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Purpose: We propose that the literature on customer engagement has emphasized the benefits of customer engagement to the firm and to a large extent ignored the customers’ perspective. By drawing upon co-creation and other literature, this paper attempts to alleviate this gap by proposing a strategic framework that aligns both the customer and firm perspectives in successfully creating engagement that generates value for both the customer and the bottom line. Design/methodology/approach: A strategic framework is proposed that includes the necessary firm resources, data, process, timeline and goals for engagement, and captures customers’ motives, situational factors, and preferred engagement styles. Findings: We argue that sustainability of data-driven customer engagement require a dynamic and iterative value generation process involving 1) customers recognizing the value of engagement behaviours and 2) firm’s ability to capture and passing value back to customers. Originality/value: This paper proposes a dynamic strategic value creation framework that comprehensively captures both the customer and firm perspectives to data-driven customer engagement. (Let me know if you need help to get access to the full paper!!!)
Customer Engagement in a Big Data World
Kunz, W., Aksoy, L., Bart, Y., Heinonen, K., Kabadayi, S., Villaroel Ordenes, F., Sigala,
M., Diaz, D., Theodoulidis, B., (2017) Customer Engagement in a Big Data World,
Journal of Services Marketing, 31 (2), pp. 161-171
Werner Kunz, Associate Professor of Marketing and Director of the Digital Media Lab
University of Massachusetts Boston, 100 Morrissey Blvd. Boston, MA 02125
Phone: (617) 287-7709; Email:
Lerzan Aksoy, Associate Dean of Undergraduate Studies and Professor of Marketing
Fordham University, Gabelli School of Business, Hughes Hall, Room 426
441 East Fordham Road, Bronx, NY 10458
Phone: (718) 817-4157; Email:
Yakov Bart, Assistant Professor
Northeastern University, D’Amore-Kim School of Business
202G Hayden Hall, 360 Huntington Avenue
Boston, Massachusetts 02115-5000
Phone: (617) 373-8386; Email:
Kristina Heinonen, Professor of Service and Relationship Marketing, Centre director
Department of Marketing, Centre for Relationship Marketing and Service Management (CERS),
Hanken School of Economics, Arkadia building, Room F.2.22
Arkadiankatu 22, FIN-00101 Helsinki, Finland
Phone: +358 40 3521 436; Email:
Sertan Kabadayi, Area Chair Marketing and Associate Professor of Marketing
Fordham University, Gabelli School of Business
140 West 62nd street, Suite 446 New York, NY, 10023
Phone: (212) 636-7804; Email:
Francisco Villaroel Ordenes, Assistant Professor of Marketing
University of Massachusetts Amherst, Isenberg School of Management
121 Presidents Drive Amherst, MA 01003
Marianna Sigala, Professor of Tourism
University of South Australia, School of Management, Business School,
Elton Mayo Building, Room 3EM16A
North Terrace, Adelaide, South Australia, SA 5001, Australia
Phone: + 61 8 8302 0353; Email:
David Diaz, Assistant Professor
University of Chile, School of Business and Economics,
Diagonal Paraguay 256, Oficina 1102, Santiago, Chile
Phone: (562) 29783708; Email:
Babis Theodoulidis, Associate Professor
The University of Manchester, Alliance Manchester Business School,
Room G25 Sackville Street building, Booth Street West, Manchester M13 9SS, United Kingdom
Phone: +44 161 306 3309; Email:
Acknowledgement: This research was developed from work at the 1st Academic-Practitioner
Research with Impact Workshop (Customer Experience Management (CEM) and Big Data) at
Alliance Manchester Business School 18th-19th January 2016.
Customer Engagement in a Big Data World
Purpose: We propose that the literature on customer engagement has emphasized the
benefits of customer engagement to the firm and to a large extent ignored the customers’
perspective. By drawing upon co-creation and other literature, this paper attempts to
alleviate this gap by proposing a strategic framework that aligns both the customer and
firm perspectives in successfully creating engagement that generates value for both the
customer and the bottom line.
Design/methodology/approach: A strategic framework is proposed that includes the
necessary firm resources, data, process, timeline and goals for engagement, and captures
customers’ motives, situational factors, and preferred engagement styles.
Findings: We argue that sustainability of data-driven customer engagement require a
dynamic and iterative value generation process involving 1) customers recognizing the
value of engagement behaviours and 2) firm’s ability to capture and passing value back to
Originality/value: This paper proposes a dynamic strategic value creation framework
that comprehensively captures both the customer and firm perspectives to data-driven
customer engagement.
Keywords: Customer engagement, Big Data, value creation, data-driven engagement
1. Introduction
Over the last few years “Big Data” has altered the business landscape creating
opportunities and challenges for companies. In comparison to traditional data, Big Data is
different in the nature of volume, variety, velocity, veracity and has the potential to create
substantial value (Wedel and Kannan 2016). Navigating the potential of Big Data to
enrich the process of creating, developing and nurturing deeper customer engagement has
become a key business and research (MSI 2016). Information and communication
technologies provide immense opportunities for organizations to engage with customers,
because of their capabilities to capture, analyse and exchange an enormous amount of
customer intelligence through Big Data approaches. Moreover, these approaches open
numerous opportunities to capture value resulting from customer engagement behaviours,
and at the same time lead to advanced competences, helping firms to sustain value
creation over time.
Customer engagement has been long recognized as one of the key drivers of a firm’s
financial success. However, despite the widely recognized importance of creating a
highly engaged customer base, many companies still struggle to reach this goal. Although
the possibilities provided by the new digital landscape seem to be endless, firms often
find it challenging to leverage these opportunities in a sustainable and long-lasting
We propose that one of the main reasons is that the extant literature on customer
engagement has overemphasized the benefits of customer engagement to the firm, while
mostly ignoring the customers’ perspective. By drawing upon co-creation and the service
literature in general, this paper attempts to alleviate this gap by proposing a dynamic
strategic value creation framework that comprehensively captures both the customer and
firm perspectives for data-driven customer engagement.
This strategic framework identifies the necessary firm resources, data, process,
timeline and goals for engagement, and captures customers’ motives, situational factors,
and preferred engagement styles. We propose that Big Data opportunities transform into
long-lasting success only for companies that focus on managing synergy between a firm’s
execution of engagement efforts and the customers’ experience, motivation, preferences,
and expectations.
This research contributes to the literature in several ways. First, the framework we
propose is the first one to emphasize the challenge of balancing the needs of customer
and firm associated with the engagement activities to ensure positive impact on both
customer and firm values. Second, the framework differentiates between engagement
value for the firm and the customer and shows how Big Data capabilities can be used to
generate value for both parties. Third, the framework allows for dynamic interactions
between firm and customer value capturing processes through several feedback loops. By
integrating such loops into the framework, we emphasize the potential for both parties to
optimize their approach and involvement in engagement activities over time in adaptive
In the following sections, we explain the characteristics of a Big Data world and
illustrate briefly the current conceptual understanding of customer engagement and how
we propose to enhance it. We then introduce our strategic dynamic framework, discuss
resulting theoretical and practical recommendations, and conclude with an agenda for
future research in customer engagement based on Big Data.
2. The New Big Data Environment
The term used to describe the impact of the technologies and the nature of the new
digital world is “Big Data.” Akter and Wamba (2016, p. 178) define it “as a holistic
process that involves the collection, analysis, use, and interpretation of data for various
functional divisions to (or “intending to”) gaining actionable insights, creating business
value, and establishing competitive advantage.” Big data captures the intensity of the
creation and movement of data but also, the variability, duplication, inconsistency,
quality and compatibility issues that are emerging on a large scale (Anuradha 2015). “Big
Data” has been characterized regarding five different dimensions, known as the 5Vs
(Anuradha 2015; Wedel and Kannan 2016).
The Volume dimension relates to the sheer amount of data that are generated in a
given context (Anuradha 2015; Forbes 2015). One of the best examples of Big Data
volume is in astronomy according to Wikipedia where new telescope technologies
generate one exabyte of data every day.
The Velocity dimension relates to the speed of data generation and frequency of data
changes, which necessitates (near) real-time analysis and decision making (Anuradha
2015; Forbes 2015). For instance, businesses like Walmart handle one million
customer engagement transactions per hour from their channels (Forbes 2015).
The Variety dimension relates to the different formats that data is available (Anuradha
2015). The Big Data world is a multimedia world where data can be in structured or
unstructured formats such as images, video, audio, text or mixed. For example, in
Facebook, the content includes photos, videos, chats, calls, likes, emotions, etc. The
increased variety of data in the Big Data world makes the integration of the various
formats one of the most important challenges.
The Veracity dimension relates to data quality, context and accuracy and the plethora
of sources for data, meaning that it is difficult to understand where it comes from,
who the originator is, whether it is accurate/correct, what the meaning of data is etc.
(Anuradha 2015; Forbes 2015). For example, Twitter posts relating to company
products and services might originate from genuine customers but also from
competitors that are interested in negative marketing or “mudslinging.”
The Value dimension relates to data helping to generate business value especially
within the context of data analytics and business intelligence (Chen et al. 2012;
Parmar et al. 2014). Return-On-Information (ROI) and has been arguably one of the
important considerations when businesses are involved with Big Data.
Digital media has created over recent years an unprecedented capacity to generate and
capture customer Big Data in a variety of forms and through different channels. Such
data can provide businesses with opportunities for innovation in ways that can
significantly impact their business model and value proposition. However, while this
explosion of data availability has generated increasing interest by businesses to deploy
Big Data for deeper customer engagement, it has also challenged firms’ ability to capture
a more comprehensive understanding of the customer.
One significant Big Data challenge for customer engagement stems from difficulty of
collecting and reconciling customer data from a variety of channels relating to customer
identity, profile, engagement history, preferences, decision making and consumer
behaviour. Typically, this data is stored and managed within a firm’s CRM system, but
this might not be the case in a Big Data world, where data is often collected by external
parties/platforms (such as Google, Facebook, cloud-based email systems). Consequently,
most of Big Data ends up being outside the control and ownership of the company,
creating the challenge of getting access to, collecting and integrating the data within the
CRM system and transforming into a dynamic 360° view of the customer. Moreover,
accessing such outside customer Big Data is often further complicated by surrounding
complexities related to ethical and legal issues.
Nonetheless, analysing this data creates a plethora of possibly relevant insights that
can be acted upon. In the context of Big Data, these insights can be generated in real-time
and directly through customer touch-points, leading to generation of sustainable value for
the firm and customers.
3. Existing Perspectives on Customer Engagement
The concept of ‘engagement’ has been widely explored by scholars from different
disciplines, including management, marketing, and information systems. It is a
phenomenon and a branding practice that stands at the crossroads of the relationship
marketing (Brodie et al. 2011; Sashi 2012) and the value co-creation paradigms (Brodie
et al. 2011; Sawhney et al. 2005). However, there is no consensus in the current literature
about the conceptualization and definition of customer engagement. The literature
indicates that there are three main perspectives from which researchers have defined and
studied the concept of ‘customer engagement’ in prior studies: psychological process
(Bowden 2009), motivational psychology perspective (Brodie et al. 2011) and
behavioural manifestation (Van Doorn et al. 2010).
Bowden (2009) conceptualizes customer engagement as a psychological process
comprising cognitive and emotional aspects that lead to loyalty for both new and existing
customers. On the other hand, some other studies conceptualize engagement as a
psychological state that reflects a customer’s particular psychological state induced by
the individual’s specific interactive experiences with a focal engagement object (e.g. a
brand) (Brodie et al. 2011; Brodie et al. 2013; Vivek et al. 2012). For example, Hollebeek
(2011, p. 785) defines customer brand engagement as the level of a customer’s
motivational, brand related and context dependent state of mind characterized by specific
levels of cognitive, emotional and behavioural activity in brand.” Similarly, Patterson et
al. (2006) define customer engagement as a psychological state that is characterized by a
degree of vigour, dedication, absorption and interaction.
Brodie et al. (2011) conceptualize customer engagement from a motivational
psychology perspective and define customer engagement as “a psychological state that
occurs by interactive, cocreative customer experiences with a focal agent/object (e.g., a
brand) in focal service relationships” (p. 260). In their understanding based on various
literature streams, they stress that customer engagement is a multidimensional concept
and subject to a context- and/or stakeholder-specific expression of relevant cognitive,
emotional and/or behavioural dimensions. Thus, in this wide conceptualization besides
observable customer behaviour, engagement also entails emotional and conative
Alternatively, other studies focus solely on the behavioural aspects of customer
engagement in their conceptualizations. Van Doorn et al. (2010, p. 254) define customer
engagement as “behaviours [that] go beyond transactions, and may be specifically
defined as a customer’s behavioural manifestations that have a brand or company focus,
beyond purchase, resulting from motivational drivers”. Similarly, Verhoef et al. (2010, p.
247) recognize engagement as a behavioural manifestation toward the brand or firm that
goes beyond transactions.” While other conceptualizations of customer engagement are
sometimes used to denote the highest form of loyalty (Bowden 2009; Roberts and Alpert
2010), this behavioural manifestation includes all kinds of behaviours, not only those that
are characteristic of high degrees of loyalty (Libai 2011). Customer engagement with a
behavioural focus recognizes that consumers carry out a number of company-related
behaviours of which many did not exist a decade ago. This type of customer engagement
is directly related to the emergence of new media and all the new ways in which
customers can interact with companies, including purchase and non-purchase behaviour
(Jahn and Kunz 2012; Libai 2011).Some later studies, e.g. Javornik and Mandelli (2012)
and Coulter et al. (2012) also adopt a behavioural perspective of customer engagement,
arguing that the emphasis on the behavioural manifestation highlights the active role of
consumers, as passive consumption does not reflect the reality any longer.
While the importance of this behavioural approach has been emphasized, capturing
the active role of consumers through conventional methods and data was challenging in
the past (Vivek et al. 2012). This issue has been remedied with the increasing use of Big
Data analytics that allows both academics and practitioners to track consumer behaviour
across various platforms (Choudhury and Harrigan 2014). The interactive nature of new
digital technologies enables both customers and companies to share and exchange
information with one another and offers numerous opportunities to engage with others.
The Big Data obtained from multiple sources including social and digital media offer a
much richer context in which behavioural aspects of customer engagement can be
captured and examined. Furthermore, with the help of Big Data, it is now possible not
only to observe and record the types and volume of various customer engagement
behaviours but also to process and make sense of such behaviours (Akter and Wamba
2016). Therefore, as we explain below, the behavioural approach to engagement and the
use of Big Data should go hand in hand to have a deeper understanding of customer
4. A Dyadic Framework of Customer Engagement
4.1 Customer Engagement as Co-Creation Process
The Co-Creation Process
Value creation is commonly seen as synchronous and interactive (Grönroos 2012;
Vargo and Lusch 2016). “Value is not simply ‘added,’ but is mutually ‘created’ and ‘re-
created’ among actors with different values” (Ramirez 1999, p. 50). Customers play an
active role in this process (Prahalad and Ramaswamy 2000).Value is co-created by
multiple actors, therefore the customer, as beneficiary, is always a co-creator of value
(Vargo and Lusch 2016).
Co-creation has been defined as “joint activities by parties involved in direct
interactions, aiming at contributing to the value that emerges for one or both parties”
(Grönroos 2012, p. 1520). As such, company and customer are acting together in a
merged and interactive process that creates value for both parties (Grönroos 2012). Based
on a comprehensive review of earlier research, (Ranjan and Read 2016, p. 305) conclude
that value co-creation is a theoretical representation of an extended exchange process of
joint production and consumption of value.” In other words, value emerges in customers’
and companies’ separate but related processes and co-creation occurs where these
processes overlap (Heinonen and Strandvik 2015).
The engagement co-creation process is nested in the value co-creation process. For
engagement co-creation to occur it thus means that both parties must be aware of the
intentions to co-create, i.e. both the firm perspectives of engagement are considered in the
evaluation and preparation phases of engagement.
A One-Sided Perspectives on Customer Engagement
One common theme across prior research of customer engagement has been a firm-
centric perspective rather than a customer perspective. The nexus of firm-centric
perspective is the company, focused on customers' positive and negative expressions
related to the company and the benefits of customer engagement for the company. In
other words, the main focus of customer engagement has traditionally been on what the
company does in its domain to induce firm-beneficial engagement from customers.
For example, studies that look at the effects of brand community engagement
(Algesheimer et al. 2005) include variables like brand-related purchase behaviour and
community recommendation behaviour as their outcome variables while the effects on
individual customers have largely been ignored. This includes lack of customer focus in
behavioural conceptualizations as well. Van Doorn et al. (2010) for example, discuss
consequences of customer engagement for companies but do not include any explicit and
direct benefits for customers (except for the financial gains through participation in
reward- or loyalty-based programs). Similarly, in their conceptual model of customer
engagement, Verhoef et al. (2010) focus primarily on the impact that customer
engagement has on metrics like customer retention, customer lifetime value and new
product performance, which in turn contribute to firm value.
The value fusion concept as developed by Larivière et al. (2013) is one exception to
the dominant focus on firm-related outcomes. This concept argues that a joint focus on
the value derived both by the firm and by the customer can produce an interaction in
which both parties benefit. Adopting this dual-focus including both customer and firm in
customer engagement, and understanding each party’s potential benefits from
engagement behaviours could lead to synergies and better outcomes for all parties
involved. Awareness of customer-related benefits of engagement with companies could
help maximize efforts aimed at building strong engagement with customers, and help
firm-focused outcomes as well. Larivière et al. (2013) provide examples showing how a
joint focus on the value derived both by the firm and by the consumer — called value
fusion — can produce an interaction in which both parties benefit. In this vein, it
becomes evident that customer engagement should be viewed and managed from a
combined approach that merges the customers and firms view. Similarly, Malthouse et
al. (2013) suggest that the strategic objective of social CRM should go beyond just
including multiple forms of value for the firm like customer referral or influence value,
and should include value to the consumer as well.
The Firm Perspective
When customers share, distribute and discuss their experiences, reviews, and brand
enthusiasm or delight in interactions with others via social networks, companies can
benefit on three distinct levels. First, at a firm level, they collect valuable market insight
for managing their reputation, complaints, and intelligence for improving processes
(Sigala et al. 2012). Second, at a market level, customers can become strong brand
advocates and e-marketers of the brand, and companies can build an enduring
relationship with them (Malthouse et al. 2013; Munzel and Kunz 2014; Sigala 2016;
Wirtz et al. 2013). Finally, at a customer level, customers enhance their self-brand
connection and brand usage intent (Hollebeek et al. 2014), the trust level attributed to the
brand, their subsequent brand loyalty and customer-brand relations (So et al. 2016) as
well as enriching and personalising their experiences (Campos et al. 2016). Customer
engagement requires the companies to migrate from a transactional management mindset
to a broader understanding and management of the customers and their value. In order to
design an optimal customer engagement approach and effectively integrate customers
into the company’s value chain operations as value co-creators, companies need to
answer the following critical questions:
Why does the company need to trigger customer engagement?
What are the considered engagement approaches?
Who (e.g. employees, customers, online communities) should be empowered to
participate in co-creation?
Which channel will be used to engage customers (where)?
At what stage(s) of the customer experience (before purchase, during consumption,
after consumption) should the customers be engaged (when)?
The Customer Perspective
Many companies invest in engagement with the premise that this will lead to positive
financial outcomes but this depends to a large extent on an organization’s ability to
cultivate these interactions effectively with its customers. To be able to do this, it is
critical to understand the customers’ perspective on why, how, where and when they
would like to engage. Such motivations or orientations can vary across customers, and a
one size fits all approach is unlikely to yield desired outcomes. For example, the literature
demonstrates that customers are motivated to communicate information based on
disparate goals such as through sense of obligation, a desire to help others/altruism
(Mazzarol et al. 2007), and/or a feeling of pleasure from telling others about products or
gaining social capital. Customers can also be driven to engage with a company to justify
their decisions (generate approval), achieve social status or to increase self-esteem, self-
enhancement, and visibility (De Matos and Rossi 2008). Understanding these motivations
and crafting an engagement strategy targeted to customers with different needs and
motivations is likely to improve response.
It is also important to understand individuals’ innate preferences toward building
relationships (Hazan and Shaver 1990): not every customer welcomes engagement efforts
or prefers engaging in a particular way. Since relational orientations vary across
customers, marketing activities should also be customized to individuals or market
segments. Unfortunately, little is known about the underlying preferences for closeness
that influence how customers want to engage. We propose that attachment styles—the
systematic patterns of relational expectations, emotions, and behaviours that result from a
particular personal history—can help explain different motivations for customers to
engage with companies (Hazan and Shaver 1990).
The three customer attachment styles include 1) secure, 2) anxious and 3) avoidant
and are characterized by a combination of factors (Mende and Bolton 2011). Research in
psychology has shown that attachment styles are best conceptualized and measured along
two continuous, quasi-orthogonal dimensions called "attachment anxiety" and
attachment avoidance" (Brennan et al. 1998). Attachment anxiety is the extent to which
a person worries that relationship partners might not be available in times of need, has a
need for approval and fears rejection and abandonment. Attachment avoidance, on the
other hand, is the extent to which a person has a need for self-reliance, fears depending
on others, distrusts relationship partners' goodwill, and strives for emotional and
cognitive distance from partners. Individuals who score low on both these dimensions are
considered having a “secureattachment style and welcome building relationships and
interacting with others. Research finds that relationship or context specific attachment
style (such as customer attachment style) is a closer predictor of outcomes related to a
focal partner (such as a company or a brand) compared to general attachment styles
(Mende et al. 2013).
This presents the opportunity for companies to include customer attachment measures
with other market segmentation variables and use the results to allocate resources and
tailor marketing activities (Mende et al. 2013). Managers can design engagement
programs to recognize attachment styles and customize how they implement such
programs. For example, those high on secure attachment (with low levels of attachment
anxiety and avoidance) toward the company are likely to be receptive to engagement
efforts and are prime candidates for programs that are more focused on relationship
building whereas those high on avoidance may not necessarily welcome engagement
efforts. Furthermore, the goal and framing of an engagement initiative can be tailored to
match the customers’ preferred situation specific attachment style.
The Dual Perspective
To summarize, we argue that when the fit between customer expectations and the
execution by the company of customer engagement efforts is high, customers will
evaluate it more favourably, have a higher likelihood to engage in the activity, derive a
higher value from the engagement and have a higher tendency to re-engage in the future.
In line with Grönroos and Voima (2013) and Heinonen et al. (2010), we propose that
value creation in engagement initiatives occurs in three distinct domains: the firm
domain, the customer domain, and the joint domain (see Figure 1). Hence, value emerges
in the joint interaction between the customer and firm and more importantly in
customers’ individual and social behaviour in the customer domain (Grönroos and
Gummerus 2014; Heinonen and Strandvik 2015). As a result, customer’s perspective to
value can be used to supplement firm’s perspective to value measured by outcomes such
as customer referral value (e.g. acquiring other new customers via referral programs),
customer influence value (e.g. spreading word-of-mouth) and customer knowledge value
(e.g. providing feedback to the company) (Kumar et al. 2013), to more fully capture value
generated by engagement.
Furthermore, this presents opportunities for companies to increasingly integrate
customers and their online communities in various operations along the company value
chain (e.g. new product and service development processes) (Verleye 2015) and
empower them to co-create value as co-designers, co-marketers, co-distributors and co-
producers of products/services. Indeed, customer engagement in non-transactional
activities expands the role of consumers as co-creators of value (Sashi 2012), and leads to
creating, building and enhancing customer-firm relationships (Malthouse et al. 2013;
Sigala 2016; Vivek et al. 2012) and empowering customers to co-create their own
valuable, seamless and personalized experiences.
4.2 Typology of Engagement Using a Dual Perspective
Drawing on this firm-customer dual framework perspective to customer engagement,
we suggest that there are different types of engagement activities that vary in terms of the
level/depth of involvement from a customer as well as a firm perspective (Wirtz et al.
2013). We propose a typology of four types of engagement that can be determined in a 2-
dimensional model by the level of resources (e.g. time, money, efforts, passion, and
manpower) each party is investing in the engagement activity (see Figure 2).
The four engagement types underline the fact that engagement varies depending on
the activity, the motivation of the customer and firm as well as the investment by both
parties. The exact position of an engagement activity within the 2-dimensional model
depends on the individual specification of the activity by the actors. Further, engagement
approaches can develop over time. So, for example, some approaches could start rather
passively, but can be later driven by the customer.
1. True collaborative approaches: These are engagement activities where there is a high
level of investment and commitment from the customer as well as the firm. These
type of engagement activities are of mutual interest and benefit for the firm and the
customer. They require a firm to invest resources in the offer of an engagement
activity and a customer to actively participate. This type of engagement activities is
frequent in business contexts, where both parties need to be involved in generating
real value (Weinberg et al. 2015). A good example is IoT (Internet of Things) in
various contexts, including wearable devices, smart cities, and energy optimization.
In these cases, both the providing firm and the customer need to participate to garner
value. Other examples include crowdsourcing activities (such as ideation contests) or
firm-offered online community and web forums. Although the firm invests heavily,
success depends on the participation of the customer.
2. Customer driven approaches: This type of engagement activities is of direct benefit to
the customers and does not require much support from the firm, alternatively
customers prefer not to involve the firm in the engagement activities. As suggested by
Van Doorn et al. (2010), customer behavioural manifestations related to WOM
(word-of-mouth), customer blogging, helping other customers and writing online
reviews are examples in which it is the customer driving the engagement.
Importantly, relatively low firm involvement does not mean it has no value for the
firm. For example, UGC (user-generated content) aggregation like a hashtag-based
user contest often generates useful new resources that firm can leverage in own
marketing activities.
3. Firm driven approaches: This type represents engagement activities in which there is
a high level of investment by the firm but not necessarily by the customer. Examples
include online brand communities that companies create in order to actively
encourage customers to exchange opinions and information related to the brand
(Wirtz et al. 2013). The development of a firm profile/page on social networks (such
as a YouTube brand channel or Facebook Page), is another example of firm
investment in building an audience and customizing content to trigger engagement
and customer conversations related to brand content (Smith et al. 2012).
4. Passive engagement approaches: Passive engagement approaches include minimal
investment from both the customer and the firm - such as collection of data generated
by past consumer engagements. In line with Maslowska et al. (2016), in this type of
activities, the customer is in a passive mode mainly as an observer of brand
communication activities (i.e. monologues).
The proposed typology helps differentiate various engagement activities and
underlines the need to consider the customer and the firm jointly as co-creators in
engagement initiatives. Also another issue to consider is the potential lack of active
involvement of either party in the engagement activities, such as the case in firm-driven
or customer-driven engagement approaches. In the next section, we take a deeper look
into the potential of Big Data to enable dynamic feedback processes and suggest several
ways of improving customer engagement over time.
4.3 Dynamic Nature of Customer Engagement Framework Using Big Data
We argue that Big Data capabilities, processes and related infrastructure play a
crucial role in ensuring sustainable engagement activities that reinforce positive value
creation for both customer and firm over time. Our model emphasizes an iterative
improvement in value for both the customer and the firm. Specifically, the model is based
on the customer motivation to derive additional value from engaging in firm-related
activities (for customer value) and firm motivations to derive actionable insights from
customer engagement behaviours (for firm value). Embedded in our general framework,
feedback loops reflect the dynamic nature of the relationship across customer
engagement outcomes over time (see Figure 3).
We posit that Big Data is of critical importance for companies looking to introduce
and support this reinforcing cycle of positive value generation. In every engagement
episode, individual customer data is systematically stored in a data store and can be used
on aggregated or individual level to optimize further engagement activities. In the
remainder of this subsection, we outline our dynamic conceptualization by providing
examples of various engagement-value feedback loops from both the customer and firm
perspective (please see Figure 3, red arrows). In these feedback loops, the goal is to
reinforce customer engagement behaviour by providing superior value based on
individual and aggregated data.
Individual data insights: Wearable devices like the Fitbit tracker or the Apple watch
are good examples of how value can be generated by individual customer data. By
engaging in a physical activity, customers automatically share their real-time multi-
dimensional data with the company. The data is stored in the company’s database and
could be processed in real-time using Big Data analytics tools. Such tools allow
companies to generate value for customers by offering them summaries of their daily
physical performance based on their own individual data. These summaries tend to keep
customers engaged, and lead to reinforcement of positive behaviours associated with
physical activities. Therefore, it is essential that the company makes the customer aware
of the personal benefit to be obtained by engagement and sharing data (Walker 2015).
Aggregated data insights: Recommendation systems are a great example to illustrate
how aggregated customer choices and preferences can be used to generate customer value
and encourage reinforcement. Many major online retailers (such as Amazon) emphasize
the importance of recommender systems in helping customers to simplify their decision
making through providing personalized suggestions. The feedback loop starts with
customer interactions providing valuable behavioural data. For example, by clicking on
“Recommendations” link on Amazon website, customers can filter their
recommendations by product attributes and rate the recommended products (Linden et al.
2003). This data is captured across a large number of consumers sharing their likes and
dislikes, and used by Amazon as a marketing tool to support targeted product
recommendations and email campaigns (Koren 2008).
Customer decision support: Big Data analytics can support the customer evaluation
process to make better-informed decisions. For instance, prior research has shown that
many consumers do not choose mobile data plans optimally when compared to their
actual usage, resulting in overpayment for services (Bar-Gill and Stone 2009). When a
customer’s data consumption can be monitored, and the service provider is able to help
with choosing the right plan against the actual usage, the customer is more likely to be
engaged and satisfied with the service provider and less likely to defect to a competitor
(e.g., Lambrecht and Skiera 2006, Iyengar et al. 2007; Ater and Landsman 2013).
KPI system: From the company’s perspective, Big Data can improve the
measurement of ROI resulting from engagement (Wedel and Kannan 2016). Advances in
digital marketing allow companies to receive real-time feedback on how successful their
marketing efforts are. This requires companies to establish reliable Key Performance
Indicators (KPIs) to validate the incremental Return on Investment (ROI) resulting from
customer engagement (Horst and Duboff 2015). Kumar et al. (2013) demonstrate the
relevance of Big Data by running and capturing the ROI of a promotional campaign on
Twitter and Facebook. The ROI on Big Data capabilities will allow companies to
leverage new services and offer better and more personalized engagement opportunities
for customers.
For example, companies can perform sentiment analysis (Ordenes et al. 2014; Pang
and Lee 2008) through real-time tracking of customer sentiment related to their brands,
product, services, customers, activities, and resources. This can be achieved by investing
into high customer interactivity in social media networks. Successful implementation of
such KPI-based feedback loops would allow companies to adjust their strategy and tactics
dynamically. Specifically, having such system in place would provide CMO with
objective ROI-related data that is often necessarily for convincing the rest of senior
management to continue investing in Big Data capabilities for enhancing customer
Strategy Support: Customer engagement behaviour data can support companies’
strategic marketing plans. For instance, companies can use the consumer perspective to
tackle innovation and product development problems by building online communities of
collaborators who can create their own solutions to business problems. This requires
companies to have open innovation structures in place, in order to increase customer
participation, collect innovative ideas and implement them in the organization (Dittrich
and Duysters 2007). Companies must have a system in place for collecting customer
ideas and the adequate structure to implement and reward successful innovation
collaborators (Repenning 2002).
Crowdsourcing is a typical example of such a customer-based product innovation
feedback loop (Di Gangi et al. 2010). By enabling high customer participation through
providing relevant intrinsic (such as higher status in a brand-driven social community)
and extrinsic (such as monetary awards in contests) incentives, companies can overcome
the barrier of knowledge and innovation by getting creative ideas and solutions from own
Customization & Targeting: Targeting customers through customized content
presents another important opportunity that companies can pursue using Big Data
infrastructure. Firms can use Big Data analytics to obtain data-driven insights for
developing narrowly targeted features or entirely new customized products based
on estimated customer preferences. For example, 3M’s implementation of a
personalized content marketing strategy designed to reduce smoking and increase
healthy behaviours has shown how content marketing can successfully change
consumer behaviour and transform lives (Content Marketing Institute, 2015).
Creating a positive feedback loop through a targeted content marketing strategy
also helps companies to react dynamically to changes in consumer content
5. Conclusions and Future Agenda
Customer engagement has emerged in the last years as a topic of great interest to
managers across sectors and industries. With the rise of digital technologies and the
diffusion of social media, many companies are attempting to use explicit strategies to
foster customer engagement (Javornik and Mandelli 2012). But customer engagement
also entails that firms reassess the ways in which they view and manage the customers
and their communities, the processes of creating value, the use and design of technology,
the scope and focus of interactions and the customer insights. Similar to the reasons
behind the failure of CRM systems (Rigby et al. 2002), much of the firm-driven customer
engagement efforts have historically been focused solely on the value to the firm,
ignoring the value it creates for the customer.
Our framework emphasizes a dualistic perspective of engagement on both the firm’s
expectations and activities, as well as the customer’s experiences and goals. Thus, we
propose customer engagement be thought of ,not only as a managerial value enhancing
tool to strengthen the relationship with (non-)customers, but also as an individual
experience for the customer as to showing the relevance of engagement in supporting key
individual goals and generating customer value. In other words, our framework proposes
customer engagement will increase if managers execute engagement activities that meet
or exceed the customers’ expectations. Fortunately, Big Data analytics allows firms to
measure both firm and consumer value in real-time and allows companies the opportunity
to understand the customer through multidimensional profiles abd dynamic adjustments
of their marketing instruments, targets and budgets.
Further, we argue the value generation process is dynamic and continuously evolving
through an iterative process starting with the customer recognition of the value of
engagement and continuing with the company’s ability to discover further value adds
from the generated data. Therefore, our proposed framework is also dynamic in nature, as
it emphasizes the challenge of maintaining a dynamic balance between customer
expectations from engagement and companies ability to create value for both the
customer and themselves. Developing Big Data capabilities and processes would enable
firms to meet this challenge through dynamic generation of creative insights related to
new consumer engagement initiatives (Davenport et al. 2012).
From a managerial perspective, the increased understanding of the customer using
Big Data analytics has the potential for companies to help their customer simplify choices
and make better decisions. This in turn encourages increased customer motivation for
engagement. Consequently, we suggest firms focus on the collection and analysis of
consumer data to develop strategies to educate consumers on how their collaborations
leads to value adds. In other words, it is importantfirms not only deploy data-based
approaches to improve consumer decisions but also actively position and communicate
consumer benefits associated with engagement tools. This would require managers to
invest into finer behavioral segmentation of their customer base and enable a highly
targeted approach to generate engagement.
While most of the prior research on consumer engagement has focused on linking
customer actions to firm value, we argue ignoring the perceived value accruing to
consumers from their actions can be detrimental to companies in the current era of Big
Data. If customers do not perceive any value derived from their engagement with a firm,
the firm’s risks losing customer engagement, threatening, in turn, firm’s ability to learn
and innovate based on these important sources of Big Data. Companies that are not able
to maintain continuous improvement of the value-generating feedback loops might lose
their share of engagement.
From a research perspective, more research is needed to fully explain the fit between
the firm’s and the customer’s engagement behavior, the dynamic nature of the feedback
loops and the of role Big Data. In Table 1 we identify potential areas for future research.
For instance, there is a need to understand under which circumstances customers
contribute and engage the best. In this context, our framework addresses the importance
of the fit between engagement approach, social media channel, customer expectation and
preferences. Given the plethora of existing social media channels and specific
engagement approaches, research should focus on how Big Data finds the appropriate
channels and engagement approaches. Further, Big Data analytics can relate customer
engagement to various customer variables such as demographics, psychographic, online
behavior and more. Future research should also consider how Big Data approaches can be
developed to focus on the right customer group for increasing engagement. Lastly, we
showed in this article different engagement approaches require different investment from
company and customer. This raises the question who is driving the engagement processes
over time and which contributions are essential for the long-term success of engagement
initiative. How can big data analytics be used to help the manager to identify the success
factor of a specific engagement approach?
While this article is a starting point for a holistic customer engagement management
that builds on the opportunities and challenges of Big Data, it is not without limitations.
The concepts introduced do not account for the contextual and situational limitations that
a company maybe experiencing as part of engagement efforts such as the challenges
associated with gathering accurate and reliable data, the ability to integrate them
successfully across various channels, company culture related obstacles, acquiring the
necessary analytic skills and tools, and of course time and costs involved in undertaking
these initiatives. Nevertheless, this framework has the potential to move the dial towards
a conversation that recognizes the dynamic and intertwined nature of engagement so as to
benefit both companies and customers.
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Figure 1: Customer Engagement as a Co-Creation Process
Figure 2: Typology of Engagement Using a Dual Customer-Firm Perspective
Figure 3: Dynamic Nature of Customer Engagement Framework Using Big Data
Table 1: Future Research Opportunities
How can Big Data help us identify the more effective touch
points to create better engagement?
Which data deserve more priority and are likely to be more
Which types / personalities of customers and with what
attachment styles are more prone to get engaged?
What metrics of customers should be used for selecting
customers to be engaged?
How do customer attachment style relates to the design of
customer engagement activities and how do firms can match
engagement activities to customer types?
How can customer activities and experiences in their own
domain be integrated and aligned with the firm's activities to
enable mutual engagement co-creation?
Does the origin of the engagement influence its success?
Can Big Data help us understand if consumer-initiated
engagement is more effective than firm-initiated engagement?
What are the analytical, interpretation and business skills that
employees should possess in this new environment?
How can business schools develop these skills in their
What are the effective analytics and measures that can be
created using Big Data to measure the effectiveness of firm's
engagement initiatives?
How do competitors' engagement activities influence the
companies own performance?
What are the ethics and privacy issues related to Big Data?
What is the role of legislation?
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Second home tourism, which is not recognised as a part of tourism activities or its place in tourism is relatively insignificant, has started to gain importance today. The advantages of renting a second home include its proximity to the city's tourist attractions, being in touch with the local culture, making you feel at home, not being tied to a single point, and offering many options at lower costs. Second home tourism, which appears as an alternative to organised tourism businesses, can quickly enter the potential accommodation inventory, mainly thanks to electronic platforms that support the sharing economy. When the literature is analysed, it is understood that there are very few studies examine second homes through the sharing economy. These studies deal with exchanging second homes for a certain period (house swap). The neglect of user views on utilising second homes represents a gap. In the study, second home with increasing online popularity was approached from a different perspective and benefited from social platform reviews. The research is aimed to analyse the consumer evaluations for second homes in different geographies. The data is customer reviews on social media of second homes in popular tourist destinations (Palma-Spain, Chania-Greece, and Fethiye-Turkey). It has been collected automatically from the social travel platform (TripAdvisor) with the help of a program developed in the Python programming language. The number of comments for Palma, Chania and Fethiye is 215, 951 and 693, respectively. To evaluate the comments, topic model analysis was used, and those were clustered under "value", "experience", and "location" titles. In addition, name-entity analysis was used to identify top products and services such as "food", "room", "pool", "shop", and "beaches". The sentiment analysis was used to score the determined products or services. For Palma, "beautiful beach", "local restaurant", and "spacious room"; for Chania, "fresh eggs", "clean water", and "minute walk"; for Fethiye, "jeep safari", "private pool" and "local restaurant" were the most prominent features. Findings indicate that second homes in similar destinations have parallel consumer review content. Also, factors that generate demand for second homes (being at home and being-feeling local) are included in the literature supported by findings. Turizm faaliyetlerinin bir parçası olarak kabul edilmeyen ya da turizmdeki yeri görece önemsiz değerlendirilen ikinci konut turizmi günümüzde önem kazanmaya başlamıştır. Şehrin cazibe merkezlerine yakınlığı, yerel kültürle iç içe olma, kendini evinde hissetme, tek bir noktaya bağımlı olmama esnekliği ve daha düşük maliyetlerle birçok seçenek sunması ikinci konut kiralamanın avantajları olarak sayılabilir. Organize turizm işletmelerine alternatif olarak ortaya çıkan ikinci ev turizmi, özellikle paylaşım ekonomisini destekleyen elektronik platformlar sayesinde potansiyel konaklama envanterine hızlı bir şekilde dâhil olmaktadır. Literatür incelendiğinde, ikinci evleri paylaşım ekonomisi üzerinden inceleyen az sayıda çalışma olduğu ve bu çalışmaların da ikinci evlerin belirli bir süreliğine karşılıklı değişimini (takasını) ele aldığı anlaşılmaktadır. İkinci konutlara ilişkin kullanıcı görüşlerinin ihmal edilmiş olması literatürde bir boşluğa işaret etmektedir. Çalışmada, çevrimiçi popülaritesi giderek artan ikinci konutlara farklı bir açıdan yaklaşılmış ve bir sosyal platformdaki kullanıcı değerlendirmelerinden faydalanılmıştır. Araştırma, farklı coğrafyalardaki ikinci konutlara yönelik tüketici değerlendirmelerini analiz etmeyi amaçlamaktadır. Kullanılan veri popüler turistik destinasyonlardaki (Palma-İspanya, Hanya-Yunanistan ve Fethiye-Türkiye) ikinci konutlara ilişkin sosyal medyadaki müşteri yorumlarından oluşmaktadır. Python programlama dilinde geliştirilen bir program yardımıyla sosyal seyahat platformundan (TripAdvisor) otomatik olarak toplanmıştır. Palma, Hanya ve Fethiye için yorum sayısı sırasıyla 215, 951 ve 693'tür. Yorumları değerlendirmek için konu modelleme analizi (topic model analysis) kullanılmış ve bunlar "değer", "deneyim" ve "konum" başlıkları altında kümelenmiştir. Ayrıca, "yemek", "oda", "havuz", "mağaza" ve "plajlar" gibi en önemli ürün ve hizmetleri belirlemek için isimlendirilmiş varlık analizi kullanılmıştır. Belirlenen ürün veya hizmetleri puanlamak için de his analizinden istifade edilmiştir. Palma için "güzel plaj", "yerel restoran" ve "geniş oda"; Hanya için "taze yumurta", "temiz su" ve "bir dakikalık yürüyüş"; Fethiye için ise "jeep safari", "özel havuz" ve "yerel restoran" en çok öne çıkan özellikler olmuştur. Bulgulardan benzer destinasyonlardaki ikinci konutların paralel tüketici yorumu içeriğine sahip olduğu anlaşılmaktadır. Ayrıca, ikinci konut talebi yaratan faktörler (evde olma ve yerel hissetme) literatürdeki bulgularla benzerlik göstermektedir.
... Furthermore, Wei et al. (2013) segmentate customers and develop marketing strategies by adopting data mining techniques to apply in RFM (recency, frequency and monetary). Finally, Kunz et al. (2017) propose a strategic framework based on Big Data to align both the customer and firm perspectives Case studies (30) This category includes case studies where SCRM is applied to specific companies or business sectors. Different business sectors have been considered: tourism industry, manufacturing industry, finance sector, hospitality sector, education sector, etc. ...
Purpose Sustainable customer relationship management (SCRM) is a combination of business strategy, customer-oriented business processes and computer systems that seeks to integrate sustainability into customer relationship management. The purpose of this paper is to contribute to the body of knowledge of marketing, business management and computer systems research domains by classifying in research categories the current state of knowledge on SCRM, by analysing the major research streams and by identifying a future research agenda in each research category. Design/methodology/approach To identify, select, collect, synthesise, analyse and evaluate all research published on SCRM, providing a complete insight in this research area, the PRISMA methodology, content analysis and bibliometric tools are used. Findings In total, 139 papers were analysed to assess the trend of the number of papers published and the number of citations of these papers; to identify the top contributing countries, authors, institutions and sources; to reveal the findings of the major research streams; to develop a classification framework composed by seven research categories (CRM as a key factor for enterprise sustainability, SCRM frameworks, SCRM computer tools and methods, case studies, SCRM and sustainable supply chain management, sustainable marketing and knowledge management) in which academics could expand SCRM research; and to establish future research challenges. Social implications This paper have an important positive social and environmental impact for society because it will lead to an increase in the number of green and socially conscious customers with an ethical behavior, while also transforming business processes, products and services, making them more sustainable. Originality/value Customer relationship management in the age of sustainable development is an increasing research area. Nevertheless, to the authors' knowledge, there are no systematic literature reviews that identify the major research streams, develop a classification framework, analyse the evolution in this research field and propose a future research agenda.
... Big data is currently available from both firm and consumer activities, making it possible to understand consumer behaviour (Grover et al., 2020;Kunz et al., 2017;Tan et al., 2015;Zhan & Tan, 2020) and consequently formulate more effective customer engagement strategies (Li et al., 2018a(Li et al., , 2018bLiu et al., 2019;Mishra & Singh, 2018). In this research, we first predicted the viewing duration time and extracted other viewer behavioural data (number of messages, number of virtual gifts, and value of virtual gift) based on large-scale real viewers' behavioural data on sports SLSSs. ...
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The newly emergent social live streaming services (SLSSs) provide the sport consumers with a synchronised and more interactive viewing experience. In order to help the sport SLSSs firms understanding and engaging with the viewers effectively, this research aims to classify the sports SLSS viewers based on their engagement behaviour, and identify the perceived value and value contribution of each group of viewers. Firstly, 52,545 sports SLSSs viewers’ viewing duration time is predicted by a feedforward neural network. Second, the predicted viewing duration time and other extracted viewer behavioural data (number of messages, number of virtual gifts, and value of virtual gifts) are analysed through two-step clustering in SPSS, and classified viewers into four types. Semi-structured interviews were then conducted to understand how each type of viewer co-creates value. The results identified four groups of viewers, namely content consumers, super co-creators, co-creators, and tourists, and identified their distinct value co-creations and perceived value. This study sheds light on combining engagement behaviour and value co-creation literature to classify the sports viewers in the context of SLSSs. This understanding assists the decision-making processes of marketers and operators to promote viewers’ co-creation effectively.
... This is particularly true with openness and proactivity with customers in terms of them knowing how their data is used and how they are benefitting from the use of their data. Such engagement and interaction give the opportunity that each buyer journey should be handled uniquely, with analysis of the multiple data collection sources to properly and successfully segment customers based on browsing behaviour (Kunz et al., 2017). Ramani and Kumar (2008) suggest that the technological progress has resulted in increasing opportunities for interactions between firms and customers for those who will take advantage of information obtained from these successive interactions in order to achieve profitable customer relationships. ...
... However, the advent of social media and the explosion of user-generated content have made utilizing conventional data analysis approaches inefficient (Devesa et al., 2010;Leung et al., 2013) and even impossible (Amado et al., 2018;Canito et al., 2018;Pizam et al., 2016). Thus researchers and hoteliers have realized that machine learning algorithms and NLP techniques are necessary to facilitate the analysis of such a considerable amount of unstructured data more accurately (Kunz et al., 2016;Sigala et al., 2019) and develop insights into customers' perceptions in shorter periods (K. Kim et al., 2017;McAfee & Brynjolfsson, 2012;Sharda, 2018;Xiang et al., 2017). ...
The information obtained from customers’ feedback can help hotel managers improve their provided services in a targeted manner according to customers’ expectations. Besides, other customers consider online hotel scores an efficient tool for quickly evaluating the quality of hotels’ services. Therefore, a higher online score indicates customer satisfaction and would lead to more bookings, price acceptance, and higher financial performance. In this article, we extracted the shortcomings related to hotel attributes utilizing a novel methodology that comprises machine learning algorithms, text mining, and a combination of customers’ comments and scores. Then we examined the quantitative effect of fixing these problems on hotels’ online scores. Furthermore, considering the origin of the problems, the cost required for fixing them, and the quantitative effect of solving them on improving the hotels’ online scores, we provided some prescriptions for hotel managers as the last phase of business analytics. This model and its resulting prescriptions can be used to increase hotels’ online scores significantly by improving service quality at the lowest cost. Finally, to describe the most important attributes, we used The Nordic European School of thought and classified them based on the technical and functional dimensions of Grönroos’ service quality model.
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Purpose Despite the increase in studies focused on analyzing the potential of big data analytics capability (BDAC) as a driver of product and process innovation, it is still necessary to understand how the use of insights generated by BDAC in innovation may be maximized through articulation with individuals' intellect and other processes involving the assimilation and transformation of knowledge. This study thus aims to analyze the impact of BDAC's deployment on innovation capability (IC – process and product innovation capabilities), taking absorptive capacity (AC) as mediating variable in this relationship. Design/methodology/approach Structural equations were used to test the research model with survey data from 112 firms located in an emerging country that is one of the digital transformation leaders in the region. Findings The results show that 37% of process IC variance is explained by the indirect relationship via the variable mediator (AC), while in the case of product IC this percentage is 34%. Originality/value These results allow us to ascertain the extent to which individuals continue to be relevant to generating product and process innovation in the digital age at a time when the literature anticipates a total loss of prominence due to the arrival of new digital technologies. However, in the case of the relationship between BDAC and ICs, the existence of a partial mediation of AC indicates that individuals continue to play a role that, albeit not being the most prominent, remains relevant in ensuring that a company maximizes the assimilation and transformation of the insights generated by BDAC in new products and processes.
Predictive analytics, the process of using current and/or historical data with a combination of statistical techniques to assess the likelihood of a certain event happening in the future, has become increasingly prevalent in interactive marketing. However, previous research on predictive analytics in interactive marketing has mostly assumed customers’ voluntary participation and engagement in predictive analytics, overlooking the role of customers’ psychological factors in driving customer engagement. Based on self-determination theory, this chapter provides a theoretical framework to understand customer engagement in predictive analytics. Specifically, this chapter proposes that predictive analytics positively influences customer engagement through need for meaningful affiliation, which is moderated by self-construal, and that predictive analytics negatively influences customer engagement through sense of control, which is moderated by data use transparency and trust. This chapter presents both theoretical contributions and practical implications to interactive marketing.KeywordsInteractive marketingPredictive analyticsCustomer engagementNeed for meaningful affiliationSense of controlPrivacy concerns
Despite a general awareness of the potential of big data in terms of public interest, several obstacles prevent their effective sharing. This study, linking the discourse on data to the concepts of data value and accountability, aims at emancipating the scientific debate from the emphasis on administrative transparency and the protection of privacy, tracing new perspectives for future research. The present research examines the main peer-reviewed articles published by journals that have dealt with data value and accountability across the public and private dimensions. The bibliometric analysis carried out indicates a propensity by current literature to consider the issue of data value creation either only in the private (data as input to improve business performance or customer relations) or in the public dimension (open data government models). This means that research on behavioral data for public governance has so far been underestimated. Evidence shows that big data value creation is closely associated with a collective process where multiple levels of interaction and data sharing develop among private and public actors in a multilayered accountability environment.
Technology has played a role to revolutionise the food delivery service from phone based to online ordering to satiate consumers’ ever-shifting demands, making its way to the top. Today, the business of food delivery services is one of the fastest segments of e-commerce. This study is conducted to know how consumer expectations from online food service providers. Customer value creation is the utmost requirement for the sustainability of these services. To create value the bottlenecks need to be removed from the service process to enhance highest quality of customer experience through the application of blockchain technology. This paper studies the relationship between information quality and availability at various levels with customer satisfaction through a technology adoption model and value creation through blockchain.
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Deciphering consumers' sentiment expressions from big data (e.g., online reviews) has become a managerial priority to monitor product and service evaluations. However, sentiment analysis, the process of automatically distilling sentiment from text, provides little insight regarding the language granularities beyond the use of positive and negative words. Drawing on speech act theory, this study provides a fine-grained analysis of the implicit and explicit language used by consumers to express sentiment in text. An empirical text-mining study using more than 45,000 consumer reviews demonstrates the differential impacts of activation levels (e.g., tentative language), implicit sentiment expressions (e.g., commissive language), and discourse patterns (e.g., incoherence) on overall consumer sentiment (i.e., star ratings). In two follow-up studies, we demonstrate that these speech act features also influence the readers' behavior and are generalizable to other social media contexts, such as Twitter and Facebook. We contribute to research on consumer sentiment analysis by offering a more nuanced understanding of consumer sentiments and their implications.
Technological advances and consumer behaviour changes are transforming CRM from a transactional to a conversational approach (called social CRM) that empowers customers as relationships’ co-creators. Limited research has examined the firms’ capabilities required to effectively integrate social media into CRM. To address this gap, a literature review produced a preliminary list of social CRM capabilities which was further refined and enriched by collecting data from the Greek tourism industry. Findings from various stakeholders (tourism professionals, scholars and IT vendors) and sources (observations, documents, job descriptions) produced a thorough list of social CRM capabilities and revealed the social CRM readiness of Greek tourism firms. The following capabilities are discussed: organisational culture and management, information resource management, information technology infrastructure, business strategy, customer-centric processes, communication, performance measurement.
Purpose: As the Internet has become an increasingly relevant communication and exchange platform, social interactions exist in multiple forms. Our research integrates a multitude of those interactions to understand who contributes and why different types of contributors generate and leverage social capital on online review sites. Design/methodology/approach: Based on the literature about social capital, social exchange theory, and transformative consumer research, we carried out a study of 693 contributors on a hotel review site. Content analysis and a latent profile analysis were used to research the contribution types and the underlying motives for generating and leveraging social capital. Findings: Through the integration of various customer-to-customer interactions, our results reveal a three-class structure of contributors on review sites. These three groups of individuals show distinct patterns in their preferred interaction activities and the underlying motives. Research limitations/implications: We develop the existing literature on transmission of electronic word-of-mouth messages and typologies of contributors. Future research should seek to expand the findings to additional industry and platform contexts and to support our findings through the inclusion of behavioral data. Originality/value: The research contributes to researches and marketers in the field by empirically investigating who and why individuals engage in online social interactions. We expand upon the existing literature by highlighting the importance of social debt in anonymous online environments and by assessing a three-class structure of online contributors.
Advancements in information communication technologies (ICTs) and internet tools have transformed the way businesses are run in the service and manufacturing industries. The tourism industry is no exception to this transformation; and with its increased accessibility, travellers have become more dependent on the internet not just for seeking out basic information, but for planning an entire trip. Today, travellers can easily access information about any destination in the world, plan and book their trips, and share memories of their vacations via their computers and mobile devices. For instance, a recent study conducted by Google (Google/Ipsos Media, 2014), which surveyed 3,500 US leisure travellers who made at least one trip for personal reasons, revealed that travellers routinely turn to the internet during the early stages of trip planning. © 2018 selection and editorial matter, Marianna Sigala and Ulrike Gretzel; individual chapters, the contributors.
The authors provide a critical examination of marketing analytics methods by tracing their historical development, examining their applications to structured and unstructured data generated within or external to a firm, and reviewing their potential to support marketing decisions. The authors identify directions for new analytical research methods, addressing (1) analytics for optimizingmarketing-mix spending in a data-rich environment, (2) analytics for personalization, and (3) analytics in the context of customers' privacy and data security. They review the implications for organizations that intend to implement big data analytics. Finally, turning to the future, the authors identify trends that will shape marketing analytics as a discipline as well as marketing analytics education.