, and social/independent/external (figure 1). In order to assign a certain touchpoint to one of the four categories, one can think about who exerts control over the touchpoint. For instance, the website of L’Oréal Paris is owned by the brand itself, whereas the product in the shelf is owned by a partner of the brand, a drugstore. The moment of usage of one of the products is owned by the customer and a social media post about L’Oréal Paris is external in many cases. However, sometimes the distinction between the four categories may become blurred. When a customer uses “Makeup Genius”, which is a mobile app working with Augmented Reality (AI) to let customers try makeup by using their smartphones instead of applying it on their skin (Trefis Team 2016), it is not clear whether L’Oréal Paris owns the touchpoint, or the customer does. If updates by Google or Apple require L’Oréal Paris to improve the functionality of their app, also partners may influence the touchpoint (Lemon and Verhoef 2016).


9 a multi-channel management. A a consequence of digitalization, mobile channel and omni-channel management became relevant. Many new technologies, such as smartwatches, AI, virtual reality, or service robots, are changing the retail industry. This leads to an even larger amount of customers’ touchpoints with a firm (Baxendale, Mcdonald and Wilson 2015; De Haan, Wiesel, and Pauwels 2016; Foroudi et al. 2018; Inman and Nikolova 2017; Van Doorn et al. 2017; Wang, Malthouse and Krishnamurthi 2015; Xu et al. 2016). Especially, due to their interchangeable usage in the omni-channel world and firms aiming to create a seamless customer experience (Verhoef, Kannan and Inman 2015) the different types of touchpoints are interacting with each other (Srinivasan, Rutz and Pauwels 2016; Wiesel, Pauwels and Arts 2011). Thus, identifying patterns in touchpoint usage would provide valuable insights for marketing managers (Herhausen et al. 2019; Li and Kannan 2014). Moreover, to allocate marketing budgets appropriately, managers need to identify the ‘moments of truth’, i.e. the most relevant touchpoints for creating their customers’ experience (Klaus and Maklan 2012; Lemon and Verhoef 2016; Voorhees et al. 2014).

2.4 Customer experience

In the 1960s Kotler (1967) and Howard and Sheth (1969) developed the initial seminal theories on marketing and consumer behavior making these years to the roots of customer experience research (Lemon and Verhoef 2016). Mehrabian and Russell (1974) elaborated on the experience economy and experiential marketing. The concept of customer experience itself was introduced by Holbrook and Hirschman (1982).


10 Levy and Kumar (2009) build on that model and show the relevance of customer experience in retailing as it mediates the impact of firm-controlled factors, such as price, promotion, merchandise, supply chain, and location, on marketing and financial metrics and is influenced by macro factors, i.e. economic and financial uncertainty. As the digitalization is going forward, customer journeys are evolving and therewith the stages and touchpoints creating the customer experience (Lemon and Verhoef 2016). Thus, this thesis follows the revised definition 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” (Lemon and Verhoef 2016, p. 71).


brand-11 owned, partner-owned, customer-owned, or social/independent/external in order to influence them accordingly.

2.5 Approaches for measuring customer experience

Already in 1994 Holbrook developed a scale for the consumption experience by measuring the intrinsically motivated and hedonic enjoyment. Babin et al. (1994) differentiate between hedonic and utilitarian values. Berry et al. (2002) focus more on the aspect of expectation management by sending the right ‘clues’ to the customer. As integral part of the entire customer experience, Brakus et al. (2009) develop a scale to measure the brand experience. Using a multiple-item scale for measuring customers’ service experience (EXQ scale) was suggested by Klaus and Maklan (2012, 2013). They identify four dimensions as relevant: product experience, outcome focus, moments of truth and peace of mind. Deshwal (2016) applies the EXQ scale in the Indian retailing sector. Bustamante and Rubio (2017) introduce the in-store customer experience scale for offline retailers. Recently, Kumar and Anjaly (2017) proposed a scale to measure the online customer experience in the post-purchase stage of the customer journey.

However, the large amount of different approaches reveals that there is no established scale to measure the entire online and offline customer experience amongst researchers and practitioners, such as service quality (SERVQUAL) by Parasuraman, Zeithaml, and Berry (1988) (Lemon and Verhoef 2016). Therefore, Lemon and Verhoef (2016) emphasize the widespread practice of measuring customer perceptions by customer feedback metrics. The authors clarify that customer feedback metrics usually capture only parts of the entire customer experience, but that at the same time, it is their simplicity which make them easily understandable and often used in practice.


12 Verhoef 2008). For instance, customer satisfaction can be influenced by expectations created through social media reviews (Ramanathan, Subramanian and Parrott 2017). There are many further studies about the antecedents customer satisfaction and its impact on customer behavior and firm performance (e.g. Anderson, Fornell and Lehmann (1994); Anderson and Mittal 2000; Bolton and Drew 1991; De Haan, Verhoef and Wiesel 2015; Gupta and Zeithaml 2006; Herhausen et al. 2019; Ittner and Larcker 1998; Rose et al. 2012). Customer satisfaction correlates strongly with customer behavior and financial performance (Gupta and Zeithaml 2006; Rose et al. 2012). Petersen et al. (2018) identify customer satisfaction as especially appropriate to measure marketing effectiveness.

Whereas customer satisfaction is classified as backward-looking, Reichheld (2003) introduced the net promoter score (NPS), a more forward-looking metric which captures loyalty (Gupta and Zeithaml 2006; Zeithaml et al. 2006). Instead of complex satisfaction surveys Reichheld (2003) proposed to solely ask customer about their willingness to recommend the firm on a scale from 0 to 10. Also, seminal theories in the domain of quantifying customer values highlight the importance customers influencing others and referring the firm (Kumar et al. 2010). The NPS categorizes customers, who answer with a number between 0 and 6, as detractors, those, who answer with a 7 or 8, as passively satisfied and those, who answer with a 9 or 10, as promoters. Whereas only some researchers classify the NPS as a relevant metric (Petersen et al. 2009), many firms adopted the NPS, which may be either due to its simplicity and the intuitive aim of increasing the number of promoters while decreasing the number of detractors or because firms often do not accomplish in changing their customers satisfaction scores significantly (Lemon and Verhoef 2016).

Dixon, Freeman and Toman (2010) introduced the customer effort score which asks customers to indicate how much effort they had to put forth to handle their request. The authors claim that this customer feedback metric would be a better predictor of repurchase intentions and higher customers’ spending than satisfaction and NPS.


13 limited. Instead, they recommend implementing dashboards of multiple customer feedback metrics including the average customer satisfaction and the top-2-box satisfaction.

De Haan, Verhoef, and Wiesel (2015) investigate the predictive power of some traditional as well as transformed customer feedback metrics for customer retention. Their results differ from those of the former study in that they find only small differences between the NPS and customer satisfaction, which both strongly outperform the customer effort score. Additionally, the results of De Haan, Verhoef and Wiesel (2015) depict that transformed metrics, i.e. the top-2-box satisfaction, should be considered to capture nonlinear experiential effects like customer delight (Oliver, Rust and Varki 1997, Anderson and Mittal 2000). Finally, the authors conclude as well that combining different metrics improves performance.

According to Gupta and Zeithaml (2006) these three metrics are perceptual and not directly observable. Contrary, customer acquisition, customer retention, cross-selling, the CLV and customer equity are also common customer metrics but rather behavioral and thus directly observable. Consequently, in order to understand how a customer experiences and perceives what is offered to him, finding ways to measure perceptual (Gupta and Zeithaml 2006) customer feedback metrics is recommended (Lemon and Verhoef 2016; McColl-Kennedy et al. 2019).

Summarizing the above, prior findings show that customer satisfaction and the NPS are the two most appropriate customer feedback metrics to measure customer experience. They are strongly associated with all dimensions of customer experience and perform well in predicting behavioral and financial metrics. Whereas the concept of customer satisfaction has a strong theoretical background, the NPS is supported by the vast number of firms using it in their daily business. Instead of relying on a single metric, combining and transforming several metrics improves their performance, considers the multidimensionality of customer experience, and captures potential non-linear effects.

2.6 Marketing and customer experience across the customer journey


14 In today’s fast-moving world, technological developments continuously generate new types of customer touchpoints. They are influencing each other, and the emergence of a new touchpoint may lead to a decrease in relevance of the established ones (Bolton et al. 2018; Foroudi et al. 2018). Touchpoints are the crucial moments under investigation when determining the effectiveness of marketing across the customer journey. In order to avoid relying on results which are outdated, managers should focus on studies which take the current digital environment with its various touchpoints into consideration. Table 1 presents a selection of meaningful studies published in A+ and A marketing journals3, which elaborate on the effectiveness of marketing across the customer journey in the last decade.

Path-to-purchase or attribution models focus on the interplay of online and offline touchpoints. Using aggregated sales data, De Haan, Wiesel, and Pauwels (2016) compare the effectiveness of nine forms of online and offline advertising on traffic, conversion and revenue. Similarly, Srinivasan, Rutz, and Pauwels (2016) examine the impact of traditional marketing mix instruments and online consumer activity metrics on sales. Clickstream data provides insights on customers’ behavior at the individual level. For instance, the last-click attribution metric identifies the last touchpoint of each customer as leading to the final purchase (Berman 2015; Kannan, Reinartz and Verhoef 2016). Since this heuristic ignores interaction and spillover effects, researchers developed more holistic attribution modelling approaches considering the pre-purchase and purchase stage of the online customer journey (Anderl, Schumann and Kunz 2016; Kannan, Reinartz and Verhoef 2016; Li and Kannan 2014). Only a few studies link online and offline individual level customer data to examine the effectiveness of touchpoints on sales (Kumar et al. 2013). However, they do not investigate the patterns of usage across touchpoints. Kumar et al. (2016) integrate this in their analysis but, like the previous study, their offline data solely consists of sales data, ignoring offline touchpoints in the pre-purchase stage.

Baxendale, Mcdonald and Wilson (2015) used the real-time experience tracking approach via mobile handsets. They measure frequency and valence of touchpoints occurring during the post-purchase stage. Combining this method with two online surveys, they can control for demographics and are able to determine the impact of the touchpoints on brand consideration. Survey data has the advantage of providing more comprehensive insights in the customers’ perspective than clickstream data. That is why McColl-Kennedy et al. (2019) suggest using text-mining tools to let them emerge from open-ended questions in survey data. However, their

3 “A+ and A marketing journals” are the highest ranked marketing journals according to the VHB-JOURQUAL3


15 approach does not provide them with usage patterns of touchpoints. In addition, their categories of touchpoints are only offline and rather broad as they do not analyze them on a granular level like a banner advertisement or personalized mailing. Finally, De Haan et al. (2018) and Herhausen et al. (2019) are one of the very few authors who investigate explicitly on perceptions about product or category related attributes which may also influence the relevance of touchpoints for customers.

In sum, there are various approaches to determine the effectiveness of different parts of the customer journey, often with a strong focus on short-term outcomes, like sales or conversion rates. However, the concept of dual value creation (chapter 2.1) emphasizes that a thorough understanding of how to deliver value to the customer is a necessary antecedent of extracting sustainable value for the firm. Taking over the customers’ perspective implies that the entire customer journey, including all stages, is relevant for creating a customer experience that matters (chapter 2.2). Especially, in today’s digitalized omni-channel environment the interplay of various online and offline touchpoints as well as usage pattern across touchpoints are important (chapter 2.3). Analyzing narrowly defined touchpoints reveals more detailed and thus more valuable insights for managers. Further, an in-depth understanding of the customer experience cannot be achieved by analyzing objectively sales data or conversion rates but by asking customers about their perceptions and customer specific factors, like their degree of product involvement (chapter 2.5).


16 Table 1: Previous measurement of marketing along the customer journey in the digital world.

Authors Outcome variables

Touchpoints Customer perspective Customer-product relationship Stages of customer journey Online / offline Touchpoint specification Usage patterns across touchpoints Anderl et al. (2016) Conversion Pre-purchase,

purchase Online Narrow Yes No No

Anderl, Schumann and Kunz

(2016) Time to purchase


purchase Online Narrow Yes No No

Baxendale, Mcdonald and

Wilson (2015) Brand consideration Pre-purchase Both Narrow No Yes No

De Haan et al. (2018) Conversion Pre-purchase Online Narrow Yes No Perceived risk

De Haan,Wiesel, and Pauwels (2016)

Online traffic, Conversion,



purchase Both Narrow No No No

Herhausen et al. (2019) Customer Loyalty Pre-purchase,

purchase Both Narrow Yes Yes Involvement

Kumar et al. (2013) Sales, ROI, WoM Pre-purchase Online Narrow No No No

Kumar et al. (2016) Sales Pre-purchase Both Narrow Yes Yes No

Li and Kannan (2014) Purchases Pre-purchase,

purchase Online Narrow Yes No No

McColl-Kennedy et al. (2019) Customer satisfaction

Entire journey for some customers

Online Broad No Yes No

Srinivasan, Rutz, and Pauwels

(2016) Sales Pre-purchase Both Narrow Yes No No

This thesis Customer satisfaction, NPS


journey Both Narrow Yes Yes Involvement



3 Conceptualization

3.1 Conceptual framework

The conceptual framework of this thesis is based on the idea that the customer experience evolves along a customer’s journey with a firm as shown in figure 1. The conceptual framework combines the customer journey concept (chapter 2.2) with findings about the formation of customer experience (chapter 2.5). Figure 2 proposes that each stage of the customer journey is characterised by its specific touchpoints which can be brand-, partner-, customer-owned or social/independent/external and differ in their relevance. What customers experience at each stage can be assessed by the customers’ satisfaction (chapter 2.5). These levels of satisfaction then lead to an overall judgement, the current overall customer experience. In addition, I propose that the variation in levels of satisfaction at the three stages has an impact on the current overall experience which is measured by customer satisfaction and the NPS. Since perceptions of customer experience are subjective (Verhoef et al. 2009), I do not only control for product categories but also for sociodemographic customer characteristics. Moreover, I investigate the role of product involvement and prior experience in the process of touchpoints usage and experience creation.

Figure 2: Conceptual framework.


18 3.2 Hypothesis development

3.2.1 Touchpoint usage, order and relevance

Researchers call for measuring the sequential order of touchpoints (Batra and Keller 2016; Baxendale, Mcdonald and Wilson 2015), because customers show browsing behaviour across different touchpoints (Xu et al. 2016). Marketing activities influence each other across offline and online channels (Wiesel, Pauwels and Arts 2011). For example, during the pre-purchase stage the very first touchpoints lead to general product awareness, whereas the following touchpoints make the product more salient and deepen the customers’ knowledge about product attributes (Lavidge and Steiner 1961). Thus, knowing the order in which customers perceive the different touchpoints would enable brand managers to provide the relevant information at the appropriate touchpoints so that they are interacting most efficiently and effectively. However, only a few studies took that macro perspective (Batra and Keller 2016). That is why, I will investigate the sequence of touchpoints. Due to limited prior findings in this domain, I do not formulate explicit hypotheses.

During the pre-purchase stage customers recognize their needs and collect information. It starts with an initial impulse, like a TV advertisement or seeing peers consuming a product, making a customer aware of a product or his need (Batra and Keller 2016; Howard and Sheth 1969; Lavidge and Steiner 1961). Hence, these touchpoints are more likely to be owned by other market actors than the customer himself. When it comes to deepening the product knowledge the customer can either still passively receive information or actively search for it at different touchpoints. However, the purpose of providing new information to the customer implies that the touchpoints may be more valuable if they are not customer-owned. It may either be the brand itself or in cooperation with a partner who generates the information. Additionally, social/external/independent market actors further distribute this information and can add their own opinion, for example through blog posts on the Internet, which may provide valuable information for potential customers. Thus, I stipulate hypothesis 1a:

H1a During the pre-purchase stage customer-owned touchpoints are the least relevant.


19 When customer tell their friends about the product, use social media, or visit review sites during the post-purchase stage, it is mainly to share their experiences (Pansari and Kumar 2017). Independently of whether the engagement takes place online or offline (Van Doorn et al. 2010), customers actively reflect about the products. The same holds for more purchase related customer-owned touchpoints, such as the decision to return a product (Wood 2001). That is why, customer-owned touchpoints may be especially relevant in determining the level of post-purchase satisfaction. Customers may also perceive brand-owned, partner-owned or social/external/independent touchpoints, like banner adds, loyalty programs, or peer observation. However, they do not necessarily comprise the active reflection about the own experience to the same extent as the customer-owned touchpoints do. Firstly, since customers already made their own experiences, those may be of higher importance when deriving judgements than the image of the product created by other market actors. Secondly, the active customer engagement might make the related touchpoints more salient than touchpoints, like banner ads, which customers receive only passively. Thus, I suggest the following hypothesis:

H1b During the post-purchase stage customer-owned touchpoints are the most relevant.

3.2.2 Development of customer satisfaction across the stages

Customer experience and customer satisfaction are cumulative in nature (Bolton 1998; Frow and Payne 2007; Jüttner et al. 2013; Rose et al. 2012) and develop throughout the customer journey (Stein and Ramaseshan 2016). Each stage of the customer journey is important for creating the overall customer experience (Lemon and Verhoef 2016). By assigning the customer’s levels of satisfaction at different points in time with individual weights they lead to the customer’s overall evaluation (Bolton 1998; Van Doorn and Verhoef 2008). Thus, if a customer is highly satisfied during the pre-purchase, purchase and post-purchase stage, he will probably judge the overall experience more positively, than if the experience in one of the stages has been less satisfying.

H2a Pre-purchase satisfaction positively influences the current overall customer experience. H2b Purchase satisfaction positively influences the current overall customer experience. H2c Post-purchase satisfaction positively influences the current overall customer experience.


20 satisfaction further implies that also the customer satisfaction derived from prior experiences is likely to influence the current overall customer experience.

H2d In case of prior experiences, the average level of customer satisfaction during these experiences

positively influences the current overall customer experience.

As touchpoints influence each other across stages of the customer journey (Batra and Keller 2016; Wiesel, Pauwels and Arts 2011), the purchase stages may also influence each other. Thus, customers may not only derive their level of satisfaction from their experiences at the current stage. Additionally, it may be influenced by the level of satisfaction perceived during the preceding stage. In line with the halo-effect, which posits that a customer’s brand image affects his perceptions of product attributes, a satisfying pre-purchase impression of a product is more likely to be followed by a satisfying purchase experience, because the customer is already primed to perceive something positive (Wirtz 2001; Wirtz and Bateson 1995). According to the anchoring and adjustment model by Hogarth and Einhorn (1992) the prior opinion serves as an anchor and is adjusted by new experiences. Based on this, I expect the direct impact of the pre-purchase and pre-purchase satisfaction on the overall customer experience to be partially mediated by the level of satisfaction of their following stages:

H3a The impact of pre-purchase satisfaction on the current overall customer experience is mediated by

the level of purchase satisfaction.

H3b The impact of purchase satisfaction on the current overall customer experience is mediated by the

level of post-purchase satisfaction.

H3c In case of prior experiences, the impact of average satisfaction levels during prior experiences on the

current overall customer experience is mediated by the level of pre-purchase satisfaction.


21 overview of studies who found different forms of asymmetries in the relationship between past and current service satisfaction. The authors further suggest that in a service context the relationship depends on the existence of negative critical incidents and the type of satisfaction (service, attribute, or price) under consideration. However, since the present thesis aims to explain the formation of the current overall customer experience by measuring the more holistic construct of customer satisfaction for products in the food industry, I will investigate which form of (non-)linear relationship is the most appropriate.

According to the satisfaction disconfirmation theory (Oliver 1980), satisfaction depends on the prior expectations and the difference between expectations and current perceptions (Anderson 1994; Anderson and Sullivan 1993). As the focus of this thesis is to explain the development of satisfaction levels within the current customer experience by distinguishing three purchase stages, the theory suggests that variation in satisfaction levels between different purchase stages may influence the overall evaluation. Since customers care more about extreme experiences (Oliver, Rust and Varki 1997; Van Doorn and Verhoef 2008) and in line with the theory of contrast and assimilation effects (Bolton 1998), customers may weigh satisfaction levels at a specific purchase stage more heavily, if they deviate largely from the preceding level, than if the differences are small. For instance, customers who were highly satisfied when purchasing a product, but had a dissatisfying experience post-purchase, may become heavily disappointed. Consequently, customers may weigh the dissatisfying post-purchase experience more heavily and perceive the overall experience less positively, than they would with less variation in satisfaction levels. Similarly, customers may weigh their post-purchase experience more heavily, if it is much more satisfying than their purchase experience. This expectation is supported by the role of customer delight in building customer satisfaction (Anderson and Mittal 2000; Oliver, Rust, and Varki 1997) which implies that satisfaction levels which significantly exceed prior satisfaction levels improve the overall perception even further. Thus, I expect that large differences in satisfaction levels between two successive stages strengthen the impact of the second stage on the overall customer experience.

H4 Customers weigh satisfaction levels at a specific stage more heavily, if they deviate largely from the


22 3.2.3 The role of involvement and prior experiences

If a customer is highly involved with a product, it is of higher value to him (Zaichkowsky 1985). That is why, involvement influences the consumption experience (Mano and Oliver 1993). Involvement is one of the core consumer moderators in the context of customer experience creation (Verhoef et al. 2009) as well as satisfaction (Pansari and Kumar 2017) and influences touchpoint usage (Herhausen et al. 2019; Jerath, Ma and Park 2014; Konus, Verhoef, and Neslin 2008; Nakano and Kondo 2018). Involved customers research more information about the product, critically evaluate its performance and are more likely to engage post-purchase by sharing their knowledge and experiences (Pansari and Kumar 2017; Suh and Yi 2006). Therefore, I further explore the role of product involvement in determining the relevance of touchpoints and stages within the customer journey.

Since repeated decisions become routinized (Sheth and Parvatiyar 1995), customers may care less about certain touchpoints. If a customer perceives the acquisition of a product as less risky, this may lead to less active information search during the pre-purchase stage (Gensler, Neslin and Verhoef 2017). Consequently, I will also explore the impact of prior experiences on the relevance of different touchpoints within the customer’s journey.

4 Methodology

4.1 Data collection


23 appropriate to represent the diverse grocery market, to explore the role of product involvement in customer experience formation and to guide brand managers in their marketing decisions across touchpoints.To avoid potential misinterpretations, I pre-tested the online survey before I distributed it online via social networks and e-mail to German participants by means of direct messages and the snowball sampling technique (Goodman 1961). By using the critical incident technique (Gremler 2004) participants are asked to remember the purchase of a productduring the last month. They are further asked to write down the name of the product, the brand and the retailer to improve their memory and to ensure that they are referring to a specific purchase journey when answering the remaining questions. Relying on the recall of past behavior is an established practice among studies on decisions and behavior during the customer journey (De Keyser, Schepers and Konus 2015; Gensler et al. 2017; Heitmann, Lehmann, and Herrmann 2007; Herhausen et al. 2019). The short time frame of one month is even shorter than the common practise and thus, minimises the likelihood of a potential recall bias among participants (Gremler 2004; Herhausen et al. 2019).

4.2 Measurement of customer touchpoints

4.2.1 Identification of relevant touchpoints


24 misperception bias, I provided respondents with clear definitions of the touchpoints. Appendix B presents the questionnaire and appendix H the transcribed protocols of the interviews.4 As a third step, I built the questionnaire for the online survey and pre-tested it among 14 participants. Based on these results and personal feedback of the respondents, I finalized the questionnaire. Table 5 provides an overview of the final list of touchpoints as derived from literature, the qualitative pre-study and the pre-test of the online survey. It further classifies the touchpoints based on the conceptual framework of Lemon and Verhoef (2016) according to their ownership. The fourth step comprises the questioning technique of the actual survey. By using a three-step process (Herhausen et al. 2019), the relevance and sequence of touchpoints are measured per stage of the customer journey. First, the participants are provided with the initial list of touchpoints and asked to indicate which touchpoint they have used for each of the three stages. They also have the opportunity to add touchpoints, if they are missing (McColl-Kennedy et al. 2019). Second, the participants are asked to state the relevance of each touchpoint they used. Third, they must order the touchpoints according to the sequence in which they had perceived them. In all three cases the list of touchpoints presented to the participants is randomized to avoid order bias (Ieva and Ziliani 2018). By confronting the participants several times with the touchpoints and providing the possibility to return to preceding questions, I avoid incomplete journey notations (Herhausen et al. 2019).

Hypothesis 1a and 1b are about the relevance of touchpoints and their differences across ownership groups of touchpoints. For example, when comparing, the average relevance of customer-owned with brand-owned touchpoints during the pre-purchase stage, the two variables are related since they belong to the same set of participants. Hence, I test these hypotheses by means of a paired-sample t-test which examines differences in the mean value of a characteristic between two connected samples. Besides, I measure for each touchpoint separately the usage frequency and the average position in the sequence of touchpoints.

4 The personal interviews were conducted in cooperation with another master student, Nerina Dombrink. Each of


25 4.2.2 Involvement and prior experience

Customers are in general less involved with snacks (Geuens, De Pelsmacker and Faseur 2011) but medium to highly involved with coffee, beer and wine (Kim, Lee and Kim 2016; Mittal 1989; Mittal and Lee 1989; Zaichkowsky 1985). However, the degree of involvement does not only vary between product categories but also between customers (Brown, Havitz, and Getz 2007; Mittal 1989). Although the average involvement for groceries is low (Smith and Carsky 1996), food purchases are not necessarily characterized by low involvement (Beharrell and Denison 1995). Due to these reasons, controlling for the product category may not suffice in order to explore the impact of product involvement5 on the relevance of touchpoints. Instead, the degree of product involvement is measured on a seven-point Likert-scale by asking the respondents how important the chosen product is for them (Herhausen et al. 2019; Slama and Tashchian 1985).

A one-way ANOVA analyses the relationship between a metric dependent variable (DV) and one categorical independent variable (IV). Survey data measured on a Likert-scale like the level of product involvement can be treated as metric variable. The dummy variable product category is categorical. Thus, I use a one-way ANOVA to examine whether the level of product

involvement differ across product categories.

Furthermore, I investigate whether the average relevance of touchpoints differs between high and low product involvement, or in case of prior experiences. To increase the size of the subsamples and to receive more balanced better interpretable groups, I categorize the values of

product involvement into high [5;7] and low involvement [1;4]. A second dummy variable, dummy prior experience, measures the information whether the respondents already bought the

product before the purchase experience under consideration. Since the average relevance of touchpoints per ownership group is a metric variable, again a one-way ANOVA is suitable to test for differences across both dummy variables separately and a two-way ANOVA additionally compares the interaction of both product involvement and dummy prior experience.


26 4.3 Measurement of customer experience

4.3.1 The current overall customer experience

Chapters 2.5 and 2.6 reveal that in order to understand the customers’ experience measuring their perceptions is crucial and that perceptual customer feedback metrics are a suitable measurement tool. Among them customer satisfaction and the NPS are the two most appropriate metrics in the retail context since they are strongly associated with all dimensions of customer experience and perform well in predicting behavioral and financial metrics. Whereas the concept of customer satisfaction has a strong theoretical background, the NPS is supported by the vast number of firms using it in their daily business. Since many researchers suggest not to rely on one metric (De Haan, Verhoef, and Wiesel 2015; Keiningham et al. 2007; Morgan and Rego 2006) and due to the disunity on whether customer satisfaction or the NPS is superior (Lemon and Verhoef 2016), this thesis considers both for measuring the current overall customer experience. As the NPS asks for the willingness to recommend, it is related to loyalty intentions (Morgan and Rego 2006). Thus, by measuring both metrics this thesis captures the customer experience more holistically than only one metric would allow. Selecting one backward- and one forward-looking metric (Zeithaml et al. 2006), is also in line with the cumulative nature of customer experience and the purpose to examine the entire journey of a customer with the firm. A further reason to measure NPS in addition to customer satisfaction is that seminal theories in the domain of quantifying customer values highlight the importance customers influencing others and referring the firm (Kumar et al. 2010).

Keeping the questionnaire short in order to avoid biases due to fatigue effects among the respondents, I measure customer satisfaction by a one-item seven-point Likert-scale. The usage of one item is well-established in marketing literature (Baxendale, Mcdonald and Wilson 20156; De Haan, Verhoef and Wiesel 2015; Mittal, Ross and Baldasare 1998; Van Doorn and Verhoef 2008) and supported by empirical findings (Ittner and Larcker 1998; Van Doorn, Leeflang and Tijs 2013). In the following, I use the term overall satisfaction (OS) when referring to the current overall customer experience measured by customer satisfaction. Emphasized by Reichheld (2003) as one of its prevailing advantages, the NPS comprises a one-item scale as well. Respondents are asked to indicate on a scale from 0 to 10 their likelihood to recommend the product to a colleague or friend. A score of 0–6 classifies respondents as “detractors”, a 7 or 8 as “passives”, and a 9 or 10 as “promoters” (Reichheld 2003).


27 4.3.2 The purchase stages of the current and prior experiences

In order to measure the customer experience at each of the three current purchase stages, I use again the one-item seven-point Likert-scale for customer satisfaction as a feedback metric. Customers are asked to indicate retrospectively the level of satisfaction they perceived at each of the purchase stages (S1, S2, S3). As a post consumption evaluation customer satisfaction can

be directed at the overall evaluation of a firm or brand as well as at a specific action (Anderson 1994; Oliver 1980). Since the current experience cannot be considered in isolation, I also ask the respondents whether they had prior purchase experiences with the product. If that was the case, they are further asked how satisfying these experiences were (Van Doorn and Verhoef 2008). Based on this, I construct the following variables: A dummy variable (PX) controlling for differences between customers with or without prior experiences and a second variable (S0)

containing the valence of a potential prior experience. By multiplying S0 with PX, I create the

variable prior experience (S0 PX), which is zero if there was no prior experience and contains

the valence of the prior experience otherwise.

(1) OS = αCS + ß0 PX + ß1.0 S0 PX + ß1.1 S1 + ß1.2 S2 + ß1.3 S3 + εCS

In order to elaborate on a potential nonlinear relationship between the satisfaction at purchase stages and the overall experience, I will test for different nonlinear effects in the model specification. Alternatively, I test the top-2-box satisfaction as recommended by De Haan, Verhoef and Wiesel (2015). The top-2-box satisfaction measures the proportion of respondents per firm who filled in the two highest scoring points of the satisfaction scale (Morgan and Rego 2006). As this thesis is conducted at the customer level, respondents who answered with a score of 6 or 7 are assigned with a 1, all others with a 0 (De Haan, Verhoef and Wiesel 2015).

The conceptual framework (figure 2) proposes that the overall customer experience is influenced by the levels of customer satisfaction at each purchase stage as well as the difference between them. Again, taking into account that customers care more about extreme experiences (Oliver, Rust and Varki 1997; Van Doorn and Verhoef 2008), the dummy variables difference

in satisfaction (DS j-(j-1)) turn 1, only if the absolute difference between two successive satisfaction levels (X j) exceeds a certain threshold (X). j = 1-0 (2-1, 3-2) labels the difference in satisfaction levels of the pre-purchase experience minus prior experience (purchase minus pre-purchase, post-purchase minus purchase):

(2) X j = | S j - S j-1 |


28 The terms six to eight of the model describe the direct effect of a large difference in successive satisfaction levels and terms nine to eleven describe its moderating effect on the impact of the subsequent level of satisfaction on the overall experience:

(3) OS = αCS + ß0 PX + ß1.0 S0 PX + ß1.1 S1 + ß1.2 S2 + ß1.3 S3

+ ß2.1 DS1-0 + ß2.2 DS2-1 + ß2.3 DS3-2

+ ß3.1 DS1-0 S1 + ß3.2 DS2-1 S2 + ß3.3 DS3-2 S3

+ εCS

I do not measure the NPS per stage because asking customers retrospectively about the recommendation intention they had before purchasing or consuming the product could lead to irritations. Instead of being driven by truly bad experiences, customers may have the tendency to answer with low scores because they are not used to recommend a product which they did not test yet.

4.3.3 Involvement and control variables

To explore the impact of product involvement on the relevance of purchase stages, I measure

product involvement as explained in chapter 4.2.2 and include term ten (I) in the equation. In

order to control for the impact of different customer characteristics in customer experience creation, the survey finishes with questions about age (A), gender (G), income (In), education (E), household size (H), and urbanization (U). The control variable for the product category (P) eliminates biases due to unobserved category specific differences (Herhausen et al. 2019). An overview of all variable names and a description of their scales are provided in appendix C. Equation (4) and (5) describe the entire model which I will test for the overall satisfaction and the NPS, separately.7 Therefore, I will first test whether both variables are indeed two separate factors by means of a factor analysis. Since the assumed relationships between the IVs and both DVs are at least linear additive or even linear in the parameters, I perform an ordinary least squared (OLS) regression to estimate the parameters in a way, which minimizes the sum of squared residuals. In order to explore moderation and mediation effects I use the software package PROCESS for SPSS programmed by Hayes (2018) which uses bootstrapping to estimate mediation effects.


29 (4) OS = αCS + ß0 PX + ß1.0 S0 PX + ß1.1 S1 + ß1.2 S2 + ß1.3 S3 + ß2.1 DS1-0 + ß2.2 DS2-1 + ß2.3 DS3-2 + ß3.1 DS1-0 S1 + ß3.2 DS2-1 S2 + ß3.3 DS3-2 S3 + ß4 I + ß5 A + ß6 G+ ß7 In + ß8 E + ß9 H + ß10 U + ß11P + εCS (5) NPS = αNPS + γ0 PX + γ1.0 S0 PX + γ1.1 S1 + γ1.2 S2 + γ1.3 S3 + γ2.1 DS1-0 + γ2.2 DS2-1 + γ2.3 DS3-2 + γ3.1 DS1-0 S1 + γ3.2 DS2-1 S2 + γ3.3 DS3-2 S3 + γ4 I + γ5 A + γ6 G+ γ7 In + γ8 E + γ9 H + γ10 U + γ11P + εNPS

5 Data analysis and results

5.1 Results of data collection and sample reliability

I received 326 responses on the online survey. After deleting participants which did not complete the survey, the final sample consists of 262 observations. Table 2 shows a description of the sample and table 3 presents the descriptive statistics of the satisfaction variables, NPS and product involvement. Almost two thirds of the sample are female. 37.79% each chose beer/wine or snacks and 24.43% chose coffee. Therewith, the product categories are rather equally represented in the sample. Only 12.60% of the respondents had no prior purchase experience, which is common for products with high purchase frequencies like groceries. The customers in the sample are on average rather satisfied with their experiences as the mean of

overall satisfaction is 5.802. The mean NPS of 7.900 reveals that the average respondent should

be classified as “passive” (Reichheld 2003). The average level of product involvement is mediocre with 4.030. Since all IVs are built on Likert-scales there are no extreme outliers in the data. However, there are some participants (n = 6) who completed the entire survey but indicated not having any touchpoint during at least one of the three purchase stages. Further, some other participants (n = 4) did not remember the brand or retailer name. When filtering out these observations the results of the analysis do not change. Thus, the observations do not have to be deleted from the sample. Finally, all main IVs for which I hypothesized an effect (table 4: variables 1 to 5) are significantly correlated at a 5% level with the DVs overall satisfaction and


30 Table 2: Description of sample (n = 262).

Product category Frequency Gender Frequency Household size Frequency Beer/wine Coffee Snacks 37.79 % 24.43 % 37.79 % Male Female 35.50 % 64.50 % 1 person 2 persons 3 persons

More than 3 persons

30.53 % 37.40 % 18.32 % 13.74 % Prior experience Frequency Age groups Frequency Household income Frequency No Yes 12.60 % 87.40 % 18 to 24 25 to 34 35 to 44 45 to 54 54 and older 48.47 % 36.64 % 2.29 % 6.11 % 6.49 % below 1000€ 1000€ - 2000€ 2001€ -3000€ 3001€ - 4000€ More than 4000€ Not specified 38.93 % 23.66 % 12.60 % 5.34 % 9.92 % 9.54 % Urbanization Frequency Educationa Frequency

Rural Urban 25.19 % 74.81 % Hauptschule Realschule (Fach-)Abitur (Fach-)Hochschulstudium 1.91 % 4.96 % 30.15 % 62.98 %

a As the survey was distributed among German participants, the categories are the different graduation levels which

can be reached in the German education system in an ascending order.

Table 3: Descriptive statistics of satisfaction variables, NPS and product involvement.

Variable Mean Std. Deviation N

Prior satisfaction 5.843 1.368 229 Pre-purchase satisfaction 5.172 1.318 262 Purchase satisfaction 5.183 1.461 262 Post-purchase satisfaction 5.832 1.329 262 Difference pre-purchasea -.607 1.565 229 Difference purchaseb .012 1.530 262 Difference post-purchasec .649 1.485 262 Overall satisfaction 5.802 1.216 262 NPS 7.900 2.390 262 Product involvement 4.030 1.711 262

a Pre-purchase satisfaction - prior satisfaction, b purchase satisfaction - pre-purchase satisfaction, c post-purchase


31 Table 4: Pearson Correlation coefficient of IVs and DVs.

1 2 3 4 5 6 7 8 9 10 11 1 1 2 .835 1 3 .128 .267 1 4 .071 .231 .397 1 5 .256 .455 .257 .436 1 6 .165 .360 .313 .497 .810 1 7 .211 .400 .308 .244 .492 .495 1 8 .227 .185 -.348 -.063 .148 .069 .075 1 9 -.034 .013 -.045 -.142 .065 .044 .092 .124 1 10 -.073 -.031 -.124 -.458 .065 -.035 .002 .206 .174 1 11 .148 .239 .278 .163 .193 .171 .305 .005 .002 -.051 1

1: dummy prior experience, 2: prior experience (= dummy prior experience*prior satisfaction), 3: pre-purchase satisfaction , 4: purchase satisfaction, 5: post-purchase satisfaction, 6: overall satisfaction, 7: NPS, 8: large difference pre-purchase, 9: large difference purchase, 10: large difference post-purchase, 11: product involvement. Bold values are significant at 5% level. Please, see appendix D for correlations with the control variables.

5.2 Analysis and results of research focus 1

5.2.1 Touchpoint usage and order





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