Measuring consumer brand engagement by using big data: The case of mobile phone brands in Vietnam
Vu Huy Binh, Vo Quang Tri, Duong Thi Lien Ha/ MICA 2018 Proceedings
2.
International Conference on Marketing in the Connected Age (MICA-2018), October 6th, 2018
Danang City, Vietnam
Measuring Consumer Brand Engagement by Using Big Data:
The Case of Mobile Phone Brands in Vietnam
Vu Huy Binha, Vo Quang Trib*, Duong Thi Lien Hab
aTien Phong Technology Company, Vietnam
b
University of Economics – The University of Da Nang, 71 Ngu Hanh Son Street, Danang City, Vietnam
A B S T R A C T
Consumer Brand Engagement (CBE) is getting great attention from the marketers because of its explanatory
and predictive value. However, CBE measurement faces many obstacles, both theoretical and practical. This
study, by using a big data mining tool, demonstrates the ability market measuring CBE as well as CBE's value
in explaining and forecasting the real-time status of the market and competition such as market share, revenue,
customer interest in Vietnam mobile phone market. The results confirmed the ability to use big data mining
tools in Vietnam, especially for businesses that are limited size and resources.
Keywords: Consumer Brand Engagement; big data; engagement measuring; mobile phone brands
1. Introduction
Since the term of customer engagement is recognized as a major priority in research (Bolton, 2011), there is
growing interest in understanding consumer behavior and interaction engagement on the social media. There are
many studies of consumer engagement in social networking environments (e.g., Lorenzo-Romero et al., 2011;
Park et al., 2009, Shao, 2009; Raacke and Bonds-Raacke, 2008). However, these studies primarily explored the
motivations for using or not using social network sites without providing insight specific users’ behavior. To fill
in these research gaps, Ross et al. (2009) have pointed out that future research should focus on a specific
behavior or activity rather than general research, which would be a comprehensive way to understand users’
behavior on social network sites. Behavior engagement on social network sites is described as "Likes" or
"Comments", which are important for making brand interaction strategy. The popularity of likes and comments,
it is a great challenge for researchers and practitioners to gather, select and timely update the acts of millions of
consumers on social network sites. In addition, there is very few research on consumer engagement because of
the limited methods of data collection. Therefore, big data seems to be a useful mining tool to gather timely the
acts of millions of consumers on social network sites at the same time (Mayer-Schonberger & Cukier, 2014).
Recently, there are many tools in the world as well as in Vietnam, which have been launched to measure
customer brand engagement using big data platform. However, the reality has shown some limitations of these
tools as follows:
Cost-in-use these tools are very large from several thousand dollars to tens of thousands of dollars. Thus,
they are only suitable for businesses of major brands.
Not focusing on comparisons on specific a customer need. These tools are able to be customizable upon
suggestions but the cost is very high.
* Corresponding author. E-mail address: voquangtri@due.edu.vn
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The data source is not reliable and repetitive. Some companies are addressing this issue because of using
humans to review the accuracy and reliability of the data.
This study aim, on the one hand, is to examine the feasibility of measuring consumer brand engagement by
using big data mining tool in the context of web 2.0. On the other hand, this study finds the relationship
between consumer brand engagement and several factors such as sales, market share and sales of each
product line.
2. Conceptual Background
Customer brand engagement derived from relationship marketing concept (Fournier, 1998). It has significant
improvements over current customer-related theories and brand relationship. Vivek, Sharon, and Morgan (2012)
have found out that the added value has been created through customer engagement, which is described as the
number of customer interactions and the customer relationship experience by extending customer purchase
behavior concept. While customers created engagement with a specific brand, they are able to show more the
traditional buying behavior and relationship which logically are mutually generating value from the customers’
perspectives (Vargo & Lusch 2004). Although the notion of customer brand engagement is a new academic
concept, there is the debate; however, its application efficiency has been practically proven. According to
McKinsey (2014), Gallup (2016), and PeopleMetrics (2008) consumer engagement would bring many practical
benefits such as increasing revenue, brand loyalty, the number of purchase per buyer, and the purchase rate of
value-added services and products. Therefore, consumer engagement has become a conspicuous issue for
operators to preserve and enhance their competitiveness. It's also a challenge for researchers and practitioners to
understand how a customer interaction platform enrich the customer interactions and manage relations properly.
The work of Brodie, Hollebeek, Juric & IIic, (2011) have risen the greater interest in explaining and applying
consumer brand engagement in the context of customer behavioral interplay and brand efficiency field.
Techniques as well as influenced on practical applications (Vivek, Beatty, Dalela and Morgan, 2014).
According to the dictionary.com website (2017), engagement refers to the act of engaging or the state of
being engaged. Different scholars deal in very different ways, only concentrate on the act of engaging, or on the
state of being engaged from psychology perspectives. Thus, engagement is not well understood in previous
studies.
2.1. Application perspectives
Within the interactive context, the phenomenon of customer engagement refers to customer interactions.
Consumer engagement is the action which involved in social network sites and therefore must be recorded by
measuring customer behavior (e.g., likes, comments, shares) (ARF, 2006; Econsultancy, 2008).
2.2. Academic Perspectives
The notion of customer engagement was studied in many academic fields including education (e.g., student
interaction), psychology (e.g., social activities), sociology (e.g., civic engagement), political science (e.g.,
political commitment), and organizational behavior (e.g., work/commitment). Marketers have different
perspectives on how they reach out to engage consumers with the brand. Some scholars argued that the
engagement must be the concept of the single attribute. Other scholars view engagement as multi-attribute
including cognition, emotion, and behavior, in which engagement subjects are addressed by both consumers and
customers.
Some researchers considered engagement as participatory behavior from physical aspects. Van Doorn et al.
(2010, p. 254) have defined customer engagement behavior as the customer behavior with brand and business,
not merely a purchase, or the result of a motivational factor. It can be argued that the behavior engagement is
similar to the practitioner's perspective on engagement such as focusing on customer-to-customer interactions,
blogging, or so on in interaction context (Verhoef et al., 2010). In this regard, customer brand engagement is
described as a psychological state (Schaufeli & Bakker, 2003, Kahn, 1990). Hollebeek (2011b, p. 560) has
indicated consumer brand engagement refers the level of consumer motivation, brand awareness, context-
dependent cognitive status, forms, emotions, and behaviors that focus on specific brand interactions. Other
researchers supported both state and behavior, which aims to make the approach the psychological perspective.
Reitz (2012) argued that the notion of consumer brand engagement should include measurement of status
categories and engagement behavior.
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The concept of customer engagement should be considered with dependence and the process, which develops
and fluctuates over time. According to Hollebeek (2011a, 2011b), there are two-way interactions in term of
related objects and objects in specific contexts that are able to increase the degree of engagement at a particular
moment, represent related states, and be inherent in change but involve interaction process. Van Doorn et al.
(2010) has shown that engagement is classified as positive or negative from the valence standpoint. Customer
brand engagement is also based on motivation. Motivation is defined as “the internal factors that stimulate desire
and energy in people to be continually interested and committed to making an effort to attain a goal” (Higgins &
Scholer, 2009) or "the reasons for behavior" (Guay et al., 2010). Von Krogh et al. (2012) has shown that
consumer brand engagement would be a motivational process, formed by internal and external motives. Therein
external motives derived from important aspects of social practice (e.g., social media).
2.3. Consumer Engagement and Social Media
Social media facilitates the creation and sharing of information, ideas, career interests and other forms of
expression via virtual communities and networks, which is able to reach out to many people including customers
and social network friends and followers. Brands have engaged in these channels to establish long-term
relationships with customers and reach potential customers (Kaplan & Haenlein, 2010). While users’ social
media are able to create content, the brand still plays an important role. Customers share their favorite brands via
Twitter, YouTube, and Facebook. Some even help their customers deal with the issues that related to the product
for free, reducing the cost of services (Mathwick, Wiertz, & De Ruyter, 2008). Therefore, social media is not
merely exchange, social media provides the various ways to reach customers, communicate with them, and
measure their communication, or browsing and shopping (Hennig-Thurau et al., 2010). These choices related
specifically to the management of customer brand relationship, using individuals’ knowledge to make marketing
action planning (Hennig-Thurau et al., 2010). Take advantage of social media requires operators to understand
why consumers are attracted and how social media affects users’ emotions and behaviors (Hennig-Thurau et al.,
2010). Consequently, new marketing methods must be developed and consistent with the characteristics of social
media. Some important characteristics of social media are that the value does not come from the platform but
from the way in which social media platform is used for various purposes (Culnan, McHugh, and Zubillaga,
2010).
3. Research Objectives and Research Methods
The research measurement method in this study is created by using big data and knowledge from the
behavioral perspective, which used to measure consumer engagement with mobile phone brands on the two
The primary data is collected from using big data. The measurement objects are selected by CBE that is clear
and able to collect through "Comments" of the consumer. At the same time, the secondary data is collected from
the company’s announcements and the publications of market research firms. The purpose of this study is to
demonstrate the feasibility measurement of using big data as a platform to build consumer brand engagement in
Vietnam, and more importantly to find the relationship between consumer engagement with mobile brands and
market share, sales of each brand, sales of each product line.
Two types of data collection are conducted. Frist, the primary data in this study is collected consumer brand
engagement by using big data tool. And then, the second data including sales and market share is gathered
directly from the publications of companies and the market research firms. The study aim is to find the
relationship between the consumer brand engagement and sales, market share. Therefore, the researchers in this
study compared the primary data with the secondary data. To collect engagement data, the scholar in this study
created the software that can read and count “Comments” functions for cell phone brands and automatically save
to the databases. Therein, the "Comments" of each brand equals sum the "Comments" for all phone lines of each
brand. The software operates continuously and collates the existing database with the new data. It is also able to
update any changes at the same time and the results instantly displayed in real time.
Time to collect data: From 1 June 2017 to 15 July 2017
4. Results and Discussion
By comparing the consumer brand engagement data from using big data mining tools to sales and market
share from the publications of companies and market research firms. This study examined the relationship
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between consumer brand engagement and market share, consumer brand engagement and sales of different
channels, and consumer brand engagement and sales of each product line.
4.1. Consumer brand engagement and market share
As shown in Figure 1, consumer engagement with mobile phone brands collected from using big data and the
market share of the brands is announced by GFK.
Source: This research
Source: GFK (Gia Hung, 2017a)
Fig. 1. Consumer engagement with mobile brands and market share
As shown in Figure 1, the scholar suggests the comparable results in Table 1.
Table 1. Consumer engagement and market share of the five best-selling mobile phone brands
No. CBE (using Big Data) Sales
Interference
1
2
3
Samsung
iPhone (Apple)
Sony
Samsung Samsung
OPPO
Sony
OPPO
Sony
4
5
OPPO
Asus
Asus
Asus
Nokia
This study pointed out that the market share of brands is quite similar to the level of consumer engagement
with those brands. While the Samsung, OPPO, Sony, Asus brands get the high level of the consumer brand
engagement. They are able to get high sales. The relationship would explain as follows:
Samsung achieved the greatest customer interest and its corresponding sales are also the highest.
OPPO, Sony, and Asus are voted on the list of top five brands which consumer interest and sales of these
brands also ranked among the top five.
Interference rates are high. While four brands are voted on the list of top five brands, these brands are also in
the list of the top five highest sales.
The iPhone (Apple) has gained consumer interest; however, it is not listed among the high-margin brands,
which can explain as follows:
In terms of brand names, the iPhone (Apple) currently is one of the biggest global brands, not only in the
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Vietnamese market (T.Thanh, 2017). Therefore, the iPhone easily gained a lot of attention from Vietnamese
consumers.
The iPhone's market share is counted from the genuine while there are many Vietnamese consumers use
Apple's portable iPhones (non-genuine).
The iPhone's product portfolio is less than the rest of the brands. However, if this study only considered the
smartphone line, the iPhone would vote on the list of the top three highest sales (Duong Le, 2017).
4.2. Consumer brand engagement and sales of different channels
According to market reports (Ha Duong, 2017; Doan Phong, 2016), OPPO sales in 2016 at Thegioididong are
higher than FPT shops. As shown in Figure 2, this study pointed out that the level of consumer engagement with
the OPPO brand at Thegioididong is far better than in FPTshop (36,496 comments at Thegioididong, 9,412
comments at FPTshop). Therefore, there is the relationship between sales and consumer engagement with the
same brand in different channels.
Source: This research, 2017
4.3. Consumer engagement and sales of each product line
We compare the CBE by product line to sales of each product line. Figure 3 shows the products with high
CBE levels. Top 10 CBE products were compared to the top 10 best sellers in the first quarter of 2017 (Table 2).
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Fig. 3. CBE by product line (number of comments)
Source: This research, 2017
Table 2. Consumer Engagement and Top Selling Q1, 2017
No. CBE (using Big Data)
Sales ( Q1/2017)
Interference
1
2
3
4
5
6
Galaxy J7 Prime
iPhone 5s 16GB
Galaxy J5
Galaxy J7 Prime
Galaxy J7 Prime
iPhone 5s 16GB
Galaxy J5 Prime
Galaxy J2 Prime
Oppo A37
Oppo F1s
Galaxy J5 Prime
Galaxy J2 Prime
Oppo A37
Sony Xperia XA
Galaxy A5 2016
Asus Zenphone 3Max
iPhone 7 Plus 128 GB
Galaxy A5 2016
7
8
9
Galaxy J5 Prime
Oppo A37
iPhone 7 Plus 32 GB
Oppo A39
Oppo A39
Galaxy J2 Prime
Galaxy A5 2016
10
Oppo A39
iPhone 5S
As shown in Table 2, this study pointed out the relationship between consumer engagement and sales of
each product line. The relationship would explain as follows:
Samsung Galaxy J7 Prime has the highest consumer engagement, and its corresponding sales is also highest.
The product lines with much interest and engagement of consumer are voted on the list of 10 best-selling
products of the first quarter of 2017 such as Samsung Galaxy J5 Prime, Samsung Galaxy J2 Prime, OPPO
A37, Galaxy A5, Oppo A39.
The ratio of the engagement/sales is 7/10, which presents a very close relationship.
5. Limitations and suggestions for future research
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Although this study had taken an initial step in measuring consumer brand engagement by using big data and
provides some interesting findings. Nonetheless, several limitations remained and are worth to be explored in the
future research. First, the data came from two websites. It is not claimed that the results can be generalized to all
sites. To increase generalizability and ensure accuracy, the future research needs to consider sampling views
from more websites. Second, the time is limited to collecting data. Thus, this study has not made comparable
cycles, seasons, and years to understand the multidimensional perspectives. Third, the research measurement
method by using big data hasn’t measured and analyzed the content of comment such as positive, negative
content, or related to shape, design, price, color, feature, so on.
State in these points of view above, we suggest the future research should, firstly, extend the research
measurement method by using data on social networking sites (e.g., Facebook, Zalo, etc.) for comprehensive and
multi-dimensional information. The research aim is to find other factors such as influencer, key opinion leader to
help marketing managers easily make their marketing strategy or discover the different sources with spread
news. In order to have a way to prevent and deal with rumors that something bad is going to happen at the
beginning. Second, future research should create the software with sentiment tool to help identify the positive or
negative comments so that there are timely solutions or make policy formulation aim is to prevent spreading
rumors in negative situations. Third, the research measurement method in this study only measures consumer
brand engagement in the mobile retail industry. We suggest that future research should develop a measurement
method that can be able to measure in many industries. Therefore, the findings will help to make a forecast, or
assess the market, and serve a wide range of users in different regions such as education (e.g. school, study
choice), retail (e.g., consumer orientation), and fashion (e.g., fashion trends). Fourth, to understand deeply
consumers, future research should synthesize, extract, and analyze customer behavior to identify trends, needs,
personal characteristics, and take the appropriate approach. Finally, this study proposes a number of broader
applications such as the construction of the emotional barometers to measure the people and customer emotions
in an event and to provide measurement method to manage movements.
6. Conclusion
From the successful research measurement method, which created by using big data in measuring the
relationship of consumer engagement with mobile brands, demonstrating the feasibility of measuring consumer
brand engagement on social media. In addition, the research measurement method by using big data is a useful
tool in tracking branding as well as tracking competitors’ brands. By comparison the secondary data from the
publications of companies and market research firms with the primary data from using big data, this study
indicated that there is the relationship of consumer brand engagement and sales, market share, and sales of each
product line. This research measurement method also offers benefits in terms of quick, updated, versatile,
multidimensional, and useful tool for collecting data from the various audiences rather than traditional
measurement method.
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Appendix A. The definitions and the scope of engagement
Research
Field and Scholar
Definition
Scope of engagement
Method
Organization behavior
Multi-attribute:
Physical
Awareness
Emotion
People engagement
(related to work)
Kahn (1990)
Experiment
Experiment
Multi-attribute:
Schaufeli et al.
(2002)
Absorption (awareness)
Dedication (emotion)
Willpower (behavior)
Staff engagement
Work engagement
Multi-attribute:
Physical
Awareness
Emotion
Rich et al. (2010)
Experiment
Marketing
Multi-attribute:
Algesheimer et al.
(2005)
Social Brand
Engagement
Benefit (awareness)
Enjoy (emotion)
Social (behavior)
Experiment
Multi-attribute:
Patterson et al.
(2006)
Consumer
engagement
Absorption (awareness)
Dedication (emotion)
Willpower / Interaction
Concept
Single attribute:
Awareness
Higgins (2006)
Heath (2007)
Degree of engagement Concept
Advertising
Single attribute:
Emotion
Experiment
engagement
Multi-attribute:
Stimulation and inspiration
Society promotion
Temporary
Self-esteem and citizenship
Inner enjoyment
Interaction/
Communication
Engagement
Calder & Malthouse
(2008)
Experiment
Uncomfortable
Involvement and Socialization
Public
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Higgins & Scholer
(2009)
Single attribute:
Awareness
Engagement
Concept
Multi-attribute:
Personal
Social interaction
Interactive/Online
engagement
Calder et al. (2009)
Sprott et al. (2009)
Experiment
Brand engagement
itself
Experiment
Concept
Single attribute
Multi-attribute:
Awareness
Behavior
Consumer
engagement
Bowden (2009a)
Emotion
Multi-attribute:
Awareness
Behavior
Pham & Avnet
(2009)
Behavior engagement
Concept
Multi-attribute:
Awareness
Consumer
engagement
Enthusiasm
Interaction
Vivek (2009)
Experiment
Activity
New Experience
Multi-attribute:
Awareness
Behavior
Consumer
engagement
Vivek et al. (2010)
Experiment
Concept
Emotion
Multi-attribute:
Awareness
Emotion
Mollen&Wilson
(2010)
Online brand
engagement
Multi-attribute:
Valance:
Van Doorn et al.
(2010)
Consumer behavior
engagement
Form
Scope
Concept
Essence
Target customer
Multi-attribute:
Customer value
Customer value
Customer reference value
The influence value of customer
Consumer value
engagement
Kumar et al. (2010)
Concept
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The knowledge value of customer
Multi-attribute:
Customer - Customer Interaction
(e.g., word of mouth)
Co-creation
Consumer
engagement
Verhoef et al. (2010)
Concept
Concept
Blogging
Multi-attribute:
Awareness
Behavior
Consumer Brand
Engagement
Hollebeek (2011)
Emotion
Multi-attribute:
Awareness
Behavior
Brodie et al. (2011b) Consumer
engagement
Concept
Concept
Emotion
Multi-attribute:
Intrinsic motivation
Behavior Engagement
Online Brand Society
Sashi (2012)
Engagement
Multi-attribute:
Community behavior engagement
Transaction behavior engagement
Gummerus et al.
(2012)
Consumer
engagement
Experiment
Experiment
Consumer behavior
engagement
Similar with Van Doorn et al.
(2010)
Jahn&Kunz (2012)
Consumer behavior
engagement
Similar with Van Doorn et al.
(2010)
Verleye et al. (2013)
Wirtz et al. (2013)
Experiment
Concept
Online Brand Society
Engagement
Concept - not presented
Concept - not presented
Jaakkola &
Alexander
(2014)
Consumer
Engagement
Experiment
Concept
Multi-attribute:
Temporary engagement
Relationship engagement
Chandler&Lusch
(2014)
Engagement
222
Vu Huy Binh, Vo Quang Tri, Duong Thi Lien Ha/ MICA 2018 Proceedings
Multi-attribute:
Hollebeek et al.
(2014)
Consumer brand
engagement
Cognitive processing
Affect
Experiment
Active
Multi-attribute:
Not clear
Franzak et al. 2014)
Wallace et al. (2014)
Brand engagement
Concept
Consumer
engagement
Single attribute
A number of “like”
Experiment
Multi-attribute:
Awareness
Affect
Consumer brand
engagement
De Villiers (2015)
Dwivedi (2015)
Experiment
Behavioral trends
Multi-attribute:
Willpower
Dedication
Absorption
Consumer brand
engagement
Experiment
Experiment
Experiment
Schamari &
Schaefers
(2015)
Consumer
engagement
Single attribute
Intention engagement
Multi-attribute:
Affect
Awareness
Behavior
Consumer
engagement
Dessart et al. (2015)
Source: Authors, 2017
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