customer engagement research paper

A Customer Engagement Literature Review and Research Directions: An Abstract

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• Liliane Abboud 5 ,
• Helen L. Bruce 6 &
• Jamie Burton 5  

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As academics and scholars are increasingly recognizing customers’ active role in shaping their service experience and co-creating value, the marketing literature has witnessed a significant rise in research on customer engagement . According to Brodie et al. (2011), customer engagement denotes customers’ psychological state with a particular focal object (i.e. a brand or a firm) occurring during interactive service experiences. It is manifested through engagement behaviors that go beyond the fundamental purchase, and has ensuing mutually beneficial outcomes for both firms and customers, including increased satisfaction, loyalty, and competitive advantage (Brodie et al. 2013; Kumar et al. 2010; van Doorn et al. 2010).

Existing literature is currently fragmented in regards to several aspects related to the construct. These include definitions, conceptualizations, conceptual relationships, as well as notions of engagement intensity and valence. Recent calls have been made for further theoretical advancement of customer engagement research (Hollebeek et al. 2019). Therefore, the purpose of this paper is to draw from extant theoretical and empirical customer engagement literature to critique existing findings, highlight areas of inconsistency, and present future areas of research in order to advance a more holistic theoretical understanding.

The discussion leads to the identification of a number of gaps in knowledge, and subsequently a number of research directions destined to advance customer engagement marketing theory. Suggested themes for future research include: (i) examining the specific nature of engagement dimensions through empirical study; (ii) the dynamics, drivers, and outcomes of varying engagement intensities and valences (Li et al. 2018); (iii) disengagement as a potentially multi-dimensional construct; (iv) the role of external and contextual factors in shaping engagement; and (v) the managerial application and value of firm-driven engagement strategies. These have potential implications for practice, since understanding customer engagement should aid firms to effectively design engagement-informed marketing strategies to achieve desired outcomes.

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Abboud, L., Bruce, H.L., Burton, J. (2020). A Customer Engagement Literature Review and Research Directions: An Abstract. In: Pantoja, F., Wu, S., Krey, N. (eds) Enlightened Marketing in Challenging Times. AMSWMC 2019. Developments in Marketing Science: Proceedings of the Academy of Marketing Science. Springer, Cham. https://doi.org/10.1007/978-3-030-42545-6_173

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Journal of Product & Brand Management

ISSN : 1061-0421

Article publication date: 13 October 2021

Issue publication date: 3 February 2022

In recent years, customer engagement (CE) with brands, which has been shown to yield enhanced firm sales, competitive advantage and stock returns, has risen to occupy a prominent position in brand management research and practice. Correspondingly, scholars have explored CE’s conceptualization, operationalization and its nomological networks as informed by different theoretical perspectives. However, in spite of important advances, the knowledge structure of the overall corpus of CE research remains tenuous. Therefore, the purpose of this paper is to explore the intellectual structure of CE research.

Design/methodology/approach

Based on this gap, this study deploys bibliometric and network analysis to map CE’s literature-based landscape. Using bibliometric analysis, important CE-publishing journals, authors and influential CE articles (2005–2020) are uncovered. Using network analysis, prominent CE themes are also unearthed.

The results document key CE-publishing journals and authors and their respective contributions to the literature. Five CE themes are also identified, including CE measurement/methods, online CE, CE’s value co-creating capacity, CE conceptualization and customer/consumer brand engagement. Further, an agenda for future CE research is provided based on the presented network analysis results.

Practical implications

The reported findings generate important implications for brand managers. For example, the identified critical role of online (vs offline) CE offers a range of strategic opportunities, as outlined.

Originality/value

This paper offers a pioneering bibliometric and network analysis of the CE literature, thus mapping the field. From the identified CE themes, important avenues for further CE research are also identified.

• Customer engagement
• Brand engagement
• Bibliometric analysis
• Network analysis

Acknowledgements

The authors thank Dr Pantea Foroudi and Dr Reza Marvi for several discussions on this topic and a related work-in-progress, in addition to a general discussion with Dr Tahereh Saheb, Dr Iman Raeesi and Dr David Gligor.

Hollebeek, L.D. , Sharma, T.G. , Pandey, R. , Sanyal, P. and Clark, M.K. (2022), "Fifteen years of customer engagement research: a bibliometric and network analysis", Journal of Product & Brand Management , Vol. 31 No. 2, pp. 293-309. https://doi.org/10.1108/JPBM-01-2021-3301

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ORIGINAL RESEARCH article

Effect of brand experience on customer engagement through quality services of online sellers to students in bekasi.

• Faculty of Psychology, Bhayangkara Jakarta Raya University, Jakarta, Indonesia

Customer engagement refers to the emotional attachment a student experiences as a customer during repeated and ongoing interactions. Engagement occurs through satisfaction, loyalty, and excitement about the brand experience. Organizations engage customers at the point of behavioral change by exploring opportunities for emotional connection through continuous and consistent positive experiences. When customers engage with a brand experience, they feel emotionally connected and excited about the product and the service quality. This study’s purpose is examining the effect of brand experience on customer engagement by using service quality as a mediator variable; this research was conducted by collecting data from 254 students of the iGeneration born in 1995. Overall, 254 students participated in this study. Of them, 172 people or 68% of the total respondents in this study were women, and 82 people or 32% were males. The results show no direct effect of brand experience on customer engagement, and there is a role for service quality mediators that mediate brand experience and customer engagement. The results are discussed, and the implications for the organization are mentioned.

Introduction

In the past few years, we have seen that research on customer attachment is on the rise. Initially, the attachment was in human resource management as a psychological connection to increase employee loyalty ( Schaufeli et al., 2002 ); however, many researchers developed studies on attachment to the realm of marketing. Marketing has gone from transactional to relationships that emphasize the importance of interaction and value-laden, long-term customer relationships ( Boulding et al., 2005 ; Rosenbaum et al., 2017 ; Sareen, 2018 ). In line with this shifting perspective, new concepts have emerged, including customer engagement ( Vivek et al., 2014 ; Islam and Rahman, 2016 ). Customer engagement is a process not an end because it maps out the various customer behaviors and attitudes that result in positive, loyalty-focused brand consequences ( Bowden, 2009 ; Verhoef et al., 2010 ; Hollebeek, 2011 , 2015 ; Gummerus et al., 2012 ). According to Hollebeek et al. (2014) and So et al. (2014) , measuring customer engagement can use two dimensions: cognitive and emotional.

The shift in these new concepts did not escape. The escalation of transactions over the internet, the development of information technology, and the influence of the Covid-19 pandemic contributed significantly to the popularity of online purchases that tend to lead to iGeneration students rather than traditional buyers. According to generation theory ( Codrington and Grant-Marshall, 2004 ), there are five generations of humans based on the year of birth: Baby Boomer , born 1946–1964; Generation X, born 1965–1980; Generation Y, born 1981–1994, often called millennials; and Generation Z, born 1995–2010 and also called iGeneration, Net Generation, Internet Generation; and Generation Alpha, born 2011–2025. The focus on this study is on the iGeneration born in 1995; they are the generation that, since childhood, have known technology and are familiar with advanced gadgets that indirectly affect their personality. According to Kim and Ammeter (2008) , young people are not only more familiar with e-commerce, but they also process website information five times faster.

Recently, competition between online sellers has become more intense, and online service quality sellers are receiving more attention than ever before. High-quality service has become a requirement among online sellers, and quality service helps companies get and keep customers engaged. According to Mittal et al. (1999) , service quality is a focused evaluation of consumer perceptions of service quality components, such as interaction, physical environmental, and outcome quality. According to Sahin et al. (2011) , brand experience is not a concept of emotional connection. Over time, a brand’s experience can produce an emotional bond, but emotions are just one internal result of stimulation that evokes the experience. Because brand experience differs from brand evaluation, attachment, and consumer pleasure, brand experience is also conceptually and empirically different from personality.

Marketing literature tends to see online brands as additional products or services that meet specific customer needs through interaction in a computer-mediated environment ( Hoffman and Novak, 1996 , 2009 ). A product can provide maximum emotional benefits to students as consumers; the brand must have a characteristic or uniqueness that distinguishes it from its competitors and provides a pleasant experience. Definition of brand experience is proposed by Brakus et al. (2009) as a bundle of feelings, sensations, cognitions, and behavioral responses elicited by brand-related stimuli that are brand identity elements. Marketing experts emphasize the emotive aspects of brand experience and subjective evaluation of brands, emphasizing the importance of brand personality ( Okazaki, 2006 ), images [( Da Silva and Alwi, 2008a , b ; Kwon and Lennon, 2009 ), or brand equity ( Furrer et al., 2004 ; Christodoulides et al., 2006 )].

This paper intends to examine the effect of the brand experience on customer engagement to quality services as mediators. The goal is important because customer attachment research is mainly fragmented and requires a general theory that is empirically verified, particularly among Generation Z.

Hypothesis Development

Brand experience on customer engagement.

According to Mollen and Wilson (2010) ; Hollebeek (2011) , and Vivek et al. (2012) , there is marketing research that defines customer engagement as the emotional, cognitive, and behavioral attachment of customers with brands. Customer engagement is embodied in four different sources of the value obtained from consumers: lifetime value (purchase), incentive referral, influence value, and knowledge value ( Kumar and Pansari, 2016 ).

In line with the importance of customer attachment, the brand experience has also reached a significant place in recent marketing research, mainly due to its essential role in offering a competitive advantage to business organizations ( Khan and Rahman, 2015 ). Attachment implies a two-party relationship ( Vivek et al., 2014 ; Dessart et al., 2016 ) based on interactivity ( Brodie et al., 2011 ; Hollebeek et al., 2019 ).

According to Carvalho et al. (2018) , by buying one product, consumers take an active process to learn about the brand, which shapes the brand’s expectations. This process directs consumers to be more informed, connected, empowered, and active, and these experiences impact customer feelings positively. Customer attachment can be classified as a positive or negative feeling ( Brady et al., 2006 ). Positive customer engagement includes positive consequences in the short and long term that are financial and non-financial for the company.

According to the research of Prentice et al. (2019) , passengers’ experiences with airlines affects their emotional attachment and attitudes toward their choice, behavioral engagement with the airline, and ultimately loyalty behavior. The findings are consistent with those in the studies of Roberts and Alpert (2010) ; Kumar and Pansari (2016) , and Pansari and Kumar (2018) although research exists in a variety of industry and study settings. Findings consistently show that customer experience with brands and related organizations is critical to engage customers to achieve customer engagement actively. The results of previous research are consistent with a view on the proposed hypothesis.

Hypothesis 1: It is suspect that there is a positive influence of brand experience on customer engagement.

Brand Experience on Service Quality

There are divergent definitions of service quality in the existing literature. Some researchers study service quality as a general service evaluation. Service quality often reflects customer perception and value assessment of a product or service ( Parasuraman, 1998 ), whereas others study it as a multidimensional construction shaped by service attributes. Service quality is a focused evaluation of consumer perceptions of service quality components, such as quality of interaction, of the physical environment, and of the results ( Mittal, 1999 ).

Quality of service is widely recognized as an antecedent of customer satisfaction and behavioral intent that, in turn, leads to organizational profitability ( Zeithaml et al., 1996 ; Alexandris et al., 2002 ; Wirtz et al., 2013 ; Shi et al., 2014 ; Kim et al., 2016 ). Its researchers argue that customers’ perceptions of service performance over each service experience determine the quality of a company’s services ( Cronin et al., 2000 ).

Although the quality of service can be judged on a single meeting experience, another case in the brand experience is not limited to just one experience and one touchpoint only. However, it involves the experience from different touchpoints in different phases of the preconsumption journey, for example, when consumers consume the experience, alternative valuations, and anticipated experiences in brand organizations, including perceived sensations and memories of postconsumption experiences ( Carù and Cova, 2003 ; Laming and Mason, 2014 ). The brand experience results from a series of interactions between brands and consumers during service meetings ( Jiang et al., 2018 ). According to Sahin et al. (2011) , customers need to have brand experience in marketing practices. This brand experience positively affects the quality of consumer–brand relationships.

The results of the research from Şahin et al. (2017) show that the relationship between brand experience and service quality is substantial.

The customer experience at each meeting is considered a quality snack that can emotionally improve customer feelings on the service manifested in purchasing behavior. Consistent with this view is the following hypothesis:

Hypothesis 2: It is suspect that there is a positive influence of brand experience on service quality

Service Quality and Customer Engagement

It is imperative to engage customers and increase customer loyalty ( Prentice and Wong, 2019 ). According to Chiou and Droge (2006) , service quality generates overall satisfaction and trust and promotes purchasing intentions. As a result, in transaction relationships between brands and consumers, certain levels of trust and intention can increase a consumer’s willingness to continue relationships in the future or be attached. Verleye et al. (2014) assert that the overall quality of service significantly influences customer engagement behavior. On the other hand, Ahn and Back (2018) state that the customer brand experience is a positive and significant antecedent of customer engagement. On the other hand, the brand experience is related to the perceived quality of service. However, the research results from Prentice et al. (2019) show results in moderated mediation and post hoc testing or direct effects, suggesting that quality of service played a less significant role in customer engagement. The results provide empirical evidence of the gap between service quality and customer engagement, providing insight into the following service quality research. As such, we argue that, when a customer has better quality service, it means that he or she has a better brand experience and has the intention to engage with the product or brand.

The results of previous research consistent with a view on the proposed hypothesis are the following:

Hypothesis 3: It is suspect that there is a positive influence of service quality on customer engagement.

Effect of Brand Experience on Customer Engagement With Service Quality as a Mediator

Brand experience includes cognitive and affective states ( Bhat and Reddy, 1998 ; Mollen and Wilson, 2010 ), and several authors recognize the importance of both perspectives ( Bridges and Florsheim, 2008 ; Hausman and Siekpe, 2009 ; Caruana and Ewing, 2010 ). Further evidenced by Gambetti and Graffigna (2010) and Brodie et al. (2011) , brands are the most distinctive objects of engagement in business literature.

Perceived quality of service is defined as a global assessment or attitude relating to service superiority ( Bitner et al., 2010 ). Recently Prentice and Loureiro (2018) conducted research closely from a customer’s perspective and examined how customers’ psychological desires, perceived benefits, and social values affect their engagement with brands and organizations, and they argue that customer-based antecedents better reflect their genuine engagement and willingness, leading to positive organizational outcomes in consumer behavior. Marketing shows that consumers no longer buy products and services, but rather buy experiences around what is sold ( Morrison and Crane, 2007 ).

In their review, Prentice et al. (2019) state that the relationship of service quality is not so significant, but overall, research shows that customer-based factors are significantly related to customer engagement. In particular, brand experience has a significant direct and indirect effect on customer engagement.

Unlike most service research that models service quality as a predictor of customer engagement, this study proposes the quality of online seller services rated by students as customers acting as mediators in the chain effect of brand experience on customer engagement.

The results of previous research are consistent with a view on the proposed hypothesis:

Hypothesis 4: It is suspect that there is a role of mediator service quality on the effect of customer engagement with brand experience.

Materials and Methods

Participants and procedures.

A quantitative approach is used in research. The data analysis technique uses path analysis and the value of direct and indirect effects and regression analysis with intervening variables. To test the three hypotheses uses a quantitative approach, the data collection tool uses a psychological scale, and the research respondents are students in Bekasi. According to the research objectives, the analysis method uses the structural equation model (SEM) based on variance or variance based-SEM.

Respondents in this study were students in Bekasi with an average age of 23–30 years who were engaged in buying online, totaling 254 respondents, from a population of 750, a sample taken with table Krecie with research confidence 95% with alpha 5%. A total of 172 people or 68% of the total respondents in this study were women, and 82 people or 32% of the total respondents were male, so the total respondents were 254 people. All respondents were voluntary, and all respondents received approval of the form by providing information about the purpose of the study.

This project involves human subjects. The research protocol was approved and reviewed by academics. Ethical approval is not needed following applicable educational guidelines and regulations. Informed consent from the participants is implied through the completion of the survey.

To collect research data, researchers used the Likert-type psychological scale. To measure customer engagement we used two dimensions: cognitive and emotional engagement, proposed by Hollebeek et al. (2014) and So et al. (2014) , whereas dimensions of brand experiences suggested by Brakus et al. (2009) are sensory, affective, intellectual, and behavioral. For the service quality measure, according to Mittal et al. (1999) , service quality is the focused evaluation regarding the consumer’s perception of quality components of service such as interaction, physical environment, and outcome quality. The researchers developed all scales and items rated on frequency ratings ranging from 1 (never) to 5 (always) or a five-point scale.

Data Analysis Method

To describe the statistical methods and data using mean, median, and standard deviation to determine the normality of data, multivariate normality testing using SPSS 21 software and testing the hypothesis using data analysis methods were used; we modeled structural equations based on SEM (VB-SEM) variants using AMOS version 24 software. To analyze descriptive statistical data, we underlined the correlation between variables focused on describing or explaining variables. By looking at the correlation between research variables, it is expected to understand the three variables studied to test the hypothesis. Data analysis methods were used to model structural equations based on SEM-based variants (VB-SEM) using AMOS version 24 software.

This model is a set of statistical techniques that allows simultaneous examination of a series of relationships. In the SEM, variables that are not affected by other variables are called independent or exogenous variables, whereas other variables that are affected by other variables are called dependent variables or endogen.

Based on the path analysis model in Figure 1 , they use the AMOS version 24 program for processing data to form an estimation equation. After they are formed, a suitability test, goodness of fit, and hypothesis tests are performed.

Figure 1. Path model. Customer engagement (cognitive and emotional), brand experience (sensory, affective, intellectual, behavioral), service quality (interaction quality, physical environment quality, outcome quality).

Based on data processing results and acceptance criteria on model testing, match sizes determine the overall model’s predicted rate on correlation and a good covariance matrix. It can be seen with the chi-square value of 33,813, where the chi-square value result is small, the better, and the model is good (see Table 1 ).

Table 1. Model results.

The value of chi-squares probability is 0.249 > 0.05, indicating that empirical data are identical to the theory/model. The value of the result, the root mean square error of approximation (RMSEA) is 0.040, indicating that the model is close to fit (see Table 1 ). Whereas the incremental suitability measure contrasting the proposing model with the base model looks very good by seeing at the value produced by the goodness of fit index (GFI) 0.973, where GFI is an index that describes the suitability of the overall model calculated from the predicted residual square of the model compared with the actual data. So the GFI > 0.90 (see Table 1 ) indicates that the model tested is suitable.

Adjusted GFI (AGFI) 0.949, Tucker Lewis index (TLI) is a cumulative conformity index by comparing the model tested with the baseline model. TLI is used to address problems arising due to the complexity of the model. The recommended acceptance value is TLI 0.996 > 0.90, and the normed fit index (NFI) measure compares the proposed and null models. The recommended value is NFI 0.99 > 0.90. The comparative fit index (CFI), 0.997, is also an incremental conformity index. The magnitude of an index ranges from zero to one; a value close to one indicates that a model has a good degree of conformity; this index is highly recommended to use because it is relatively less sensitive to sample size and less influenced by the complexity of the model. The recommended acceptance value is CFI 0.997 > 0.90

Influence analysis intends to see how strong the influence of a variable is with other variables indirectly, but directly variable BE low. The results of calculations of direct and indirect influence are shown in Table 2 .

Table 2. Standardized total effect.

Through the calculation results in Table 3 and Figure 1 , the direct effect of brand experience on service quality can conclude that brand experience has a direct effect of 0.272 (sig.). Similarly, the direct influence of service quality on customer engagement has an effect of 0.971 (sig.). Whereas between brand experience and customer engagement has a low direct effect, which has a value of 0.22 (0.452 > p ). The following calculation results show that the indirect effect of brand experience on customer engagement through quality service is 0.934 (sig.). Because indirect influence is more significant than direct effects, it can be concluded that the service quality mediators play a full role in this study.

Table 3. Standardized direct and indirect effect.

Based on the results of the structural model analysis and the testing of the goodness of fit for this study, the effect of brand experience on customer engagement with service quality as a mediator gave the results that the research had a complete role—the statistical hypothesis testing of the effect of each variable to the other variables as follows. Of the four proposed hypotheses, only three were accepted (H2, H3, H4), and one was rejected (H1).

In hypothesis 1, there is no proven influence of brand experience on customer engagement. Although some studies say that brand experience has a significant effect on customer engagement, research conducted by Prentice et al. (2019) shows that passengers’ experience with airlines not only affects their emotional attachment and attitude toward the airline of their choice, but also their behavioral engagement with and on the airline. Finally, loyalty behavior, likewise, in research conducted by Roberts and Alpert (2010) ; Kumar and Pansari (2016) , and ( Pansari and Kumar, 2018 ). The research from Ahn and Back (2018) assert that customer brand experience is a positive and significant antecedent of customer engagement. This topic is particularly intriguing because the relationship between experience and engagement is controversial ( Calder et al., 2009 ; Hollebeek et al., 2014 ). Given the intense focus on experience in modern marketing (e.g., Pine and Gilmore, 1998 ; Brakus et al., 2009 ), this can be considered surprising. However, other types of experiences may be better able to predict consumer behavior. According to Aldrin and Merdiaty (2019) , today’s brand experience is no longer in demand, especially for young people or students (Gen Z).

In hypothesis 2, there is a proven positive influence of brand experience on service quality although research value is significant but not quite intense. This result is paradoxical to the finding research of Carrizo-Moreira et al. (2017) ; the study’s main conclusion is that brand experience is an essential antecedent of service quality, trust, satisfaction, and loyalty. Also, Devia et al. (2018) find that loyalty and brand experience significantly influence service quality to improve customer loyalty. Furthermore, loyalty and brand experience influence the improvement of service quality on customer loyalty.

In hypothesis 3, there is a proven positive influence of service quality on customer engagement. From the research we conducted, quality service relationships are very influential and significant to customer engagement firmly. This finding supports several prior studies that find service quality may lead to customer engagement: Ahn and Back (2018) ; Roy et al. (2018b) ; Lee et al. (2019) . Roy et al. (2018a) investigated the impact of service convenience, fairness, and quality on customer engagement, finding that service quality has a significant effect on customer engagement. They are strongly supported by other research from Verleye and Aghezzaf (2013) , asserting that overall service quality significantly influences customer engagement behavior. Also related to research from Abror et al. (2019) , this study examined the link between service quality and customer engagement. The research finds that service quality is a significant and positive antecedent of customer engagement.

Finally, in hypothesis 4, there is a proven role of mediator service quality of customer engagement with brand experience, but it is very low. Unlike most service research that models service quality as a predictor of customer attitude and behavior outcomes, this study proposes that the quality of online seller services that students judge plays a role in mediation in customer attachment relationships. There is still little research on quality service as a mediator to customer engagement, so we compared the research from Prentice and Loureiro (2018) contending that customer-based factors are more reflective of customers’ volition to engage with a brand. Service quality is reflective of the cognitive assessment of the services provided by the brand organization. From the customers’ perspective, the firm should provide quality service to be competitive.

Limitation of Study, Suggestion for Future Research

The study proposes that customer-based factors play a dominant role in engaging customers, and customer-based factors serve as mediators. There are several implications in this study. First, this study contributes to empirical customer engagement testing customer online seller-based factors among students with customer engagement. The results are extraordinarily challenging about the similar effects of customers and customer factors on customer engagement. Customer-based factors play a more prominent role in engaging customers. Second, the number of respondents is not too large, so it cannot be generalized to other students. A more significant number of respondents will give better and more accurate results. Therefore, future studies should test with a more significant number of respondents. Third, because respondents are students in generation Z, subjective possibilities must exist. It is hoped that future research can test more mature students or millennials or even customers of Generation X. Differences in views and cultures need to be considered, especially in the culture in Indonesia, and especially the Bekasi area needs to be the next concern in research.

It can also be caused by the ongoing Covid-19 pandemic, because of which changes occur in almost all sectors. Similarly, the local culture that influenced Gen Z decided to engage.

Implication for Organization

Similarly, the findings of this study have important implications for the industry of online merchants in Indonesia and abroad. Regardless of the level of service offered to students, particularly among students, marketing efforts to engage customers among students need to be focused on improving customer experience based on customer psychology. In recent decades, service marketing researchers have widely promoted service quality in customer satisfaction and loyalty but have less involved the role of psychology in preparing its programs. The study shows that quality of service plays a role in mediation of customer-based outcomes. Although the results are identical or different with this study, they could be in a small portion or may contradict some of the results of previous studies; precisely, the effect of brand experience on customer engagement has positive but shallow results. It could be due to local cultural differences, considering that Indonesia has many cultures and cultural influences.

Furthermore, influence the way of thinking and making decisions for students to engage a customer. In terms of practical implications for the seller and providing an understanding of the attention to its customers, it is essential that the seller firmly understands the role of customer engagement for the continuity of its organization, for it requires awareness and attention for every employee in the seller’s organization to provide services following customer needs. Thus, seller organizations must pay attention to positive effects and experiences regarding the brand of an organization and the behavior of employees when interacting with customers. Because customer engagement does not show up instantly, seller organizations need to expand and improve other experiences that impact customer engagement. Hence, this research can guide seller organizations that want to increase their customer engagement.

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Ethics Statement

Ethical review and approval was not required for the study on human participants in accordance with the local legislation and institutional requirements. The patients/participants provided their written informed consent to participate in this study.

Author Contributions

NM: study conception and design, data collection, and draft manuscript preparation. NA: analysis and interpretation of result. Both authors reviewed the results and approved the final version of the manuscript.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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Keywords : customer engagement, brand experience, service quality, student, Gen Z

Citation: Merdiaty N and Aldrin N (2022) Effect of Brand Experience on Customer Engagement Through Quality Services of Online Sellers to Students in Bekasi. Front. Psychol. 12:801439. doi: 10.3389/fpsyg.2021.801439

Received: 25 October 2021; Accepted: 30 November 2021; Published: 14 January 2022.

Reviewed by:

Copyright © 2022 Merdiaty and Aldrin. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Netty Merdiaty, [email protected]

† ORCID: Netty Merdiaty, orcid.org/0000-0002-1162-8368 ; Neil Aldrin, orcid.org/0000-0001-8990-4695

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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Measuring customer engagement in social media marketing: a higher-order model.

1. Introduction

2. theoretical perspectives and hypotheses, 2.1. theoretical foundation and conceptualization of customer engagement, 2.2. customer engagement antecedents, 2.2.1. involvement, 2.2.2. customer participation, 2.2.3. commitment, 2.3. the impact of customer engagement on customer loyalty, 3. research method, 3.1. measures, 3.2. data collection and respondents’ profile, 3.3. analyses, 4. empirical analysis and results, 4.1. exploratory factor analysis, 4.2. confirmatory factor analysis, 4.3. structural equation model, 5. discussion, 5.1. theoretical contributions, 5.2. managerial implications, 5.3. study limitations and future avenues for research, author contributions, institutional review board statement, informed consent statement, data availability statement, conflicts of interest.

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Share and Cite

Vinerean, S.; Opreana, A. Measuring Customer Engagement in Social Media Marketing: A Higher-Order Model. J. Theor. Appl. Electron. Commer. Res. 2021 , 16 , 2633-2654. https://doi.org/10.3390/jtaer16070145

Vinerean S, Opreana A. Measuring Customer Engagement in Social Media Marketing: A Higher-Order Model. Journal of Theoretical and Applied Electronic Commerce Research . 2021; 16(7):2633-2654. https://doi.org/10.3390/jtaer16070145

Vinerean, Simona, and Alin Opreana. 2021. "Measuring Customer Engagement in Social Media Marketing: A Higher-Order Model" Journal of Theoretical and Applied Electronic Commerce Research 16, no. 7: 2633-2654. https://doi.org/10.3390/jtaer16070145

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Table of contents

Top 11 customer engagement metrics to measure.

With just a few taps on their phone, customers can quickly compare product prices, check reviews, and easily switch brands . So, how does one earn a customer’s loyalty? The secret is tracking your customer engagement. 

But evaluating your customer engagement isn’t just about looking at the sales figures. Sure, metrics like sales and profits are important, but they don’t give you a complete picture. To understand more, you need to dig into how your customers are actually interacting with your brand. 

Are they talking about you on social media? Are they coming back for more, or are they quietly slipping away? And if they are, what is making them switch brands? Tracking these actions can tell you a lot about what your customers feel about your brand.

And, how do you track these behaviors? By measuring customer engagement metrics. In this article,  I give you the top customer engagement metrics that matter the most today. These metrics will help you understand how your customers perceive your brand—and how you can keep them engaged. 

Table of Contents

Why measuring customer engagement metrics matters.

First, let me quickly walk you through what customer engagement really means. 

Customer engagement is all about building a strong connection between your business and your customers. It goes beyond just making a sale—it’s about creating meaningful interactions that make customers feel valued and understood. 

Engagement can happen in many ways. For instance, a customer can leave a positive review, participate in one of your social media challenges, open your emails, or even just spend more time browsing your website. 

The important thing is that these interactions show a deeper level of interest and involvement with your brand. This is something every business should strive for because engaged customers are more likely to stick around. 

Now, coming to the important part –  Why does measuring customer engagement matter? because simply guessing how engaged your customers are just won’t cut it. You’d need numbers and metrics to understand your customer engagement. 

Here’s a list of benefits of measuring customer engagement:

Top 11 Customer Engagement Metrics To Track

Now, let’s get straight into the important metrics you should be tracking. Each one of these will provide you with a distinct perspective on the ways your customers are engaging with your brand. 

These metrics will give you a comprehensive understanding of what’s working, what’s not, and which areas need improvement. 

1. Website traffic

If you own a business, chances are that you have an official website through which you sell your products or services to your customers. So, the very first thing to analyze is your website traffic to check how many customers visit your website. 

So how do you define website traffic?

Website traffic is a crucial customer engagement metric that refers to the volume of users who visit your website. This can be tracked daily, monthly or even quarterly – according to your business needs. 

Website traffic can also be segmented across various channels:

To track your website traffic, you’ll need to invest in a website analytics tool like Google Analytics . All you need to do is set up Google Analytics and add a tracking code to your website. This code will collect all the data about visitors and their activities on your site.

Website traffic is the starting point for measuring your customer engagement. It gives you a fair idea of the visibility and reach of your website, if users aren’t visiting your website, there’s no opportunity for them to engage with your content, products, or services. Conversely, High traffic volumes increase your chances of engaging a broader audience.

2. Session duration

The next important metric to track in your website is the session duration. So, what do you mean by session duration?

Session duration is the total time a user spends on a particular page of your website during a single visit. It is tracked from the moment they land on your site to when they leave or become inactive.

If you use Google Analytics, you’ll be able to find your average session durations when you check the insights of your website pages. 

What do session durations tell you about your website?

Average session duration is an important metric that shows genuine user engagement. It’s not something you can easily manipulate with ads, keyword stuffing, or catchy headlines. It provides a true measure of how much time visitors are actually spending on your site. This indicates how interested and engaged they are with your content.

Recommended read: 18 Key Customer Service Metrics + How to Use Them

3. Pages per session

Another important website metric that you should be aware of is – pages per session. 

Pages per session is the average number of pages a visitor goes through during a single visit to your website. 

It gives you an idea of how much your visitors are exploring about your brand when they come to your site. From the moment a visitor lands on your site to when they leave or become inactive, every page they click counts towards this metric.

You can use any website analytics tool like Google Analytics or Amplitude to track this metric. 

So, what do Pages per session tell you about customer engagement?

Pages per session is a metric that tells you how users are interacting with your site. It reflects how curious and engaged your visitors are, showing whether they’re just skimming the surface or diving deep into your content. Just like session duration, you cannot manipulate this metric with marketing tricks or flashy headlines. Instead, it gives you a clear picture of the user experience on your site. 

4. Bounce rate

Let’s talk about Bounce rate—one of those metrics that often gets overlooked but is quite important for understanding your website’s performance.

Bounce rate is the percentage of visitors who land on a page and then leave without clicking on anything else or visiting another page of your website. So basically, they just “bounce” away after viewing just one page. This could mean they either found what they needed right away, or, they didn’t find what they were looking for and left immediately.

Bounce rate gives you a quick snapshot of how well your website is holding your customers’ attention. If someone lands on your site and leaves almost instantly, it could indicate a few things: maybe your page didn’t load fast enough, the content wasn’t relevant to what they were looking for, or the user experience wasn’t that great.

But here’s the thing— bounce rate isn’t always a bad thing . For example, if you have a single-page site or if the goal is to provide a quick answer (like on a blog post),a high bounce rate could mean that the user found exactly what they were looking for and didn’t need additional exploring. It all depends on the context of your website.

So, to sum up:

5. Active user metrics (DAU, MAU)

Active user metrics like Daily Active Users (DAU) and Monthly Active Users (MAU) give you a snapshot of how engaged your customers are with your website on a daily or monthly basis. 

To give you an in-depth definition of these two metrics:

Daily Active Users (DAU) measures the number of users who interact with your website on a daily basis. And when you track this metric on a monthly basis, it becomes Monthly Active Users (MAU).

You can track these metrics through most website analytics tools like Google Analytics or app analytics tools like Clevertap or Mixpanel. By closely monitoring these numbers, you’ll be able to see trends and identify spikes or drops in user engagement .

So, what does a high or a low DAU or MAU tell you about your customer engagement?

DAU and MAU are good indicators of user engagement and stickiness. These metrics don’t just tell you how many people are visiting your website or app, but also how many are actually coming back and using it regularly. High DAU and MAU numbers suggest that your platform is providing ongoing value to your customers.

6. Email open rates and CTRs

Email engagement metrics are as important as your website or app analytics. Your digital marketing efforts might also include email marketing campaigns, and it’s important to track their performance. So, if you’re sending cold emails or even newsletters and promotional emails to your prospects or subscribers, you might want to measure their open rates and Click-through rates (CTR).

Let’s understand these two metrics one by one. 

Email open rates measure the percentage of recipients who open your email out of the total number of emails sent. It’s a powerful metric that tells you how effective your subject lines are and whether your emails are catching your audience’s attention. 

A high open rate indicates that your subject lines are compelling and your audience is looking forward to reading your email content. On the flip side, a low open rate might suggest that your emails are getting lost in your audience’s inbox. It can also imply that your subject lines might need some more thinking to urge your audience to open them. 

Click-through rate (CTR) is the percentage of recipients who click on one or more links in your email. This metric goes a step further than the open rate by showing how engaged your audience is with the content of your email.

It’s one thing to get someone to open your email, but getting them to click through to your website, product page, or another piece of content is a stronger indicator of email engagement. 

A high CTR means your email content is compelling and relevant, encouraging readers to take action. A low CTR , on the other hand, could mean that your content isn’t connecting with your audience or that your CTA isn’t appealing enough.

You can track these metrics through any email analytics tool like GMass or Mailtracker. 

Both open rates and CTRs help you to measure your email marketing efforts. They help you make informed decisions, like optimizing your email campaigns for better results. Tracking these metrics can also help you experiment with different subject lines, email content, and CTAs to see what resonates most with your audience.

7. Conversion rate

Whether you’re running an e-commerce store or a service-based business, your conversion rate is a direct indicator of how well you’re achieving your business goals. 

Conversion rate is the percentage of users visiting your website who take a desired action. This action can vary depending on your business objectives. For an online store, a conversion might be a completed purchase. For a SaaS company, it could be signing up for a free trial. For a blog, a conversion might mean subscribing to a newsletter. 

Here’s how to calculate the conversion rate for your business:

So, what do conversion rates tell you about your business?

Now that you know what conversion rates tell you about your customer engagement, you must be wondering what the ideal rate should be. The truth is, there is no right answer. It really depends on the industry, your business size, and your goals. 

Here’s what a Reddit user says when asked what is a good conversion rate:

Conversion rate is one of the most telling metrics for understanding how well your website and marketing efforts are performing. This metric tracks if your customers complete the actions that align with your business goals.

8. Social media metrics

Till now we’ve covered the important metrics to track in channels like website and email. Now let’s move to another important channel: social media. You use social media to engage with your audience, build relationships, and create content that resonates. But how do you track if your social media strategy is working or not? By tracking the right metrics. 

Social media metrics are insights that help you measure the performance of your social media efforts. These metrics can tell you everything from how many people are seeing your posts to how engaged your audience is with your content. 

Here are some of the metrics that you should keep a tab on:

Today, most social media channels, such as Facebook, Instagram, and LinkedIn, have built-in analytics and reporting dashboards. These dashboards give a pretty good idea of customer engagement. 

Social media metrics help you gauge your audience’s preferences and behaviors. By tracking these metrics, you can tailor your social media strategy to better meet your audience’s needs and interests and build a stronger online presence for your brand.

Recommended read: Social Media Data Mining to Improve Customer Experience

9. CSAT Score

Another metric that businesses use to gauge how engaged their customers are is Customer Satisfaction (CSAT) score. It’s a direct way to measure short-term satisfaction and pinpoint areas for improvement.

Customer Satisfaction Score (CSAT) measures how satisfied your customers are with your product, service, or a particular interaction. Typically, it involves asking customers, through a survey, to rate their satisfaction on a scale of  1 to 5, with 1 being “Not at all satisfied” and 5 being “very satisfied.” 

This survey is usually done after a customer interaction – possibly right after your suppor team resolves a customer query, or after a customer purchases a product from your store. 

How to Calculate CSAT Score

Once you gather all the ratings given by your customers, use this formula to find your business CSAT score

How does calculating CSAT score help your business?

Customer Satisfaction Score (CSAT) is a valuable metric for gauging how satisfied customers are with specific aspects of your business. It provides direct feedback that you can use to enhance customer experiences, boost satisfaction, and ultimately drive higher engagement.

10. Customer Lifetime Value (CLV)

This metric is all about the long-term value. Unlike other metrics that measure short-term performance or immediate outcomes, CLV helps you understand the overall value a customer brings to your business over the entire duration of their relationship with you.

Customer Lifetime Value (CLV) represents the total amount of revenue you can expect from a single customer account throughout their entire relationship with your business.

This metric includes not just one purchase but every purchase a customer makes—from the first to the last—including any recurring revenue in the form of subscriptions or repeat purchases. 

There are several ways to calculate CLV, but the simplest formula is:

So, for example, if we assume the following inputs:

Average Purchase Value: $50

Average Number of Purchases per Year: 5

Average Customer Lifespan: 3 years

So, CLV = $50 * 5 * 3 = $750

This means that, on average, each customer brings $750 in revenue over their entire relationship with your business.

Customer lifetime value (CLV) is a metric used to understand the long-term value of your customers. It’s not just about how much a customer spends in one transaction but how much they’re worth over the entire time they’re with your company. By focusing on increasing CLV, you’re not only improving your revenue but also building stronger, more loyal relationships with your customers. 

Recommended read: Top 17 Customer Success Metrics & How to Track Them

11. Net Promoter Score

Net Promoter Score (NPS),is a popular metric  to measure customer loyalty and satisfaction. NPS is unique because it focuses not just on what customers are currently doing, but on how likely they are to recommend your business to others—an indicator of long-term satisfaction and brand advocacy.

Net Promoter Score (NPS) is a customer loyalty metric that gauges how likely your customers are to recommend your product, service, or brand to others. 

The NPS survey usually consists of a single, simple question: “On a scale of 0 to 10, how likely are you to recommend our company to a friend or colleague? 0 being least likely and 10 being most likely.”

The customers are then classified into 3 groups based on their responses. 

Based on your classification, here’s how you calculate NPS:

For example, after conducting an NPS survey, you find you have 60% promoters, 25% passives and 15% detractors. 

So your NPS score would be = 45%

Now, you may ask what is a good NPS score? Although it may vary from one industry to another, in general, according to Bain and Company , 

Here’s why you should measure NPS to gauge customer engagement:

Net Promoter Score (NPS) is a valuable metric for measuring customer engagement because it captures the depth of a customer’s relationship with your brand. It reveals not just how satisfied customers are, but how committed they are to supporting and advocating for your business. 

Improve Customer Engagement By Tracking The Right Metrics

We hope that by tracking these top customer engagement metrics you’ll be able to gain valuable insights on your customer behaviour, preference and overall satisfaction. Please bear in mind that the metrics you track should align with your business goals and the strategies you implement to improve your customer engagement. So feel free to pick and choose the metrics that matters the most. 

Remember, it’s not just about collecting data—it’s about understanding the insights you get from them and how you use them to create meaningful customer interactions – right from when your customer discovers your brand, makes a purchase and reaches out to you for customer support. The right tools and strategies can make all the difference in building stronger, more loyal customer relationships.

Speaking of customer support, you might want to check out Hiver to streamline your support operations. With Hiver, you can manage customer queries directly from your inbox, ensuring timely and efficient responses that enhance customer satisfaction. 

Want to check out more? Start a free trial

Frequently Asked Questions (FAQs)

Customer Lifetime Value (CLV) helps you understand the total revenue you can expect from a single customer over the duration of their relationship with your business. By tracking this metric, you can implement customer retention strategies to potentially increase your business’s CLV. 

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