How Does Retargeting Work For Different Gen Z Mobile Users?
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* Yllka Azemi
* Wilson Ozuem

## Customer Expectations and Evaluations of Retargeting via the Expectancy-Theory Lens

## Abstract

Scholars of retargeting have increasingly recognized that consumers’ choice decisions can often be affected by the stage of their decision making. The challenge for both researchers and practitioners is to understand and provide retargeting that leads to conversions by the second round of communication. Drawing on an interpretive perspective and utilizing the expectancy theory of motivation, the authors of the current study interviewed 40 Gen Z mobile phone customers ages 18–24 years across four settings (U.S.A., Germany, Switzerland, and Kosovo) about their experiences of retargeting in the luxury fashion industry. Results show that customers share similarities in their evaluation of the first retargeting advertisement, but analysis revealed three types of customers (indifferent, seeker, and meticulous) with differing evaluations of the second retargeting advertisement.

Keywords:
*   Retargeting
*   social media retargeting
*   mobile phone customers
*   luxury fashion
*   early stage of decision making
*   luxury fashion retail
*   Gen Z
*   mobile marketing

## MANAGEMENT SLANT

*   This study provides a model that supports practitioners’ application of retargeted advertisements as a dyadic process that leads to customer engagement with the first retargeted advertisement and conversion with the second.

*   The model serves as a set of guidelines for luxury fashion retailers to successfully acquire Gen Z customers.

*   The authors decipher differing expectations and evaluations for three types of customers (indifferent, seeker, and meticulous) that luxury fashion retailers should retarget.

## INTRODUCTION

Customers are exposed to an increasing abundance of mass promotional advertisements (Holmes, 2019). This abundance overwhelms customers and leads most customers to bypass most of these advertisements; this decreases customers’ ability to observe messages of interest in the pool of mass online advertising. Retailers recognize the critical competitiveness of online advertising and have been exploring retargeting advertisements as a marketing strategy to replace mass communication (Méndez-Suárez and Monfort, 2020; Li, Luo, Lu, and Moriguchi, 2021). Retargeting is customer behavior-based advertising that is displayed to individuals who have previously expressed interest in the company (Carmicheal, 2021). A company notes a consumer searching for information on one of its products, for example; it then targets the customer with further information about that product (Villas-Boas and Yao, 2021). Companies seek to provide retargeted advertisements that consist of information that is personalized to the customer’s latest behavior, interest, and expectations (Bleier and Eisenbeiss, 2015b; Yoon, Lee, Sun, and Joo, 2023). Retailers can capture individuals’ behavior and interest in their company by placing an unobtrusive piece of JavaScript on individuals’ browsers when they interact with the retailer through a platform (*e.g.*, the retailer’s website); the browser gets “cookie-d,” which tells the retailer to send a retargeted advertisement to the customer (Carmicheal, 2021). Customers can also express interest in a retailer by visiting its e-commerce store and placing a product in a shopping cart (Li et al., 2021; Mailchimp, n.d.).

In general, researchers have explored retargeting across three constituents, namely content, placement, and the timing of the advertisement exposure (Bleier and Eisenbeiss, 2015a; Zarouali, Ponnet, Walrave, and Poels, 2017). Most of the research focused on exploring and disclosing individual constituents of retargeting, rather than all the constituents. A study exploring the content of retargeted advertisements compared the effects of dynamic retargeted advertisements, which “feature pictures of the exact product consumers previously browsed,” with the effects of generic retargeted advertisements, which showed a random generic message (Lambrecht and Tucker, 2013, p. 561). This revealed that generic advertisements triggered more conversion, except when customers were at the stage of evaluating different brands (Lambrecht and Tucker, 2013). Other research demonstrated the success of the placement of retargeted advertisements in banners that had high-fidelity content “featuring products from a consumer’s most viewed category and brand combination,” which were exposed immediately after the consumer had visited the retailer’s store (Bleier and Eisenbeiss, 2015a, p. 670). Research observed the highest success rate of advertisements to be within the first week of advertisements, with “approximately 33 percent of the total effect” reached in the first day of the advertisement exposure (Sahni, Narayanan, and Kalyanam, 2019, p. 412).

The focus on specific constituents of retargeting advertisements provides limited insight into customers’ overall expectations of retargeting or of the parameters customers utilize to evaluate the messages. A lack of understanding of customers has resulted in mistargeted advertisements. According to the results of a Statista survey conducted in June 2022, 70 percent of customers “received mistargeted advertisements—including inaccurate promotional e-mails or suggestions unconnected to previous purchases—at least once a month” and 24 percent received mistargeted advertisements daily (Faria, 2023). An aggregate understanding of retargeting could aid understanding of customers’ expectations and evaluations, which could serve as a blueprint for the construction of messages that generate engagement and lead to conversion.

An emerging stream of research into customers’ expectations and evaluations of retargeted advertisements has started to generate insights that could inform how mobile social media directly influence marketing practices (Azemi, Ozuem, Wiid, and Hobson, 2022; Dixon, 2022). Four themes — for example, relatable, attainable, socially effectual, and presence—can explain customer behavior and customer acquisition in relation to the constituents (*i.e.*, content, timing, and placement) of retargeted advertisements (Azemi et al., 2022). Relatable retargeted advertisements are “congenial to [customer’s] personality … and the content and visuals meet their preferences” (p. 6); attainable retargeted advertisements “reveal accessibility in terms of product price … and support” (p. 7); socially effectual messages present “the positive authentic social impact of the company”; finally, presence refers to “the channels utilized to target the customer … and the frequency of the exposure of a company’s mobile marketing messages per weekly timeframe” (Azemi et al., 2022, p. 8). For customer acquisition to occur, there should be five instances of customer engagement with the retargeted ad: the first and last retargeted messages to be presented through Instagram, and the three in-between retargeted messages presented across Twitter, TikTok, and Facebook (Azemi et al., 2022). This associates the retargeted advertising with a three-week process for customer acquisition to occur. The prolonged retargeting process is in line with some researchers’ work, which associates multiple customer–company retargeted interactions with conversion, but goes against other researchers who argue that there is no need for a second round of retargeted advertisements for conversion to occur.

Though insightful, this work leaves two issues unresolved. First, the findings do not provide insight into customers who are at an early stage of decision making. Companies might need multiple company communications (Kireyev, Pauwels, and Gupta, 2016; Nisar and Yeung, 2018; Lesscher, Lobschat, and Verhoef, 2021) to optimize conversion of customers at an early stage of decision making. A prolonged retargeting process could have consequences, however. This approach would not work with customers who are exposed to multiple companies and who are time-sensitive to making a purchase. In turn, this could result in companies mismanaging their retargeting processes and marketing budget. Developing a dyadic retargeting that leads to customer engagement with the first retargeted message and that generates conversion with the second retargeted message among customers at an early stage of decision making could address such challenges. Second, customers are pluralistic and complex (Ozuem, Ranfagni, Willis, et al., 2021). Customers’ perceptions are influenced by multiple factors, such as emotions, critical thinking, inherited clusters of thoughts and beliefs, other social actors, and even the devices through which they receive marketing communications (Dahana, Miwa, and Morisada, 2019; Kallevig, Ozuem, Willis, et al., 2022). The complexity of the merging of influences and development of customers’ perceptions throughout their purchasing journeys and decision making make it impossible for marketers to approach all customers with a single marketing program. Instead, marketers are required to classify customers into segments and approach them with tailored marketing programs (Scheuffelen, Kemper, and Brettel, 2019). Marketing practices that speak to each customer segment increase the possibility of maximized mutual satisfaction for both customer and company. It is crucial that retailers account for differing expectations and evaluations across segments in the customer pool when developing retargeting practices.

> Marketing practices that speak to each customer segment increase the possibility of maximized mutual satisfaction for both customer and company.

The current study further develops Azemi et al.’s (2022) work; it aims to understand customers’ expectations and evaluations of retargeting at an early stage of their decision making. More specifically, it seeks to understand customers’ evaluations of a first retargeted advertisement and to conceptualize the origin and development of their evaluations and expectations of the second retargeted advertisement. To do so, the current study addresses three questions:

*   RQ1: How do customers explain their expectations of retargeting that leads to conversion on the second retargeting encounter?

*   RQ2: How are the differences between different customers’ expectations and evaluations of retargeting explained?

*   RQ3: How can retargeting be constructed as a dyadic process in which a retailer’s first message creates engagement and their second message leads to customer conversion?

Drawing on an interpretive methodological perspective and the expectancy theory of motivation, the current study explores mobile social media retargeting as it applies to Gen Z luxury fashion customers between 18 and 24 years of age. Data were collected during interviews with 40 Gen Z participants from the USA, Switzerland, Germany, and Kosovo. Theoretically, the current study extends understanding of customers’ expectations and evaluations of social media retargeting as a dyadic process for mobile Gen Z luxury fashion customers in the early stages of their purchase decision. The current study concludes with a model that displays how managers in luxury fashion could utilize mobile social media retargeting for Gen Z customers to generate engagement after the first retargeted communication and conversion after the second.

More specifically, the current study presents a model that provides practitioners with guidelines on how to develop and implement a dyadic retargeted advertising process that concludes with customer conversion. The model invites practitioners to develop an engaging first retargeted advertisement that meets customers’ expectations of the presentation of the advertisement across design and content, and that provides information that is core to the brand/product and that allows for competitive comparisons. The model requires practitioners to develop a second, conversion-aimed, retargeted advertisement, which, first, conveys accessibility in terms of brand affordability and is a self-reflection of customers’ brand expectations, and, second, supports trust-building through message credibility and authenticity. The model reveals three customer groups—indifferent, seeker, and meticulous—each with unique evaluations of the second retargeted advertisement. It includes customer approaches and intensity of information search, the main purpose of customers’ information searches, and frequency of customers’ purchasing as moderators of customers’ positioning across the segments.

## LITERATURE REVIEW

### Retargeting Ads: Content, Positioning, and Timing of Exposure

Most investigations into retargeting have been produced in the last few years (Méndez-Suárez and Monfort, 2020; Li et al., 2021). Scholars have acknowledged retargeting as a marketing opportunity for retailers to increase customer acquisition, and have explored this mainly across three research streams. The first research stream investigates the content of retargeted advertisements and acknowledges the thematic foreground of these advertisements. Researchers found that content that consists of price incentives is the key mediator of the effectiveness of retargeted advertisements (Bruce, Murti, and Rao, 2017). Later, researchers utilized textual debriefing (*i.e.*, they placed in the advertisement “a text informing the adolescents they were exposed to either retargeting or non-retargeting”) to explore customers’ evaluations of advertisements (Zarouali et al., 2017, p. 160). This disclosed that retargeted advertisements lead to greater skepticism and lower purchasing, which implied that retargeted advertisements should avoid content that highlights information about the nature of the advertisement. This stream of research mostly ignored the specifics of the visualization of advertisements. Marketing literature has acknowledged that visualization attributes (such as color) are vital to the effectiveness of advertisements (Kareklas, Muehling, and King, 2019; Sharma, 2021).

The second research stream explores the positioning of retargeted advertisements (Bleier and Eisenbeiss, 2015a; Huang, 2018). Research suggests that banner advertisements placed in motive-congruent (*i.e.*, shopping-related) versus motive-incongruent (*i.e.*, non-shopping-related) websites were more effective as customers view these advertisements as informative rather than intrusive (Bleier and Eisenbeiss, 2015a). Other studies investigated central slots versus the right sidebar of a website’s portal, and advertisements exposed next to non-apparel-related articles versus apparel-related articles (Huang, 2018). Centrally positioned retargeted advertisements and advertisements placed by apparel-related articles seem to be the most successful. Some researchers argued for the positioning of advertising across multiple channels; they suggested that multiple channels generate higher customer conversions (Klapdor, Anderl, Schumann, and Van Wangenheim, 2015). It is surprising that mobile social media as a channel of retargeting was the least explored channel. Social media is a key channel to discover new products and make purchases (Chevalier, 2022). Disregarding social media in marketing programs impedes customer communication and acquisition (Scheinbaum, 2016; Yang, Lin, Carlson, and Ross, 2016; Lee Burton, Mueller, Gollins, and Walls, 2019).

Finally, the third stream of research in retargeting focuses on the timing of advertisement exposure (Sahni et al., 2019; Li et al., 2021). This stream suggests that the timing of “re-advertisement” is a principal factor influencing customer behavior toward the company that is advertising. This accords with other marketing literature that has traditionally acknowledged timing as a crucial factor in customer satisfaction across multiple fronts of customer service (*e.g.*, Liu, He, Gao, and Xie, 2008; Azemi, Ozuem, Howell, and Lancaster, 2019). Some studies have explored timing of retargeted marketing in the context of customer engagement (e.g., Sahni et al., 2019). Findings revealed a 14.6 percent increase in product viewers who visited websites following exposure to a retargeted advertisement, and a 5.43 percent increase in cart creators (Sahni et al., 2019, p. 402). Such information is pivotal to deciphering the retargeting parameters of customer engagement, yet it does not explain the impact that retargeting has on purchasing. The end purpose of engagement and retargeting is the acquisition of customers (Eisingerich, Marchand, Fritze, and Dong, 2019; van Heerde, Dinner, and Neslin, 2019). Other studies explored the effect of the timing of retargeted advertising exposure on customer purchasing; for example, presenting retargeted advertisements within 24 to 72 hours, rather than immediately, generated greater customer acquisition (Li et al., 2021).

In general, these study streams do not provide sufficient knowledge about customers’ expectations and evaluations of retargeting across content, placement, and timing for researchers and practitioners to be able to describe retargeting practices that lead to customer engagement and conversion. In this context, a recently developed model of five instances of retailer–mobile customer communication provides valuable information (Azemi et al., 2022). This model proposed that the first communication message should be “congenial to [customer’s] personality,” which leads to customer engagement in the first week of communication, and that in the last communication “the content and visuals meet [customer] preferences” to generate conversion in the third week of communication (Azemi et al., 2022, p. 6). The model acknowledged Instagram as the channel of engagement and conversion; it assigned Facebook, Twitter, and TikTok as channels where three reminder messages should be sent out to the customers. This model does not seem to address expectations and evaluations of retargeting among customers at an early stage of decision making.

Integrated messages increase the likelihood of conversion of customers at the beginning of their purchasing journey (Klapdor et al., 2015). The nature of information presented (*i.e.*, informational versus goal-orientated), however, determines the likelihood of customer conversion (Klapdor et al., 2015). The current lack of insight serves the assumption that prolonged instances of communication could increase the risk of companies placing information in poor positions. Also, multiple instances of communication could be problematic in situations when customers’ decision making is timebound and they come across other competitor messages; however, this is also the case for single retargeted messages in situations when a single exposure does not convince a customer.

### Expectancy Theory of Motivation, and Customers’ Expectations and Evaluations of Retargeting

A dyadic retargeting model seems ideal to address the drawbacks of single and multiple communications with customers at an early stage of decision making. This requires a higher level of decoding of customers’ underlying motivations to engage and convert, specifically of their expectations and evaluations of two rounds of messages. Victor Vroom’s expectancy theory of motivation has been recognized as a theory that “offers critical value for marketing research” when seeking to understand customers’ motivations that lead to conversion (Lee, 2007, p. 789). In the current study, the expectancy theory of motivation was used to discern customers’ motivations to engage with the first retargeted advertisement and convert with the second retargeted advertisement. Vroom explains expectancy with three components: expectancy, instrumentality, and valence (*e.g.*, Chopra, 2019). Expectancy refers to “a person’s estimation of the probability that effort will lead to successful performance” (Lee, 2007, p. 789); instrumentality refers to “the person’s perception of the probability that performance will lead to a specific outcome” (Lee, 2007, p. 790); and valence is one’s inclination to attain versus not attain an outcome (Lee, 2007). The three components aid understanding of the gradual transition of customers’ motivations starting from their evaluation of the degree to which a company meets their expectations to their realization that purchasing versus not purchasing the product provides more benefit. Accounting for this transition is important as it supports understanding of the directions that lead to conversion (Nisar and Yeung, 2018). In the context of the current study, this also ensures a comprehensive understanding of the retargeting constituents (*i.e.*, content, placement, and timing) that motivate customers to engage and purchase (Lambrecht and Tucker, 2013; Hoban and Bucklin, 2015; Sahni et al., 2019).

According to the expectancy theory of motivation, one’s motivation toward actions and outcomes is a compound of “situations,” “settings,” and other individuals (Lee, 2007, p. 788). These compounds have been recognized to support greater understanding while decoding differences in perceptions among customers (*e.g.*, Ozuem et al., 2021). Customers are dynamic; understanding them is challenging (Kietzmann, Paschen, and Treen, 2018); however, having retargeting practices that account for multiple customer groups could help maximize customer engagement and conversion.

## METHODOLOGY

### Paradigm of Inquiry

The current authors adopted an interpretive research approach to this qualitative study, which gave voice to the participants who had experienced retargeting advertisements (Gioia, Corley, and Hamilton, 2012). The participants’ viewpoints were the foundation of analysis (Van Maanen, 1979). This study is based on the authors’ interpretation of the participants’ voices. Interpretation was co-created based on participants’ discussion and their contextual factors. The researchers’ background and their knowledge of existing studies and the application of retargeting in real settings were also crucial.

A researcher adopting an interpretive approach is inclined to show empathy and understanding in the meaning-making (Denzin and Lincoln, 2000). The primary goal of interpretive research is to explore particular motives and experiences in the phenomena of interest through “thick descriptions” that are time-bound and context-bound (Geertz, 1973). Based on this perspectival epistemology, the interpretive approach holds that the researcher and subject interact with each other to provide depth of insight and illuminate meanings that are grounded in experience. This approach facilitated an understanding of customers’ expectations and evaluations of the timing, placement, and content of retargeted advertisements.

Data were collected from Gen Z luxury fashion mobile customers of U.S. luxury fashion brands (Azemi et al., 2022). These were social media customers at an early stage of decision making. With a few exceptions (Zarouali et al., 2017), existing studies in retargeting do not focus on specific customer age cohorts. The uniqueness of customers and their perceptions of marketing messages can be explained by their age cohort (Herrando, Jimenez-Martinez, and Hoyos, 2019). U.S. luxury fashion brands have been experiencing steady growth (Pasquali, 2022), which provides an expanded scope for understanding participants’ experiences across different settings. Gen Zers are heavy and competent users of mobile social media and are the most prominent customer group to shape U.S. luxury fashion brands (Azemi et al., 2022). Gen Z mobile customers of U.S. luxury fashion brands decode the complex nature of retargeting and its effects on engagement and conversion of customers at the early stage of their decision making. Most investigations in retargeting have been produced in the last few years and are quantitative in nature (Huang, 2018; Méndez-Suárez and Monfort, 2020). An in-depth interpretivist approach helps to overcome the partial, and often contradictory, results of quantitative studies of retargeting.

> Gen Zers are heavy and competent users of mobile social media and are the most prominent customer group to shape U.S. luxury fashion brands.

Data were collected through in-depth interviews with 40 Gen Z customers. The sample size in the current study was informed by other qualitative and interpretivist studies in marketing (*e.g.*, Lee, Choi, and Kim, 2020). Participants were identified through a snowballing process. One of the authors identified four participants who were heterogeneous in terms of gender and occupation to begin the interviews (see Appendix A). Heterogeneity in these dimensions was purposefully considered. In general, existing studies highlight these as key dimensions to control for heterogeneity, which in turn support a greater depth and richness of understanding of the subject matter (Ozuem et al., 2021). At the end of each interview, the participant was asked if they could suggest other potential participants (Zanette, Pueschel, and Touzani, 2022). The initially approached participants were in the U.S.A. Participants’ recommendations directed data collection from customers of U.S. luxury fashion brands in four different settings. This is not surprising as one of the key advantages of snowball sampling is that it reaches a highly diverse population, which allows for a higher level of understanding (Kirchherr and Charles, 2018). Also, the online environment provided the opportunity for purchasing and communication beyond isolated settings.

The settings and percentage of participants per setting were: the U.S.A. (27 percent), Switzerland (24 percent), Germany (24 percent), and Kosovo (25 percent). The authors did not exclude any contacts and/or country that was brought up due to snowballing. This is in line with online customers’ research, and the current study’s interpretivist methodological and theoretical approaches (*i.e.*, expectancy theory of motivation), which perceives online customers’ expectations and evaluations to be an outcome of the context and their authentic and emerging idiosyncrasies that are influenced by others, such as peers and companies’ messages (Ozuem et al., 2021. Therefore, the researchers did not seek to compare customers’ expectations and evaluations across the four countries. The four countries provided grounds for improved holistic interpretation of customers’ evaluations, customers’ expectations, and retargeting (Kallevig et al., 2022; Naeem, Ozuem, Howell, and Ranfagni., 2022).

> The in-depth interviews were conducted via Zoom. Before the interviews, participants were provided with information about the study and given time to reflect on their experiences.

These countries cover multiple levels of the market share continuum of luxury sales. The U.S.A. is at the top end of the continuum of luxury product sales (Statista Research Department, 2022), whereas Kosovo is situated at the lower end of luxury product markets, with Switzerland and Germany listed within the continuum. These four countries have experienced different rates of sales growth for luxury goods. Switzerland’s revenue in the luxury goods market, for example, was US$4.29 billion in 2022 (Statista, 2022a), whereas Germany’s revenue was US$12.14 billion (Statista, 2022b). This difference elucidates how luxury markets are evolving. Finally, these countries are in different economic and social development stages. Kosovo is one of the newest countries worldwide. The U.S.A., Germany, and Switzerland are worldwide economic powers, whereas Switzerland has the greatest social and economic development levels (Boyle, 2021). Social and economic maturity levels inform unique customer expectations and evaluations (Kallevig et al., 2022; Naeem et al., 2022).

The in-depth interviews were conducted via Zoom. Before the interviews, participants were provided with information about the study and given time to reflect on their experiences. The interviews lasted approximately 35 minutes. This time length is within the interview span that supports rich and deep understanding (Dicicco-Bloom and Crabtree, 2006; Athota, Pereira, Hasan, et al., 2023). The interview consisted of questions that took participants through their exposure to retargeted advertisements in a sequential way. Questions such as “How do you explain the first time you were exposed to a luxury company on your mobile device?” set the stage for understanding customers’ evaluations of the first retargeted message. The question, “How do you explain the other times the company showed up on your mobile device?” supported understanding of customers’ evaluation of the second retargeted advertisement. In line with expectancy theory of motivation, questions such as “How do you explain the interaction with the company after you were first exposed to the company?” helped to delve deeper into customers’ evaluations and the gradual emergence of their expectations of the retargeted advertisement, which concludes with conversion.

Other questions and conversations emerged during the interviews that were inspired by the participants’ responses, existing literature, and/or previous interviews (Ozuem et al., 2021). This developed a data collection setting of “learning, listening, testing … binding, and sharing” (Dicicco-Bloom and Crabtree, 2006, p. 317) with the goal “to co-create meaning with interviewees” (Dicicco-Bloom and Crabtree, 2006, p. 316). It precipitated the emergence of new in-depth insights into the subject matter. This did not direct responses or discussion; alterations to interview questions and prompts were informed by the participants themselves. Instead, this served as a triangulation mechanism, which ensured the validity of the evolution of new insights (Chopra, 2019). In some instances, notions such as “first time” and “the second time” were used to keep participants’ discussion within the dyadic retargeting topic. This lay the grounds for conceptualizing the authentic expectations and evaluations of customers, which supported a gradual understanding of how customers can be segmented in relation to dyadic retargeting.

### Data Analysis: From Key Concepts to Conceptual Themes

Any analysis of qualitative data is a difficult, intuitive, creative, and dynamic process. The goal of the analysis was to understand the assumptions, categories, patterns, and relationships that constituted the situated experiences of the participants (Basit, 2003). The interpretivist stance during the data collection helped familiarization with the data and understanding of the contextualities of emerging insights. The core of the data analysis occurred once the interviews were transcribed. This followed the traditional thematic analysis approach: key patterns within the data were identified, relationships within the data were recognized, and key conceptual themes were developed. Participants’ use of language was analyzed manually. This allowed the researchers to delve deeply and discuss the obvious and the hidden meanings in the participants’ use of language. The researchers had the opportunity to utilize their real-world expertise in the subject matter and their knowledge of retargeting extant literature to maximize the opportunity to create new knowledge. This was an iterative process; the researchers analyzed “the text in reflective cycles until interpretations intuitively crystalize[d]” (DiCicco-Bloom and Crabtree, 2006, p. 318).

To control for the researchers’ participation in the co-creation of meanings to the level that maximizes understanding yet does not endanger the validity of the data, data analysis followed a structured three-stage thematic analysis approach (Gioia et al., 2012). This process includes analysis that generates (1) first-order concepts, (2) second-order themes, and (3) aggregate dimensions (Gioia et al., 2012). During the first-order concept stage, the most repetitive words and important concepts that customers utilized to reveal their expectations and evaluations of retargeted communications were identified. The researchers’ interpretivist approach and the existing literature on retargeting informed the authors of the current study of the most important concepts (*e.g.*, Kallevig et al., 2022). To ensure “accuracy” in the identification of the most important concepts, the study used inter-coding agreement (Bidar, Barros, and Watson, 2022, p. 903) in which a concept was recognized as important only if both authors of the current study were in agreement.

Data analysis followed with the identification of second-order themes utilizing “nodal structure stemming” (Zanette et al., 2022, p. 788). Here the first and second retargeted advertisements were used as nodes to further analyze the flow of the key concepts and words based on their similar versus their different meanings. This stage consisted of an iterative review of the data, discussions, and actual drawings of nodal structure charts. This directed the classification of the words/concepts into seven themes, namely (1) design, (2) content, (3) core and competitive comparison, (4) self-reflection, (5) affordability, (6) credibility, and (7) authenticity in relation to the first and second retargeted advertisements (See Figure 1). The stage concluded with the first three themes assigned to the first retargeted advertisement and the remaining four themes assigned to the second retargeted advertisement.

![Figure 1](http://www.journalofadvertisingresearch.com/https://www.journalofadvertisingresearch.com/content/jadvertres/63/4/384/F1.medium.gif)

[Figure 1](http://www.journalofadvertisingresearch.com/content/63/4/384/F1)

Figure 1 
The Emergence of First-Order Concepts, Second-Order Themes, and Aggregate Dimensions as Suggested by Gioia et al. (2012)

Finally, in the aggregate dimensions stage, the components of the expectancy theory of motivation (*i.e.*, expectancy, instrumentality, and valence) directed a further exploration of and reflection on the themes in relation to customers’ expectations and evaluations, and customer differences across the two. The analysis at this stage began with reflection on the data regarding the expectancy and instrumentality components of the theory. Given that expectancy refers to “actions that lead to a desired outcome” (Chopra, 2019, p. 322), expectancy in the current study was assigned to the key principles that the customers expected retargeted advertisements to possess. The seven themes that emerged during the identification of second-order themes (*i.e.*, design, content, core and competitive comparison, self-reflection, affordability, credibility, and authenticity) represented customers’ expectations.

When analyzing the data in the context of instrumentality, which is defined as the “performance … [that] aids … [the] outcome” (Chopra, 2019, p. 338), the researchers explored customers’ evaluations of the performance of advertisements in relation to the level that the retargeted advertisement met their expectations related to the seven themes. To support the interpretation of data and discussions, another iterative round of nodal structure charts, review of data in relation to existing literature and practitioners’ insights, and discussions among researchers took place. Here, customers with three different evaluative approaches were recognized and identified: indifferent customers; seeker customers; and meticulous customers (See Table 1).

View this table:
[Table 1](http://www.journalofadvertisingresearch.com/content/63/4/384/T1)

Table 1 
Indifferent Customers, Seeker Customers, and Meticulous Customers

Customers’ evaluations of the performance of retargeted advertisements seemed to be explained by three components, namely: (1) involvement in an information search in-between the first and the second retargeted advertisement exposures, (2) the purpose of the information search, and (3) the frequency of purchasing. To control for the validity of the categorization of customers, the authors of the current study followed a “devil’s advocate” approach, which invites one of the authors in the project to look for inconsistencies in the data structure (Gioia et al., 2012, p. 19). In the current study, in an iterative and rotating process, the authors took turns being the devil’s advocate until the point where agreement was reached (Bidar et al., 2022, p. 903).

Following the valence component of the expectancy theory of motivation, which refers to “satisfaction,” “rewarding experience,” and “trust in the usage” (Chopra, 2019, p. 338), and guided by the aggregate stage of data analysis, a higher-level interpretation of the most repeated and important first-order concepts and emerging second-order themes took place (Gioia et al., 2012). This stage facilitated a comprehensive overview of the data and ensured that the interpretation of the new insights was logical and coherent. This resulted in four aggregate dimensions, namely: (1) “presentable” (an outcome of design and content), (2) “informational” (an outcome of core and competitive comparison), (3) “accessible” (an outcome of self-reflection and affordability), and (4) “trust-building” (an outcome of credibility and authenticity). The four aggregate dimensions and the three types of customers informed a model that outlines customers’ expectations and evaluations of retargeting that concludes with customer acquisition (See Figure 2).

![Figure 2](http://www.journalofadvertisingresearch.com/https://www.journalofadvertisingresearch.com/content/jadvertres/63/4/384/F2.medium.gif)

[Figure 2](http://www.journalofadvertisingresearch.com/content/63/4/384/F2)

Figure 2 
Model of Mobile Customers’ Expectations and Evaluations of Social Media Retargeting

To control for data validity, the current study followed validity criteria of refutability, which “requires researchers to critically examine whether the assumed relationships among emergent constructs hold true across contexts,” and deviant case analysis, in which “researchers actively look for cases that are substantially deviant (*i.e.*, outliers) and that bring into question the overall findings of the study” (Malshe, Hughes, Good, and Friend, 2021, p. 8). The emergent insights were representative of all four settings (U.S.A., Germany, Switzerland, and Kosovo) and no outliers were found.

## FINDINGS

The seven emerging themes (*i.e.*, second-order themes in Figure 1)—design, content, core and competitive comparison, self-reflection, affordability, credibility, and authenticity—revealed customers’ explanations of their expectations of retargeted advertisements. This addressed the first research question of the current study (*i.e.*, “How do customers explain their expectations of retargeting that leads to conversion on the second retargeting encounter?”). Findings revealed that customers evaluate the first retargeted advertisement identically but evaluate the second retargeted advertisement differently. This is not surprising because customers’ unique perceptions and evaluations of an advertisement evolve as their decision-making journey progresses. Findings revealed three customer segments, named indifferent customers, seeker customers, and meticulous customers (as presented in the evaluative approaches, Figure 1). Such information addresses the second research question of the study (*i.e.*, “How are the differences between different customers’ expectations and evaluations of retargeting explained?”). Finally, the emerging aggregate dimensions (See Figure 1)—presentable, informational, accessible, and trust-building—allow for a holistic yet critical approach to the findings (*i.e.*, to the themes that present customers’ expectations and the customer segments) within a dyadic retargeting process. This answers the third research question of the study (*i.e.*, “How can retargeting be constructed as a dyadic process in which a retailer’s first message creates engagement and their second message leads to customer conversion?”*)*. The aggregate dimensions of presentable and informational present customers’ expectations and evaluations of the first retargeted advertisement, which happened to be identical across all customers. The aggregate dimensions of accessibility and trust-building identify the diverse evaluations and expectations of customers. Indifferent and meticulous customers seem to evaluate mostly on the grounds of accessibility and trust-building, respectively, whereas seekers’ evaluations seem to be based on a combination of the two. The following two subsections present the findings and extracts from participants’ interviews to support interpretation.

### Presentation of Findings and Interpretations

**Presentable**. Gen Z customers expect to be exposed to retargeted advertisements that are presentable in terms of content and design (Sharma, 2021; Azemi et al., 2022). Although the scholarly focus on retargeting highlights the content of the advertisement (Lambrecht and Tucker, 2013; Zarouali et al., 2017), marketing scholars acknowledge both content and design as important mediators of positive customer evaluations of advertisements (Kareklas et al., 2019; Sharma, 2021). The current study revealed that the content of the retargeted advertising should take the form of multiple pictures or videos. The preference for these modes of message delivery, rather than single picture advertisements, is explained by the increased amount of information that customers can get, as highlighted in the following statement by a female participant: “the ad was not visually pleasing and [there] was not much going on; it was very minimal, very simple, [and] did not give much information.” Customers seemed to seek information regarding different product options. This was captured in the following statement of a female participant: “the perfect ad to me would be: being able to see more than one item.” Customers seemed to assign equal importance to the visual aspects of the advertisement and to the message information. They identified pleasing advertisements as those that are visually attractive. This seems to be achieved by advertisements that are colorful, convey a luxurious mood, and have an appealing message as stated by a female participant: “it should be aesthetically pleasing … it should have … a nice message.” Gen Z luxury fashion customers disregard long texts within advertisements. Instead, they seek highlights about the benefits of products and financial information, such as price, discounts, and shipping.

**Informational**. Gen Z luxury fashion customers expect retargeted advertisements to provide information that helps to optimize their understanding of possible product options within the company, and the competitive edge of a product in comparison to other products on the market. More specifically, they view the advertisement as a starting point to explore the differences between products in terms of product benefit and financial acquisition (Huang, 2018; Azemi et al., 2022). This is evident in both message delivery modes (*i.e.*, images and videos) expected by customers. Rather than cross-sectional information, Gen Z appears to seek advertisements that help to determine whether a company makes an effort to provide an enhanced experience for the customer over time. They seem to evaluate such efforts in two ways: namely, the company’s use of sales promotions and the improved uniqueness of the product. These are captured in the following statements: “[the ad should] tell me ‘O hey, this is the price it was, and what it is now’ and [that is when I] click on [the ad]” (female participant); “the product is different from what I have seen before” (female participant). Additionally, customers seek information that showcases product being used in real life. If an advertisement is delivered through video, then the customer looks for a demonstration of how the product is used. The following statement speaks to this: “I think it is very different to see just a picture of the products than to see like … for example, I was trying to buy a phone case and just the picture … did not show me enough. I would prefer to see real-life people using the phone case so that I can review [it]. That influences my decision more than just the picture” (female participant). An advertisement that lacks a demonstration of product being used seems to be detrimental to customers’ engagement with the advertisement.

**Accessible**. For Gen Z customers, retargeted advertisements should provide products that speak to their style and are financially affordable (Smith, 2019; Jäger and Weber, 2020). Rather than just focusing on style in terms of their appearance, customers seem to expect exposure to retargeted advertisements that align with their ideology of the societal impact of the company. The importance of a societal ideology within Gen Z’s concept of style is revealed in the following statement: “when I am looking [at] a new purse or a new perfume, I try to do it as ethically” (female perticipant). Customers seem to forego companies that do not apply practices that have a societal impact, yet they are not willing to give up luxury products. This reveals the increased interest of Gen Z in owning luxury items. They appear, however, to be conscious buyers and will bypass luxury products in situations where companies do not provide financial incentives. The price of the products should be in line with customers’ beliefs in monetary exchange for value as implied in the following statement: “The best ad that I have ever seen is actually for a jewelry company … they actually give you a free chain [when] you purchase a chain” (male participant).

These customers appreciate multiple forms of financial incentives. They are very careful about their evaluation of incentives. They do not seem to be driven by low discounts, which are perceived as providing no financial support. This is captured in the following statement: “Some of them jump the discount from like $80 to $70—that’s not really gonna catch my eye. … if it is like $20 off [it] will catch my eye faster than $5 or $10” (female participant). High discounts appear to make customers question the luxury prestige of the company and diminish their trust in the product.

**Trust-Building**. Refers to retargeted advertisements that consist of messages that convey credibility (Hussain, Meleware, Priporas, et al., 2020; Choi, Hwang, and McMillan, 2008). In the current study, credibility seems to be explained by the authenticity of the deliverer of the message. Customers might bypass some advertisements, however, even if they were delivered by an influencer. This is highlighted in the following statement: “I am not necessarily going to think ‘oh this is good’ just because [the influencer] is advertising it” (female participant). For Gen Z customers, the advertisement should address any potential doubts that they might have. They seem to appreciate influencers as a means of message delivery. They seek sincerity, however, in terms of how the influencer conveys the message. This is implied by the following participant: “It is a person that has a luxury product but makes it look very casual … especially influencers like [name of the person], who [has] a lot of wealth but makes the product look like it is really part of their everyday life” (female participant). Therefore, customers expect the message to posit the product as a luxury, yet everyday, item. This could be influenced by Gen Z customers’ ideology of social support and equality as explained in the accessibility/self-reflection theme. Finally, these customers seem to disregard any type of message that appears superficial or untrustworthy.

### Customer Evaluative Groups: Indifferent Customers, Seeker Customers, and Meticulous Customers

The findings suggest that Gen Z customers expect the first retargeted advertisement to be presentable and informational, whereas they expect the second retargeted advertisement to convey accessibility and support trust-building. All customers seemed to identically evaluate the first retargeted advertisement in terms of its presentability and information. A variety of customer evaluations of accessibility and trust-building were, however, evident. They evaluated the two in relation to the importance that the four constructs of accessibility and trust-building (*i.e.*, self-reflection, affordability, credibility, and authenticity) had for them. Their evaluative differences were explained mainly by approaches to, and the intensity of, information searches between the first retargeted advertisement and the second retargeted advertisement exposures, followed by the purpose of the information search, and the frequency of purchasing. This generated three Gen Z customer groups that luxury fashion companies should retarget: (1) indifferent customers, (2) seeker customers, and (3) meticulous customers (See Figure 2). Each of the three customer groups is now described; the descriptions are based on the findings and extracts from participants’ interviews to support interpretation across the customer groups.

**Indifferent Customers**. Indifferent customers positively evaluated second retargeted advertisements that consisted of a message dominated by self-reflection and affordability (*i.e.*, accessibility) rather than credibility and authenticity. Once exposed to an advertisement that met the accessibility criteria, these customers browsed the website to learn more about the company. This is captured in the following statement: “I will see an ad … so I just browse the website, though I won’t stay at it for too long” (female participant). In comparison to other customers, they engaged in information searches within the shortest timeframe after they saw the first advertisement. These customers seemed curious to learn more about the company’s product once exposed to the first retargeted advertisement as encapsulated in the following statement: “I am intrigued to click on and learn more” (male participant). These customers did not seem to compare information about advertised products with information about other available products on the market. They had a medium purchasing frequency in comparison to seekers and meticulous customers. This is implied by the following notion: “I typically don’t buy luxury products for myself. Sometimes I do, but not too often. Typically, it’s for a friend or a family member” (female participant); therefore, indifferent customers seem to often purchase products for others rather than for themselves.

**Seeker Customers**. Seeker customes perceived the second retargeted advertisement positively if its message was dominated by affordability and credibility constructs. These customers seemed to purchase products that they believed would benefit their wellbeing. Seekers were the least frequent buyers in comparison to other customers as implied in this statement: “I buy for special occasions, and that is very rare” (female participant). They were the only group, however, to search for information about products across online and offline media. They seek validity of information about the product before the decision as they pursue high quality as implied in the following statement: “When I think luxury, I think [of] the highest quality [products] … I want to get myself” (female participant). They only considered media sources that they believed to be trustworthy, such as their online media friends and immediate family. Additionally, they searched Google to triangulate data across multiple companies. Finally, as stated by the following female participant: “I do like to look at reviews … I look up videos about their products. I try to do a lot of research before I spend money on things”; to get a more thorough insight, seekers observed videos of other people revealing product use and benefits.

**Meticulous Customers**. Meticulous customers positively evaluated a second retargeted advertisement that consisted of a message dominated by credibility and authenticity (*i.e.*, trust-building) rather than self-reflection and affordability. Out of all the customer groups, meticulous customers were the most frequent purchasers of luxury products. This is encapsulated in the following statement: “[I purchase] a lot. I will say probably once a month. Even during Covid I was buying a lot” (female participant). Therefore, in comparison to seekers, they seemed to understand the perceived benefits of luxury products to a greater extent. They looked for validity in the retargeted message; hence, they were the one group to initiate data collection while utilizing the advertisement as a starting point. This is captured in the following statement: “I will take a picture [of the product from the ad] and post and ask if anyone has heard about such and such company, or if anyone has a bad review” (female participant). They utilized the greatest number of modalities to collect data, such as website browsing, social media, browsing online comments about products, and evaluating the number of followers. Finally, they seemed to be the only customer group to utilize their own social media to collect information about the brand/product. This is implied by the following narrative: “I will take a picture [of the product from the ad] and post that in my story and ask if anyone has purchased this before” (female participant). Meticulous customers ask for reviews from other people by posting the advertisement or the product on their social media.

## DISCUSSION

The emergent model of the early stage of mobile customers’ decision making suggests that retargeting in social media needs to be a two-stage activity, rather than a single marketing activity, for optimal customer acquisition to occur (See Figure 2). This goes beyond the existing literature, which merely identifies the time lag between a customer’s website visit and retargeting exposure as the key determinant of retargeting effectiveness (*e.g.*, Sahni et al., 2019; Li et al., 2021). Moreover, the model challenges studies on multiple mobile communications that identify more than two instances of communications for customer conversion to happen (*e.g.*, Azemi et al., 2022). The model suggests that customers’ expectations of the first advertisement differ from their expectations of the second retargeted advertisement, and expectational construals for each should be met for customer acquisition to occur. Existing studies focused on specific components of retargeting, such as content, timing, and the placement of the retargeted ad (Sahni et al., 2019; Li et al., 2021). The current model reveals that expectations and evaluations originate across all three components. Also, the current study differs from existing studies because it explores retargeting among customers who are at the early stage of decision making. According to the emergent model, Gen Z customers expect the first retargeted advertisement to be presentable in terms of content, design, and visuals.

> The current literature on retargeting does not seem to provide insight into customer groups with distinct evaluations in retargeting.

Additionally, the findings show the value of visuals in the successful use of retargeting. The existing literature places importance on the content of the retargeted advertisements (*e.g.*, Bruce et al., 2017; Zarouali et al., 2017, p. 160). Customers expect the first retargeted advertisement to be informational (*i.e.*, it serves as the source for customers to compare multiple product options within the company and with its competitors). Customers’ expectations for the second retargeted advertisement are that the communication must generate a feeling that the product is accessible in relation to its affordability and as a reflection of the customer’s personal stance. It should also convey authenticity and credibility to make the customer trust the company.

The model reveals three types of customers (indifferent, seekers, and meticulous) with identical evaluations of the first retargeted advertisement, and different evaluations of the second retargeted advertisement. The current literature on retargeting does not seem to provide insight into customer groups with distinct evaluations in retargeting. The digital marketing literature argues for the use of pluralistic customer evaluations, however, and recommends multiple marketing practices that best fit each customer type (Patten, Ozuem, and Howell, 2020; Ozuem et al., 2021). In the current study, retargeting advertisements that prioritize self-reflection and affordability seem to convert indifferent customers, whereas seekers and meticulous customers are converted through advertisements that prioritize affordability and credibility, and credibility and authenticity, respectively. The differing evaluations across the groups are mediated mainly by the approach they utilize to collect data about the product/company presented in the first advertisement. Also, customers appear to have different rationales for information searches and diverse purchasing frequencies. Although meticulous customers purchase luxury products the most, their main reason for searching for information is to validate the information presented in the first retargeted advertisement. They conduct information searches through multiple online platforms (such as social media) and are the only customer group to invite others to engage in conversation about products. By contrast, seekers put less effort into data collection, but only approach sources that they consider credible (*e.g.*, immediate family and close friends). This is in line with existing literature that outlines the engagement of specific customer groups by word of mouth with people whom they feel close to (*e.g.*, Lam and Mizerksi, 2005; Azemi, Ozuem, and Howell, 2020). Finally, seekers look for some validation of the benefits of the product presented in the first retargeted advertisement. For indifferent customers, the first retargeted advertisement sparks curiosity, which directs them to the website to browse for data collection.

### Theoretical Contribution

The theoretical contribution of this study is important, and it opens new avenues for future research in retargeting. First, the study sets out a theoretical explanation of retargeting as a dyadic process, unlike existing literature that views retargeting as a single marketing practice to generate customer conversion (*e.g.*, Zarouali et al., 2017). The dyadic approach to retargeting helps to form an understanding of how customers who are in the early stage of decision making perceive retargeted advertisements, and the construals that lead to their engagement and conversion. This is important considering that previous research efforts mostly explored retargeting effectiveness among customers who have decided to purchase products (*e.g.*, Li et al., 2021). The assumption that all customers convert on the first retargeted advertisement leaves the retargeting discipline with a partial exploration of customers’ expectations and evaluations. Existing studies in marketing have traditionally acknowledged the necessity of multiple communications/channels for customer conversion to occur (*e.g.*, Slack, Rowley, and Coles, 2008). This is relevant specifically in the conversion process of customers who are in the early stage of their decision making.

Second, existing studies (with very few exceptions) seem to have investigated retargeting across specific components, such as content, timing, and placement while approaching all customers as identical in their expectations and evaluations of retargeting (*e.g.*, Bleier and Eisenbeiss, 2015a; Li et al., 2021). The exploration in the current study approaches the components as a single unit, which allows for an enhanced theoretical understanding of overarching yet distinct expectations and evaluations of retargeting across customers. This has led to the identification of three customer groups with distinctly different evaluations of retargeting. The digital marketing literature has traditionally emphasized the importance of customer segmentation and the customization of messages as mediators of conversion (*e.g.*, Patten et al., 2020).

Last, this study focuses specifically on customers who are in the early stage of decision making. Researchers of retargeting have generally approached customers without specifying their stage of decision making (Azemi *et al.*, 2021). Customers are complex and the nature of decision making for those who are in the early stages of decision making requires closer examination. Understanding the engagement and purchasing of this customer group sets the stage for retailers’ maximization of marketing programs and an increased customer pool.

### Managerial Contribution

The emergent model of customers’ expectations and evaluations supports luxury fashion retailers that target Gen Z early in their decision-making process on numerous fronts: First, the model reveals the retargeting journey that digital demand-generation and data-analysis managers should utilize to ensure customer conversion. The dyadic retargeting journey offers a guideline for managers to follow to lead customers from engagement to conversion. To be able to appropriately assign advertisements to customers relative to their customer segment, demand-generation managers, marketing-data analysis managers, and social media managers should keep an eye on the moderators of customer segmentation, specifically the frequency of purchasing (for existing customers) and customers’ involvement in information search. For small businesses, this would require a manual review of customers’ engagement in social media. For large retailers, advanced customer-relationship management platforms could support data collection. Second, the expectation and evaluation construals in the model should direct communications managers’ decision making and attention toward the construction of retargeted advertisements. This would ensure that no customer is lost during the first retargeted advertisement, and it would support a smooth customer transition to engagement and conversion with the second retargeted advertisement.

Data about customer engagement in information searches would support customer service and customer success managers to track potential customers across online and offline media and their influencers (*e.g.*, friends and family), and communicate with them at the timing that best influences customer acquisition. Both customer success managers and customer service managers can use the purposes of information search as a source of conversation with customers. This allows managers to tailor their language to fall in line with customers’ expectations. Finally, the outcome of retargeting based on the model could support marketing managers in their decision making and plans for the inclusion of retargeted advertisements in integrated marketing communications plans. If applied appropriately, integrated marketing techniques support an effective usage of marketing (Bakopoulos, Baronello, and Briggs, 2017). Therefore, overall, the model supports a productive allocation of the marketing budget, and the tracking, understanding, and conversion of customers.

### Limitations and Future Research

The current study utilized interviews with customers as the main source for data collection. The authors recommend that future research explores retargeting from the perspective of retailers. This would optimize understanding retailers’ efforts and evaluations, given that current insight is limited to customers’ interpretations. The current study did not aim for generalizability. Therefore, it would be interesting if other scholars tested the emergent model in real settings. Also, the focus of the current study is on Gen Z mobile customers of the luxury fashion industry positioned at an early stage of decision making. Gen Z luxury fashion customers are a complex group, particularly in terms of their decision-making processes; they seek personalized marketing experiences for conversion to occur. Given the pluralistic nature of customers and the idiosyncratic expectations and evaluations of customers that originate in specific age cohorts, future research should expand the understanding of retargeting to mobile customers across other age groups as well as other industries. Finally, the study did not explore customers’ expectations and evaluations of retargeting in relation to whether customers were exposed to specific brands or whether the advertisements were identical and/or different messages. It would be interesting if comparative studies explored customer engagement and conversion across retargeting with identical and different messages across different brands.

## ABOUT THE AUTHORS

**Yllka Azemi** is an assistant professor of marketing at the Indiana University Northwest’s School of Business and Economics. Her research interests include online service failure and recovery strategies, social media marketing, mobile marketing, consumer journey and behavior. Azemi’s work can be found in the *Journal of Business Research, Psychology & Marketing*, and *Journal of Retailing and Consumer Services*, among other journals.

**Wilson Ozuem** is an associate professor of management at Anglia Ruskin University, U.K. His general area of expertise lies in digital marketing and fashion marketing with a specific focus on understanding the influences of emerging computer-mediated marketing environments (CMMEs) on the fashion industry. Ozuem’s research is published in the *European Journal of Marketing, Journal of Business Research, Information Technology & People, Psychology & Marketing*, and *Journal of Advertising Research*, among others.

## Appendix A Participants’ Characteristics

View this table:
[Table2](http://www.journalofadvertisingresearch.com/content/63/4/384/T2)

*   Received January 23, 2023.
*   Received (in revised form) July 9, 2023.
*   Accepted July 11, 2023.


*   Copyright © 2023 ARF. All rights reserved.

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