ii
The effectiveness of customer centricity in search engine advertising
Freek Kuper
University of Groningen Faculty of Economics and Business
Department of Marketing
PO Box 800, 9700 AV Groningen, The Netherlands MSc. Thesis Marketing Management
January 2018
Telephone number: +316 53 99 54 18 E-mail address: freekkuper@gmail.com
iii Abstract
A number of trends caused a stronger position of the customer in the relationship with the business. To respond to this position, organizations and literature adapted a customer centric perspective, which was investigated extensively in offline organizations. However, online businesses and digital marketing are becoming more important, especially search engine
marketing. The present study investigates whether a customer centric perspective is effective for SEA marketing for internet based organizations. A field experiment of 18 days is conducted, search advertisements were manipulated to compare customer centric advertisements to product centric advertisements. Key performance indicators were click-through rate and conversion rate. Moderating effects of target group customers, product category and higher levels of
iv Executive summary
A number of trends caused a stronger position of the customer in the relationship with the business, such as intensifying pressures to improve marketing productivity, increasing market diversity, intensifying competition, more demanding and well-informing customers and consumers, and accelerating advances in technology. For organizations it is thus important to have a loyal customer base, as loyal customers result in profitable relationships. To respond to this position, organizations and literature adapted a customer centric perspective, which was investigated extensively in offline organizations. However, currently online businesses are becoming more important as well as digital marketing, especially search engine advertising. It was not yet investigated whether a customer centric perspective is effective within online
businesses, the present study makes the first steps in that direction by investigating the effects of customer centric product positioned search engine advertising on the click-through rate (CTR) and conversion rate (CVR) of customers (customer responses). Furthermore, higher levels of personalization (in the form of location data) and product category (experience vs. search products) were expected to have a negative moderation effect on the relation between product positioning and customer responses. It is also expected that when a customer belongs to the target group, it positively moderates the relation between customer centric product positioned advertisements and customer responses. A field experiment was used to test the proposed
predictions. Two product groups will be used as experience products: fashion unisex and fashion children; and the product group toys will be used as search products. The experiment will run for 18 days. For each product group two customer centric product positioned were created, one with location data and one without. These were compared to product centric product positioned advertisements. One of those was manipulated with location data. A survey was used to validate that the product groups are indeed experience or search goods. The manipulated customer centric product positioned advertisements were also validated using the same survey. There were 18 participants who completed the survey.
v instead of the expected negative moderation effect of location on the relation between product positioning and CTR, a small positive moderation effect was found. However, there was no moderation effect of location on the relationship of the effect of product positioning on CVR. Furthermore, no moderation effects were found of product category and target group customers on the relation between product positioning and customer responses. However, there was a stronger direct effect of experience goods on customer responses than search goods.
Furthermore, CTR was higher for both target and non-target group customers when they were exposed to customer centric product positioned advertisements. Lastly, target group customers had a higher CVR when they were directed to the website by product centric product positioned advertisements and non-target group customers had a higher CVR overall.
The present study concludes that customer centric perspective in SEA does not result in the same rewards for the ecommerce business that offline organizations can reap using a customer centric perspective. However, some promising results were found to which future research can build upon. For example, the CTR were higher for customer centric product positioned advertisements, but the bounce rate was higher as well, resulting in lower CVRs. This might mean that the
landing pages were not optimized to fit the same customer centric perspective. Furthermore, there was a small positive moderation effect of location, meaning that customers might want higher levels of personalization. Future research should find out to what extend customers accept personalization. Besides future research recommendations, there are also managerial
vi Preface
By finishing my master thesis, I mark the end of my academic journey at the University of Groningen. First of all, I want to thank my parents for enabling me to make this journey and supporting me through my years of studying.
Secondly, I want to thank my supervisor P.C. Verhoef for giving me valuable feedback to further improve my thesis. I also want to thank Jelle Bouma for notifying me of the opportunity to combine writing my thesis with an internship. Besides, he also made a clear manual about writing your master thesis which helped me greatly structure my thesis.
vii Table of contents
Chapter 1: Introduction 1
1.1 Search engine advertising 2
1.2 Product positioning within SEA 4
1.3 The present research 5
Chapter 2: literature review 7
2.1 The road towards a customer centric organization 7
2.2 Conceptual model 8
2.3 Responding to customer needs 9
2.4 Target group customers 11
2.5 Personalized location data in search engine advertising 12
2.6 Product categories 14
Chapter 3: Method 17
3.1 Population 17
3.2 Research design 17
3.3 Procedure 17
3.5 Manipulating the advertisements 19
3.6 Statistical tests 20 Chapter 4: Results 23 4.1 Descriptives 23 4.2 Hypothesis testing 25 4.2.1 Product positioning 25 4.2.2 Location 25 4.2.3 Product category 27 4.2.4 Target group 29 4.3 Control variables 31
Chapter 5: Conclusion and discussion 34
5.1 Limitations and future research 37
References 40
Appendix 46
Appendix A: Survey 46
viii Appendix C: Advertisements including character variations 50 Appendix D: Advertisements as the customer would see them 56
Appendix E: Control variables 58
Appendix F: Interaction graphs of Ancova with impressions as covariate 60
1
Chapter 1: Introduction
“It is the customer who determines what a business is, what it produces, and whether it will prosper.”
Drucker (1954)
More than 60 years ago Drucker (1954) wrote the above quote in his book. 40 years after he wrote this, his ideas were accepted by the business community (Shah, Rust, Parasuraman, Staelin, and Day, 2006). Currently, literature and organizations have accepted the idea that customers play a large part in the success of a business (Day, 2006; Shamma and Hassan, 2013) as they can make or break a business by getting into a relationship with a company and being a loyal and profitable customer, or churn to other companies. Different trends have caused the current position of the customers: Intensifying pressures to improve marketing productivity (Sheth, Sisodia, and Sharma, 2000), increasing market diversity (Sheth, Sisodia, and Sharma, 2000), intensifying competition (Ramani and Kumar, 2008; Shah et al., 2006), more demanding and well-informing customers and consumers (Ramani and Kumar, 2008; Shah et al. 2006; Shamma and Hassan, 2013), and accelerating advances in technology (Ramani and Kumar, 2008; Sheth, Sisodia, and Sharma, 2000). For organizations it is thus important to have a loyal customer base, as loyal customers result in profitable relationships (Shah et al., 2006).
The best means to achieve profitable customer relationships is by transforming the company into a customer centric organization (Shah et al., 2006; Tan, Yen, and Fang, 2002). An organization is customer centric when the organization emphasizes on satisfying the needs, wants and resources of individual customers. A customer centric organization assess each customer
individually and makes a decision whether and how to serve customer needs (Sheth, Sisodia, and Sharma 2000). The difficult part is that not every customer wants to have the same relationship (Avery, Fournier, and Wittenbraker, 2014). One customer might want to engage with the
2 2017). Nevertheless, the effects of a customer centric approach within online marketing
messages have not been investigated yet.
Shah et al. (2006) investigated how an organization could transform to a customer-centric organization in an offline setting. They argue that future research could explore how
technological advances and online business could impact its customer centricity standpoint. Furthermore, according to Shamma and Hassan (2011), a customer centric perspective is a necessity for organizations if they want to excel in the marketplace. Namely, they argue that customer driven organizations’ end goal is to satisfy the customer by fulfilling their needs. The customer then decides whether price, delivery, and quality of goods and services are satisfactory. When satisfactory the customer might become a loyal customer and engage with the organization (Kumar, Aksoy, Donkers, Venkatesan, Wiesel, and Tillmanns, 2010; Palmatier, Dant, Grewal, and Evans, 2006; Shah et al., 2006; Van Doorn, Lemon, Mittal, Nass, Pick, Pirner, and Verhoef, 2010). Furthermore, according to Day (2000), an organization's ability to maintain a relationship with its valuable/loyal customers is a basis for a competitive advantage.
Because of these advantages it is important to investigate whether a customer centric perspective will hold up, and result in the same advantages, within an internet based organization and
whether it is suitable for digital marketing. It is especially important as the internet has become one of the largest marketplaces for goods and services (Leeflang, Verhoef, Dahlström, and Freundt, 2014). Besides, digital marketing is becoming more important for organizations, which requires rethinking the current marketing strategies if organizations want to be competitive online (Baltes, 2015).
1.1 Search engine advertising
Digital marketing has been a popular topic within the literature and consists of email marketing, search engine optimization (SEO), search engine advertising (SEA), affiliate marketing, and social media marketing. The present study has a focus on SEA, which is one of the dominant forms of online advertising (Olbrich and Schultz, 2014; Rutz and Trusov, 2011). Search
3 advertisements based on a format defined by the search engine. Subsequently, advertisers are able to target customers based on specific keyword strings, which are defined by the advertiser (Agarwal, Hosanagar and Smith 2011). When (potential) customers search on a specific keyword string, like “red dress”, the customer will be exposed to organic search results and search engine advertisements which are triggered by the keyword string. The appearances of these
advertisements are called impressions. The advertiser does not have to pay for impressions, instead the advertiser only pays when the customer clicks on the advertisement, which is called cost per click (CPC) (Ghose and Yang, 2009). The number of clicks divided by the amount of impressions is called the click-through rate (CTR). Next, the advertiser must decide what the maximum is that they are willing to pay for a click, which is called the bid. The bid, combined with the quality score of the advertisements (Agarwal et al., 2011), determines whether the advertisement will be shown and on which position, compared to competitor’s bids and quality scores. Quality scores are calculated using the quality of the landing page, expected CTR and advertisement relevance. When the customer actually buys the advertised product after clicking the advertisement, a conversion has been made. Conversion rate (CVR) is the percentage of conversions from the total amount of clicks. In general, a conversion can be anything that is valuable to the advertiser, from requesting extra information to pressing on a phone number, or simply buy the advertised product (Zenetti, Bijmolt, Leeflang, and Klapper, 2014).
4 1.2 Product positioning within SEA
SEA has been researched extensively on subjects as advertisement position, personalization and privacy. Advertisement position has been discussed and the latter two will be discussed in a subsequent chapter. However, research has neglected the effect of product positioning within SEA. Product positioning is ‘where the company wants its product to be placed in the customer’s mind so that it will achieve optimal utilization’ (Vanderveer and Pines, 2007). According to Shah et al. (2006) product positioning can also be transformed to a customer centric product
positioning. Product positioning presents the benefits, attributes and/or features of the product to the target audience in, for example, a marketing message (Aaker and Shansby, 1982). A
customer centric product positioning focuses on the individual customer needs. It highlights product’s benefits, attributes and/or features in terms of meeting individual customer needs (Shah et al., 2006). In contrast, a product centric product positioning (also referred to as
standardized advertisements) focuses on selling as many products to as many customers possible and only highlights general product features and advantages (Shah et al., 2006).
Customer centricity is a part of personalization. According to Chellappa and Sin (2005),
personalization is ‘the ability to proactively tailor products and product purchasing experiences to tastes of individual consumers based upon their personal and preference information.’ A customer centric product positioning also tries to tailor the advertisement message to the tastes of individual consumers based upon the preferences by fulfilling their customer needs. However, there are a few differences between the two. According to the definition of Chellappa and Sin (2005) personalization is dependent on the willingness of the consumer to share private information and use personalized services, and on the ability of organizations to acquire and process personal information of their customers. With the current tools and advances within technology and digital marketing it is possible to personalize advertisements based on demographics, location, interests, and online behavior (Chen and Stallaert, 2014).
5 value-in-use for the customer is the service the product can deliver to them to fulfill their needs. They define service ‘as the application of specialized competences through deeds, processes, and performances for the benefit of another entity.’ In other words, goods deliver a service to
customers at the time of use, which is value-in-use. This is known as the service dominant logic perspective and is aimed at fulfilling customer needs by interacting with the good. Gummesson (2008) uses a car as an example to explain this phenomenon: when the customer interacts with the car it creates the service, which could be to get to places comfortably or getting enjoyment out of having a certain brand and/or the car’s design.
Thus, where personalization most often is about the inclusion of personal data such as location, demographics, interests, and online behavior, a customer centric product positioning is a form of personalization by highlighting the value-in-use for the individual customer. The main difference is that a customer centric product positioning is a form of personalization without the inclusion of personalized data.
1.3 The present research
The present study will investigate whether a customer centric product positioning within search engine advertising has a positive effect on CTR and CVR (customer responses) of customers of an internet based organization. This will be investigated by building up on existing research and theories about customer centricity within offline organizations. When there is indeed a positive relation between customer centric product positioning and customer responses, the present study might have set the first steps towards investigating whether a customer centric approach is achievable within an internet based organization and digital marketing. New research could build upon the current study and investigate effects for a longer time period to find out whether it does lead to loyal customers, higher profitability, and a sustainable competitive advantage. From the perspective of internet based organizations, it could lead to new opportunities in digital
marketing, such as increasing loyalty of customers by adjusting old manners of digital marketing to customer centric marketing.
6 measured for target group and non-target group customers. It is important to note that customers can only be assigned to the target group or non-target group when he/she converts (buys the product) or accepted cookies. Only then there is enough data available to assign the customer to one of the groups. Besides measuring CVR, it is also important to measure CTR, because a significant increase in CTR can be seen as a successful campaign (Kim, Qin, Liu, and Yu, 2014).
Furthermore, the present study investigates whether product groups have a different effect on customer responses. A distinction between experience and search products will be made to investigate whether one of these product groups has a stronger effect for a customer centric product positioning. Furthermore, personalization in the form of personalized location data will be added to find out whether personalization has any effect on customer centric product
positioned advertisements. Lastly, customers are divided into two groups, target and non-target group customers to investigate whether customer centric product positioning has a stronger effect for target group customers.
7
Chapter 2: literature review
This section dives deeper into the relevant literature and will introduce the hypotheses. First, it will be discussed how an organization can transform itself to a customer centric organization as this should be the first step for organizations before adjusting their communications to a
customer centric perspective. Secondly, the conceptual model is introduced. Third, search engine advertising will be discussed and how personalization plays a role in this relatively new form of marketing. Next, a distinction will be made between product categories and it will be discussed why this classification is important. Lastly, a comparison between target and non-target group customers is made.
2.1 The road towards a customer centric organization
An organization that decides to make the transition towards a customer centric approach needs to transform large parts of its organization (Naumann and Shannon, 1992; Shah et al., 2006;
Woodruff, 1997). According to Naumann and Shannon (1992), marketing strategists need to recognize that retaining an existing customer base is at least, if not, more important and more profitable than acquiring new customers. Instead of being reactive in studying opportunities and constraints in the environment and directing the marketing mix toward a target, the marketing department needs to be proactive in defining its marketing strategy. This is possible, because technology advances have made making direct contact with customers much easier and more cost effective. Due to these advances it is easier to actively adopt a customer marketing strategy, which can result in improved marketing practices.
According to Shah et al. (2006) and Woodruff (1997) not only the marketing department needs to change, the whole organization needs to adjust their practices. Shah et al. (2006) describe the path organizations should take when transforming into a customer driven organization. First, the organizational culture needs to be changed. Customer-centered cultures understand that long-term profitability results from customer loyalty. Next, instead of having functions or departments organized around functional silos, a customer centric structure must have all functional activities integrated and aligned with each other to deliver superior customer value. Furthermore,
8 customer’s requirements with the right services and/or products. It also includes changing
marketing metrics toward, for example, share of customer wallet and away from metrics such as market share. Lastly, financial metrics needs to be revised as well. Customer centric metrics are in general long-term metrics such as customer lifetime value (CLV) and customer satisfaction. To successfully change the organization, as Shah et al. (2006) discuss, management commitment is important. Without commitment from the top, changes in culture, structure, processes and financial metrics are almost impossible.
The current study investigates whether a customer driven approach is also effective within an internet based organization.
2.2 Conceptual model
Figure 1 presents the conceptual model. The independent variable is the degree to which the advertisement is positioned as customer centric. The lower the degree the more product centric (standardized) the positioning is of the advertisement. The dependent variable are customer responses which can be divided into the degree to which customers click on the advertisement (CTR) or purchase the advertised product (CVR). It is expected that there is a positive direct effect between a high degree of customer centric product positioning and the degree of customer responses. Furthermore, the extent to which customer belongs to the target group is included as a moderator. It is expected that when the customer belongs to the target group, it will positively affect the direct relationship between a customer centric product positioning and the customer responses. Next, personalized location data is also added as a moderator with an interaction effect. When personalized location data is added, it is expected that this will negatively affect the relation between the customer centric product positioning and the customer responses. However, it is expected that personalized location data will have a positive effect on customer responses. Lastly, product category is added to the model, which is divided into two groups: search products and experience products. It is expected that experience products will negatively affect the
9 2.3 Responding to customer needs
As discussed before and argued by Olbrich and Schultz (2014) SEA is extremely relevant for customers as they are only exposed to an advertisement message when they are actively searching for it. From the perspective of advertisers, SEA is also relevant. Namely, advertisers are able to selectively reach the target group at a point in time when potential customers are already involved and activated, because of the search term they have entered (Olbrich and Schultz, 2014). The customer is thus more likely interested in the search engine advertisement than, for example, an advertisement during a commercial break on the TV, because they are not actively searching for a product or service when watching TV.
10 value-in-use (service dominant logic) (Vargo and Lusch, 2004). Lastly, standardized
advertisements focus on trying to sell as many products to as many customers possible (Shah et al., 2006; Vargo and Lusch, 2008), which makes customers less inclined to click on the
advertisement, because customer needs are not taken into account.
Besides, that the advertisements focus on customer needs, they are also new and more vivid for customers. Due to the customer centric product positioning, these advertisements will have a unique positioning within search engine advertisements, because most other organizations have a product centric product positioning in their advertisements. The advertisements are thus
new/novel to the customer. Novelty is a stimuli that attracts customer attention (Kardes, 2002). Furthermore, due to the customer centric product positioning taken in the advertisements, it might also be emotionally interesting to the customer as it responds to their needs. This can increase the vividness of the advertisement (Nisbett and Ross, 1980). According to Fennis and Stroebe (2015), novelty and vividness can increase the focal attention of customers, which means that the advertisement message is more likely transmitted into the conscious awareness of the customer, where it is identified and categorized. As a consequence, customers are more likely to remember the advertisement and are more inclined to click on the advertisement.
Due to the search term entered by the customer, they are already interested in buying the product, thus SEA is highly relevant for customers. Furthermore, a customer centric product positioning focuses on fulfilling customer needs, which is more likely to result in increased purchases and higher profits. Besides, due to the fact that the advertisements focus on customer needs, it also increases vividness of those advertisements. This is a novel way of advertising within search engines, thus the advertisements stand out more. The present study expects thus that a customer centric product positioning used in SEA will result in higher CTR and CVR compared to standardized search engine advertisements.
11 2.4 Target group customers
The present study does not only focus on target group customer, but also on non-target group customers. Target group customers are those customers that the company is able to serve well by fulfilling their customer needs in a profitable manner. Non-target group customers are the
opposite, the company is not able to serve their needs well and/or build a profitable relationship with them (Jones and Sasser, 1995). Within the customer relation management (CRM) literature, non-target customers are studied extensively. Tan et al. (2002) consider CRM to be the same as a customer centric strategy. CRM can be defined as “a comprehensive strategy and process of acquiring, retaining, and partnering with selective customers to create superior value for the company and the customer” (Parvatiyar and Sheth, 2001). CRM and customer centricity strive for the same goals, namely building a loyal profitable customer base.
Previous CRM literature does not have a general consensus whether or not to serve non-target group customers, which results in different strategies firms could take regarding these customers. First, Cao and Gruca (2005) argue that organizations should completely exclude non-target group customers from their advertisements. Second, Ryals (2013) takes another approach and argues that organizations should add most value into acquiring and retain the (potentially) most profitable customers as long as the cost do not outweigh the benefits. Next, Lewis (2005) argues that for different segments different pricing schemes should be used. The goal of the pricing scheme proposed by Lewis (2005) is to acquire and use knowledge about customers to extract more value from those customers, which results in increased value for the organization. Fourth, Thomas and Sullivan (2005) propose a decision support system in which the organization can decide which messages to send to what customer. The decision support system must make it possible to shift individual customers to more profitable channel. Finally, Nguyen, Li, and Cheng (2012) and Nguyen and Simkin (2013) argue that non-target customers respond more strongly when price is included in the advertisement.
12 target group. However, when using a customer centric product positioning the message is less relevant for non-target group customers, because the advertisement is focused on fulfilling specific needs of the target group. An advertisement that is not relevant for the customer might induce irritation (Thota and Biswas, 2009). In this case a standardized advertisement should generate higher CTR and CVR for non-target group customers as these advertisements highlight general product features and advantages (Nguyen et al, 2012; Nguyen and Simkin, 2013). Besides, advertisers only pay for their advertisements when the customer expects the advertisement to be relevant for them and click on it. Compared to traditional marketing channels, where marketing messages to non-target group customers always results in higher marketing costs (Cao and Gruca, 2005), it does not directly add to the marketing costs within SEA.
Naturally it follows that when a customer from the target group is exposed to a customer centric positioned advertisement relevance and fit is increased. The customer centric advertisement is focused on fulfilling customer needs of the target group and leads to more beneficial
relationships for the customer (Nguyen et al., 2012; Nguyen and Simkin, 2013). Thus, customer centric advertisements that are exposed to target group customers should result in increased relevancy and fit (Shah et al., 2006). Furthermore, by communicating the positioning more clearly, target group customers respond more strongly to the advertisements (Nguyen et al, 2012). It is expected that when target group customers are exposed to customer centric
advertisements, the effect of customer centric product positioning advertisements on CTR and CVR will be higher.
H2: Whether the customer belongs to the target group positively affects the relationship between a customer centric product positioned search advertisement and customer responses.
2.5 Personalized location data in search engine advertising
13 (LAM) and geolocation personalization. LAM can be defined as “targeted advertising initiatives delivered to a mobile device from an identified sponsor that is specific to the location of the consumer” (Unni and Harmon, 2007). Geolocation personalization is about personalization of search engine results based on the customer’s location (Hannak, Sapiezynski, Kakhki,
Krishnamurthy, Lazer, Mislove, and Wilson, 2013), which is not only about SEA, but also about optimizing organic results to the location of the customer. The present study discusses the first and extends its definition by including laptops and desktops, since it is possible to have location tracking on these devices as well. LAM could be highly relevant for customers searching for a nearby shop and is becoming extremely important as mobile searches increase rapidly (Uni and Harmon, 2007; Xu, Luo, Carroll, and Rosson, 2009). Due to LAM customers are able to find a nearby shop while they are on the road.
From a customer’s perspective including personalized location data in the advertisement can increase relevance and fit (Chellappa and Sin 2005; Okazaki et al. 2009; Tam and Ho 2006). From the perspective of the advertiser, advertisements that have high fit and relevance can increase customer’s purchase intentions (Goldfarb and Tucker, 2011). As location increases the fit and relevance of advertisements, it naturally follows that personalized location data will have a positive effect on customer responses. However, van Doorn and Hoekstra (2013) argue that high degrees of personalization can result in feelings of intrusiveness, especially when personal identification or transaction data is used in the advertisement. Furthermore, they argue that feelings of intrusiveness have a negative impact on customer responses, which is in line with findings of White, Zahay, Thorbjornsen and Shavitt (2008), who investigated different degrees of personalization in marketing emails.
14 advertisements. She argues that when customers have no control over their personal data, online advertisements do not perform as good. After she gave them more control, the advertisement did perform well, while there were no changes in how personal information was collected and used. This is in line with research from Xu, Luo, Carroll, and Rosson (2009) who found that customers feelings of intrusiveness decrease when organizations have transparent privacy policies.
Thus, there is a trade-off between using personalized advertisements and customer responses. Due to high fit and relevance, personalized advertisements increase purchase intentions. However, when the degree of personalization is too high, feelings of intrusiveness among
customers increases, which in turn decreases customer responses. Furthermore, without overt and distinct personal data collection policies customer’s feelings of discomfort increases when they receive a personalized advertisement, which in turn decreases customers responses.
Using personal location data in customer centric product positioned search advertisements might be too intrusive for customers, as these are two forms of personalization. The present study expects that when personalized location data is included, it will negatively affect the relation between a customer centric product positioned advertisement and its customer responses, due to high levels of personalization.
H3: Personalized location data in the advertisement will negatively affect the relationship between a customer centric product positioned search advertisement and customer responses.
2.6 Product categories
Within online markets, it is more difficult for customers to evaluate products. Product
15 evaluated after the customer uses the product (fit, taste, etc.). This classification scheme has been widely discussed and has been accepted in the literature (Korgaonkar Silverblatt, and Becerra, 2004; Weathers, Sharma and Wood, 2007).
An article in the Wall Street Journal from 2008 reported that product returns are mainly due to the fact that the product does not meet customer needs (Lawton, 2008), due to product
uncertainty, which is identified as an obstacle within online markets (Dimoka et al., 2012; Ghose, 2009; Kim and Krishnan, 2013). The effects of search vs experience products on online purchase intentions have been investigated extensively. Lim, Al-Aali and Heinrichs (2014) investigated the effect of search and experience products on customer loyalty and website satisfaction, as this is crucial in understanding customer online shopping behavior. They concluded that search products have a stronger effect on website satisfaction and customer loyalty compared to experience products. Mainly because features of search products can be communicated more effectively than features of experience products. This is connected to perceived risk customers experience when buying experience goods from an e-retailer. Because customers are unable to evaluate an experience product when buying online, which makes it more difficult for customers to evaluate the product, which increases product uncertainty (Weathers et al., 2007). Product uncertainty is thus higher for experience products than it is for search products. In contrast when buying experience products in a brick and mortar store, the customer is able to sense the sensory aspect of the product, which results in a better judgement whether the product will perform up to their expectations and needs. This increases the perceived risk for customers to buy experience products online (Campo and Breugelmans, 2015; Chiang and Dholakia, 2003; Lian and Yen, 2013). As a consequence, customers are more likely to buy search goods online than they are to buy experience goods (Chiang and Dholakia, 2003; Girard and Dion, 2010).
This also has implications for internet based organization to sell experience goods, because they cannot reduce the perceived risk by providing a real physical or sensory sense of the product to the customer (Lian and Yen, 2013). Previous research has found diversified approaches and strategies customers take when searching for experience goods to which e-commerce
16 pictures and, where possible, increase the vividness of pages that show experience goods. For search goods, on the other hand, e-retailers should focus on giving the customer more control by, for example, being able to sort reviews. Huang, Lurie, and Mitra (2009) found that the total time customers spend online searching a product is the same for both search and experience goods. However, evaluating experience attributes online, require increased cognitive effort by the customer. Online customer behavior is different when searching for experience goods than when searching for search goods. Customers searching for experience goods view fewer pages but spend more time per page. Furthermore, communication mechanisms, such as consumer reviews, only increase time spent on a web page for consumers searching for experience goods. In the present study, landing pages will not be customized to fit the outcomes of those investigations. However, it is important to discuss these, as it gives a clear view of the difference between search and experience goods in an online setting.
Although a customer centric product positioning does focus on fulfilling customer needs, the current study does not expect that that would be sufficient to take away the risk customers perceive when buying experience products online. It is expected that experience goods negatively affect the relationship between customer centric product positioned search advertisements and the customer responses.
17
Chapter 3: Method
This section will first discuss the research design, procedure, the manipulated advertisement and statistical tests will be discussed.
3.1 Population
The population exists of Dutch customers who are searching for keyword(strings) that enable advertisements for the products used in the present study. As explained before, a customer will only be exposed to an advertisement when he/she is using the keywords triggering the
advertisement. In general, customers are between 25 and 55 years old, 63.3% is female, 41.2% of the visits occur via desktop, and 39.3% are directed to the website via SEA (which is the largest channel). 53% of the total customers belongs to one of the main target groups, they are
responsible for 57% of all customer demand and have an overall conversion rate of 7%.
3.2 Research design
In order to get insights to the hypothesized effects a field experiment is conducted. The present study follows a 2 (target group customer vs. non-target group customer) x 2 (customer centric product positioning vs. product centric product positioning) x 2 (location vs. no location) x2 (search product vs. experience product) factorial between-subject design. Eight advertisements are necessary for the current experiment, to obtain all necessary data (Table 1).
Table 1. Advertisements necessary for the present study Target group customers/Non-target group customers
Customer centric product positioning Product centric product positioning Experience goods Search goods Experience goods Search goods Location Advertisement 1 Advertisement 3 Advertisement 5 Advertisement 7 No location Advertisement 2 Advertisement 4 Advertisement 6 Advertisement 8
3.3 Procedure
18 products as experience or search products, these included: apparel, movies, dishwashers,
monopoly, step, Barbies, marble tracks, Lego, and toys. Based on the definition of search and experience products (Nelson, 1970; Nelson, 1974) the current study classified apparel and movies as experience products and the other seven products as search products. Apparel and movies were grouped together with a mean of 1,868 and the other seven were also grouped together with a mean of 1,105 where 1 was a search product and 2 was an experience product. An independent sample t-test showed that the classification between those groups differ significantly with t(18) = -13.082 and p = 0,000. Based on those results, the current study includes apparel as experience goods and toys as search goods.
Furthermore, in the survey participants were explained the difference between a customer centric product positioned advertisement and a product centric product positioned advertisement. Next, they were asked to classify 15 different advertisements as customer centric or product centric using a Likert scale of 1 till 5, where 1 was product centric and 5 was customer centric. The advertisements were made for three products and for every product, five different advertisements were made. Those five advertisements consisted of one product centric advertisement without location, two customer centric advertisement without location, and two customer centric advertisements with location. There were two different customer centric variations (A and B). For the two variations there was one advertisement which included location and one without location data. The product centric advertisements were grouped together into one variable, just like both variations of customer centric product positioned advertisements (see Appendix B). The descriptives of those groups can be found in table 2.
Table 2. Descriptives of advertisements groups
Mean Median
Product centric advertisements 1,649 1,333 Customer centric advertisements A 4,202 4,667 Customer centric Advertisements B 3,895 4,333 1 = product centric, and 2 = customer centric.
With a Wilcoxon matched pair signed rank test the product centric advertisements were
19 0,000, and r = -0,850. The product centric advertisements were also significantly different from customer centric advertisements B (median = 4,333) with Z = -3,643, p = 0,000, and r = -0,836. Both customer centric advertisements A and B were significantly different from the product centric advertisements, however the differences were very small. It was decided that customer centric advertisements A had a better fit with their core target group’s needs, because these advertisements had a greater emphasis on taking care of the children and getting ready for Christmas. Customer centric advertisements A were thus used in the present study.
3.5 Manipulating the advertisements
The advertisements used in the present study will be optimized to respond to the needs of the two target groups, which results in customer centric product positioned advertisements.
For both product categories (experience and search products) four advertisements are needed each to test the proposed hypotheses (see table 5). All four advertisements for both product categories, including character variations, can be found in Appendix C. The product centric advertisements consists of numbers 3, 4, 7, and 8 (table 5). It was not necessary to manipulate those advertisement, except a small change to reset the quality score. For advertisements 3 and 7 location needed to be included into the advertisement. For the present study the USP in the description is changed to the location variable. For advertisements 1, 2, 5 and 6 new
advertisements will be made, which can be found in appendix C. Validated in the survey, these advertisements are made to fully focus on the customer needs of the customers from the target groups. There is always at least one unique selling propositions (USP) included, most often in title line 2.
Table 5. The necessary advertisements Target group customers/Non-target group customers Experience products Search products
20 There are eight examples in Appendix D of the advertisement and how the customer would see them. These are in Dutch. Some advertisements include review ratings, sitelinks or snippets. This is randomized by Google AdWords and could not be left out and are essential, as competitors use those as well, to attract customers.
3.6 Statistical tests
The dataset used in the present study will be aggregated to advertisement level as in table 5. Every advertisement will be included four times in the dataset, e.g.: an experience product had both a customer centric and a product centric product positioned advertisement, both with and without location data (advertisements 1, 2, 3, and 4 from table 5).
To analyze the direct effects of product positioning on CTR and CVR an One Way Anova seems to be the appropriate analysis. Even though, according to the Kolmogorov-Smirnov test (P < 0,05), the data is not normally distributed. However, according to Field (2013), for large sample sizes normality statistics are not really appropriate, because according to the central limit
theorem large sample sizes will be normal distributed no matter what the shape of the population actually is. The current study has a sample size of 3.987 advertisements, which should be large enough. Thus, an One Way Anova with product positioning (customer centric vs product centric) as independent variable and CTR as dependent variable will be conducted. The same test is conducted with CVR as dependent variable.
The moderators location (yes vs. no), product category (experience products vs. search products), and target group (target group customers vs. non-target group customers) will be tested by a Two Way Anova. First it will be investigated if there is a direct effect of the moderator variable on customer responses and secondly, it will be tested if there is an interaction effect between product positioning and the moderator. Lastly, when there is an interaction effect, a linear regression will be conducted to test the strength of the moderation effect. In that case, the moderator will be constructed into a dummy variable and to avoid high levels of
21 consists of the interaction term between the mean centered predictor and the mean centered (dummy variable), for which a new variable will be constructed.
For the last moderator, target group, a few changes will be conducted to the data set, because it was impossible to collect individual customer data based on the four advertisement combinations (table 5). Instead customer data will be collected through AdWords on campaign level.
Campaign level is one level higher. Each campaign consists of the four advertisements (table 5). Three groups will be made to analyze whether there was a difference between customer
responses for target and non-target group customers. The first group consists of campaigns to which more target group customer had responded. The second group consist of campaigns to which most non-target group customers have responded. The third group consists of campaigns without clicks and conversions. The third group will be excluded from the analysis since it was impossible to assign campaigns without clicks to target or non-target group customers.
Excluding the third group from the analysis results in different sample sizes for both CTR (table 6) and CVR (table 7).
Table 6. Sample size with target group data excluding cases without clicks for CTR analyses.
\
Table 7. Sample size with target group data excluding cases without conversions for CVR analyses.
22 customers, the same One Way Anova’s will be conducted with target group customers who clicked on the advertisements as independent variable.
23
Chapter 4: Results
4.1 Descriptives
The experiment ran for a period of 18 days from November the 28th, till December 14th. During those 18 days there were a total of 3.943.145 impressions, 152.692 clicks and 3.573 conversions combined over all three product groups with a total of 7.137 advertisements. However,
advertisements with zero to 4 impressions were deleted as well as advertisements which had a CTR or CVR of over 100% (which is possible due to the attribution period of 7 days. Which means that it is possible that a customer was exposed/clicked on the advertisement before the current experiment, but clicked on the advertisement/bought the product during the experiment). Deleting those advertisement leads to a total of 3.932.661 impressions, 152.160 clicks, and 3.542 transactions. The CTR was 3,87% and the CVR was 2,33%. The precise numbers per product category can be found in table 8. Furthermore, the average CTR of customers from the target and non-target group are also reported in table 8. Furthermore, table 9 shows the descriptives per advertisements group based on table 5.
24 Table 8. Descriptives
Average position is the position within the search results, where 1 = the top position, and 8 = the lowest position
Table 9. Descriptives per cell based on table 5.
25 4.2 Hypothesis testing
Table 12 at the end of this chapter shows which hypotheses are accepted and which are rejected.
4.2.1 Product positioning
In the One Way Anova between product positioning and CTR, homogeneity of variance was violated according to Levene’s statistic, with F(1, 3985) = 8,302, p = 0,004. To adjust for this matter, Welch’s F test is used. Customer centric product positioned advertisements (mean = 5,11) had a significantly higher CTR than product centric product positioned advertisements (mean = 3,72), F(1, 2182,214) = 107,128, p = 0,000. Furthermore, in the One Way Anova between product positioning and CVR, homogeneity of variance was violated according to Levene’s statistic, with F(1, 3985) = 7,301, p = 0,007. The results of the Welch’s F test were reported. The CVR of customer centric product positioned advertisements (mean = 2,03) did not differ significantly from the CVR of product centric product positioned advertisements (mean = 2,41), F(1, 2435,163) = 1,719, p = 0,190. The first hypotheses was accepted in regard to CTR, but was rejected in regard to CVR as a customer centric product positioning only resulted in a higher CTR.
Table 10. Descriptives direct effects of product positioning on customer responses
4.2.2 Location
26 differed from customer centric positioned advertisements with location (mean = 5,44, SD = 4,17) and customer centric positioned advertisements without location (mean = 4,81, SD = 3,39).
Next, a linear regression was conducted to test whether location had a moderation effect on the relation between product positioning and CTR. The results of this analysis can be found in table 11. By adding the interaction term in the model, there was a significant increase in R² of 0,002 with F(1, 3983) = 8,719, p = 0,03. There was a small positive moderation effect of location on the relation of product positioning on CTR.
Table 11. Results linear regression location on CTR
27 The third hypothesis expected a negative moderation effect of location on the relation between product positioning and CTR however, the present study found a positive moderation effect. Thus, in regard to CTR, H3 was rejected.
Next, the Two Way Anova reported a non-significant main effect of positioning on CVR, with F(1, 3983) = 1,090, p = 0,296 (see table 10 for the descriptives). Furthermore, there was also a non-significant main effect between advertisement with location (mean = 2,10) and without location (mean = 2,44) on CVR, with F(1, 3983) = 0,486, p = 0,486. Lastly, the interaction term of positioning and location was not significant, with F(1, 3983) = 0,638, p = 0,425. This means that the effect product positioning on CVR was not affected by location (figure 3). In regard to CVR, H3 was also rejected, because no negative moderation effect of location on the relation between product positioning and CVR was found.
4.2.3 Product category
28 interaction effect of product positioning and product category on CTR, with F(1, 3983) = 0,397, p = 0,529 (figure 4). This means that the effect of product positioning on CTR is not affected differently by experience or search products, thus H4 was rejected in regard to CTR. H4 expected a negative moderation effect of product category on the relation between product positioning and CTR.
29 4.2.4 Target group
According to the One Way Anova the CTR of target group customers was significantly higher for customer centric product positioned advertisements (mean = 0,054) than for product centric product positioned advertisements (mean = 0,040), with F(1, 3985) = 41,243, p = 0,000.
Furthermore, the CTR of non-target group customers was also significantly higher for customer centric product positioned advertisements (mean = 0,047) than for product centric product positioned advertisements (mean = 0,039), with F(1, 3985) = 8,028, p = 0,005. Next, the same analysis was conducted to compare the CVR of target and non-target group customers to analyze the differences between product and customer centric product positioned advertisements.
Homogeneity of variances was violated for the CVR of target group customers with F(1, 3985) = 12,243, p = 0,00, thus Welch’s F was used. According to the Welch’s F test, CVR was
significantly higher for target group customers who were exposed to product centric advertisements (mean = 0,042) than when they were exposed to customer centric product
30 Next, a Two Way Anova was conducted to analyze whether there was an interaction effect between product positioning and target group customers on CTR. To conduct this analysis the dataset was adjusted to correct for the fact that impressions could not be assigned to target or non-target group customers (see table 6 for insights about the adjusted dataset). There was no difference between product centric advertisements (mean = 5,25) and customer centric
advertisements (mean = 5,11) on CTR, with F(1, 3152) = 0,319, p = 0,572. There was also no significant difference between target group customers (mean = 5,43) and non-target group customers (mean = 5,18) on CTR, with F(1, 3152) = 2,024, p = 0,155. Lastly, there was no interaction effect between product positioning and target group customers on CTR, with F(1, 3152) = 2,385, p = 0,123 (see graph 5). This means that the effect of product positioning on CTR was not affected differently by target group or non-target group customers. In regard to CTR, H2 was rejected as it expected a positive moderation effect of target group customers on the relation between product positioning and CTR.
31 adjusted to correct for the fact that impressions could not be assigned to target or non-target group customers (see table 7 for insights about the adjusted dataset). Product centric product positioned advertisements (mean = 11,95) had a significantly higher CVR than customer centric advertisements (mean = 8,37), with F(1, 847) = 6,832, p = 0,009. Non-target group customers (mean = 11,95) had a significantly higher CVR than target group customers (mean = 8,80), with F(1, 847) = 4,659, p = 0,031. However, there was no interaction effect between product
positioning and target group customers, with F(1, 847) = 1,349, p = 0,246 (see graph 6). This means that the effect of product positioning on CVR was not affected differently by target group or non-target group customers. In regard to CVR, H2 was also rejected as it expected a positive moderation effect of target group customers on the relation between product positioning and CVR.
4.3 Control variables
32 The customer centric product positioned advertisements (mean = 35,91) had a significantly higher bounce rate than product centric product positioned advertisements (mean = 24,08), with F(1, 3985) = 127,274, p = 0,00. Thus, when customers were exposed to customer centric product positioned advertisements, they had a higher tendency to leave the landing page, without any clicks or actions. Furthermore, the effect of target group customers on the bounce rate was investigated using a One Way Anova. However, Levene’s statistic was significant, with F(1, 3154) = 65,127, p = 0,00, thus Welch’s F test was reported. There was no significant difference in the bounce rate between target (mean = 32,42) and non-target group customers (mean = 34,47), with F(1, 374,870) = 0,935, p = 0,334. There is no difference between the bounce rate of target and non-target customers.
A Three Way Ancova was conducted with the number of impressions as covariate. The assumptions of the Ancova were tested. These results can be found in appendix G. Adding impressions as covariate to the model results in a significant effect of product positioning on CTR, with F(1, 3978) = 109,126, p = 0,000 where customer centric product positioned advertisements (mean = 4,99) had a significantly higher CTR than product centric product positioned advertisements (mean = 3,46). Furthermore, there was no significant difference on CTR when the advertisements included location data (mean = 4,31) or excluded location data (mean = 4,14), with F(1, 3978) = 1,355, p = 0,244. Next, experience products (mean = 4,78) had a significantly higher effect on CTR than search products (mean = 3,67) with, F(1, 3978) = 55,973, p = 0,000. Furthermore, the interaction effects were tested. There was a significant interaction effect between product positioning and location, with F(1, 3978) = 12,071, p = 0,001. Lastly, the interaction effect between product positioning and product category failed to be significant, with F(1, 3978) = 1,370, p = 0,242. The interaction graphs can be found in appendix F, because there were barely any differences between the results of the Ancova and the Two Way Anova’s in chapter 4.2.
Lastly, the same Three Way Ancova was conducted, but with CVR as dependent variable instead of CTR. There was no significant difference in CVR between customer centric product
33 (mean = 2,20), with F(1, 3978) = 0,107, p = 0,744. There was also no significant difference when location was added to the advertisements (mean = 1,68) and when it was not (mean = 2,25) on CVR, with F(1, 3978) = 2,973, p = 0,085. Experience products (mean = 2,52) had a higher CVR than search products (mean = 1,43), with F(1, 3978) = 11,124, p = 0,001. The interaction effect between product positioning and product category failed to be significant, with F(1, 3978) = 3,280, p = 0,070. Lastly, the interaction effect between product positioning and location data also failed to be significant, with F(1, 3978) = 0,609, p = 0,435. The interaction graphs can be found in appendix F. Apparently, the outcomes of the currents study are not controlled by the number of impressions.
Table 12. Hypothesis testing
¹ Accepted in regard to CTR, rejected in regard to CVR ² No moderation effect was found
34
Chapter 5: Conclusion and discussion
The present study investigated whether customer centric product positioned search engine advertisements could have a positive effect on customer responses and whether this effect was moderated by target group customers, higher levels of personalization in the form of location data, and product category. Firstly, it was found that customer centric product positioned advertisements had a higher CTR than product centric product positioned advertisements. However, there were no differences between customer centric and product centric product positioned advertisements on CVR. There might be two reasons why customers might be more inclined to click on customer centric product positioned advertisements. First, the advertisements were novel and more vivid compared to product centric product positioned advertisements, because these are used more often by other organizations. This resulted in a higher focal
attention of the customer (Fennis and Stroebe, 2015), who were in turn more inclined to click on the customer centric advertisements. Secondly, it was clear from the advertisements how the product could fulfill customer needs. As argued in chapter 2, fulfilling customer needs can lead to higher purchases (Cooper, 1983; Gupta and Wilemon, 1985; Hise, O’Neal, Parasuraman, and McNeal, 1990). Customers were thus more inclined to click on advertisements that made clear how the product could fulfill their needs, instead of clicking on product centric advertisements.
35 Secondly, a negative moderation effect was expected, when higher levels of personalization were added to the advertisements in the form of location data, on the relation between product
positioning and customer responses. With regard to CTR a positive moderation effect was found instead of a negative moderation effect. According to Van Doorn and Hoekstra (2013) higher levels of personalization can result in feelings of intrusiveness, which in turn decreases customer responses. However, they also argue that feelings of intrusiveness appear when advertisements reveal personal identification data about the customer. In the current experiment, location data was added, which is a form of personal identification data, but the customer centric product positioning does not reveal any personal identification data. Thus, the combination of location data and focusing on customer needs might increase fit and relevance of the advertisements (Chellappa and Sin 2005; Okazaki et al. 2009; Tam and Ho 2006), without increasing feelings of intrusiveness. According to Goldfarb and Tucker (2011) increased relevance and fit should result in increased purchase intentions, however there was no evidence found in the present study that location data also moderated the relation between product positioning and CVR. This could be channeled back to the fact that the landing pages were not optimized to fulfill customer needs, which disrupted the customer journey as they might have expected a customer centric landing page after they clicked on a customer centric product positioned advertisement.
Furthermore, according to the literature (Chellappa and Sin 2005; Okazaki et al. 2009; Tam and Ho 2006) implementing location data leads to increased relevance and fit for customers.
However, there was no prove in the current study that location data actually makes a direct difference on both CTR and CVR. This is not totally unexpected as Uni and Harmon (2007) and Xu et al. (2009) argue that location data is more relevant for customers looking for a nearby shop on their mobile devices, which was not the case in the present study as there were no physical stores.
36 as perceived risk for customers (Campo and Breugelmans, 2015; Chiang and Dholakia, 2003; Lian and Yen, 2013). Lastly, the campaigns ran during the Dutch holiday “Sinterklaas” and a few weeks before Christmas. Which might mean that a lot of customers were browsing for toys (search goods) as presents, but found better options at shops that are more specialized in toys or offer them more cheaply.
Fourth, there was no moderation effect of target group customers on the relation between product positioning and customer responses, as well as no difference in the CTR between target and non-target group customers. However, the most remarkable finding was that non-non-target group
customers had higher CVRs than target group customers, which was contradictory to previous research. Another remarkable finding was that target group customers had higher CVRs when they were exposed to product centric product positioned advertisements than when they were exposed to customer centric product positioned advertisements, even though the customer centric product positioned advertisements were made to better fulfill their needs and thus should have performed better for target group customers (Cooper, 1983; Gupta and Wilemon, 1985; Hise, O’Neal, Parasuraman, and McNeal, 1990). Main reason might be that target group customers felt more attracted to the customer centric product positioned advertisements, but it also increased their expectations of the landing pages, which were not optimized to fulfill their needs. These higher expectations were not met, resulting in a lower CVR compared to non-target group customers. Lastly, customer centric product positioned advertisements should not influence non-target group customers’ expectations, as those advertisements did not respond to their specific needs, which was supported by the fact that there were no differences for non-target group customers on CVR when they were exposed to product centric or customer centric product positioned advertisements.
37 rates and a lower CVR. Nevertheless, there are a few limitations and future research
opportunities which will be discussed in the next chapter.
5.1 Limitations and future research
Future research is necessary to validate and generalize the results of the current study, as for now there is no reason why customer centricity can only work within offline organizations, because within SEA it is possible to personalize and target specific individual customers to better fulfill their needs. To validate the current results, future research should overcome the following limitations.
First off, as discussed in chapter 3, a seven days attribution model was used. However, this model attributes the whole conversions to the last marketing click. This might not be fair to the different channels as the customer journey is often longer than just one touch point, which can result in unfair attributions of conversions. Future research should consider using a data driven attribution model, which is able to divide conversions over the different touch points the customer has experienced, to avoid unfair distribution of conversions.
Secondly, the current study was not able to collect customer data from every impression, click and conversion due to privacy violations. Another problem with collecting this data is that it was impossible to collect the data per advertisement individually. Namely, there were four
advertisements (table 5) in each campaign, however AdWords only collected target group data per campaign and not per advertisement. Thus, based on a click, conversion, and cost share, target group data per campaign was divided along the advertisements in those campaigns which was not completely fair as advertisements with more clicks would be assigned more target group customers. Future research should be able to collect this data per advertisement, which can be achieved by making campaigns with a single advertisement or conduct the same experiment in a country with less strict online privacy regulations.
38 evenly, which caused uneven impressions between advertisements. The present study tried to correct for those variances using an Ancova with impressions as covariate, however it had barely any impact on the current results. This might be caused due to the fact that a few assumptions of the Ancova were not met (see appendix G). Furthermore, due to those large variances, the data was also not normally distributed, even though it should be no problem to have non-normal data in large sample sizes (Field, 2013), normally distributed data would have made the statistical tests more reliable (more assumptions would have been met). Future research should make sure that sample sizes are of equal size. This is almost impossible when conducting a field
experiment, but is achievable when doing a controlled experiment.
Third, the use of product feeds limited the power of true customer centric product positioned advertisements, because the advertisement must be relevant for the whole product category. For example, the current study used “create easily your Christmas outfit with our newest collection” for fashion, however this product category also includes products that do not fit the stated description such as socks, sportswear, and underwear. Beforehand, the revenue share of those products was investigated. However, it is possible to create better advertisements when focusing on one product instead of a product category. Future research should thus create customer centric advertisements based on products instead of product categories to generate more relevant
advertisements for the customers. Ideally these advertisements direct the customer to landing pages that are also optimized to fulfill customer needs, as this might have been a large
contributor to the high bounce rates and low CVRs found in the present study for the customer centric advertisements.
40
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