Online Retargeting Based on Consumer Behavior: a Comparison Between Models

Online retargeting based on consumer behavior: a comparison between models

Master thesis

Menno Nelis Student no: 5929040 Supervisor: Prof. Dr. Dick Heinhuis

University of Amsterdam Amsterdam, The Netherlands Faculty of Science Information Studies Track: Business Information Systems

August 2017 Abstract

Behavioral retargeting is a widely used technique within the field of online marketing to generate sales out of customers that already visited your website. These campaigns are based on the online behavior and interests of the customer. Although there is a huge growing market for behavioral retargeting, current online campaigns do not yet use models of consumer behavior or decision making as input for their campaign design. Behavioral retargeting is seen as a trick by marketers and their campaigns are optimized empirically. This research selected three models of consumer behavior or decision making; the Howard-Sheth model, the EBM model and the Bettman model. The phases of these models were translated from offline to online behavior and used to create three different retargeting campaigns. The fourth campaign is the control group, which is not based on a model. A Dutch travel company, Corendon, provided a platform to run the different experiments. Their visiting customers on the website were used in the experiment to sub divide customers among the different experimental campaigns, based on the selected models. The results obtained from these experiments were measured in six online metrics: the bounce rate, click through rate, conversion rate, cost per action, cost per click and the return on investment. After the campaigns ran for a week, results showed that the campaigns based on a consumer behavior or decision making model all scored better than the control group. Comparing the experiments on the different metrics, it can be concluded that a retargeting campaign based on the Bettman model scores highest on five of the six metrics. Therefore the Bettman model is identified as the most suitable model for the design of an online retargeting campaign, out of the three models used during this study. This research shows that marketers should not neglect models of consumer behavior or decision making, but should embrace and implement them to improve their behavioral retargeting campaigns.

Keywords

Behavioral retargeting, consumer behavior, buyer intention, decision making process, online banners

Acknowledgments

A lot of time and effort has been put into this thesis. I would like to thank all people who have been supporting me during this research. First of all I would like to thank my supervisor Prof. Dr. Dick Heinhuis for his support, patience and shared knowledge. Second I would like to thank Corendon for sharing information and the freedom of running my experiments with their data and assets. Special thanks to Stefan van den Berg with his help to set up the experiments. Last but not least I’d like to thank my friends and family for their support, special thanks to Lianne Wensveen, Baukje Faber, Saskia Epping and Michelle van der Klauw for their support.

Contents

Abstract ...... 2

Acknowledgments ...... 3

Contents ...... 4

List of Figures ...... 7

List of Tables ...... 8

1. Introduction ...... 9

1.1 What is behavioral retargeting? ...... 11

1.2 How does behavioral retargeting work? ...... 11

2. Literature review ...... 13

2.1 Which theories of consumer behavior or decision making are applicable for behavioral retargeting? ...... 13

2.1.1 Selection criteria of theories ...... 13

2.1.2 Theories of consumer behavior and decision making ...... 14

2.2 Which recent studies are conducted on behavioral retargeting? ...... 16

2.3 Which models will be selected for this research?...... 18

3. Experiment ...... 21

3.1 Hypotheses ...... 21

3.2 Method ...... 24

3.3 Experiment scope ...... 25

3.4 Data collection ...... 26

3.5 Advertisements ...... 27

3.6 Models translated into online behavior ...... 28

3.6.1 Howard-Sheth model ...... 28

3.6.2 EBM model ...... 31

3.6.3 Bettman model ...... 35

3.6.4 Traditional behavioral retargeting campaign ...... 39

3.7 Summary of selections ...... 39

4. Results ...... 41

4.1 Overall results ...... 41

4.2 Metric 1: Bounce rate ...... 42

4.3 Metric 2: Click Through Rate...... 43

4.4 Metric 3: Conversion Rate ...... 43

4.5 Metric 4: Cost per Action ...... 44

4.6 Metric 5: Cost per Click ...... 45

4.7 Metric 6: Return on Investment ...... 45

5. Discussion and conclusion ...... 46

5.1 Discussion ...... 46

5.2 Conclusion ...... 48

5.2.1 Limitations...... 50

5.2.2 Future work ...... 50

References ...... 52

Appendices ...... 55

Appendix 1: calculations Bounce Rates ...... 55

Appendix 1.1: Bounce Rate Experiment 1 vs control group ...... 55

Appendix 1.2: Bounce Rate Experiment 2 vs control group ...... 56

Appendix 1.3: Bounce Rate Experiment 3 vs control group ...... 57

Appendix 1.4: Bounce Rate Experiment 1 vs 2 ...... 58

Appendix 1.5: Bounce Rate Experiment 1 vs 3 ...... 59

Appendix 1.6: Bounce Rate Experiment 2 vs 3 ...... 60

Appendix 2: calculations Click Through Rates ...... 61

Appendix 2.1: Click Through Rate Experiment 1 vs control group ...... 61

Appendix 2.2: Click Through Rate Experiment 2 vs control group ...... 62

Appendix 2.3: Click Through Rate Experiment 3 vs control group ...... 63

Appendix 2.4: Click Through Rate Experiment 1 vs 2 ...... 64

Appendix 2.5: Click Through Rate Experiment 1 vs 3 ...... 65

Appendix 2.6: Click Through Rate Experiment 2 vs 3 ...... 66

Appendix 3: calculations Conversion Rates ...... 67

Appendix 3.1: Conversion Rate Experiment 1 vs control group ...... 67

Appendix 3.2: Conversion Rate Experiment 2 vs control group ...... 68

Appendix 3.3: Conversion Rate Experiment 3 vs control group ...... 69

Appendix 3.4: Conversion Rate Experiment 1 vs 2 ...... 70

Appendix 3.5: Conversion Rate Experiment 1 vs 3 ...... 71

Appendix 3.6: Conversion Rate Experiment 2 vs 3 ...... 72

List of Figures

Figure 1: Summary of technology used for behavioral retargeting at Corendon ...... 25 Figure 2: Online advertisements from Corendon used for behavioral retargeting ...... 27 Figure 3: Online advertisements from Corendon used for behavioral retargeting ...... 27 Figure 4: Howard-Sheth model (Howard & Sheth, 1969) ...... 30 Figure 5: EBM model (Engel et al, 2005) ...... 32 Figure 6: Hotel page on Corendon website with salient attributes tabs...... 34 Figure 7: Bettman model (Bettman, 1979) ...... 35 Figure 8: Screenshots of free text search (with search on ‘Lara’) and quick search box on the Corendon website ...... 38

List of Tables

Table 1: Recent studies conducted on behavioral retargeting ...... 16 Table 2: Scorecard of theories and models from literature research...... 19 Table 3: Online metrics used for the experiment ...... 21 Table 4: Howard-Sheth model phases linked to online behavior ...... 28 Table 5: EBM model phases linked to online behavior ...... 32 Table 6: Salient attributes on the Corendon website overview ...... 34 Table 7: Bettman model phases linked to online behavior ...... 36 Table 8: Summary of selections used for the experiment ...... 39 Table 9: Overall results experiments ...... 41 Table 10: The key metric results of the experiments ...... 42 Table 11: Calculated bounce rates of experiments ...... 43 Table 12: Calculated Click Through Rates of the experiments ...... 43 Table 13: The calculated Conversion Rates of the experiments ...... 44 Table 14: The calculated Cost per Actions of the experiments ...... 44 Table 15: The calculated Cost per Clicks of the experiments ...... 45 Table 16: The calculated Returns on Investment of the experiments ...... 45

1. Introduction

Retargeting is a widely used method to reach out to potential customers on the internet. Retargeting, as the word reveals, is the process of targeting visitors who left a website without making a sale on it. Retargeting is the way to reconnect with these visitors through online banners and other expressions to let them revisit your website. When this process is done based on data of the customers, it’s called behavioral retargeting or Online Behavioral Advertising (OBA) (Smit et al, 2014; Chen & Stallaert, 2014). The market for behavioral retargeting in Europe is increasing heavily and the spend on these online advertisements in Europe has grown every year since its beginning. In 2006, the value of the market stood at €6.6 billion, versus €30.7 billion in 2014 (IAB Europe AdEx Benchmark, 2014). When retargeting started it was a technical process and not many tools were available. Nowadays, many organizations offer tools and assistance to setup, run and improve behavioral retargeting campaigns. Together with this development, the market is even more increasing since adapting the technology to use it is made more easy now. According to the Behavioral Retargeting Blog, ‘personalized retargeted banners perform 6x better than general banners and 4x better than basic retargeted banners’ (Behavioral Retargeting Blog, 2010). However, using behavioral retargeting does not automatically mean that your sales or conversion rates on online advertisements will grow. In fact, your dynamic retargeted ads can be less effective when used on the wrong moment or customers (Lambrecht & Tucker, 2013). This can be improved to make it more effective than general advertisements by adjusting the campaigns. But how will they improve these campaigns? The technique is used as a trick by marketers and improvements are made empirically. Therefore it’s not about predicting consumer behavior or decision making, but about testing and improving the results. This results in neglecting years of experience and developed theories about consumer behavior or decision making. In fact, it comes down to play with campaigns which are costly for firms and base the results on the current market values, standards and changes. And as the numbers point out, there is a huge market for behavioral retargeting. If there could be a small improvement, it can have a huge impact on money spent and earned in this market. There seems to be a big gap between theories of consumer behavior or decision making and behavioral retargeting. “Consumer behavior is the study of how individuals, groups and organizations select, buy, use and dispose of goods, services, ideas, or experiences to satisfy their needs and wants” (Kotler and Keller, 2006, p. 62). Decision making is about the process of decision making, including all the factors that influence this process (Strydom et al, 2000). Since decades research is done into these areas, and many theories have been found usable to predict consumer behavior or decision making. However, these theories do not seem to be used as input for behavioral retargeting campaigns. Most of these campaigns are set up by the idea that a customer views a product and leaves the website, and is therefore interested in this product. Then marketers try to get them back with advertisements showing these products and possible sales are made. This retargeting does not take the state or stage the customer is in into account. Campaigns like these are the current and accepted way of

9 implementing behavioral retargeting. Why start with a simple basis and then try to improve these campaigns? Isn’t it possible to create a solid basis, based on theory, to start a campaign? And when doing this, which theories are suitable for behavioral campaigns? Therefore, this paper tries to find an answer on the question:

To what extent is behavioral retargeting that is based on theories of consumer behavior or decision making, more effective than campaigns without this basis?

To make this research question measurable, online campaign metrics can be used to measure the effectivity of the campaigns. For this research, the bounce rate (BR), click through rate (CTR), cost per click (CPC), cost per action (CPA), conversion rate (CVR) and the return on investment (ROI) will be used. These metrics are the most commonly used metrics for retargeting campaigns and combined they can give an overview of how a campaign performs (Batra, 2014). The metrics are explained in detail in chapter 3.1, where the hypotheses will be set up based on these metrics. To answer the main research question, the following sub questions should be answered:

1. Which theories of consumer behavior or decision making are applicable for behavioral retargeting? 2. Which recent studies are conducted on behavioral retargeting? 3. Which applicable theories will be selected for this experiment? 4. What is the empirical validation of the experiment?

In the first part of this paper definitions will be given as explanation on the main research question and for further reading this paper. This includes an (technical) explanation or the process of behavioral retargeting. In the literature review sub question 1 and 2 will be answered. For sub question one, general theories of consumer behavior or decision making will be selected based on defined selection criteria. Sub question 2 is added to see if new developments in the literature of behavioral retargeting can be found, as well in the literature that is created during the rise of the internet. This literature will also be used as input for the experiment. The conditions for selecting studies and theories will be given in chapter 2.1.1. For sub question 3, a scorecard will be created to score the theories and select the appropriate theories usable for this research. After this selection, hypotheses will be defined for the experiment. The second part of this paper will describe the empirical experiment performed on real time data in cooperation with a Dutch travel company (sub question 4) to validate the and answer the hypotheses. The experiment will be a quantitative research. In this case, the output of the qualitative research (sub question 1 till 3) will be used as input for the quantitative research (the experiment). The combination of these two methods of data collection creates a more comprehensive research and has several benefits over an experiment without this combination (Bryman, 2006). The last part contains the discussion on the results of the experiment and the outcomes of this research.

1.1 What is behavioral retargeting?

Retargeting already existed before the internet was in use. Retargeting means basically to try to reconnect with customers you already know to create conversions. Where an offline shop can do this for example with flyers or mouth-to-mouth advertisements, online shops can do this with online advertisements. The advantage of online retargeting is that it can be used for a lot of persons in just a matter of seconds and everything can be measured. Nowadays online retargeting has shifted from general ads to recommendation ads, and is now at the level of personal ads. Customer data is needed for personal ads. Travel organizations can gather data when a customer is visiting their website. Every view, click or even interests in certain types of content can be saved to a customer specific profile. All the actions a customer performs on the website can be saved and combined to create this profile. All these profiles can be grouped into groups of customers that have the same interests or value, determined by the travel company in this case. In this way, possibilities to target a group of customers with marketing campaigns arise. All this behavioral data of visitors has value for organizations. Having the right data can attract the right customers which leads to higher conversion rates. Gartner understates this by saying “personalization technology drives relevant experiences that augment the customer experience and drive higher levels of satisfaction and engagement, which help to increase revenue and return business” (Gartner, 2015). Beales (2010) also understates this statement and claims that conversion rates are more than doubled when using behavioral retargeting, although advertising rates are increasing as well. Criteo, one of the largest retargeting organizations in the Netherlands, got from their data that personalized retargeted banners perform six times better than general banners and four times better than basic retargeted banners (Behavioral Targeting Blog, 2010).

1.2 How does behavioral retargeting work?

For understanding how online retargeting works, a description in steps below:

1. A customer visits the website. In this step, the customer views products and clicks through the website. On certain types of pages a pixel is downloaded and is added to the customer cookie to create a profile of the visitor. These pixels can also be send to advertisers to use the profile on their side as well. If the visitor returns to the website, the same cookie will be used and extended. Together with creating a cookie, a system is used to create a more detailed customer profile, which can be used to personalize the advertisements (step 4). 2. The customer leaves the website and did not make a sale. If the customer browses the internet afterwards, advertisers will try to recognize the customers on one of their channels, by trying to match their cookies with the customers cookies. Once they have a cookie sync, they know which person is browsing and a banner can be displayed. 3. In this step, a bid manager is used to check if the customer will get an advertisement on his screen or not. Advertising companies have their own algorithms written for this core functionality. If advertising channels like Google Doubleclick (DoubleClick by Google, 2016)

are used, the advertiser is more in control of the bids he wants to make to serve an advertisement. How much to bid on a customer, depends on a complex combination of factors. This is a market where all the advertisers are competing with each other. A bid is nothing more than an amount of money you want to spend on the advertisement to show. These bids can be defined based on the type or value of the customer that is matched, and algorithms are used to see which bid is the winner (has the highest bid). Other parties might also bid on the same customer, therefore it’s calculated within milliseconds who has the highest bid and can show his advertisement. 4. When the bid is accepted, or in other words, the algorithm has decided to show the advertisement, the banner will be shown. This banner is in case of behavioral retargeting dynamic. Different from traditional targeting where the advertisement has a layout that is shown to everybody in the same format, the content can now be managed based on the customer's profile. This makes it possible to show banners that are more related to the person which is browsing, based on his prior actions done on the website. 5. Measurements can be done with an analytics system that can translate campaign variables into readable dashboards or overviews. The most common system to use is Google Analytics. These systems can show the results in terms of clicks, impressions, conversions and other online measurements.

In this five step explanation it has become clear that multiple systems are linked to be able to do behavioral retargeting. This requires a precise communication between all these systems in order to create successful campaigns. The tweaking that marketers do to improve these campaigns can be done in all systems, except for the analytics tool, which only gives insights.

2. Literature review

2.1 Which theories of consumer behavior or decision making are applicable for behavioral retargeting?

First, before selecting the theories, a scope will be defined for selecting theories in the literature review. Secondly the theories found will be described.

2.1.1 Selection criteria of theories

In order to make a selection of the theories, requirements and boundaries for selecting them need to be defined. This research focuses on the path customers go through before buying a product. This path is linked to consumer behavior, how do customers behave through the whole buying process and which stages are there? Decisions need to be made during these stages, therefore decision making is also reviewed. It is important to note that decision making in this research focuses only on the consumer perspective within the marketing field, since decision making exists in many fields. Within these two fields of research, many theories have been developed during time. Where some have a clear distinction between one or two ways of thinking, others describe a more detailed process with multiple steps, explained or visualized in models. Therefore, a short review will be done on high level of decision making theories to create an overview, but the focus will be on the more detailed decision making theories. The theories that will be selected should have a certain value in the areas that they are in. As a start for discovering these theories, the 10th edition of the book Consumer behavior, written by Engel, Blackwell and Miniard (Engel, Blackwell & Miniard, 2005) is used. This book describes the EBM model, but also gives an overview of relevant theories. One important measurement for selecting the theories is the number of citations in scientific journals. This value is relative and should give an indication of how widely accepted and used the theory is against other theories. The relatively most popular theories will be selected in this paper. The journals which are used for research are Springer, ScienceDirect (Elsevier), JSTOR, Business Source Premier and EBSCOhost. These journals cover the scientific disciplines used for this research and contain relevant peer-reviewed articles and publications. All journals are accessible via the University of Amsterdam. The last criterion is the year of publication. In this paper a distinction is made between recent studies and older studies, where the rise of the internet plays an important role. Since the mid-1990s the internet became available for consumers. The online marketing world, and so also the retargeting part, is a highly changing area where new technologies are rapidly evolving since the start. For this reason, studies from the mid- 1990s till now are defined as recent in this research. This makes the timespan of recent studies small. Theories founded before the rise of the internet are seen as non-recent theories of consumer behavior and decision making, which can be selected for this research.

2.1.2 Theories of consumer behavior and decision making

Andreason (1965) proposed one of the first models of consumer behavior. This model points out the importance of information available for the consumer during the decision-making process. A year after, Nicosia (1966) created a model which focuses on the relationship between a company and the consumer. Four areas are defined in the decision-making process according to Nicosia: the first area is the consumer attitude based on information from the company, which is divided into two subfields: the created marketing field created by the company that affect the consumer's attitude and the consumers characteristics such as experience, personality etc. that create an attitude towards the product based on his or hers interpretation of the message. The second area is search and evaluation, the act of purchase is the third and last is the feedback area (Nicosia, 1966). Fishbein and Ajzen created the theory of reasoned action in 1967 (TRA) (Fishbein & Ajzen, 1977). This theory predicts consumer behavior based on the intention of the consumer. The foundation is based on the attitude of the consumer towards the behavior and the subjective norm. The attitude towards the behavior is a positive or negative evaluation of performing the behavior. The subjective norm ‘is a person’s belief about whether significant others feel that he or she should perform the target behavior’ (Hale et al, 2002, p. 260). This theory is seen as one of the foundations of attitudes and behavior within human action. Two years later, Howard and Sheth created the Howard-Sheth model (1969). This model explains the rationality of choices made by the consumer, focused on choosing between brands. An important addition to the field was that inputs to the consumer play an important role in the buying process. The inputs are categorized in significative stimuli, symbolic stimuli and social environment stimuli. Significative stimuli are physical characteristics of a product such as price, color or quality. Symbolic stimuli are verbal or visual characteristics, such as advertisements. Environmental stimuli are social related factors of the consumer, such as family (Howard & Sheth, 1969). The buying process, seen as a decision making process, is applied to consumer behavior with the Engel-Kollat-Blackwell (EKB) model (Engel et al, 1968). A five stage problem-solving process is defined. The five stages are: identification of the problem, search for information, alternative evaluation, choice and outcomes. Where the Nicosia model does not take the attitude of the consumer towards the product into account, the EKB model pays attention to internal factors. Secondly, steps in the EKB model can be skipped when a repeat purchase by the consumer is made (Engel et al, 1968). The Bettman-model (Bettman, 1979) has seven major stages in the buying process defined: processing capacity, motivation, attention and perceptual encoding, information acquisition and evaluation, memory, decision process and consumption and learning process. Every step in this model is a choice, based on the rational thinking of the consumer. Bettman states that that consumers have limited capacity for processing information, resulting in using a simple strategy to make decisions (Bettman, 1979). After the Bettman model, dual processing theory is founded to describe how a decision can be made in two different ways. One applied theory based on the dual processing theory is the Elaboration Likelihood Model of Persuasion

(Petty & Cacioppo, 1984). This model describes two routes, the central and the peripheral route. The central route is a well thought, elaboration on information way of thinking and making a decision. The peripheral route occurs when a person does not think carefully, and motivation and ability are low to make the decision (Petty & Cacioppo, 1986). Later, an extension of TRA is created: the theory of planned behavior (TPB), where perceived behavioral control is added. ‘Perceived behavioral control is the perception of ease or difficulty in performing a behavior’ (Ajzen, 1991, p. 188). In further research, the Engel-Blackwell-Miniard model (EBM) was created, where the customer decision process consists of six phases, which later has changed into seven phases in an evolved model: need recognition, search for information, pre-purchase evaluation of alternatives, purchase, consumption, post-consumption evaluation and divestment. In addition to the Howard-Sheth model, human processes like memory and information processing are added. The post consumption outcomes can be both positive and negative in this model (Engel et al, 1990; Engel et al, 2005). In much later studies, in this research defined as a recent study, Kahneman (2011) describes a dichotomy between two modes of thought on a high level: system 1 is a fast, automatic, intuitive and largely unconscious mode of thinking. System 2 is our slow, deliberate, analytical and consciously effortful mode of reasoning about the world. These models are a result of research into attitude formation, attitude change and attitude measurement, together with research into external cues of internal psychological processes. “Marketing strategies evolving out of this approach focus less on specific product/service attributes and more on understanding the effects of contextual cues and heuristics on evaluation and decision making” (Bitner & Obermiller, 1985). Besides theories of consumer behavior, theories of technology adoption are created with consumer behavior or decision making in relation to technology acceptance. The Technology Acceptance Model (TAM) is seen as one of the most well-known models in this area (Davis et al, 1989). This theory describes how consumers come to accept the use of a technology. Two factors have influence on their acceptance: perceived usefulness and perceived easy-of-use. It is seen as an extension on the TRA, where it replaces factors of the TRA with perceived usefulness and perceived easy-of-use (Davis et al, 1989). After reviewing the TAM model, others created the TAM2 model (Venkatesh & Davis, 2000). In this model, more determinants are added to perceived usefulness: subjective norm, image, job relevance, output quality and result demonstrability. Determinants added to perceived ease-of-use are experience and voluntariness. Several years later, Venkatesh created the Unified Theory of Acceptance and Use of Technology (UTAUT) (Venkatesh et al, 2003). This theory is a combination of eight prominent models of technology acceptance, including TRA, TAM and TPB. These theories of technology adoption are useful as support for the experiments that will run for this experiment. They are not included in the selection of models to test whether these theories can be used as input for behavioral retargeting campaigns, since this research does not focus on the acceptance of the technique of behavioral retargeting, but on the process of decision making of customers.

2.2 Which recent studies are conducted on behavioral retargeting?

Recent studies conducted on behavioral retargeting can support this research and the setup of the experiments. These conducted studies have a model of consumer behavior applied to retargeting or did research into behavioral retargeting. Therefore, a summary will be given in table 1 with recent studies. As explained, these studies should not be older than the rise of the internet. In table 1 an overview is given: the paper, authors, year of publication and a short overview is given of every research.

Table 1: Recent studies conducted on behavioral retargeting

Research Authors Year of Short summary publication

The theory of planned Lim, H., 2005 Back in the days, you could touch, feel or taste every behavior in e‐commerce: & physical product you wanted to buy in shops, or the Making a case for Dubinsky, service you bought was explained by a person. interdependencies A. J. Nowadays, the internet does not have those options, between salient beliefs which increases hypotheticality. Therefore content on the websites or advertisements with information to the customers becomes more important

Construal levels and Trope, Y., 2007 Consumer decision making consists of different psychological distance: Liberman, stages as described before. Together with these Effects on representation, N., & stages, some dimensions in the psychology of prediction, evaluation, Wakslak, consumer decision making are highly relevant. Four and behavior C. dimensions of psychological distance: temporal distance, spatial distance, social distance and hypotheticality are discussed. Temporal distance is based on time, an online sale can be made and can be used at a later point. Spatial distance has been covered by the internet because it’s not necessary anymore to reach out to physical shops. Social distance can be an interesting aspect, since organizations try to get more socially close to the customers. At last, hypotheticality occurs when people make choices that have uncertain outcomes, something that has been increased since online shopping exists.

The Effects of Visual and Kim, M., 2008 How do different product presentation formats (visual Verbal Information on & vs. verbal) influence consumer attitudes toward Attitudes and Purchase Lennon, S product and purchase intentions in internet shopping. Intentions in Internet Shopping

Toward an integrated Darley, 2010 A review of recent empirical studies dealing with framework for online W. K., online consumer behavior and decision-making consumer behavior and Blankson, processes. During this review they used the EKB and decision making process: C., & EBM model as backdrop. They found that research in A review Luethge, online decision making is still in its early stages of D. J. development. Although they suggest that new technologies of the latest years enable more possibilities online, the focus should be also on satisfaction and better understanding of choice decisions.

When does retargeting Lambrech 2013 A division between two types of customers is made, work? t, A., & those who have narrowly construed preferences and Tucker, those who have a high-level goal. Narrowly C. construed customers know more precise which product they want to buy, high level construed customers only know a direction or high level decision of e.g. the product category. Their findings suggest that dynamic retargeting is effective on narrowly construed customers, but less effective on high level construed customers. For this group, general advertisements have better results.

How to use multichannel Klapdor 2015 Little is known about how prior user behavior across behavior to predict online et al these advertising channels can be used to predict conversions behavior conversions. To address this issue, the authors of the patterns across online current paper drew on advertising-response and channels inform strategies purchase decision making theory, as well as findings for turning users into about user search and information processing on the paying customers web.

2.3 Which models will be selected for this research?

In chapter 2.1 a selection and description of all theories is given based on the selection that was defined. However, some theories fit more into a translation to online behavioral retargeting than others. A scorecard is defined to select the most usable models. In the scorecard three measurements are of importance: the possibility to measure online, clear defined multi step process and the link to advertisements used for behavioral retargeting. The possibility to measure online is important, when this is not possible, the translation from offline models to online behavior cannot be made and the experiment will fail. This translation from offline to online is based on rational insights. A simple example: somebody that makes the decision to go into a physical store (offline), can be translated to somebody who made the decision to enter a website (online). A second example: if a person gets an article in a physical shop and puts it in his or hers basket, this could be online seen as the action that somebody selects a holiday and puts it in his or her virtual basket. Secondly, if a model has a clear defined multi step process, it’s possible in the experiment to recognize customers in a certain phase. This will help to select the right customers at the right time. An example: when a customer is asking for more information about a product at a certain moment in his buying process to a person (offline), can be translated to somebody who is looking for more information about the product on certain web pages or information fields (online). At last, the link to advertisements needs to be made, if a model does not have an stimulus like an advertisement or product trigger, it does not seem to fit this research. A more describing explanation about the choices made in the scorecard is given first, then the scorecard will follow. As Lambrecht & Tucker (2013) stated, it is of importance to learn ‘about consumers’ responsiveness to ads in the context of a multistage decision process in which ad effectiveness may change with their decision stages’ (Lambrecht & Tucker, 2013, p.575). For this reason, it is of great value to select theories which have a multi-stage decision path (in a model) for customers. Dual processing theories such as the Elaboration Likelihood model of Persuasion and Kahneman’s model would be too general to test and results will not be very enlightening, since only two ways of thinking are discussed. It’s also hard to select online, since these phases are very comprehensive. For diving more into the rationality of choices by customers in detail, the Howard-Sheth theory applies. The different inputs (significative stimuli, symbolic stimuli and social environment stimuli) have influence on the customer's buying process. In the proposed model it is assumed that the process is repeatable and the behavior of the customers is rationally, based on the inputs. The significative stimuli can be seen as the characteristics of the online products that are sold, including all the information that can be found online. The symbolic stimuli are the advertisements, the visuals that the customers will see. The environmental stimuli might be more difficult to measure online, since an online visit is hard to link to other visits of related people, without knowing that these people are related to each other.

The buying process is well defined in five (EKB model) or seven (EBM model) stages. The EBM model is seen as an revision of the EKB model, therefore the EBM model will be used in this research. In the EBM model, seven phases are defined (need recognition, search for information, pre- purchase evaluation of alternatives, purchase, consumption, post-consumption evaluation and divestment). These phases can be linked to online behavior and can be identified during the process. The phases will be linked to online behavior in chapter 3. The Theory of Reasoned Action and the Theory of Planned Behavior are difficult to connect to online behavior. These theories are based on the intention of the customer. The positive or negative evaluation of the behavior, or the perception of ease or difficulty in performing a behavior, are hard to measure just from online behavior on a website. The customers should be asked directly to get information to answer these questions, but no questionnaire will be used in this research. The Bettman-model however does have a clear seven stage buyer's process defined. The seven stages (processing capacity, motivation, attention and perceptual encoding, information acquisition and evaluation, memory, decision process and consumption and learning process) are based on choices that a customer can make. It is said that due to the limited capacity for processing information, every step is a new, separate step in the process. These steps can be translated to steps made online before making a purchase. Models of technology acceptance that are discussed, such as TAM, TAM2 and UTAUT can be used when setting up the experiments to consider choices and review outcomes. These models however do not have a role in the consumer decision process of buying a product online as input for this research.

Table 2: Scorecard of theories and models from literature research

Model Possibility Clear defined Link to advertisements Total to measure multi step used for behavioral score online process retargeting

Elaboration Likelihood Model No No No NNN of Persuasion (Petty & Cacioppo, 1984)

Kahneman model (Kahneman, No No No NNN 2011)

Andreason model (Andreason, No Yes/No No NYNN 1965)

Nicosia model (Nicosia, 1966) No Yes Yes NYY

Howard-Sheth model Yes Yes Yes YYY (Howard-Sheth, 1969)

EKB model (Engel et al, 1968) Yes/No Yes No YNYN

Bettman model (Bettman, Yes Yes Yes/No YYYN 1979)

TRA (Fishbein & Ajzen, 1977) No No Yes/No NNYN

TPB (Ajzen, 1991) No No Yes/No NNYN

EBM model (Engel et al, Yes Yes Yes YYY 2005) Possible scores: Yes means it’s applicable, No means not applicable.

As can be seen in the scorecard, two theories stick out with a yes score on all three measurements: the Howard-Sheth model and the EBM model. These will be used in the experiment. As third model the Bettman model has a good score, where the possibility to measure online and the multistep process are well defined. This will be the third selected model for the experiment.

3. Experiment

3.1 Hypotheses

Seven metrics have been defined to measure the success of the campaigns. Based on these metrics, hypotheses can be set up to test our findings. Table 3 gives an overview of these metrics including definition and formula to calculate them (Batra, 2014).

Table 3: Online metrics used for the experiment

Metric Explanation

Bounce rate A bounce is registered when a customer opens a website (eg. via an advertisement) and directly leaves the website without browsing any further to another page. When a customer bounces, it means that the opening page is not interesting enough to continue the visit on the website.

Click The percentage of customers that had an impression of an advertisement and also Through clicked on it, versus the customers that had an impression of an advertisement and Rate (CTR) didn’t click on it. The formula used: CTR = (clicks / impressions) * 100.

Conversion The percentage of customers that made a purchase versus the amount of visits of a rate (CVR) website (through advertisements in this research). The formula used: CVR = total of conversions / visits * 100.

Cost per The campaign cost divided by the total of defined actions. The CPA tells you how action (CPA) much you need to pay for an action. In this research the action is a conversion. The formula used: CPA = total costs / total of conversions.

Cost per The cost per click is the cost that you pay for each click. The formula CPC = costs / click (CPC) clicks calculated the CPC.

Cost per The cost per thousand tells you how much it costs to show 1000 impressions of an thousand advertisement, The formula: Total Impressions = (Total Cost) * (1000/CPM). (CPM)

Return on The return on investment gives a percentage of how efficient the campaign will be. investment The formula: ROI = ((gain from investment - cost of investment) / cost of (ROI) investment) * 100. The gain can be measured from the conversions.

The first metric, the bounce rate, is a special metric within online marketing. The lower this percentage is, the better it is. A bounce means, as explained, that a customer does not opens a second page on the website during one visit. One assumes that the customer did not found what he was looking for, and a sale cannot be made without at least loading another page. On 5 July 2017, the average bounce rate on the Corendon.nl website is over the year 2017: 27,43%, meaning that more than a quarter of all visitors leaves the website before entering a second page. This research tries to prove if retargeting campaigns based on theories of consumer behavior have better results, which implicates lower bounce rates. Therefore, hypothesis 1 is defined as:

H1: Behavioral retargeting campaigns based on theories of consumer behavior or decision making have lower bounce rates than behavioral retargeting campaigns without this basis

The second metric, the CTR, is more positive when the percentage is higher. A score of 100% means that 100% of the customers who had an impression of the advertisement, also clicked on the advertisement. This is separate seen from the bounce rate, since this is in the process before entering the website. However, the CTR can be influenced by several factors, such as timing, frequency and design. These factors will be elucidated in chapter 3.7. It is assumed that customers will click on banners with content that matches their requirements more than banners with less matching information. Banners are visible on the major part of websites that are online. There could be more than 20 advertisements on a single page from all different competitors visible. Therefore, the customer will probably select one of those advertisements that is the closest to their needs (if they decide to click on an advertisement). Besides, in this research it is intended to select the customers on the right time. This will result in customers viewing an advertisement that matches their needs and at the right time. Therefore hypothesis 2 is defined as:

H2: Behavioral retargeting campaigns based on theories of consumer behavior or decision making have higher CTR rates than behavioral retargeting campaigns without this basis

The third metric is the conversion rate. As explained, it doesn't matter which product is sold, as long as based on the last click attribution one of the campaigns is the last one used before the purchase. This means, if a customer comes to the website with campaign A, leaves and enters the website again via campaign B. During this visit, the customer makes a purchase, which gives the conversion to campaign B. Campaign A might have triggered the customer to click on an ad served via campaign B, but that is not taken into account. Also, a cookie has an expiration time. If the expiration time is 30 days, this means a customer can max 30 days after coming to the website with campaign B, still make the purchase, resulting that campaign B will get the credits. For this visit, a direct visit to the website will be enough to read the cookie which is still active. Google uses cookie times of 90 days, and this

22 experiment will run only for a week. Therefore, no pollution will exist based on cookie time expirations. It is suspected that the campaigns based on the theories of consumer behavior or decision making will score better than traditional campaigns, since the attempt will be made to select the customers at the time that they actually want to do a purchase. Hypothesis 3 is defined as:

H3: Behavioral retargeting campaigns based on theories of consumer behavior or decision making have higher conversion rates than behavioral retargeting campaigns without this basis

The fourth metric is the cost per action (CPA). The CPA gives the amount of money that every conversion (in this case the booking of a holiday) has cost in the campaign. The lower this number, the more effective the campaign is based on money spend per conversion. As this research implies to select customers that are more likely to make a conversion at the moment they are targeted, the CPA will be lower than standard campaigns, because more actions (conversions) will occur caused by the campaigns. Therefore hypothesis 4 is defined as:

H4: Behavioral retargeting campaigns based on theories of consumer behavior or decision making have a lower CPA than behavioral retargeting campaigns without this basis

The fifth metric is the cost per click (CPC). This value gives an indication of how much money it costs to get a visit to your website via a campaign. The higher this amount is, the more money is spent on one customer to get him or her to your website. This does not mean that this value should be as low as possible, it depends on how much money your customer is worth. However, this research implies that customers are selected more thoroughly, and therefore have more intention to click on an advertisement. This means that the CPC should be lower than normal campaigns. Hypotheses 5 is defined as:

H5: Behavioral retargeting campaigns based on theories of consumer behavior or decision making have a lower CPC than behavioral retargeting campaigns without this basis

The last metric is the return on investment (ROI). The return on investment basically tells you how much money a campaign will generate, expressed in a percentage. This percentage can be positive or negative. In this research it is suspected that the ROI will be higher than standard behavioral retargeting campaigns, since the selection of the customers is done more precise. The total earnings of the campaign will be the turnover of the conversions made by this campaign. This will not show the profit, since Corendon is not giving their profit amounts of the booked holidays. The margins on a sold holiday are really small. According to Corendon, it’s accepted to use a margin of 1% in this research

23 as a rule. It does not affect the ratios, but it increases the meaning of the outcomes, since the ROI is normally based on profits. Therefore, hypothesis 6 is based on ROI is defined as:

H6: Behavioral retargeting campaigns based on theories of consumer behavior or decision making have a higher ROI than behavioral retargeting campaigns without this basis

The cost per thousand (CPM) is not used anymore in this paper. This metric tells us more about the costs of impressions shown to customers. However, it is not valuable to see the costs of impressions since this will contribute to the customer's decision process in many possible ways which are not direct measurable. These impressions can have side effects on the other running campaigns as well and are therefore not a useful KPI to measure and draw conclusions from.

3.2 Method

The selected models will be tested by setting up different experiments to test the hypotheses. A Dutch travel company, Corendon, makes it available to run these experiments based on their data. Corendon is in the top 3 of the travel industry in the Netherlands in selling full package holidays. These holidays include a flight, transfer and stay at the hotel as a package. All visitors of the website create big data which is stored by a Data Management Platform (DMP). In the case of Corendon, Relay42 is used. This DMP collects all data from all visitors of the website and creates a profile of every customer that visits the website. Via a Tag Management System (TMS), which is included in the software of Relay42, the profiles of customers can be extended by javascripts which can add information. This added information is based on actions and events on the website, which can be custom defined (Boogert, 2013). After profiles are created, a connection to other software solutions is possible to create retargeting campaigns based on these profiles. For retargeting customers based on their behavior on the website, Google DoubleClick is connected to Relay42 to set up retargeting campaigns. “Google DoubleClick is the ad technology foundation to create, transact, and manage digital advertising for the world's buyers, creators and sellers” (DoubleClick by Google, 2016). The visitors that are selected with Relay42 based on the selections, are synced with DoubleClick. The cookies contain no personal data, only a profile and an anonymous cookie ID. The ad serving network of Google is needed to reach out to these customers, Google has a great reach on the internet by their search engine and sites where space for Google advertisements is available. The ads that will be served to the customers are managed in Relay42, which allows Corendon to quickly adjust these based on their needs. The ads are dynamic HTML5 banners. Dynamic can indicate two things in this case; the dynamic display or the dynamic content of the banners. Both is used, transforming objects are developed and put into the banner, and the data in the

24 banner is fetched dynamically from a XML feed provided by Corendon. This feed is again generated from the CMS where all the information needed from the hotels is defined. The complete setup for these experiments is visualized in the next image to summarize it:

Figure 1: Summary of technology used for behavioral retargeting at Corendon

The cookies that are served from Relay42 to Google DoubleClick are Google cookies. These cookies can be matched because Google Analytics scripts are also running on the website of Corendon via the TMS. When customers click on a served advertisement, they will be redirected to the website with campaign parameters. These parameters are recognized by Google Analytics if the format is correct, and create reports in Google Analytics to get insights of the campaign results. The campaign parameters are defined in Google DoubleClick and will be visualized in Google Analytics when a customer enters the website via one of the campaigns. All data which is reported and visualized in Google Analytics is based on the last click a customer has done, which is called Last Click Attribution. Whenever a customer makes a sale and arrived at the website via a campaign, the last clicked campaign will get the credits for the conversion. It could happen that campaign A lets the customer visit the website four times and campaign B only one time, but the customer makes the conversion during the visit from campaign B. In this case, Google Analytics will show that campaign B has made the conversion. The product that has been sold however, does not have to be particularly the product that was in the advertisement shown. Probably the shown product will be sold most, but for this experiment it’s not important which product is sold. The traditional created behavioral retargeting campaigns are in this experiment existing campaigns that are set up without a selected theory as basis. For this research, a traditional campaign will be running at the same time with other campaigns.

3.3 Experiment scope

This research contains multiple aspects of online retargeting. The research question focuses on the differences between the used models in combination with behavioral retargeting. Therefore, it’s important to set a clear scope, so that only the factors that are investigated will be measured correctly. Retargeting is based on customers that already visited a website, this research will not focus on getting

25 the customers for the first time to the website. The visitors that do reached the website can be selected for the experiments. However, it’s important that no other campaigns are running that could conflict with the experiments of this research. Therefore, the selections need to be made on people who landed on the website by a direct visit. The assumption is made that this customer has not been part of any other (online) campaign. Further, the advertisement send to the customer should always have the same layout and message. In this research, a banner will be made based on the Corendon standards. In every experiment, this banner will be shown, and only the content can differ. These differences exist because in behavioral retargeting, information is shown based on your visit to the website. In this case, the hotel and it’s information will differ per advertisement. In the travel industry, the time of the year is an important factor on the sales made. Different holidays, different (booking) seasons and factors like paychecks and holiday allowance play an important role in the booking behavior of customers. Therefore, all experiments should run simultaneously, excluding each other's customers. The advertisement shown to the customer should also take into account the period in which the experiment will run. For measuring the results of the campaigns a number of metrics are selected. This research only focuses on these metrics that are directly measurable from the campaigns. However, there are other possible metrics which can define the success of an campaign, such as the number of new newsletter subscribers or another action by the customer. This research will only focus on the bounce rate (BR), click through rate (CTR), conversion rate (CVR), cost per action (CPA), cost per click (CPC) and the return on investment (ROI). These metrics are according to Corendon the metrics they use for measuring their campaigns. Also, these are the major metrics used according Batra (2014).

3.4 Data collection

In order to measure the outcomes of the experiments, quantitative research will be performed on the data. The data will be gathered with Google Analytics, by having Google Analytics java scripts running on the website of Corendon and sending parameters in the URL’s used. This ensures that Google Analytics will create reports based on these parameters. It’s possible to define a campaign source, name, medium, and extra information within these parameters. The quantitative data is most usable to test the hypotheses. An advantage is the objective approach and the precise measurements of more large datasets (Miles & Huberman, 2013). Corendon provides full access to these numbers, but will not give full historical data about all campaigns from the past for publishing in this research. Corendon makes it possible to run the experiments for one week. This should generate more than enough data to analyze. How much customers will be reached cannot be predicted and depends on how much customers fall into the selections that will be defined in the following chapters. Also, the bidding mechanism to target customers to show advertisements is of influence. The budget made free

26 by Corendon for this experiment is 20.000 euro. How much targeted customers this will create, is unknown since prices for bidding are variable. There is no distinction made between mobile, tablet or desktop users. The campaigns will run as they normally do with Corendon, and results of these three groups will be added together. On the Corendon website, all incoming traffic is measured and used. Whenever a customer falls into a selection, a counter will be triggered in Relay42 and information will be send to Google DoubleClick. When a customer returns to the website, this will be measured and pushed to Google Analytics. At last, data will also be pushed to Google Analytics when a conversion is made.

3.5 Advertisements

In this section, the link between the selected models and online customers will be defined. Each selected model will be described separately. As a fourth experiment, a traditional behavioral retargeting campaign will be set up for comparison. As described in the scope of this research, the advertisement which will be shown to the customers will remain the same during the experiment, but the content can change. Some examples of these banners are in figure 2 shown.

Figure 2: Online advertisements from Corendon used for behavioral retargeting

Figure 2 shows different hotels, different prices and some characteristics about the hotel as USP’s. The banners are defined in four possible dimensions: 160x600px, 300x250px, 336x280px and 970x250px, see also figure 3. These are online standard sizes for banners, which are accepted by most websites that offer advertisements. If we take the first advertisement from figure 2, the complete set of banners will be like figure 3 for this hotel.

Figure 3: Online advertisements from Corendon used for behavioral 27retargeting

3.6 Models translated into online behavior

The selected models need to be translated from offline behavior to online behavior in order to recognize and select the customers. Therefore, each model will be examined and translated into online behavior and selections.

3.6.1 Howard-Sheth model

In order to select customers at the right moment for retargeting according to the Howard-Sheth model, it’s necessary to define in which stage and at what moment these customers need to be selected. In the Howard-Sheth model, three levels of decision making are defined: extensive problem solving, limited problem solving and routinized response behavior. In the extensive problem solving stage, the customer has none or not much information about the brand nor about his preferences or attributes of the products that are important to him. During the limited problem solving, the customer knows more about the products and the criteria that are important for making a choice. However, the choice between brands is still not made and comparison is now based on the products they offer. In the routinized response behavior phase, the customer has made his choice for the brand and is ready to make a purchase. All uncertainties about the product attributes are gone at this moment (Howard & Sheth, 1969).

Table 4: Howard-Sheth model phases linked to online behavior

Phase Scenario Link to online behavior

Extensive A person wants to go on holiday with his The person will browse the internet with problem family to a sunny destination. He does not general terms like ‘holiday summer 2017’ solving know with which organization, nor the and will look at different retailers websites destination or hotel he wants to go. Besides, to get an impression of what is possible he does not know yet what kind of hotel he and what he likes. wants and the criteria to choose are not

defined yet.

Limited The person knows more about his The person will look on different websites problem requirements for a hotel, and knows more of competitors for the products that he solving about the period to travel and what kind of likes to book. He will compare the same holiday he want. However, the choice of products at the different competitors and brand is not made yet, since the focus was on will switch between the websites often. the hotels and destinations.

Routinized The person has made his choice for the The customer has made his choice on one response product and the organization where to book. of the websites of the competitors and will

28 behavior He is ready to make the purchase. hit the ‘book now’ button to go into the checkout flow to make the purchase.

Looking at table 4, the limited problem solving phase is the phase where behavioral retargeting could attract the customers to Corendon, since in that phase the choice between brands is being made and the products for sale are known. If the right product is shown on this right moment, it could help the customer to get into the next phase and make the purchase. Besides the phases, this model identifies variables which influence customers during their decision phase. These variables are input variables, hypothetical variables, output variables and exogenous variables. Within input variables a distinction is made between significative, symbolic and social stimuli. Significative stimuli influence the customer directly through attributes of the product, symbolic stimuli influence the customer with factors derived from advertisements. Both significative and symbolic variables include quality, price, distinctiveness, service and availability. Social stimuli are derived from family, references or social groups (Howard & Sheth, 1969). Within hypothetical constructs, a distinction is made between perceptual constructs and learning constructs. Perceptual constructs are perceived and influenced by the input variables. The learning constructs “deal with the stages from the buyer motives to his satisfaction in a buying situation. The purchase intention is an outcome of the interplay of buyer motives, choice criteria, brand comprehension, resultant brand attitude and the confidence associated with the purchase decision” (Seborro, 2011). The output variables are reactions of the consumer on the input variables. These outputs are attention, brand comprehension, attitude, intention and purchase. The purchase is the result of the other four variables. Besides the defined variables, there are some undefined external variables possible which influence the buyer's decision, these are the exogenous variables (Howard & Sheth, 1969). The model of Howard and Sheth is shown in figure 4.

Figure 4: Howard-Sheth model (Howard & Sheth, 1969)

For this experiment, the input variables remain the same as used for the other selected models to be able to make a comparison between the different models. The hypothetical and exogenous variables are not measurable online, some of the output variables however can be translated to online behavior. Attention is based on the amount of information intake. This can be translated into the time spend on the website (during an active session), the amount or type of pages seen on the website (Knight, 2014). According to Google Analytics data of Corendon accessed on 4th July 2017 the average session duration was 5:30 minutes, based on the data of the year 2017. The average amount of pages per session was 5,57. For this experiment, customers will be selected that are above these averages. This means that customers that visit the Corendon website for more than 05:30 minutes or visit at least 6 pages will be selected. Brand comprehension can be measured on the Corendon.nl website if webpages are visited that contain specific information about Corendon. These can be promotional pages or informative pages. These pages are recognizable and when a customer will visit one of these pages the customer can be selected based on these page types. However, Corendon data reviews in Google Analytics that customers do visit 1,04 informational page per session on average (accessed on 14 July 2017, based on date of 2017). Customers can enter the website on a informational page or lookup one during their visit. For this research customer will be selected that are above this average, and therefore at least two informational pages about Corendon should be viewed to be selected. The attitude of a customer cannot be measured online since this is the emotional attitude towards the brand. The same applies for the intention of the customer. In the end, the purchases will be measured as KPI for this experiment.

3.6.2 EBM model

In order to select customers at the right moment for retargeting according to the EBM model, it’s necessary to define in which stage and at what moment these customers need to be selected. In the EBM model, seven phases are defined: need recognition, search for information, pre-purchase evaluation of alternatives, purchase, consumption, post-consumption evaluation and divestment. “Need recognition occurs when an individual senses a difference between what he or she perceives to be the ideal versus the actual state of affairs” (Engel et al, 2005, p. 71). The product to buy should be of more value than the money which will be spent on it. Phase 2, search for information, can go two directions: search can be internal (information from memory or genetic tendencies) or external (information via peers, family or marketplace). Also there is a distinction between active or passive search. Passive means that the customer will become more receptive to information around him, active means among others searching on the internet. During the search, the customer can find marketer dominated content, which the supplier provides, or non-marketer dominated information (Engel et al, 2005). In phase three consumers use different evaluative criteria, the way consumers compare different products or retailers. These can consist of environmental influences (culture, social class, personal influences, family or situation) or individual differences (consumer resources, motivation, knowledge, attitudes and personality). Attributes that consumers use to evaluate alternatives can be salient or determinant. Salient attributes are things like price, reliability and factors that vary little between similar products or types of products. Determinant attributes are more brand based. During the purchase phase, the consumer has two phases to complete. The first is which retailer to select, the second one involves in-store choices. The in-store choices can be different from what the customer intended to buy, since the retailer can influence these purchases heavily. In the consumption phase, the consumption can happen immediately or in a later stage. The satisfaction is based on how the consumers use the products. In the post-consumption evaluation, satisfaction or dissatisfaction can occur. When the needs and expectations of the consumer are matched, satisfaction will occur. Dissatisfaction occurs when the expectations are not met. Consumers tend to have cognitive dissonance, where they are reviewing their experience (Engel et at, 2005). In the seventh phase, the divestment, consumers can have several options such as outright disposal, recycling or remarketing.

Figure 5: EBM model (Engel et al, 2005)

If we would zoom in on these phases, describe scenarios in a travel environment and link them to online behavior, the selections can be defined.

Table 5: EBM model phases linked to online behavior

Phase Scenario Link to online behavior

Need A person wants to go on holiday with The person will not be that active online (yet). recognition his family to a sunny destination, his The need is clear and the decision to go on desire to go on holiday exceeds his holiday is made. value for his money. He does not know with which organization, neither the destination he or she wants to go to.

Search for The person can search active for The person is browsing on the internet and information information. “Search refers to a looks into his options on multiple websites that receptivity of information that solves offer holidays that could meet his needs. problems or needs, rather than a search for specific products” (Engel et al, 2005, p. 74).

Pre-purchase The person looks for differences To measure what kind of retailer the person is evaluation of between retailers and between products looking for (determinant attributes), is in this

32 alternatives between the brands. Also, within one case not possible. The salient evaluation retailer, differences are looked over attributes are measurable, since the person will between the products, and choice look for more information about the different alternatives are evaluated. hotels he or she is looking for. The hotels and flights differ and information about these differences is available on the website.

Purchase The person makes a choice between the It’s not possible to measure the retailer choice. retailer, and after making that choice, However, when the choice is made for in-store products are selected to buy. Corendon, the in-store products can be measured (and influenced).

Consumption The person goes on his holiday. This phase cannot be measured online.

Post- The person thinks back on his holiday, Online this can be measured with surveys, consumption can have cognitive dissonance and is which every customer gets after he is back evaluation positive or negative about the trip. from his holiday. The rates give an impression of the (dis)satisfaction of the consumer.

Divestment Divestment is not possible with a This phase cannot be measured online. holiday, after the holiday has been completed there is nothing left to divest.

To make online selections of consumers based on the EBM model, the Pre-purchase evaluation of alternatives phase is the phase where consumers can be selected, as becomes clear in table 5. The salient attributes will be used as input to make the selection. The determinant attributes are out of scope, since the customer is already on the Corendon website. Assumed is that this choice is already made on basis of determinant attributes. The following salient attributes can be found and used on the Corendon.nl website: - Prices - Information about the hotel - Ratings - Activities - Photos and videos - Location - Information about the flights and transfers

These salient attributes can be found on hotel pages as selectable tabs:

Figure 6: Hotel page on Corendon website with salient attributes tabs

To give an importance ranking to these attributes, it can be checked how much the different attributes are clicked by customers in percentages. Since the 12th of October 2016, these clicks are measured on the Corendon.nl website and registered in Google Analytics. The ranking is as following (on 31th of May 2017):

Table 6: Salient attributes on the Corendon website overview

Salient attribute Ranking Percentage

Information about the hotel 1 46,96 %

Prices 2 31,18%

Photos and videos 3 13,00 %

Reviews 4 5,44 %

Location 5 1,79 %

Information about the flights and transfers 6 1,04 %

Activities* 7 0,42 % * The activities attribute is not present for every hotel

If the top 3 rankings are combined, 91,14 % of all clicks on the possible attributes are covered. For this experiment, these attributes will be combined and if they are all clicked in one session, customers will be selected to be retargeted with ads. This ensures that customers are in their Pre-purchase evaluation of alternatives phase and have shown enough interest in this particular hotel to be retargeted according to the EBM model. When the customer views a new hotel including these three attributes, the previous hotel will be overridden and the new one will be shown in advertisements.

3.6.3 Bettman model

In order to select customers at the right moment for retargeting according to the Bettman model, it’s necessary to define in which stage and at what moment these customers need to be selected. In the Bettman model, seven major phases are defined: processing capacity, motivation, attention and perceptual encoding, information acquisition and evaluation, memory, decision process and consumption and learning process.

Figure 7: Bettman model (Bettman, 1979)

Processing capacity differs per individual and limits are severally restrictive. A way to bypass these limits is to ignore or prioritize information. Consumers will pick the easy way in this phase to search for a product (Bettman, 1979). Motivation influences the intensity and direction of the choices the consumer makes during his phases through the Bettman model. During the motivation phase, there is a hierarchy of goals that help the consumer to make a choice. However, the consumers experience plays a role in which goals he or she needs to pass before making a choice and helps to organize the consumer efforts. (Bettman, 1979; Prasad & Jha, 2014). Attention and perceptual encoding distinguish two types of attention: voluntary attention and involuntary attention. Voluntary attention is the conscious attention seeking to achieve the goals which are defined in the motivation phase. The involuntary attention is the response to newly gained information. Perceptual encoding helps the consumer to integrate the information into his own perception of needing more information or not (Bettman, 1979). The fourth phase is information acquisition and evaluation, where the customer

35 reviews the information he or she has and makes the decision to look for more external information if needed. This process will continue until the customer has made sure all relevant information is gathered or if the additional information exceeds the time or effort worth to look for it (Bettman 1979; Prasad & Jha, 2014). In the fifth phase, two types of memory exist: short term memory and long term memory. Short term memory is information from the last two minutes, long term is the rest of acquired information. If the memories are not sufficient enough to make a choice, the external search will continue. The next phase is the decision process, where the choice for a product or brand is made based on the information which is gathered and both perception and individual factors, such as personality differences. Besides, situational factors play a role, such as urgency of the decision (Bettman, 1979). In the last phase, the Consumption and learning process, Bettman highlights the capacity of the consumer to analyze the gathered information and to learn from the experience during the decision phases. This is now stored in the memory of the consumer and can be used for future decisions (Bettman, 1979). If we would zoom in on these phases, describe scenarios in a travel environment and link them to online behavior, the selections can be defined.

Table 7: Bettman model phases linked to online behavior

Phase Scenario Link to online behavior

Processing A person wants to go on holiday with The person could browse a bit on general terms like capacity his family to a sunny destination. Not ‘sunny holiday’ or the period he wants to go, but a much effort is put into his search yet, detailed search on competitors websites is not done nor he does know much about his yet. preferences yet.

Motivation The person does not know much about The person is motivated to search for a holiday and holidays offered and therefore he or by browsing the internet he discovers that a certain she makes a goal set to complete for amount of goals needs to be completed before making a choice. This goal set can making the final choice for a product. Examples of exist on goals like choosing a goals are the selection of the period, competitor, destination, a travel period, the type of hotel, price related goals etc. From his experience, accommodation etc. he can have some experience with one of the goals that helps making the decision.

Attention and During the search where all goals are During the search for hotels and flights on perceptual tried to be answered, a lot of competitors websites, the person finds that more encoding information is found, including new goals play a role in making a choice. Which insights information. This creates more goals to the person gets is hard to measure online, since it’s

think about which were not on his not known what the person defined set of goals is. mind before.

Information The newly found goals have The person is looking at all the websites of the acquisition differences amongst the competitors competitors again, and will search on the internet and websites and makes sure the person is for more information about these goals. This evaluation revisiting all products again. Until the information can be used to make a choice between person knows enough about these products of different competitors. The visits on the goals and about the differences products can be measured online. The external between the competitors, or he does search cannot be measured since this is beyond the not want to put that much effort website of Corendon. anymore in these goals, the comparison continues.

Memory During the search, the person’s During the information search, the person can get memory plays a role. Information he insights that he compares and remembers from just found can be compared with the visits of products in earlier searches. The person can information he found previously in his now go back to websites and quickly search for search. These can be short term or long these products again to see if his memory was right. term memories. The person knows quite well which products he has visited from his short term memory and can easily find them by searching for them via the website search. Also the memories could be more vague from his long term memory, which makes the person to put more effort in finding the products back again. The searches performed on the website can be measured.

Decision The person is going to choose and The person will select the product he is going to buy process book his holiday, based on his and will continue into the booking flow of the findings, individual factors and website. There will be no hesitations and the situational factors. He has made his booking flow will be completed. The bought decision of the competitor and product. products can be measured online.

Consumption The person is going on holiday and In this phase, the person actually went on the and learning will get learnings from his holiday for holiday and does not have any online activities process future choices to make. anymore. An online review could reflect his experience, besides that online measurements are

not possible.

To make an online selection of customers based on the Bettman model for retargeting, the Information acquisition and evaluation phase and memory phase are the phases where customers should be selected. However, the Information acquisition and evaluation phase is a quite general behavior phase which does not differs in behavior much more than the limited problem solving phase from the Howard-Sheth model or the pre-purchase evaluation of alternatives phase of the EBM model. Therefore, a selection based on this phase will not be a selection based on the Bettman model. The memory phase however is an important phase which is not present in the other models and is therefore a distinction which can be used for online selections based on this model. As described in table 7, a consumer can use the search fields on a website to find a product quickly which is stored in his memory. On the website of Corendon, two ways of searching are possible; an open free text field which gives options based on the input, or a quick search box with four predefined metrics and a search button. The free text search for the search term ‘lara’ gives the following result:

Figure 8: Screenshots of free text search (with search on ‘Lara’) and quick search box on the Corendon website

The customer can see all destinations or all hotels matching his search term. From a short term memory, the customer can easily use this form to get back to the product he was previously looking for. The second option is the quick search box: in this search box, the customer can fill in one, two, three or four questions with predefined values in dropdown menus to narrow down his search. The questions are location, period, travel member and budget related. When one or more filters are filled in, the button shows the amount of results the customer will get. When clicking on this button, the results will be shown on an search overview page. When a customer has a product in his long term memory, but does not know exactly the name anymore, but he does remember the set of goals that he

38 defined, this search box can easily help the customer to find back his hotel. A side note is that this quick search box can also be used for customers without a product in their memory. In this case the customer will use it as guideline to find a hotel based on his needs. For this experiment, when a customer visits the website of Corendon and uses one or two of the two possible search boxes in a session, the customers will be selected and be retargeted according to the Bettman model. The assumption is made that the customer uses these search possibilities to find the hotel from their memory back again. In case of the free text search, the hotel which is selected from that search will be used for retargeting. In case of the quick search box, the hotel that is clicked from the search result page will be used for retargeting. Every time the customer makes a new search, no matter which search box used, the hotel to be shown will be overridden and shown as advertisement.

3.6.4 Traditional behavioral retargeting campaign

For comparison and to set a control group, a traditional behavioral retargeting campaign will run simultaneously with the other three campaigns. This selection will be a normal selection of customers that visit an hotel on the website, no matter if it’s a direct visit or not. Besides, it does not matter which actions the customer performs before or on the hotel page, just like a behavioral retargeting campaign behaves at the moment for Corendon.

3.7 Summary of selections

The selections which will be used for the four different behavioral retargeting campaigns are defined and are summarized in the table 8:

Table 8: Summary of selections used for the experiment

Experiment Model Selection no.

1 Howard- Customers can be selected in three ways: Sheth model - Customers that visit the Corendon.nl website for more than 05:30 minutes in an active session - Customers that visit at least six pages at the Corendon.nl website in one sessions - Customers that visit at least two web pages that contain specific information about Corendon, such as promotional pages or informative pages.

2 EBM model Customers that click and view the top 3 attributes of a hotel in one session. These attribute tabs are the information about the hotel tab,

prices tab and photos and videos tab.

3 Bettman Customers that use one or two of the possible search boxes during a model session.

4 Control Customers that have opened a hotel page. group

These experiments will run simultaneously, and will exclude each other customers. This means that a customer can only be selected for one experiment and cannot switch afterwards to another experiment. Also, these customers will be excluded from other running campaigns set up by Corendon.

4. Results

4.1 Overall results

The campaigns have been running for one week and in total 3697,19 euro is spent to retarget customers online. The maximum amount set by Corendon was 5000 euro for one week. Due to the bidding mechanisms in Google DoubleClick, only 3697,19 euro was spent on the campaigns. This amount is not divided equally over the four campaigns, since the bidding rules decide which customer will be selected at which moment and for how much money. However, the settings for the bidding mechanism are kept the same for all campaigns. The rules that were set by the marketers of Corendon, most rules they normally use for other campaigns as well:

- Rule 1: Combined maximum budget for all campaigns: 5000 euro - Rule 2: Frequency cap: maximum of 5 exposures per day - Rule 3: Do not exceed average CPM of 20,00 euro - Rule 4: Country: Netherlands - Rule 5: Day and time: all days of the week, from 7AM till 12AM - Rule 6: Devices: desktop, tablet and smartphone - Rule 7: Exclude each other’s customers

Since the bidding rules were the same for all campaigns, it's likely this doesn't affect the results, since averages will be calculated. The overall results of the campaigns are in table 9 shown.

Table 9: Overall results experiments

Table 9 shows the total costs spend per campaign, the shown advertisements, clicked advertisements, the conversions that came out of every campaign within this week and the total turnover. The moment of exporting this data is important for the results, since cookie expire times are set to 90 days by Google. It could happen that after 89 days, the customer still makes a conversion which will attribute to one of these campaigns. This data is gathered seven days after the campaigns ended. As described earlier, it is assumed that the right customers are selected at the right moment for the purchase, and therefore the data is gathered this short after the ending of the campaigns to exclude the purchases that

41 could occur by coincidence later on. The last column is the total turnover instead of the total revenue. On the website of Corendon, there is no information available during a booking of the holiday about the net profit of that holiday. At that moment, only the complete amount of money the customer spends on his or her holiday is known and send to Google Analytics. For calculating the ROI, we will use the assumption that 1% of the turnover is margin. However, for the results of table 9, we compare the turnovers of all campaigns. The turnover is influenced by the amount of money spent per campaign. Therefore, the ROI’s will be compared with each other, since these are calculated averages. Based on these results, the metrics defined for testing the hypotheses can now be calculated. In table 10 a summary is given of all these values.

Table 10: The key metric results of the experiments

In the next subchapters, the hypotheses will be validated and compared between the experiments and the control group. With every metric, experiment 1 will be compared with the control group, experiment 2 will be compared with the control group and experiment 3 with the control group. To see differences amongst all experiments, each experiment will be held against each other also. This does not provide direct answers on the sub questions, but could support the argumentation. Chi Square tests will be performed to calculate if the differences are significant. The Chi Square test is useful to see whether there's a relation between two categorical variables. It's based on the idea of comparing the frequencies observed in certain categories to the frequencies you might expect to get in those categories by change. The Chi Square tests had a degree of freedom of 1, and the alpha used is 5% (.05). Given this, the Chi Square value (X²) should exceed 3.84 to prove significant differences.

4.2 Metric 1: Bounce rate

The first metric, the bounce rate, is summarized in table 11. In this table, experiment 1, 2 and 3 are separately tested against the control group with a Chi Square test, which is shown in the last column (X²).

Table 11: Calculated bounce rates of experiments

There is a statistically significant difference found between experiment 1 and experiment 3 compared with the control group (X² = 4.64; 4,98; p < .05), and both experiments score better than the control group. This supports hypothesis 1 that campaigns based on models of consumer behavior or decision making have a lower bounce rate than campaigns without this basis. Overall seen experiment 1, 2 and 3 all have lower bounce rates than the control group. Comparing experiment 1 with experiment 2 and 3 does not give a significant difference, neither do experiment 2 and 3 differ significant.

4.3 Metric 2: Click Through Rate

The second metric measured during the experiments was the Click Through Rate (CTR). Results are shown in table 12:

Table 12: Calculated Click Through Rates of the experiments

There is no significant difference found between the CTR of experiment 1 and the control group. However, experiment 2 and experiment 3 compared to the control group (X² = 3,84; 39,77; p < .05) have a significant difference. These results support hypothesis 2, that retargeting campaigns based on models of consumer behavior or decision making have a higher CTR than campaigns without this basis. It’s notable that the X² value of experiment 3 is much higher than experiment 2. When comparing experiment 3 with experiment 1 and 2, there is a significant difference (X² = 34,72; 38,15; p < .05) between them. Campaign 1 and 2 also do differ significantly from each other (X² = 4,45; p < .05). These results show that customers are most likely to click on an advertisement in experiment 3, and least likely to click on an advertisement in the control group.

4.4 Metric 3: Conversion Rate

The third metric used in the experiments was the conversion rate (CVR). In table 13 are the results shown. Note that the conversion rates are based on the amount of people that reach the website, not on the total amount of advertisements shown.

Table 13: The calculated Conversion Rates of the experiments

Experiment 1 and experiment 3 do not differ significantly from the control group. Experiment 2 does have a significant (X² = 13,35; p < .05) difference with the control group, and does therefore support hypothesis 3, that retargeting campaigns based on models of consumer behavior or decision making have a higher conversion rate than campaigns without this basis. When comparing experiment 1 with experiment 2, there is a significant difference (X² = 8,57; 34,26; p < .05) found. Experiment 1 and 3 do not differ significant. Comparing experiment 2 with experiment 3 gives a significant difference (X² = 4,76; p < .05). This concludes that experiment 2 has the highest conversion rate and is compared with all other campaigns significant proven to be better.

4.5 Metric 4: Cost per Action

The fourth measured and calculated metric is the Cost per Action (CPA). In table 14 are the results:

Table 14: The calculated Cost per Actions of the experiments

Looking at the differences in Cost per Action, experiment 3 scores best, experiment 2 second best. These two experiments score better than the control group and support hypothesis 4 that retargeting campaigns based on models of consumer behavior or decision making have a lower CPA than campaigns without this basis. Experiment 3 has a higher CPA and does not support hypothesis 4. The results mean that when a marketer would create a campaign based on experiment 3, the cost per action (a conversion in this case) created by this campaign will be the lowest. The control group has a CPA that is almost twice as high as experiment 3.

4.6 Metric 5: Cost per Click

The fifth measured and calculated metric is the Cost per Click (CPC). In table 15 are the results shown:

Table 15: The calculated Cost per Clicks of the experiments

Comparing experiment 1, 2 and 3 with the control group shows that only experiment 3 has a lower CPC than the control group, which is positive in this case. Therefore, there is found evidence with experiment 3 for hypothesis 5 that campaigns based on consumer behavior or decision making have a lower CPC than campaigns without this basis. Experiment 1 and 2 have a higher CPC, which means that it costs the marketer more money per click on an advertisement in the campaigns. Experiment 2 is per click almost 2,5 times more expensive than experiment 3, and almost 2 times more expensive than the control group. This means that hypothesis 4 is not completely supported, since two of the three experiments score significant lower than the control group.

4.7 Metric 6: Return on Investment

The last metric measured and calculated is the Return on Investment (ROI). In table 16 are the results and the X² values in comparison with the control group.

Table 16: The calculated Returns on Investment of the experiments

Experiments 1, 2 and 3 all have a higher ROI than the control group. Besides, experiment 3 has more than 2,5 times a better ROI than the control group, and experiment 2 two times. This proves evidence for hypothesis 6 that campaigns based on consumer behavior or decision making have a higher ROI than campaigns without this basis.

5. Discussion and conclusion

5.1 Discussion

Differences between the experiment groups and the control group are observed in the results, therefore the results and the method will be discussed before the conclusion is made. The selected models of consumer behavior and decision making, the Howard-Sheth, EBM and Bettman model, have been selected out of ten models that were created before the rise of the internet. These models were selected by the approved journals and their relative value in the field. However, there are many more models founded that might be better to translate to online behavior, but for this research a clear selection of models was made. A first selection of models was made without taking into consideration the usability of these models for this experiment. After the selection of the ten models, the usability of the models for this research was tested. The three selected models have proven to be usable for this research, but within this scope, it is unknown if there are other (more) usable models. Before starting to discuss the different metrics that have been tested with the experiments, the validity of this research is discussed. The experiments have been running on the network that Corendon uses with their daily retargeting campaigns, and the outcomes of these experiments are outcomes normally used by marketers to value their campaigns. Therefore, the test validity of the experiments is high. However, the bidding algorithm of Google Doubleclick is not entirely manageable by Corendon. As described in chapter 4, bidding rules are set for the campaigns to create boundaries for the experiments. The algorithm of Google does the bidding on customers, and outcomes are trackable afterwards. Therefore differences occurred in how much money was spent on the campaigns. Also, it is not known whether a campaign contains customers that cost more money compared to another campaign during the bidding. In the end, this can be influenced by creating more bidding rules. For this experiment, the rules applied are used for all four experiments and therefore the results seem valid. During these experiments, there was no overlap in between the different campaigns. This means that customer A can only be in one of the four campaigns. However, the customer can also see other Corendon advertisements through other (online) channels. With other online channels, it could be that the customer is taken over by that campaign. It could also be that a customer is influenced by another campaign, before he or she comes in contact with an advertisement from one of the campaigns of this experiment. This could influence the consumer behavior or decision making path a customer goes through. Besides the online campaigns, also offline campaigns such as advertisements in newspapers could have influenced the customer. This research is about behavioral retargeting, and therefore the customer is targeted on prior visits of the Corendon website. But customers could enter the website at a different moment of their decision making state, or their decision to enter the website

46 is influenced by seeing offline - and other online advertisement. This is not measurable, and therefore this research assumes that customers are not influenced by other campaigns. When looking at the overall results of the campaigns, the invested money is not divided equally over all four campaigns. As previously explained this is due to the bidding algorithm Google Doubleclick uses and the fact that Corendon stopped the campaigns at 3697,19 euro. It might have been better that the campaigns each got separately their budget (e.g. 1250 euro per campaign) and that they stopped when this amount per campaign was reached. Still the amount of advertisements would not be equal because of the bidding algorithm of Google DoubleClick, but the overall campaigns results would be more in line with each other in numbers of advertisements shown and money spend. In this experiment the least money was spent on campaign 3, more than four times less than the control group. This might have influenced the significance of the results. The six measured metrics give interesting insights. When comparing the control group with the averages of all four campaigns shown in table 10, the control group scores with almost all metrics under the averages, except for the CPC. Experiment 1 has a negative CTR, CVR and CPA compared with the averages, experiment 2 a negative CTR, CPC and ROI compared with the averages and experiment 3 only the CVR compared with the averages. This indicates that the campaigns score better overall than the control group if we compare it with the total averages, but it differs per metric. When comparing the experiments with the control group per metric, only with the CPC and ROI all results differ significantly from each other. However, the CPC from the control group scores better than the CPC from experiment 1 and 2. The ROI is the only metric that is significantly better for experiment 1, 2 and 3 than for the control group. In addition to comparisons made between experiment 1, 2 and 3 and the control group, the results of the different experiments are also compared to each other to see if the differences between all experiments could give extra insights. When looking at the bounce rate, experiment 1 and 3 differ significant positive from the control group. When comparisons are made between the experiments, no evidence is found for the differences. Based on the bounce rate, a marketer should go for experiment 1 or 3, because when comparing the experiments amongst each other there is no campaign significantly better than the others. Apparently the content of the opening webpage in the control group is the worst matching page with the needs of the customers, which indicates support that behavioral retargeting campaigns based on consumer behavior or decision making does matches the needs of the customers more at the right moment. With the CTR, only experiment 1 does not differ significantly with the control group. All other campaigns do differ with each other. This means that experiment 2 can be seen as the best campaign based on the CTR. This shows that the advertisements customers see, are most relevant for the group in experiment 3. The layout of the advertisements is kept the same, so this higher CTR is based on the content, which is in this case the hotel they are interested in. Translated into web behavior performed on the Corendon website, this means that the customers who use one or two of the

47 search boxes, have the highest intensity to click on a retargeting banner. This could indicate that these people who are searching for specific hotels or destinations, have a high intent to make a conversion. The conversion rate shows great differences in the rates. Experiment 2 is compared to the control group the campaign that scores the best, more than double the conversion rate of the control group. It seems that these customers are in a stage of their decision process where they are likely to buy a holiday at the moment of seeing the advertisements, since the results are measured maximum one week after the campaigns finished. The Cost per Action shows that experiment 3 is by far the experiment with the best score, compared with the control group and the other experiments. If a marketer bases his online marketing strategy on actions (conversions in this case), he or she should select customers which are selected according to the Bettman model. At Corendon they have a percentage of each sold holiday which they can pay on advertisements. They could do an analysis based on the average order value and this number to see if this is a valuable campaign for them to run. The Cost per Click shows that only experiment 3 has a better score than the control group. It depends on the kind of campaign a marketer would run if he or she would focus on the CPC. If the marketer wants to create brand engagement or wants to have more traffic on his website, he or she should definitely go for experiment 3. The Return on Investment is maybe the most straight forward metric to use for evaluating a campaign. All campaigns have a positive ROI, but the differences are big. Based on this metric it will be an easy choice for marketers to go for campaign 3. The Returns on Investment are now based on a 1% margin of the generated turnover. This is an average provided by Corendon as a reliable assumption for this research. However, the margin per sold holiday can differ, some sold holidays can even have a negative margin. This means Corendon sells the holiday with an operational loss. When the ROI’s are completely calculated on the real profits, different values for the ROI might be found. In that case the ROI is more valuable for a marketer than it is now.

5.2 Conclusion

Based on the selections of the models, the results in chapter 4 and the discussion in the previous subchapter, a conclusion can be drawn and an answer on the main research question can be given. This research started with sub question one, which was the research into different models of consumer behavior or decision making. Based on a set of criteria, a total of ten models was selected. The ease to translate these models into online behavior differed, but at forehand it was assumed that it must be possible to translate them into online behavior. The selected models that were used for the experiment had results that have been proven to be significant different with the control group and translations to online behavior were correctly measured. Therefore it can be stated that (at least) the three selected models were applicable for behavioral retargeting.

Sub question two focused on recent studies conducted in the field of behavioral retargeting. Despite the interesting articles found on this subject, which were usable for the setup of the experiments, no newly developed applicable theories were found to use in the experiment. The studies have proven that the field of behavioral retargeting is still in development and although they measure different aspects with their experiments, none of these studies link to models of consumer behavior or decision making or try to create a new theory. An exception on these studies is Kahneman (Kahneman, 2011). Kahneman proposes a dual processing theory. However, this theory was not applicable for this research since it was too difficult to translate this into measurable online behavior. After selecting the ten models of consumer behavior or decision making, a further selection of models was made based on their ability to be translated into online behavior. The performance of all three models was validated by an experiment. To answer sub question three, which applicable theories will be selected for the experiment, a scorecard was defined to make the selection. The Howard-Sheth model, the EBM model and the Bettman model had the highest scores and were therefore selected based on the possibility to measure them online, if the models had a clear defined multi step process and if there was a link to advertisements in the models. The fourth sub question focused on the validation of the different models through an experiment. Each model was translated into a campaign and a control group was created. The control group got a behavioral retargeting campaign without a model as basis, the campaigns Corendon normally uses. Overall seen, it can be stated that all three experiments score better that the control group on almost every measured metric. When summarizing the results and reviewing per metric which model scores best, experiment 3 scores at the bounce rate, click through rate, cost per action, cost per click and the return on investment the highest compared with the control group and compared with the other experiments. Only experiment 2 scored best on the conversion rate. Experiment 2 was based on the EBM model and experiment 3 was based on the Bettman model. This means the empirical validation of the experiment has a positive outcome, and these models can be used in practice for organizations to start or improve their behavioral retargeting campaigns. With all sub questions answered, an answer to the main research question can be given. The main research question was: To what extent is behavioral retargeting that is based on theories of consumer behavior or decision making, more effective than campaigns without this base? This research has proven that all the three conducted behavioral retargeting campaigns based on a model of consumer behavior or decision making are more effective than the control group. This conclusion is drawn on six measured metrics. The control group was in this research a standard configured behavioral retargeting campaign without a model as basis. Within the three campaigns based on a model, it is proven that a behavioral retargeting campaign based on the Bettman model has the best score. To what extent the theories score better than the control group is hard to define in an number, since it’s a combination of six metrics. It is also possible that marketers have certain goals in mind with their campaigns. Based on these goals, he or she can choose which behavioral retargeting

49 campaign based on which model of consumer behavior or decision making suits his or hers needs the most. However, this research has shown that marketers should use or implement behavioral retargeting campaigns based on models of consumer behavior or decision making to improve or surpass their current campaigns.

5.2.1 Limitations

The selected models of consumer behavior and decision making, the Howard-Sheth, EBM and Bettman model, have been translated to online behavior. This translation is done based on the translated scenario’s from offline to online behavior, but also according to the possibilities within the experiment with Corendon. This created some boundaries which were beyond the possibilities for this research. It could be that the selections of customers based on the models can be improved. During the experiment setup and after the experiments had run, it was defined that a week after the experiments had finished, the results would be collected. This ensures that customers were selected who were highly motivated to book a holiday, which supports the statement that customers were selected at the right time according to the used models. However, as explained, the cookie expire time for Google cookies is 90 days. It is possible that more results and conversions came in after the results were collected and were not taken into account for this research. In this research the return on investment is calculated from the turnover. It was stated by Corendon that it can be safely assumed that 1% can be counted as profit. As explained before, the return on investment can differ from the values calculated in this research when the real profits are calculated instead of the turnover. For this research it was not possible to get insights in the actual profits. Last limitation could be the time of the year this research has been done. The travel industry has different periods per year, such as last minute periods or holiday periods. During these different periods, sales and sold holiday types differ heavily. The experiment of this research has been performed during the so called last minute period, where most of the sold holidays are last minutes (departure within a week after date of booking). This supports the statement that more people are selected that are likely to buy a holiday at that specific moment. It could be that these campaigns have different results during other periods in a year.

5.2.2 Future work

This research did not focus on the designs of the advertisements used. Since it is assumed we know in which phase a customer is in his buying process, the advertisements shown to the customer could be adjusted to serve his or hers needs better. This could possibly increase the scores of the campaigns. Also, for this research, ten models were selected to be applicable for behavioral retargeting. Three of them were tested against a control group. It can be interesting to translate the seven other

50 theories also into online behavior and test these empirically, since the differences between the three used models and the control group are significant. This research did not focus on the question why a certain model scores better than another model, but on the question if a model scores better or not compared with the control group. Now that significant differences has been found in the results, one could investigate why e.g. the Bettman model scores better than the EBM model. This requires a thorough investigation into the models, and requires more detailed information about the customers and data gathered. Besides the differences between the models used for the experiments, it is possible that the travel industry works different than other industries. Therefore, it would be interesting to test these experiments on another market. At last, which was out of scope for this research, is to test the difference between desktop, tablet and mobile devices. In this experiment all results were combined for the campaigns, but there might be differences in scores between these mediums.

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Appendices

Appendix 1: calculations Bounce Rates

Appendix 1.1: Bounce Rate Experiment 1 vs control group

Appendix 1.2: Bounce Rate Experiment 2 vs control group

Appendix 1.3: Bounce Rate Experiment 3 vs control group

Appendix 1.4: Bounce Rate Experiment 1 vs 2

Appendix 1.5: Bounce Rate Experiment 1 vs 3

Appendix 1.6: Bounce Rate Experiment 2 vs 3

Appendix 2: calculations Click Through Rates

Appendix 2.1: Click Through Rate Experiment 1 vs control group

Appendix 2.2: Click Through Rate Experiment 2 vs control group

Appendix 2.3: Click Through Rate Experiment 3 vs control group

Appendix 2.4: Click Through Rate Experiment 1 vs 2

Appendix 2.5: Click Through Rate Experiment 1 vs 3

Appendix 2.6: Click Through Rate Experiment 2 vs 3

Appendix 3: calculations Conversion Rates

Appendix 3.1: Conversion Rate Experiment 1 vs control group

Appendix 3.2: Conversion Rate Experiment 2 vs control group

Appendix 3.3: Conversion Rate Experiment 3 vs control group

Appendix 3.4: Conversion Rate Experiment 1 vs 2

Appendix 3.5: Conversion Rate Experiment 1 vs 3

Appendix 3.6: Conversion Rate Experiment 2 vs 3