understanding how automatized personalization …1461556/...digital marketing practitioners were...
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IN DEGREE PROJECT INDUSTRIAL ENGINEERING AND MANAGEMENT,SECOND CYCLE, 30 CREDITS
, STOCKHOLM SWEDEN 2020
Understanding how automatized personalization with AI can drive value in B2B marketing
A case study of a Swedish industrial equipment manufacturer
ELIZABETH ANZÉN
LUKAS EKBERG
KTH ROYAL INSTITUTE OF TECHNOLOGYSCHOOL OF INDUSTRIAL ENGINEERING AND MANAGEMENT
1
Understanding how automatized
personalization with AI can drive value in
B2B marketing
by
Elizabeth Anzén
Lukas Ekberg
Master of Science Thesis TRITA-ITM-EX2020:138
KTH Industrial Engineering and Management
Industrial Management
SE-100 44 STOCKHOLM
2
Hur automatiserad personalisering med AI
kan driva värdeskapande i B2B-
marknadsföring
by
Elizabeth Anzén
Lukas Ekberg
Examensarbete TRITA-ITM-EX2020:138
KTH Industriell teknik och management
Industriell ekonomi och organisation
SE-100 44 STOCKHOLM
3
Master of Science Thesis TRITA-ITM-EX2020:138
Understanding how automatized personalization with AI can drive value in B2B
marketing
Elizabeth Anzén
Lukas Ekberg
Approved
2020-06-04
Examiner
Kristina Nyström
Supervisor
Mana Farshid
Commissioner
Atlas Copco
Contact person
Claire Geffroy
Abstract
In the last decade, marketing automation, a tool for automatic personalization, has been gaining
significant traction among marketing professionals. In parallel with the growing adoption trend, many
marketing automation platform providers have been extending their offers to include AI features.
However, there is a lack of research regarding how AI can enhance the process of marketing automation
in a way that creates value, which is the studied topic in this thesis.
A qualitative and exploratory case study has been conducted in collaboration with the global B2B
company Atlas Copco, a manufacturer of industrial equipment. Digital marketing practitioners were
presented with two use cases of AI, segmentation and cross-selling, for personalization and asked about
the marketing automation process and the expected impact on value.
The findings reveal what would be required in the marketing automation process for the use cases in
terms of data needs, learning about customer insights, marketing output and evaluation. In our findings
value creation strongly revolve around the value types: ‘excellence’, ‘efficiency’ and ‘privacy’.
To conclude, AI will enable more advanced personalization and value creation can be substantial if
customer sacrifices are addressed in an appropriate way. Depending on relevance, tone of voice, time and
use of channel, different feelings of value are perceived, which are factors that AI can help to determine.
Key Words
AI, B2B, Marketing Automation, Personalization, Value, Value Creation
4
Examensarbete TRITA-ITM-EX2020:138
Hur automatiserad personalisering med AI kan driva värdeskapande i B2B-marknadsföring
Elizabeth Anzén
Lukas Ekberg
Godkänd
2020-06-04
Examinator
Kristina Nyström
Handledare
Mana Farshid
Uppdragsgivare
Atlas Copco
Kontaktperson
Claire Geffroy
Sammanfattning
Under det senaste årtiondet har verktyg för automatisk marknadsföring blivit populära bland
marknadsförare. Automatiska marknadsföringsplattformar fungerar som ett verktyg för att automtiskt
leverera personaliserade marknadsföring. Många leverantörer av automatiska marknadsföringsplattformar
har utökat sina erbjudanden till att innefatta AI-tjänster. Den befintliga forskningen kring hur sådana AI-
tjänster ska utnyttjas på ett sätt som skapar värde är begränsad och därav behandlas ämnet i den här
uppsatsen.
En explorativ och kvalitativ fallstudie har genomförts i samarbete med Atlas Copco som är ett globalt
b2b-företag. Vid varje intervju presenterades antingen merförsäljning eller kundsegmentering sedan
ställdes frågor om den automatiska marknadsföringsprocess och värde. Resultaten indikerar vad som
skulle krävas för de undersökta användningsfallen i den automatiska marknadsföringsprocessen samt att
värdeskapande är starkt kopplat till värdetyperna excellens, effektivitet, privatliv och datasäkerhet.
Slutsatserna indikerar att AI kommer göra den personalisering som uppstår till följd av automatisk
marknadsföring mer avancerad. Värdeskapandet från nya AI lösningar kan vara signifikant om
implementeringen tar hänsyn och adresserar uppoffringar kunder behöver göra.
Nyckelord
AI, B2B, automatisk marknadsföring, personalisering, värde, värdeskapande
5
Acknowledgements
We would like to thank all the members of the marketing and sales department at Atlas Copco
who have been incredibly helpful during our thesis project. Without their contribution, in terms
of time, resources and domain knowledge, this thesis would never have been finished.
Moreover, our supervisor Claire Geffroy at Atlas Copco has helped us significantly by providing
us with great insights into the procedures in B2B and the latest trends in digital marketing among
many other things. Finally, we would like to thank our supervisor Mana Farshid at KTH who has
continuously challenged us to improve our thesis throughout the process.
6
Table of Content
Acknowledgements ......................................................................................................................... 5
1. Introduction ................................................................................................................................. 9
1.1. Background ........................................................................................................................................ 9
1.2. Problem Statement ........................................................................................................................... 11
1.3. Research Purpose ............................................................................................................................. 13
1.4. Research Question ........................................................................................................................... 13
1.5. Delimitations ................................................................................................................................... 13
1.6. Sustainability Aspects ...................................................................................................................... 13
1.7. Outline of Thesis ............................................................................................................................. 14
2. Literature Review...................................................................................................................... 15
2.1. Personalization ................................................................................................................................. 15
2.1.1. What is Personalization? .......................................................................................................... 15
2.1.2. Levels of Personalization ......................................................................................................... 17
2.2. Value ................................................................................................................................................ 18
2.2.1. What is Value? ......................................................................................................................... 18
2.2.2. Value in Business Marketing as a Developing Concept .......................................................... 19
2.2.3. Value Typologies and Dimensions .......................................................................................... 22
2.2.4. Value in Personalization ........................................................................................................... 27
2.3. Marketing Automation..................................................................................................................... 29
2.3.1. Marketing Automation and Personalization Processes ............................................................ 29
2.3.2. Conceptualization of the Marketing Automation Process ........................................................ 34
2.4. Artificial Intelligence ....................................................................................................................... 37
2.4.1. Big data and AI ........................................................................................................................ 37
2.4.2. How AI can Enhance Personalization ...................................................................................... 39
2.5. Frame of Reference.......................................................................................................................... 42
3. Methodology ............................................................................................................................. 43
3.1. Research Purpose ............................................................................................................................. 43
3.2. Research Approach .......................................................................................................................... 43
3.3. Research Design .............................................................................................................................. 43
3.4. Research Strategy ............................................................................................................................ 44
3.5. Case Company ................................................................................................................................. 44
3.6. Data Collection ................................................................................................................................ 44
3.7. Research Instrument ........................................................................................................................ 45
3.8. Sample Selection ............................................................................................................................. 46
7
3.9. Data Analysis ................................................................................................................................... 47
3.10. Research Quality ............................................................................................................................ 48
3.11. Ethical Considerations ................................................................................................................... 49
4. Empirical Analysis .................................................................................................................... 50
4.1. Empirical Context ............................................................................................................................ 50
4.2. Findings & Analysis ........................................................................................................................ 52
4.2.1 Marketing Automation Process ................................................................................................. 52
4.2.2. Value Types ............................................................................................................................. 58
5. Conclusion ................................................................................................................................ 68
5.1. How Can AI be Used in the Marketing Automation Process to Create Value in a B2B Context?.. 68
5.2. Limitations and Suggestions for Future Research ........................................................................... 69
References ..................................................................................................................................... 71
Appendix ....................................................................................................................................... 79
A. Value measurements .......................................................................................................................... 79
B. Interview guide .................................................................................................................................. 80
8
List of Figures
Figure 1. Sample model for perceived value as a second-order formative construct (Lin et al.,
2005: 325). Get and Give components can be any number and include dimensions as price,
privacy and security. ..................................................................................................................... 24
Figure 2. A conceptual framework of personalization (Vesanen, 2007: 4147) ............................ 28
Figure 3. The personalization process by Vesanen and Raulas (2006) ........................................ 31
Figure 4. Marketing automation framework adopted from (Heimbach et al., 2015: 131) ........... 32
Figure 5. The marketing and sales funnel related to content marketing, marketing automation,
and CRM adopted from (Järvinen and Taiminen, 2016: 170) ...................................................... 33
Figure 6. Process of automized personalization. .......................................................................... 36
Figure 7. Frame of reference used in this study ............................................................................ 42
List of Tables
Table 1. Selection of definitions of personalization mentioned in marketing literature,
(Montgomery and Smith, 2009:131; Vesanen, 2007: 412-413). .................................................. 16
Table 2. The value dimensions in PERVAL (Sweeney and Soutar, 2001: 211). ......................... 23
Table 3. Total value proposition with value dimensions (Lapierre, 2000: 125) ........................... 23
Table 4. Revised value typology adapted from Holbrook (1996: 139-140) ................................. 26
Table 5. Comparison of personalization processes, adapted from Vesanen and Raulas (2006) .. 30
Table 6. AI framework (Davenport et al., 2020) .......................................................................... 39
Table 7. Applications of AI in personalization. ............................................................................ 41
Table 8. List of contextual interviews........................................................................................... 46
Table 9. List of title of interviewees in main research stage. ....................................................... 47
Table 10. Themes identified relating to the digital marketing process ......................................... 52
Table 11. Themes identified relating to value including value description .................................. 58
9
1. Introduction
This chapter sets the scene by introducing the emergence of AI, marketing challenges in B2B and
how personalization can help to facilitate these challenges to create value. The motivation for
the research is laid out and thesis’s purpose is described along with the research question.
Finally, delimitations, sustainability aspects and the structure of the thesis is presented.
1.1. Background
The rapid development of digital solutions, fueled by progress in digitization, information and
communications technology (ICT) and artificial intelligence (AI), has sparked a belief that we
are now entering a new epoch, referred to as the fourth industrial revolution. This revolution is
believed to create a shift in decision-making, from human to machine (Chatterjee et al., 2019;
Syam and Sharma, 2018). Traditionally, information technology has helped with the processing
of data to enhance and support human decision-making. Now there are algorithms that process
data, learn from data and use data to make well-informed decisions. A process that renders jobs
both faster and easier, by being able to use more data than humans could ever dream of analyzing
by themselves. The brain behind this process is AI; the concept where machines have the ability
to mimic intelligent human behavior, including learning and problem solving (Syam and Sharma,
2018).
With AI, firms can automate some routine functions in the sales process, but the more interesting
aspect is its ability to augment sales by using personalization, customization and enhanced
service, while simultaneously increasing effectiveness (Moncrief, 2017; Paschen et al., 2019).
This includes using AI as an advanced analytics tool to engage in activities such as creating
tailor-made offers to specific customers, conversing online with virtual agents and proactively
suggesting maintenance (Balducci and Marinova, 2018; Miklosik et al., 2019).
The potential of AI opens many doors for marketing practitioners, who must be agile in an ever-
changing sales environment that is getting increasingly more complex. During the last decades,
the way customers conduct their purchasing has changed (Steward et al., 2019). With the
explosion of information, buyers are now able to do independent research and set their own
purchasing criteria. Before even interacting with a sales representative, B2B customers are
normally 50-60% down the purchasing process (Adamson et al., 2012; Gartner, 2018a). In
addition, online channels are being increasingly more used throughout the purchasing process,
10
where it has been found that 83% of buyers are accessing digital channels for more information
even during later stages of the purchasing process (Gartner, 2018b). Business customers are
content-driven, technically savvy and comfortable with engaging via digital channels (Vieira et
al., 2019). This shift in B2B buyer behavior highlights the importance of marketers to adapt their
practices towards digital solutions.
Traditional marketing with the goal to build brand awareness and to generate qualified leads that
hopefully result in transactions is no longer enough. Today, marketing must support the entire
customer journey, which is the entire process the customer through and touchpoints they interact
with (Steward et al., 2019). A challenge that has arisen due to this is how to align multiple
touchpoints with marketing actions to provide relevant information. Another challenge is
understanding both the customers, i.e. those making the buying decision, as well as the users, i.e.
those who ultimately will use the service or product (Paschen et al., 2019). In complex B2B
contexts using the correct marketing action is even more difficult. These contexts are
characterized by (Schmitz et al., 2014):
• Technical complexity;
• Few, infrequent transactions with large economic value;
• Buying-center involvement with many stakeholders;
• Heterogeneous customer requirements;
• Long-term decision processes;
• Highly individualized solutions.
At the same time customers are more demanding and more value conscious than ever before
(Sweeney and Soutar, 2001), leading to the creation of value being key in marketing (Anderson
et al., 1992; Woodruff, 1997). As a result, B2B sales in complex settings need to have a high
level of personalization and customization to provide the best possible offering, which in turn
will increase profits for the firm and value for the customer (Montgomery and Smith, 2009). To
facilitate these challenges, the emergence of marketing automation tools has become advent. At
its core, marketing automation is automatic personalization and customization of the marketing
mix (Heimbach et al., 2015). Marketing automation is seeing substantial growth in the private
sector. Forrester estimates that marketers will spend 25 billion dollars on marketing automation
11
by 2023 (Adams, 2018). In addition, the number of available marketing automation platforms
has grown drastically from 10 to 292 between 2011-2018, with tech giants like Adobe entering
the scene (Murphy, 2018).
This interest is due to many recognized benefits of marketing automation, such as increased
ability to generate a higher quantity of more qualified leads (Järvinen and Taiminen, 2016;
Sandell, 2016; Todor, 2017), multichannel view of prospect behavior (Todor, 2017), improved
lead conversion (Heimbach et al., 2015; Todor, 2017) and return on marketing investments
(Montgomery and Smith, 2009; Świeczak, 2013; Todor, 2017). The lead qualification process is
claimed to be both improved as well as accelerated by delivering personalized content to
potential buyers (Järvinen and Taiminen, 2016).
1.2. Problem Statement
Although marketing automation has been present in the literature for almost two decades
(Heimbach et al., 2015), the research field is still in its infancy (Murphy, 2018). While the
impact on research has been modest, the adoption of the technology in the business community is
surging (Murphy, 2018). The primary functionality of marketing automation platforms is to
deliver content automatically to users according to a specific set of rules (Järvinen and Taiminen,
2016). This functionality requires the user of the platform, the marketer, to define rules based on
existing customer insights. The ability to generate such insights is one of the most prominent
challenges for many marketing executives (Leeflang et al., 2014). Forrester predicts that AI
technology will emerge as a prominent feature in marketing automation platforms to ease this
challenge (Hussain, 2019). How companies should utilize these new features, and which benefits
and challenges that arise with its applications, is an area which is not present in the marketing
automation literature.
Prior related work by Järvinen and Taiminen (2016) demonstrated how marketing automation
and content marketing can be integrated in the selling process in a way that creates business
benefits. However, their work did not touch upon these new emerging features discussed above.
Additionally, their study focused solely on business benefits which does not capture the broader
concept of value creation. The need for such research is stressed further by Russo-Spena et al.
(2019), who highlights a general lack of academic examination of the impact of AI on complex
business system interactions and how the integration of resources can bring value.
12
In addition to the gap in marketing automation literature, researchers have acknowledged the
need for studies regarding AI and personalization (Kannan and Li, 2017; Syam and Sharma,
2018; Wedel and Kannan, 2016). Since marketing automation is a tool for automatized
personalization, it is highly connected to the research field of personalization.
Syam and Sharma (2018: 141) underline the research question “how can machine learning and
AI enhance smart and continuous customer targeting in real time?”. A similar topic is
emphasized by Kannan and Li (2017: 40) who point to the need of research into “methodologies
that provide real-time, accurate targeting across platforms as well as the development of
intermediaries who can help in personalization”. Furthermore, Wedel and Kannan (2016) brings
up research gaps regarding the role of AI, cognitive systems and automated attention analysis
systems in delivering personalized customer experience.
The need for further research within personalization is particularly prominent in the context of
B2B since most of the current personalization research is centered around the B2C context
(Strycharz et al., 2019). Generally, marketing research is centered around B2C, despite the fact
that B2B and B2C account for approximately the same economic value of transactions (Lilien,
2016). Lilien (2016) points out three reasons for this being the case. Firstly, the B2B problem
domain is heterogenous and complex, as discussed in the previous section. Secondly, there is a
lack of easy data availability in B2B. Data in B2B are less voluminous than in B2C, due to
comparably fewer and larger transactions. In addition, there are difficulties in collecting data,
where multiple organizations normally must cooperate and align. Lastly, there is a lack of
domain knowledge in B2B. All researchers act as consumers in their daily lives, therefore it is
easier to relate to the field of B2C. Hence, there is a lot of potential for additional research
focusing on the B2B domain.
From a broader perspective, how sales and marketing practices will be impacted by AI is an area
where several scholars stress a need for further research (Flaherty et al., 2018; Moncrief, 2017;
Paschen et al., 2019; Russo-Spena et al., 2019; Singh et al., 2019; Syam & Sharma, 2018).
Understanding and implementing AI in a successful way will be paramount for businesses to
remain competitive in the future. AI enables a deeper level of personalization, which in turn can
create additional value for the customer.
13
1.3. Research Purpose
The purpose of this thesis is to investigate how a company in a complex B2B context can benefit
from using AI in personalization to create value. Moreover, what kind of value it is perceived to
bring will be examined. Marketing automation has been found to be a prominent tool to
automatize personalization processes but has yet to reap the full benefits of AI, which will be
explored further in this thesis.
1.4. Research Question
To achieve the sought-after purpose, the following research question has been defined:
RQ: How can AI be used in the marketing automation process to create value in B2B contexts?
1.5. Delimitations
The study focuses on one large organization within industrial equipment, therefore, the findings
cannot be considered as general. As the studied phenomenon is not applied to a full extent in the
studied organizations, the results will be based on perceptions of individuals and hypothetical
situations. Real applications might differ. In addition, the amount of applications of AI in
personalization are endless and we will focus on the most prominent ones in regards of the
studied organization.
The dynamics and processes for creation of value between supplier and customer is out of scope.
Instead the study focuses on the component of value, if it is created and if so, what type of value.
Even though the research question addresses AI, the scope is limited to marketing research and
the technical details will not be central in the study.
1.6. Sustainability Aspects
This study also considers its impact on sustainability. Sustainability is often defined by three
interconnected pillars: environmental (ecological), economic and social (Elkington, 1999). The
environmental aspect is considered as the management of the physical environment in a way that
supports life on the planet within ecological limits and protection of natural resources. Social
sustainability entails the impact on people and society, often related to well-being, justice and
equality. Lastly, economic sustainability refers to practices that support long-term economic
growth and the ability to create value.
14
As value is a central building block in our study, the economic aspect will be assessed. In
addition, we’ll consider sustainability by considering social and ecological benefits & costs as
value types. More in general, AI has the potential of freeing up time from repetitive work,
leaving certain jobs redundant, therefore impacting the social aspect. Personalization on an
individual level could lead to recommendations on how to use products in a way that saves
resources and extends the lifetime of products, therefore impacting environmental aspects, which
is related to UN sustainability development goal 13: climate action. However, personalization
could also be used for buying recommendations, leading to more transactions and thus
potentially increasing total consumption. This relates to UN sustainability development goal 12:
responsible consumption and production.
1.7. Outline of Thesis
The first chapter introduces the studied topic and presents the research question. Chapter 2
discusses previous related work and lays out theory that will be used throughout the study. The
concepts of value, marketing automation, personalization and AI are discussed in more detail.
The frame of reference in this chapter presents the theory that is used a basis for the study.
Chapter 3 describes the methodology, along with the research approach, data collection and how
data will be analyzed. Also, ethical considerations are discussed. In chapter 4 the findings from
the study are laid out and analyzed with respect to theory. Lastly, chapter 5 presents the
conclusion, answering the research question and provides suggestions on future research.
15
2. Literature Review
This chapter discusses previous related research and serves as a foundation for the theories,
models and concepts used in this study. The chapter is divided into four parts: personalization,
value, the marketing automation process and AI. The chapter starts off with a presentation of the
concept of personalization and the current views present in the literature. Thereafter, the value
research field is presented, culminating in foundational characteristics of value and a value
typology. Marketing automation and personalization process are scrutinized, and the concept of
AI is introduced as well as how AI can impact the field of personalization. Finally, the
conceptualization of the most relevant theories which serves as theoretical foundation for the
study are presented in the frame of reference.
2.1. Personalization
2.1.1. What is Personalization?
The concept of personalization is broad and occurs in several research fields besides marketing,
including human-computer interaction, machine learning and data mining among others (Zanker
et al., 2019). In the context of marketing, personalization generally refers to a customer-oriented
marketing strategy that aims to deliver the right message, to the right person at the right time
(Aguirre et al., 2015; Dangi and Malik, 2017).
Even though the interest in personalization among researchers has been cultivated by increasing
internet usage (Montgomery and Smith, 2009) and rising e-commerce (Dangi and Malik, 2017),
the use of personalization preceded the internet (Montgomery and Smith, 2009). Vesanen (2007)
asserts that it is likely that personalization is as old as trade itself. Aguirre et al., (2015) describe
the existence of both online and offline personalization. Examples of offline personalization
include face-to-face service encounters where the employee adapts their behavior to the
characteristics of the customer, e.g. greets them by name (Aguirre et al., 2015). The internet has
advanced the possibilities and usage of personalization tremendously. An example of online
personalization is individualized search results. Google personalize by refining a user’s search
result based on past behavior (Montgomery and Smith, 2009).
There are several definitions of personalization in the marketing literature (Strycharz et al.,
2019). Vesanen (2007) provides a summary of available definitions and discusses the many faces
16
of personalization. Some definitions are centered around the context, e.g. (Allen et al., 2001)
whom describes personalization as individualization of web experiences. Montgomery and Smith
(2009: 130) propose the following definition where technology is required as an enabler for
personalization and thus context centered: “the adaptation of products and services by the
producer for the consumer using information that has been inferred from the consumer's behavior
or transactions”. Other early definitions are more general (e.g. Imhoff, 2001.; Wind and
Rangaswamy, 2001) and some are more focused on value creation (Peppers and Rogers, 1999).
An overview of definitions is summarized in Table 1 adopted from (Vesanen, 2007) and
(Montgomery and Smith, 2009).
Table 1. Selection of definitions of personalization mentioned in marketing literature, (Montgomery and Smith,
2009:131; Vesanen, 2007: 412-413).
Source Definition
(Peppers and Rogers,
1999: 146)
“Customizing some feature of a product or service so that the
customer enjoys more convenience, lower cost, or some other benefit”
(Allen et al., 2001:
32-33)
“Company-driven individualization of customer web experience”
(Imhoff, 2001: 467) “Personalization is the ability of a company to recognize and treat its
customers as individuals through personal messaging, targeted banner
ads, special offers on bills, or other personal transactions”
(Wind and
Rangaswamy, 2001:
15)
“Personalization can be initiated by the customer (e.g. customizing the
look and contents of a web page) or by the firm (e.g. individualized
offering, greeting customer by name etc.)”
(Montgomery and
Smith, 2009: 131)
“The adaptation of products and services by the producer for the
consumer using information that has been inferred from the
consumer's behavior or transactions”
Another aspect of the literature is the relationship between personalization and customization.
There is no consensus on the relationship between the two terms (Vesanen, 2007). Peppers and
Rogers (1999) does not acknowledge the need to distinguish between the terms and Allen et al
(2001) highlight the complexity in separating the two terms. Other scholars view customization
17
as a form of personalization (Imhoff et al., 2001) or even as an advanced form of personalization
(Wind and Rangaswamy, 2001). In more recent literature (Aguirre et al., 2015; Arora et al.,
2008), the difference is centered around who initiates the adaptions of the marketing mix (Cöner,
2003). If the adaptions are requested proactively by the customer, it is customization, while if the
adaptions are initiated by the company, it is personalization (Aguirre et al., 2015; Arora et al.,
2008). This view creates a similar relationship between the terms personalization and
customization and push and pull personalization (Wedel and Kannan, 2016).
In this thesis we build on the definition presented by Montgomery and Smith (2009), due to its
broad nature which enables a variety of data-driven applications to be within the scope of
personalization. The amendment we make is that not only products and services can be
personalized, but also other objects such as activities, experiences, technologies, etc. Therefore,
we consider personalization as “the adaptation of objects by the producer for the customer using
information that has been inferred from the customer's behavior or transactions”. Compared to
personalization, customization is viewed as adaptions of the marketing mix requested or initiated
by the customer.
2.1.2. Levels of Personalization
Personalization and customization are the two main concepts in one-to-one marketing. One-to-
one marketing is a concept that suggests that at least one part of a company’s marketing mix
should be adapted to the individual customer. It is considered as an extreme form of
segmentation where the segmentation size is one (Arora et al., 2008). Personalization can be
performed with different levels of granularity and Wedel and Kannan (2016) identified three
levels of granularity on which personalization can be performed:
1. Mass personalization is when all customers receive the same offering or adaptions are
made based on their average taste;
2. Segment level personalization is when all customers in the same segment receive the
same adaptions to the marketing mix;
3. Individual level personalization is when each customer receives adaptions that are based
on them only.
Similarly, Huang and Rust (2017) distinguishes static and dynamic personalization. Dynamic
personalization adapts to a specific customer’s preferences based on the customer’s active input
18
and by observing the customer’s behavior over the time, rather than relying on cross-sectional
customer data from similar customers as in the case of static personalization. This is comparable
with the segment and individual levels described above. Static personalization can be achieved
by analytics and big data, while dynamic personalization hinges on AI and other cutting edge
technologies (Huang and Rust, 2017).
2.2. Value
2.2.1. What is Value?
To create an understanding of the concept of value, one must delve into the development of
different value perspectives. The study of value dates to the time of Aristotle (Eggert et al., 2018;
Gordon, 1964; Grönroos, 2011). In his value theory he raised the famous value paradox,
distinguishing between two ways a product can be used (Gordon, 1964). A shoe for example can
either be used for wearing or used for exchange. Drawing on this distinction, two complementary
perspectives on customer value were introduced, value-in-use (or use value) and value-in-
exchange (or exchange value), which have been highly adopted by economics (Smith, 1791). In
addition, Gordon (1964) points out that Aristotle treats use value as the subjectively perceived
benefit, where demand is a function of use value and exchange value something that is derived
from use value.
Early, popular work on value that builds onto the perspectives is that by Lawrence D. Miles
(Lindgreen and Wynstra, 2005; Wilson and Jantrania, 1994). Miles (1961: 3) explains the elusive
nature of value: “value means a great many things to great many people because the term value
is used in a variety of ways. It is often confused with cost and with price. In most cases, value to
the producer means something different from value to the user. Furthermore, the same item may
have differing value to the customer depending upon the time place, and the use”. In addition to
use value and exchange value, Miles identifies two additional types of value.
1. Esteem value is the intrinsic attractiveness, features or properties, which causes a desire
to own it.
2. Cost value is the cost of producing a product, meaning the sum of labor, material, and
various other costs.
19
Wilson and Jantrania (1994) highlights the importance of setting the discussion of value in
relation to the social, economic, political and religious environment surrounding the affected
individual(s). They conclude that value is a problematic concept to define, but that it cannot be
ignored. Similarly, Holbrook (1994) emphasizes the difference between ‘value’ and ‘values’,
where the first is defined as preferential judgements (i.e. benefits vs sacrifices) and the second
the criteria that determine those preferential judgements (i.e. enduring beliefs). To avoid
unnecessary complex discussion, the focus in this thesis will be on the general realm of customer
value, rather than of philosophical and ethical values.
2.2.2. Value in Business Marketing as a Developing Concept
Marketing, as an offspring of economics, began with an exchange centered view on value
(Eggert et al., 2018). With this view, the supplier manufactures and distributes goods and
services that are embedded with value. The value is created and determined by the supplier, it
can be exchanged and it is the marketing’s job to understand and communicate the value to the
customer (Anderson and Narus, 1998). Zeithaml (1988) builds on this logic, emphasizing value
as subjectively perceived and as an overall assessment of the utility of a product or service. The
perceived value is described as a trade-off between what the customer receives and what it gives
in exchange (Woodruff, 1997; Zeithaml, 1988). With the competition taken into account
Anderson et al. (1992) defined value in business markets as the perceived worth in monetary
units as the set of economic, technical, service and social benefits received by a customer in
exchange for a price for the product offering, taking into consideration competitors offerings and
prices. Some scholars consider the value and price as independent elements, the price is what a
customer pays for a market offering (Anderson et al., 2000). The difference between the price
and the value is the customer’s incentive to purchase a market offering. These findings are
reflected in an extensive review by (Ulaga and Eggert, 2005) who reviewed a variety of
definitions of value and identified four recurring characteristics:
1. Customer value is a subjective concept.
2. Customer value is conceptualized as a trade-off between benefits and sacrifices.
3. Benefits and sacrifices can be multifaceted.
4. Value perceptions are relative to competition.
20
Then a shift in the marketing literature occurred, emphasizing the long-term value of
relationships. As marketing features a continuation of transactions (Dwyer et al., 1987), more
value can be accrued through relationships exchanges rather than from transactional exchanges.
In a relational context, customer value is not embedded in the transactional exchange of a
product or service for money (Grönroos, 1997). Instead perceived customer value is created and
delivered over time, as the relationship develop over time. Ravald and Grönroos (1996) explains
the value of relationships as the total episode value, or the sum of all the interactions that create
value in a relationship. However, due to difficulties in estimating the future value of relationships
the identification of high value relationships is problematic (Wilson and Jantrania, 1994).
The importance of understanding business relationships in terms of value and the creation of
value has since been highly discussed (Ulaga and Eggert, 2006). Researchers started
emphasizing the process of co-creation of value between the supplier and customer. In this point
of view, relationship value is a “measure of joint outputs, underpinned by co-operation, where
the nature of the interaction between supplier and customer is critical in the creation of joint
value” (Lindgreen et al., 2012: 209).
With a growing trend towards servitization of business markets, the traditional view of business
marketing was challenged. The economic exchange of service provision rather than goods led to
the introduction of service dominant (S-D) logic in marketing (Vargo and Lusch, 2004). This
logic emancipates the marketing discipline from its economic heritage and focuses on the
subjective value in use and integration of resources rather than resource exchange. In this sense
the customer integrates resources from the provider such as products and services with other
skills and resources to (co-)create value. With the S-D logic, value is perceived and determined
by the customer, co-created through resource integration and it is the marketing’s job to identify
and facilitate opportunities for co-creation in the use context of the customer (Vargo and Lusch,
2004). Payne et al. (2008) builds on the S-D logic and investigates how value co-creation with
the customer occurs. They developed a conceptual framework consisting of three main
components:
21
1. Customer value-creating processes is the processes, practices and resources that the
customer uses to manage its business and relationships with suppliers.
2. Supplier value-creating processes is the processes, practices and resources that the
supplier uses to manage its business and relationships with customers.
3. Encounter processes is the processes and practices that facilitate exchange and
interaction in a customer-supplier relationship, which needs to be managed to
successfully realize co-creation opportunities.
Each component consists of their own set of procedures, tasks, mechanisms, activities and
interactions which ultimately supports co-creation of value. In this sense the supplier creates
value propositions, where the customer determines value after consumption and the dialogue is
seen as interactive process of learning together (Ballantyne and Varey, 2006).
With respect to the S-D logic and previous value literature, seven foundational characteristics for
customer value have been identified (adapted from Leroi-Werelds, 2019):
1. Customer value implies an interaction between a customer and an object (e.g. a product,
service, store, technology, activity, etc.).
2. Customer value involves a trade-off between the benefits and sacrifices of an object;
3. Customer value is not inherent in an object, but in the customer’s experiences derived
from the object.
4. Customer value is subjective and personal as value perception are based on personal
characteristics.
5. Customer value is context-specific, considering situation, time frame, circumstances and
location.
6. Customer value is multidimensional and consists of multiple value types.
7. Customer value is co-created by the customer by means of resource integration between
supplier and customer.
These seven foundational characteristics will be used throughout the thesis as a common
denominator for value.
22
2.2.3. Value Typologies and Dimensions
Many attempts to conceptualize value have been made (Gallarza et al., 2017; Leroi-Werelds,
2019; Lin et al., 2005). One of the earlier defined typologies, that has gained a lot of traction
(Gallarza et al., 2017), is the one by Holbrook (1994). His typology comprises three dimensions
that emerge in the customer experience: extrinsic vs intrinsic value, self-oriented vs other-
oriented value and active vs reactive value. In turn, by combining these dimensions eight types
of value arises: efficiency, excellence, status, esteem, play, aesthetics, ethics, and spirituality
(Holbrook, 1996). His perspective is in line with the S-D logic, where value is experienced by
the customer (Holbrook, 1996) and this is reinforced in his later work where he describes that
value only resides in a consumption experience (Holbrook, 2006). The relevance of certain types
of values in Holbrook’s typology has been discussed, e.g. ethics (Smith, 1996), other has
combined spirituality and ethics value into ‘altruistic value’ and split ‘excellence’ into ‘service’
excellence’ and ‘product excellence’ (Willems et al., 2016). Also, it has been highlighted that
customers increasingly value sustainability (Sudbury-Riley and Kohlbacher, 2016). Likewise,
value creation involves a process that increases the customer’s well-being (Grönroos and Voima,
2013), making ecological and societal aspects even more relevant when assessing value. The
ethics value dimension by Holbrook (1996) has been argued to be related to “respect and care for
the environment from the organization” and “collaboration with social causes” (Gallarza et al.,
2017: 741), therefore these can be seen as the value types ‘ecological’ and ‘societal’.
Sheth et al. (1991) proposed a value construct where customer value is a function of five
independent value dimensions: social, emotional, functional, epistemic and conditional value.
These dimensions affect the perceived utility of a choice and whether a customer should make a
purchase decision. Sweeney uses the first three dimensions (see Table 2) to propose PERVAL, a
scale to measure perceived value of consumer durable goods (Sweeney and Soutar, 2001). In
comparison, he sees the dimensions as interrelated rather than independent and breaks down
functional value into the subcomponents price and quality. These dimensions are seen in a trade-
off perspective, where price is the only sacrifice component and the remaining as benefit
components.
23
Table 2. The value dimensions in PERVAL (Sweeney and Soutar, 2001: 211).
Dimension Description
Emotional value the utility derived from the feelings or
affective states that a product generates
Social value (enhancement of social
self-concept)
the utility derived from the product’s ability
to enhance social self-concept
Functional value (price/value for
money)
the utility derived from the product due to
the reduction of its perceived short term and
longer term costs
Functional value (performance/
quality)
the utility derived from the perceived quality
and expected performance of the product
Lapierre concurs with seeing value in terms of benefit vs sacrifice components. He conducted
interviews with suppliers and customers in an industrial B2B context and empirically identified
several value dimensions related to product, service and relationship (see Table 3).
Table 3. Total value proposition with value dimensions (Lapierre, 2000: 125)
Product Service Relationship
Benefit Alternative solutions
Product quality
Product
customization
Responsiveness
Flexibility
Reliability
Technical
competence
Image
Trust
Solidarity
Sacrifice Price Price Time/effort/energy
Conflict
Seeing value as a first-order multidimensional (or unidimensional) construct as in the case of
Pierre and Sweeney has been argued to be inadequate (Lin et al., 2005). Lin et al. (2005) instead
offers a formative second-order multidimensional construct to assess value perception (see
Figure 1). They conceptualize perceived value with different give-get (benefit vs sacrifice)
components and consequence dimensions of perceived value (satisfaction and behavioral
intentions), which are manifested by insdicators.
24
Figure 1. Sample model for perceived value as a second-order formative construct (Lin et al., 2005: 325). Get and
Give components can be any number and include dimensions as price, privacy and security.
Their model is tested on the web site eTail’s service value survey data, where they use the
following dimensions to conceptualize value: monetary sacrifice, web site design,
fulfillment/reliability, security/privacy and customer service.
Macdonald et al. (2011) uses a more experiential perspective and conducts a case study in B2B.
Based on the case study, they suggest a conceptual framework for assessing value-in-use, with
the resulting types of value-in-use: continuity of operation, retention of knowledge, retention of
competencies, security and time
Other researchers focus solely on the concept of relationship value. Wilson and Jantrania (1994)
examines the creation of value in industrial buyer-supplier relationships by comparing several
disciplines and proposes three independent dimensions of relationship value: economic, strategic
and behavioral dimensions. In contrast, Ruiz-Martínez et al. (2019) measure relationship value in
manufacture-supplier relationships along three axes: core axis, ICT axis and access axis. The
core axis represents the essential benefits and sacrifices in a relationship, the ICT axis reflect
dimensions related to the use of technologies and the access axis the represent importance of
social interaction (Ruiz-Martinez et al., 2018). The investigated dimensions include product
quality, low quality cost, ICT costs, customization, order delivery, personnel training, seller
25
support, ICT benefits, electronic notification, switching cost and social interaction. In their study
the most notable dimension in contributing to relationship value was seller support.
By revising the typology by Holbrook with the trade-off logic, relationships and other prominent
value dimensions, the typology in Table 4 has been proposed. These types can be considered as a
“menu card” for a study, where some values are more relevant than others depending on the
context (foundational characteristic 5).
26
Table 4. Revised value typology adapted from Holbrook (1996: 139-140)
Value types Brief description Papers
Benefits The perceived advantages
Efficiency/
Convenice
The extent to which an object makes life easier
for the customer. E.g. increased output of
products from a given time
(Holbrook, 1996; Lin et al., 2005)
Status The extent to which an object enhances a
positive impression on others
(Gallarza et al., 2017; Holbrook,
1996; Sweeney and Soutar, 2001)
Excellence The extent to which an object is of high quality.
Can both be product and service excellence.
Includes reliability and responsiveness
(Holbrook, 1996; Lin et al., 2005;
Macdonald et al., 2011)
Aesthetics The extent to which an object is appealing.
Relates to sensory appreciation
(Gallarza et al., 2017; Holbrook,
1996; Willems et al., 2016)
Escapism/
Spirituality
The extent to which an object allows customer to
relax and escape daily routine
(Gallarza et al., 2017; Holbrook,
1996)
Self-esteem The extent to which an object positively affects
the attitude and satisfaction of oneself
(Holbrook, 1996; Sweeney and
Soutar, 2001)
Relational The extent to which an object improves the
relationship with the service provider
(Lapierre, 2000)
Social The extent to which an object results in better
relationships with other parties
(Ruiz-Martínez et al., 2019;
Wilson and Jantrania, 1994)
Epistemic The extent to which an object provides novelty,
arouse curiosity or satisfy a desire for knowledge
(Sheth et al., 1991)
Ecological
benefits
The extent to which an object positively impacts
environmental well-being
(Gallarza et al., 2017; Holbrook,
1996)
Societal
benefits
The extent to which an object positively impacts
societal well-being
(Gallarza et al., 2017; Holbrook,
1996)
Sacrifices The perceived loss for the sake of other
considerations
Price The extent to which an object demands monetary
resources
(Lapierre, 2000; Lin et al., 2005;
Ruiz-Martinez et al., 2018;
Sweeney and Soutar, 2001)
Time The extent an object requires time to prepare,
use, understand, etc.
(Lapierre, 2000; Macdonald et al.,
2011)
Effort The extent an object requires effort or energy to
prepare, use, understand, etc.
(Lapierre, 2000)
Privacy The extent an object can result in loss of privacy (Lin et al., 2005)
Security The extent an object can result in security issues,
e.g. being more vulnerable for hacking
(Lin et al., 2005; Macdonald et
al., 2011)
Ecological
costs
The extent to which an object negatively impacts
environmental well-being
(Gallarza et al., 2017; Holbrook,
1996)
Societal costs The extent to which an object negatively impacts
societal well-being
(Gallarza et al., 2017; Holbrook,
1996)
27
2.2.4. Value in Personalization
Vesanen (2007) conducted a literature study and proposed a conceptual framework (Figure 2) of
personalization. He concluded that the main topics in personalization literature were execution of
personalized marketing, personalized marketing output, value for customer and value for
marketer. The value conceptualization used is that value created is the margin between benefits
and sacrifices.
The benefits of personalization for the customer include better preference match, service,
communication (Cöner, 2003; Murthi and Sarkar, 2003; Vesanen, 2007; Wind and Rangaswamy,
2001) and the experience of one (Vesanen, 2007). An additional benefit recognized in literature
(Ansari and Mela, 2003) as well as among marketing professionals (Strycharz et al., 2019) is
reduced information overload for the customer. The sacrifices for the customer listed by
Vesanen (2007) are privacy risks, spam risks, spent time, extra fees and waiting time. Successful
personalization requires data about past behavior, which can be perceived as intrusive (Miceli et
al., 2007; Montgomery and Smith, 2009; Strycharz et al., 2019; Vesanen, 2007). Strycharz et al.
(2019) interviewed marketing professionals and found that privacy risks are viewed as a
boundary condition of personalization success. Privacy concerns can negatively influence the
effectiveness of personalization. If customers get cues that their data have been collected without
their consent, they tend to have a negative reaction (Aguirre et al., 2015). This serves as the
foundation for the personalization-privacy paradox, where personalization leads to an increase of
relevance at the expense of an increased sense of vulnerability (Aguirre et al., 2015).
Additionally, Vesanen (2007) addresses value created for the marketer through personalization.
The benefits recognized for the marketer includes higher prices, better response rates, customer
loyalty, customer satisfaction and differentiation from competitors (Ansari and Mela, 2003;
Vesanen, 2007). Potential sacrifices for marketer include investments, risk of irritating customers
with marketing output and potential brand conflict.
28
Figure 2. A conceptual framework of personalization (Vesanen, 2007: 4147)
Another framework proposed by Miceli et al. (2007) uses value as a dimension of
personalization. They define value of personalization as the site-specific and content-specific
attributes and benefits expected by the customer. Site-specific attributes consist of graphics,
content layout and site updating, while content-specific attributes consist of presentation of
information. This framework covers several of the same topics as the one proposed by Vesanen
(2007) including value and customer marketing relationship. Their view on value solely deals
with benefits and not sacrifices.
29
2.3. Marketing Automation
2.3.1. Marketing Automation and Personalization Processes
Marketing automation is closely related to personalization. Heimbach et al.(2015: 130) describe
marketing automation in the following way: “the core of marketing automation is an automatic
customization or personalization of marketing mix activities”. A similar view is stated by
Järvinen and Taiminen (2016: 165) whom state that the objective of marketing automation is “to
attract, build and maintain trust with current and prospective customers by automatically
personalizing relevant and useful content to meet their specific needs”. Furthermore, several
scholars acknowledge the process nature of personalization (Vesanen, 2007). Consequently,
personalization processes are viewed as marketing automation processes if they can be
performed automatically. Thereby, the following literature review will include processes from
the personalization literature in addition to processes from the marketing automation literature.
Several scholars have attempted to describe the process of delivering a personalized marketing
output. Within the field of personalization, processes have been proposed with varying levels of
granularity (Adomavicius and Tuzhilin, 2005; Murthi and Sarkar, 2003; Pierrakos et al., 2003;
Vesanen and Raulas, 2006). Within marketing automation research, a general framework for
marketing automation has been proposed (Heimbach et al. 2015) and as a part of the wider
concept of the sales and marketing process (Järvinen and Taiminen, 2016).
Vesanen and Raulas (2006) reviewed the personalization literature and found that there are nine
required basic elements to execute a personalized marketing output. The nine basic elements are:
customer, dialogue with customer, customer data, analyses of customer data, customer profile,
customization, marketing output and delivery of marketing output (Vesanen and Raulas, 2006).
They categorized these elements further into operations or results of operations which are
referred to as objects. To incorporate the previously discussed differentiation between
personalization and customization used in this thesis, personalization will be can be seen as an
additional tenth element.
Table 5, provides a comparison of personalization processes, adapted from Vesanen and Raulas
(2006), and a mapping of the basic elements in the relevant steps. In the marketing automation
process proposed by Heimbach et al., (2015) step three and four of the process have been merged
to increase comparability.
30
Table 5. Comparison of personalization processes, adapted from Vesanen and Raulas (2006)
Heimbach
et al. (2015)
Vesanen
and Raulas
(2006)
Pierrakos et al.
(2003)
Murthi and
Sarkar
(2003)
Adomavicius and
Tuzhilin (2005)
Basic Elements of
Personalization
Relevant by
process step
1. Data
inputs
Interaction Data Collection Learning Understanding the
customer by using
data to build
customer profiles
Customer
Interaction
Data
Processing
2. Real time
decision
rules
Processing Data
preprocessing
Matching
offerings
to
customer
Deliver
personalized
offering by
matchmaking
Processing
Customer profile
Personalization
Delivery
Customer
3. Update and
optimize
rules
Personaliz
ation
Pattern
discovery
Evaluation Measure
personalization
impact and
adjusting
personalization
strategy
Interactions
4. Delivery
by chosen
medium
and
content
Delivery Knowledge
postprocessing
Delivery
5. Personalization Customer profile
Matching
6. Report Customer data
Processing
Customer Profile
The processes proposed by (Adomavicius and Tuzhilin, 2005; Heimbach et al., 2015; Murthi and
Sarkar, 2003; Pierrakos et al., 2003; Vesanen and Raulas, 2006) have strong similarities in terms
of the basic elements that they incorporate. Murthi and Sarkar (2003) and Adomavicius and
Tuzhilin (2005) both divided the process into three main steps:
1. Learning in terms of data collection and data inference.
2. Matching marketing action and customer preference.
3. Evaluating the efficiency of the action.
Adomavicius and Tuzhilin (2005) process included additional subcomponents which are detailed
in Table 5. The process by Pierrakos et al. (2003) include additional intermediary steps,
preprocessing and postprocessing, that are more relevant from a data mining perspective.
Moreover, they suggest that reports should be a part of the process which differentiates their
31
process further from the ones by (Adomavicius and Tuzhilin, 2005; Murthi and Sarkar, 2003) .
Vesanen and Raulas (2006) aimed to synthesize the previously discussed processes by
interlinking the basic elements of the personalization process: customer, customer data, customer
profile and marketing output, seen in Figure 3.
Figure 3. The personalization process by Vesanen and Raulas (2006)
Heimbach et al., (2015) proposed a general framework (see Figure 4) for marketing automation
based on Little's (2001) five levels of system operations. The five levels of system operations are
data inputs, real time decision rules, updates of the decision rules, feedback to site management,
and strategy choice. The first step, data inputs, aims to ensure data availability. Heimbach et al.,
(2015) categorize the data inputs into current information, e.g. behavior on the website and
stored information, e.g. transactional history. The second step is usage of real time decision
rules, which have been predefined based on observed patterns in the data. These rules are then
updated in the third step, which can be automated to some extent, e.g. by A/B testing (Heimbach
et al., 2015). The fourth step relates to feedback to management, which enables monitoring and
optimization of performance. The fifth step is related to the strategic choices regarding content
and medium.
32
Figure 4. Marketing automation framework adopted from (Heimbach et al., 2015: 131)
Järvinen and Taiminen (2016) used the classic sales and marketing funnel in Figure 5 to show
the role of marketing automation in the sales and marketing process by doing a case study on a
large B2B-company. Instead of describing the process of personalization, they describe
personalization as a part of a sales and marketing process. Therefore, their process is excluded
from the comparison in Table 5. The marketing and sales funnel consist of five stages: identified
contacts (or suspects), marketing leads (or prospects), sales leads, opportunities (or qualified
leads) and deals. In the first stage, marketing automation contributes by identifying suspects
through their contact info, login, cookies and IP-addresses (Järvinen and Taiminen, 2016). The
basic elements of personalization that occur in this step are customer data, analysis of customer
data and customer profile. In the second stage, marketing leads receive automatic nurturing
programs and lead scoring (Järvinen and Taiminen, 2016) which can be broken down to the basic
elements personalization marketing output and delivery of marketing output. Previous findings
by Strycharz et al. (2019) indicate that the sales funnel is a common way to evaluate
personalization among marketing professionals.
33
Figure 5. The marketing and sales funnel related to content marketing, marketing automation, and CRM adopted
from (Järvinen and Taiminen, 2016: 170)
Järvinen and Taiminen (2016) limit the applications of marketing automation to the first two
stages of the sales and marketing funnel, which significantly reduces the number of potential use
cases of marketing automation. Marketing automation can be used for promotion of additional
products for cross-selling or up-selling, which occur in the later stages of the sales and marketing
funnel. Thus, a need for extension of the process description adopted from Järvinen and
Taiminen (2016) is recognized and we argue that marketing automation can be used throughout
the sales and marketing funnel.
34
2.3.2. Conceptualization of the Marketing Automation Process
There have been several attempts to describe the process of delivering a personalized marketing
mix. Out of the reviewed processes none of the articles explicitly discuss AI despite the fact that
big data and algorithmic personalization is believed to be the future for personalized marketing
(Strycharz et al., 2019). The basic elements of the personalization process that has the strongest
relationship to AI are those that incorporate data and algorithmic aspects. Therefore, the basic
elements customer data, analysis of customer data, customer profile and personalization should
be included.
Several types of data of have been acknowledge as useful for personalization by scholars (Kumar
et al., 2019; Murthi and Sarkar, 2003; Vesanen and Raulas, 2006). Murthi and Sarkar (2003)
separates the data that can be collected through monitoring the user, into user-centric data and
site-centric data. User-centric data consist of web behavior that is stored in the user’s machine.
Site-centric data captures a subset of activities that are present in user-centric data but cannot be
used to explain competitive effects (Murthi and Sarkar, 2003). Moreover, the usage of site-
centric data is highlighted by Vesanen and Raulas (2006). Other types of data that have been
acknowledged as suitable for AI powered personalization is:
- Data on firm-customer transactions (Kumar et al., 2019; Vesanen and Raulas, 2006)
- Customers’ consumption pattern of offering (Kumar et al., 2019; Montgomery and Smith,
2009; Murthi and Sarkar, 2003; Vesanen and Raulas, 2006)
- The communication pattern about firm offerings to customers (Kumar et al., 2019)
- Clickstreams (Montgomery and Smith, 2009)
Learning about customer preference requires data collection (Montgomery and Smith, 2009;
Murthi and Sarkar, 2003). Data collection can be done overtly, where the company informs the
customer about the data collection or covertly which is when the company collect customer data
without them being aware (Aguirre et al., 2015; Murthi and Sarkar, 2003). Since data collection
and processing can have negative effects related to the value of personalization it is important to
include it in a process which is done in most personalization processes (Adomavicius and
Tuzhilin, 2005; Heimbach et al., 2015; Järvinen and Taiminen, 2016; Pierrakos et al., 2003;
Vesanen and Raulas, 2006), but not as explicitly as by Murthi and Sarkar (2003). A potential
negative effect originating from personalized marketing is customer privacy concerns.
35
Several of the reviewed processes have included an explicit step that aim to encapsulate creation
of customer understanding (Adomavicius and Tuzhilin, 2005; Murthi and Sarkar, 2003;
Pierrakos et al., 2003; Vesanen and Raulas, 2006). Marketing professionals have a recognized
difficulty in translating data into customer behavior insights (Leeflang et al., 2014). Kumar et al.,
(2019) suggested that AI can be used in personalization to automatically predict the type, timing,
and purchase of preferred firm offerings which are examples of how AI can be used to learn
about the customer.
The execution of personalized marketing output encompasses the interaction between marketer
and customer in the shape of channel and content. This is described as a process that interlinks
the customer and the marketer (Vesanen, 2007), a view that is shared by several scholars (Murthi
and Sarkar, 2003; Strycharz et al., 2019; Vesanen, 2007; Wind and Rangaswamy, 2001). The
interactions created by the personalized marketing output creates the relationship between
marketer and customer (Vesanen, 2007; Wind and Rangaswamy, 2001).
To achieve successful personalization the marketing professionals is required to understand the
customers preferences (Strycharz et al., 2019). Gaining insights about customers and their
preference is a challenge for many marketers (Leeflang et al., 2014). Therefore, a process should
include an explicit step for evaluation to ensure qualitative preference match. Research regarding
the effectiveness of personalized marketing show varied results (Strycharz et al., 2019).
Moreover, the need for evaluation is stressed further by the need to estimate the positive impact
on the company’s profits which is required for successful personalization (Kaptein and Parvinen,
2015). They also use the ability to measure effects as a requirement for successful
personalization. The personalization processes proposed by Adomavicius and Tuzhilin (2005)
and Murthi and Sarkar (2003) mention evaluation explicitly. In the marketing automation by
Heimbach et al. (2015) optimization is discussed where some form of evaluation is assumed to
be required.
The importance of evaluation underlines the advantage of relating the marketing automation
process to the sales and marketing funnel, as done by Järvinen and Taiminen (2016), compared
to viewing it as a standalone process. It enables for a structured view of the relationship between
marketing action and outcome. It creates a more prominent focus on how marketing automation
relates to marketing outcomes and thereby a link to value creation. Furthermore, Strycharz et al.
36
(2019) conclude that a funnel perspective is often used among marketers to evaluate
effectiveness. This indicate that a process where evaluation is linked to the sales funnel could be
simplify application among marketers.
In summary, the reviewed personalization literature supports that a process, that aims to describe
automatic personalization, should incorporate four process steps. The first step is data which is
achieved through data collection. The main reason for including this as an explicit step is the
associated customer sacrifices related to privacy (Aguirre et al., 2015; Vesanen, 2007) and the
potential negative consequences on value (Vesanen, 2007). The second step, learning, aims to
describe the understanding of the customer that arises as a result of data analysis. The purpose of
the understanding is to be able to match marketing content to a customer. Data analysis is a
prominent part of several processes existing in literature (Adomavicius and Tuzhilin, 2005;
Murthi and Sarkar, 2003; Pierrakos et al., 2003; Vesanen and Raulas, 2006). When the company
has gained an understanding of the customer’s preferences and created a customer profile, it
should be matched to an appropriate marketing output, thereby personalized. The importance of
these steps are highlighted by Kaptein and Parvinen (2015) who view algorithm scalability as a
prerequisite to successful personalization. The delivery of the personalized marketing is the third
step, here it is assumed that the operations can be automized. As discussed in the earlier section
another prerequisite to successful personalization according to Kaptein and Parvinen (2015) is
the ability to evaluate it which is the final step. The process summary can be viewed in Figure 6.
Figure 6. Process of automized personalization.
37
2.4. Artificial Intelligence
2.4.1. Big data and AI
With so much value hiding in data, it has been referred to as the oil of the digital economy (Yi et
al., 2014). Compared to oil, the amount of data is growing at an unprecedented speed, with an
estimated generation of 2.5 quintillion bytes per day (Dobre and Xhafa, 2014).
In 1997, Michael Cox and David Ellsworth were among the first to use the term big data. Since
then, numerous definitions of big data have appeared in literature. One of the most popular
definitions originates from IBM and describes the three V’s of big data (O’Leary, 2013). It has
later been expanded to the following five V’s characterizing big data (Ishwarappa and Anuradha,
2015):
• Volume refers to the sheer amounts of data in terms of stored terabytes, tables and files,
transactions and records. The volume of big data renders traditional data warehousing
methods unusable.
• Velocity aims to describe the increasing speed of data creation. Attributes of velocity
include number of batches, data streams or processes and the use of real time data.
• Variety is an indicator of the different types of data that is stored. There is structured data
and unstructured data, where unstructured data is significantly more complex to process.
Of all generated data, 90% is unstructured.
• Veracity refers to the degree of correct data in a large data set. Low veracity is
detrimental to the quality of analysis based on the big data set.
• Value is said to be the most significant aspect of big data. It refers to the potential value
the data can turn into.
Big data and AI are closely related. AI uses voluminous data sets to perform advanced pattern
recognition and learning tasks (O’Leary, 2013), therefore the presence of big data enables AI
(Thrall et al., 2018). Artificial intelligence is manifested as machines that exhibit some aspects of
human intelligence (Huang and Rust, 2018). Furthermore, they described four types of artificial
intelligence: mechanical, analytical, intuitive and empathic. The categorization can be used to
determine the complexity of AI in certain applications, depending on the nature of the task that it
shall perform (Huang and Rust, 2018). The four types of artificial intelligence are explained
further below.
38
1. Mechanical intelligence can handle simple and standardized tasks were limited training is
required (e.g. call center agents or retail sales agents). It solely relies on observations to
act.
2. Analytical intelligence can perform analytical, rule based, systematic and complex tasks.
This type of intelligence can make rational decisions and handle work that require
technical training and expertise (e.g. financial analysis, data science and accounting).
3. Intuitive intelligence learns and adapts intuitively based on understanding. An agent that
possess this type of intelligence can handle work that require creative problem solving
(e.g. being a doctor, management consultant or marketing manager). Tasks that can be
managed by an agent that possess this type of intelligence can be complex, chaotic and
idiosyncratic.
4. Empathetic intelligence can recognize emotions and make decisions that incorporate
emotions. Characteristics include sociability, being emotional, communicative and highly
interactive. Agents that possess this intelligence can perform tasks that require
relationship building and communication for example being a physiatrist or politician.
In comparison, Davenport et al. (2020) distinguish two levels of intelligence, task automation
and context awareness. The former is described as the case of standardized or rule-based AI,
where consistency and logic are required. Context awareness is more advanced and requires
machines to “learn how to learn” and therefore, to be able to extend beyond a certain domain or
context. The aforementioned two are highly related to the concepts of narrow AI (maps onto
analytical and mechanical intelligence) and general AI (maps onto intuitive and empathetic
intelligence) (Kaplan and Haenlein, 2019). Davenport et al. (2020) includes the intelligence
levels in an AI framework along with two other dimensions, task type and whether AI is
embedded in a robot. The proposed AI framework is derived from extant marketing literature
revolving around AI, with the goal to create an understanding of AI. Task type refers to whether
the AI application analyzes numerical or non-numerical data (e.g. voice, text or images). Usage
of non-tabular data is more complex and often translated to numerical data, e.g. pixel color in an
RGB scale. The last dimension relates to whether AI is virtual or embedded in a robot. The robot
form has elements of physical embodiment, which has been found to offer substantial
advantages, especially when customer interactions are involved. By combining the three
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dimensions, six states of AI arise, as seen in Table 6, with increasing complexity and each
enabling new applications.
Table 6. AI framework (Davenport et al., 2020)
Level of intelligence Task type Digital form Robot form
Task Automation
Analyze numbers 1. Controller of
Numerical data
3. Numerical Data
Robot
Analyze text, voice,
images and faces
2. Controller of
Data 4. Data Robot
Context Awareness Analyze numbers, text,
voice, images and faces 5. Data Virtuoso 6. Robot Expert
2.4.2. How AI can Enhance Personalization
Practitioners anticipate the impact of AI on B2B marketing to significantly amplify
personalization and customization (Paschen et al., 2019). Kumar et al. (2019) examined the
converging paths of personalization and AI and found that personalization is a major factor
behind the popularity of AI. Prior to AI, personalization was limited by volume and quality of
customer information, the firm’s ability to generate insights and their ability to implement
insights (Kumar et al., 2019). Personalization efforts in marketing were limited to be based on
predefined rules by experts (Kumar et al., 2019).
The complexity of the AI required for personalization depends on the desired degree of
personalization. Simpler forms of personalization (e.g. mass personalization) require less
complex intelligence. According to Huang and Rust (2018) mass personalization can be
performed by agents possessing analytical intelligence. To provide personalized quality service
that use intuition in the form of tacit knowledge would require intuitive intelligence (Huang and
Rust, 2018).
There is a wide variety of applications of personalization that are enhanced by AI. They include
recommender systems (Zanker et al., 2019), automatic generation of advertising copies (Deng et
al., 2019), tailored and targeted marketing for lead generation (Syam and Sharma, 2018), to
predict customer dissatisfaction (Daqar and Smoudy, 2019) and many more found in Table 7.
40
The most prominent and frequent example of personalization enabled by AI is recommender
systems (RS) (Zanker et al., 2019). According to Han et al (2004) RS is a system that helps users
find desired items by making recommendations either by the content of the recommended item
or ratings of an item by similar users. Recommender systems provide personalized product
recommendations to fulfill customers' needs (Singh et al., 2019). Many B2C companies such as
Netflix and Amazon have developed advanced RS (Gabrani et al., 2017). The use of RS in the
B2B context have so far been limited (Oprea et al., 2013). However, this can help to suggest
products to buy as in the case of cross-selling (Syam and Sharma, 2018).
Automatic generation of personalized marketing messages is an application area that is growing
rapidly within research. Deng et al., (2019) advanced a system for generation of personalized
advertising copy that can automatically personalize advertising content to align with the needs of
individual customers. Empirical research indicated higher clickthrough rate for their personalized
system.
Syam and Sharma (2018) examined the potential use cases for AI in the marketing and sales
process. They found that in the process of lead generation the main contributions of AI are the
delivery of highly personalized and individually tailored marketing. Additionally, they discussed
how robo-advisors could be a part of the sales process (Syam and Sharma, 2018). Robo-advisors
could have access to the communication history of customers and could thereby personalize their
communication to a specific customer. AI could be used to track the customer behavior and
identify dissatisfaction. Thereby, companies could proactively take action and improve customer
satisfaction (Daqar and Smoudy, 2019).
41
Table 7. Applications of AI in personalization.
Paper Application
(Syam and
Sharma, 2018)
- Targeting customers and sorting them by propensity to purchase.
- Advance warning of probability of switching to competitors.
- Suggesting cross-selling and up-selling opportunities.
- Segmentation based on segment labels: frequent buyers; purchase
amount etc.
- Tracking customer behavior over time
- Keyword analysis for prospecting.
- Text, audio and video analysis of salesperson-customer
communications for lead generation and subsequent qualifying of
prospects.
- Search engine insights. Finding keywords and phrases that potential
customers search can help a company identify prospects.
- Gain customer insights from their social media postings and
reviews.
- Text analysis of emails, audio and video records to generate and
qualify leads.
- Lead scoring for better efforts allocation based on quality of leads
(Davenport et
al., 2020)
- Suggesting next product to buy.
- Optimal and dynamic prices.
- Personalized product offering by analyzing customer data across
channels.
(Deng et al.,
2019)
- Generation of advertising copy.
(Daqar and
Smoudy, 2019)
- Predicting dissatisfaction and taking automatic actions.
(Zanker et al.,
2019)
- Recommender systems.
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2.5. Frame of Reference
The literature review includes concepts and theories that constitute the theoretical framework
displayed in Figure 7. In order to investigate how AI can be used in the process, a selection of
use cases of AI in personalization from Table 7 will be mapped to the process. In each step of the
process the impact on value will be investigated.
Figure 7. Frame of reference used in this study
The value component of the study will be investigated through the value types stated in Table 4.
As discussed in section 2.2.2 this thesis has adopted a value view which is grounded in the S-D
logic and where value has the following foundational characteristics, adapted from Leroi-
Werelds (2019):
1. Customer value implies an interaction between a customer and an object (e.g. a product,
service, store, technology, activity, etc.).
2. Customer value involves a trade-off between the benefits and sacrifices of an object.
3. Customer value is not inherent in an object, but in the customer’s, experiences derived
from the object.
4. Customer value is subjective and personal as value perception are based on personal
characteristics.
5. Customer value is context-specific, considering situation, time frame, circumstances and
location.
6. Customer value is multidimensional and consists of multiple value types.
7. Customer value is co-created by the customer by means of resource integration between
supplier and customer.
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3. Methodology
This chapter lays out the methodology used in this study, including the research purpose, design
and strategy. How data will be collected and analyzed is explained in more detail. Lastly, the
validity and reliability of the study is discussed as well as ethical considerations.
3.1. Research Purpose
Research projects can be classified as one of three categories: exploratory, descriptive, and
explanatory. The studied phenomenon, AI in marketing automation, is both novel and relatively
understudied, suggesting an exploratory purpose (Yin, 2010). Moreover, the research problem is
large and complex and therefore, exploratory work can help to create an understanding and
provide insights (Neuman, 2013).
3.2. Research Approach
Our research is conducted in the interpretivist research paradigm since the central areas of our
research question, value, has a strongly subjective nature where the perception of value can vary
depending on social context (Farquhar, 2012).
The study takes a deductive approach, since personalization, value and AI each have known
models and theories, as described in Chapter 2. However, they’ve are not well-studied together
in the context of B2B. The frame of reference based on these theories and models will be used to
guide design and analysis of data collection.
3.3. Research Design
As the purpose of study is of an open-ended character and requires an explanation rather than a
straightforward yes or no, a qualitative approach is suitable, which is described as an
investigation of an area using words to develop a deeper and contextual understanding
(Blomkvist, 2015). Qualitative studies involve collecting, analyzing and interpreting by
observing what people say and do, which will be described further in this chapter.
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3.4. Research Strategy
The type of qualitative study that will be conducted is case study. The reason for this, being the
contemporary nature of the project and that the purpose of the project has a close connection to
the real-life context (Yin, 1994). It is suitable when the purpose of study seeks to answer a ‘why’
or ‘how’ research question.
As value is described as perceived by individuals it is beneficial to give several actors a chance
to describe their point of view from a single case (Blomkvist, 2015). The studied phenomenon is
multifaceted, where rich and vivid collection from a case can help to create an understanding
(Saunders et al., 2015).
3.5. Case Company
The case study is conducted at Atlas Copco, a Swedish industrial company that manufactures
industrial equipment and tools. The Atlas Copco companies develop, manufacture, service, and
rent industrial tools, air compressors, construction and assembly systems on a global level.
Within these areas the company has a yearly turnover of roughly 100 BSEK and employs 40 000
people. As a pioneer in their industry, Atlas Copco is a good example of a company within a
complex B2B context, characterized by technical complexity, many involved stakeholders and
long-term buying decisions.
3.6. Data Collection
For the sake of this study, semi structured interviews were found to be the most appropriate
choice of data collection since they can help to develop a deeper understanding of how AI in
personalization can bring value. The data collection was conducted in two stages, first as a
contextual stage and then as a main stage.
The purpose of the contextual stage is to create an understanding of the organization, the context
it resides in and how AI in personalization fits into the organization. The contextual interviews
help to bring insights in how AI can be used in marketing and what use cases from the literature
are most relevant for the organization in question. During this stage, eight unstructured
interviews were conducted with various people working within marketing, sales or business
development.
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The purpose of the main stage is to answer the overarching research question with the help of the
theoretical framework. Interviews during this stage had an aim to capture the company’s
perspective on value of personalization and how certain use cases are described in the
automatized personalization process. To determine what value a certain use case is perceived to
bring we have used established constructs in Appendix A. The constructs are often based on a
seven-point Likert scale and have therefore been reworded to better fit a qualitative interview,
leaving room for further elaboration. A pilot interview was done to evaluate which value types
were deemed relevant for each use case. This caused a revision in the research instrument where
the value types escapism, self-esteem, social, ecological, societal and price were removed.
These interviews were conducted in a semi-structured fashion. During the interviews both of the
authors were present and took on different roles as suggested by Eisenhardt (1989). One took the
role as an interviewer and focused on asking predetermined questions and facilitating discussion.
If an answer was brief, the interviewee was asked to elaborate. The other author was responsible
for documentation and taking notes. All interviews were recorded with permission from the
interviewees. The interviews in the main stage were conducted via video conferencing mainly
due to the wide range of geographical locations of the interviewees.
3.7. Research Instrument
The purpose of a research instrument is threefold, it should describe the means by which data
collection is achieved, demonstrate that accepted protocols have been followed and help argue
for the rigor of the data (Farquhar, 2012). The research instrument used in this case study is the
interview guide presented in Appendix B. The interview guide consists of an introductory
description of the research that each interviewee was given at the start of the interview in
addition to the questions asked. The introductory description ensure that all interviews are given
the same information regarding the purpose of the interview and how their anonymity is
protected. Thereby it helps ensure that ethical standards are followed.
The use of a pre-decided set of interview questions, based on the frame of reference, assures
collection of comparable data that can be analyzed in a structured fashion while upholding
standards of validity and reliability thus ensuring the rigor of the study.
Since qualitative method enables immediate data analysis (Farquhar, 2012) preliminary data
analysis can serve as the basis for revisions in the research instrument. Our interview guide was
46
revised on one occasion following the pilot interview as describe in the section regarding data
collection.
3.8. Sample Selection
The sample selection was conducted through a snowballing approach, where each round of
interviewees identified other relevant sources of information (Farquhar, 2012). Interviewees
include employees at Atlas Copco that work with tasks closely related to sales and/or marketing,
which is the field the thesis revolves around. The initial, contextual, round of interviews included
vice presidents in the sales and marketing departments of the organization, possessing an in-
depth view of the employees and their knowledge base in their respective departments. Table 8
contains all interviewees in the contextual phase.
Table 8. List of contextual interviews
Interviewee Role Geographical Region
Interviewee 1 VP Sales Development Sweden
Interviewee 2 VP Marketing Sweden
Interviewee 3 Customer Interaction Manager Sweden
Interviewee 4 Marketing Automation Specialist Sweden
Interviewee 5 VP Global Distribution Manager Sweden
Interviewee 6 VP Marketing Spain
Interviewee 7 Business Manager Sweden
Interviewee 8 VP Digital Platform Development Sweden
This study is conducted from a marketing perspective, with technical elements, thus for the main
stage it made sense to interview digital marketing professionals who work as a bridge between
the digital and marketing perspectives. The titles of the interviewees can be found in Table 9.
Most interviewees were recommended in the contextual phase; however, some were suggested
by other interviewees in the main stage. They were chosen from different hierarchical levels in
their regional marketing organizations, ranging from directors to managers and specialists. As
the scope of this study analyzes a global organizational context, the interviewees represent a
plethora of geographical regions, including the U.S., Germany, the UK, France, Czech Republic
and Sweden.
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Table 9. List of title of interviewees in main research stage.
Interviewee Role Geographical Region
Interviewee 9 Marketing Manager United Kingdom
Interviewee 10 Digital Marketing Manager The U.S.
Interviewee 11 Digital Marketing Specialist The U.S.
Interviewee 12 Marketing Manager Germany
Interviewee 13 Director of Marketing The U.S.
Interviewee 14 Digital Media &
Communication Manager
France
Interviewee 15 Business Manager Germany
Interviewee 16 Communication and Digital
Specialist
Sweden
Interviewee 17 Digital Communication Manager South Africa
Interviewee 18 Digital Marketing Specialist Czech Republic
Interviewee 19 Digital Marketing Specialist Sweden
3.9. Data Analysis
When conducting a qualitative study, it is important that the analysis is done in a systematic way
to ensure reliability. Yin (2010) found that qualitative studies typically follows five phases. It
begins with compiling of data, which is described as collecting and arranging data. The data is
disassembled and broken down into smaller segments. The smaller segments of data are then
reassembled according to some set of categories. Finally, the data is interpreted to create a
narrative, which is used for a basis for drawing conclusions.
One approach for this is the procedure by Miles (1994). Following data collection, Miles (1994)
suggests a procedure incorporating three steps: data reduction, data display and verification. The
first step involves selecting, focusing and simplifying data according to what is relevant for the
study, which is based on the theoretical framework and value constructs. The reduction takes
place on transcribed interviews to extract valuable information. For extraction of valuable
information we’ve used thematic analysis, which is suitable for qualitative studies (Blomkvist,
2015).
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The coding procedure on transcripts to identify themes was done as described by Creswell
(2014):
1. Starting with raw data
2. Organizing and preparing data for analysis
3. Coding the data
4. Identifying themes
5. Interrelating themes
6. Interpreting the meaning of themes
The unit of analysis, the marketing automation process in the frame of reference is used as basis
for development of codes. For each stage in the marketing automation process, themes for the
stages and value types were extracted in an emergent fashion.
The second step of the procedure by Miles (1994) involves displaying and structuring the
reduced data to make it comparable and analysis more feasible. This is done by using themes
which are structured and presented in tables. Lastly, the data is interpreted to draw conclusions to
answer the research question.
3.10. Research Quality
There are a number of criteria to assess the methodological rigor of case studies (Gibbert et al.,
2008). Common criteria are internal validity, construct validity, external validity and reliability.
To ensure internal validity, the frame of reference presented in this report is derived from
literature in a transparent way. Additionally, theory triangulation is achieved by using both the
theoretical lens of personalization as well as marketing automation in the frame of reference.
Several efforts have been made to increase construct validity. The most significant is that all
interview questions relating to value have been adapted from well researched constructs which
have been empirically evaluated using factor analysis. The importance of constructs which are
empirically investigated for construct validity is highlighted by Aguinis and Edwards (2014).
Moreover, the data collection circumstances have been described which is recommended by
(Gibbert et al., 2008).
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The external validity or generalizability of this case study is low since a single case study allows
for neither statistical nor analytical generalizations (Gibbert et al., 2008). Attempts to improve it
include providing the case study context description, as well as the rationale behind using the
case study method.
The reliability of this thesis is supported through a transparent presentation of the case study
method which is done by using a case study protocol shown in appendix B. The case study
protocol has been validated by a senior marketing associate at the case company.
3.11. Ethical Considerations
Bell and Bryman (2007) investigated ethical codes used by social researchers and found the
following nine ethical principles: harm to participants, informed consent, anonymity, dignity,
privacy, confidentiality, affiliation, honesty and transparency, deception, misrepresentation and
reciprocity.
These principles will naturally guide all work in this thesis. In the data collection phase,
significant focus has been put into adhering to the principles of informed consent, honesty and
confidentiality. To ensure that all participants understood the aim of the research and what their
answers would be used for, a standard project presentation was used. Furthermore, the validation
of the case study protocol by a senior associate at the case company serves as a safeguard against
questions that could negatively affect the participants in any way.
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4. Empirical Analysis
This chapter presents the analysis of the collected data. It starts with a description of the
findings from the contextual interviews, which is followed by the analysis of the data collected
for the main stage. The presentation and analysis of data is divided into two parts, one that
pertains to the marketing automation process and one that pertains to value creation.
4.1. Empirical Context
The contextual interviews shed light upon how sales at Atlas Copco are conducted. From a
customer perspective, it typically follows a classic process from being unaware to realizing a
need, evaluating potential solutions, making a purchase and then evaluating the purchased
solution (interviewee 1). Translating this into a sales cycle, the steps goes as follows: qualifying
leads, proposing an offering, win/loss, evaluation and then the retention for new purchases
(interviewee 1-3). Traditionally, digital tools for sales, such as marketing automation, have
mostly been used to generate leads (interviewee 4), but lately it has been found that the
customers are the most digital in the evaluation step (interviewee 1), therefore the organization
has concluded that digital marketing need to facilitate the entire process.
The industrial equipment that Atlas Copco provide are often customized to customers’ needs,
which highly differs depending on the customer and their industry (interviewee 2 & 7). The
product portfolio is therefore vast. Equipment can be capital intense and of high importance to
the customer, making the duration of the purchasing process long (interviewee 2). In addition,
the customer behavior is changing, and they tend to involve even more people in the
procurement process, thus increasing decision times (interviewee 2). A reason being that
providers in the industry are becoming more software-oriented (interviewee 6), where additional
capabilities and knowledge are required to take decisions.
With the rise of industry 4.0, AI is seen to have major potential when it comes to products
(interviewee 2). At Atlas Copco it is often described as connected machines (interviewee 6 & 7),
able to communicate using big data. Recognized benefits of successful AI include increased
uptime, reduced defections, improved production, new human interactions and reduction in
energy use (interviewee 7). AI can be used to evaluate failure of products off site for remote
maintenance, for predictive maintenance and to detect misuse (interviewee 6).
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From a marketing standpoint, Atlas Copco wants to be perceived as a thought leader in their
industry; knowledgeable and in the forefront (interviewee 2 & 4). One proposed use of AI for
marketing purposes is segmentation based on customer owned products to provide relevant
content to customer (interviewee 2 & 8). From a technological perspective this means marketing
the right products to transform customers to the next generation of products. This is comparable
with the practice of upselling (interviewee 2). Having a vast and complex product offering makes
it difficult for salespeople to understand and have knowledge about all the products. In this
sense, AI can also help to suggest cross-selling opportunities (interviewee 1-3 & 6). Being able
to suggest relevant products, as in the case of successful cross-selling, has been found to drive
customer loyalty (interviewee 2).
The sales process highly relies on salespeople and that they understand the customer’s needs to
provide the right offering (interviewee 2). Here the contextual interviews reveal that AI can help
to understand when to contact the customer and to reduce manual, time-consuming operations
for salespeople, freeing up time and letting them focus on the rights things (interviewee 2 & 8).
Salespeople tend to be optimistic about qualification of leads and AI can help to focus the
resources on the right customer, with the highest likelihood of resulting in a deal (interviewee 1
& 3). Additionally, AI is thought to be able to help to identify which parameters drive successful
sales and then giving the opportunity to tweak those parameters to increase the chance of closing
a deal (interviewee 1 & 3).
Other mentioned use cases of AI are market-based pricing (interviewee 2) and scraping internet
for relevant information about the customer (interviewee 4). E.g. if it is mentioned that a
customer focus on quality in their annual report, then maybe Atlas Copco can push products
related to that. More in general, AI can help to improve and personalize customer interaction on
the website.
Recurring challenges with applications of AI are system complexity, human factors and
sustainability (interviewee 1 & 7). Combining data from different systems poses a real challenge
and AI can potentially change the roles of the salespeople. Compared to B2C, in B2B there is a
lot of less data, making it more difficult to identify patterns (interviewee 8). If the patterns are
not correctly identified, there is a risk of providing customers with bad content that can inhibit
future opportunities (interviewee 4).
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One of the most prominent use cases of AI at Atlas Copco is cross-selling, which is a strategic
goal at Atlas Copco. It is a challenge to suggest cross-selling with such a technically complex
and large product offering. The second use case that stands out is segmentation based on
technological maturity. Some products can only be sold to customer who has the technological
prerequisites. Thus, it makes sense to market those products to the customers who have the
prerequisites rather than to those who don’t. Then automated nurturing activities can be used as
actions depending on maturity. These were the two use cases that was brought up the most
during the contextual interviews and thought to have significant impact, which is why we will
use them as examples when looking at how AI in marketing automation can create value.
4.2. Findings & Analysis
4.2.1 Marketing Automation Process
This section presents the thematic analysis of the interviews relating to the marketing automation
process. For each of the marketing automation steps: data, learning, personalized marketing
output and evaluation, themes are identified and presented. A summary of the results is provided
in Table 10.
Table 10. Themes identified relating to the digital marketing process
Process Step Theme
Data
Digital customer behavior
Industry specifics
Current product portfolio for segmentation
Customer specifics for cross selling
Learning Improved accuracy of personas
Personalized Marketing Output – Channels
Social media
Website
Direct marketing
Personalized Marketing Output – Content Educational
Evaluation
Evaluation through the sales funnel
Digital engagement
Benchmarking
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Data for Segmentation
The responses from the interviewees regarding data needs for segmentation based on
technological maturity contained three main themes: digital customer behavior, industry
specifics and the current product portfolio.
Digital customer behavior was the most frequently occurring theme. Several interviewees (9-12,
15 & 18) discussed data sources such as touchpoints used, clickstreams, attitudes towards digital
customer service encounters and engagement with social media content. Clickstreams,
touchpoints and service encounters are all different types of site-centric data (Murthi and Sarkar,
2003) whereas social media engagement is site-centric data from a separate site than the website.
User-centric data consist of web behavior that is stored in the user’s machine. This can be useful
to use for personalization in addition to site-centric data (Murthi and Sarkar, 2003), but was not
suggested by any interviewees. Additionally, that data on digital customer behavior can be used
for AI driven personalization have been acknowledged by Kumar et al. (2019).
The second theme, industry specifics, reoccurred among all interviewees to some extent during
the interviews. The case company has an extensive customer base from a wide range of
industries and the manufacturing process of different industries have varying technical
complexity mainly due to differences in regulatory requirements. Several interviewees (10,11 &
17) provided examples that illustrates the differentiation in technological complexity of
customer’s manufacturing. Examples ranged from manufacturing plants that were described as
“garages” to others with extensive IT-departments. Interviewee 13 described that a data source to
capture industry specifics could be the technical requirements for different industries which Atlas
Copco R&D department already have access to. Thereby, utilizing it for marketing purpose is
more of an organizational challenge.
The final theme, current product portfolio, revolves around data regarding the current products
used in customer operations. Internally at the case company, there is a clear hierarchy regarding
technical complexity of the products and thereby the currently used products are an indication of
the technological maturity. Since the company already has accessed to this data through previous
invoicing it is a highly accessible data source. Interviewees 13, 15 & 16 mainly listed purchase
history as a data source for the current product portfolio. Presently, technical complexity of the
product portfolio is assessed manually by sales professionals and product managers at the case
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company which is a process that the interviewee 13 reckoned could be automized. Using
previously purchased products for personalization powered by AI algorithms is something that is
acknowledged in recent literature (Kumar et al, 2019).
Data for Cross-Selling
Regarding cross-selling, the themes for data requirements were less distinct. Three themes were
identified: industry specifics, customer specifics and digital customer behavior.
The answers varied greatly both in terms of content and details. They ranged from "industry or
firm specific data is something that could be useful for looking at cross selling” to "key to our
sales process is understanding the way our customers operate. Hard information to get digitally”.
However, most interviewees (9, 10, 13, 15 & 18) acknowledged that detailed data about the
customer’s industry segment would be useful, thereby it was identified as a primary theme.
Moreover, the two most senior interviewees (13 & 15) described how more firm specific data
about operations, financial performance, investments and purchase history would be useful to
find cross-selling opportunities. Sources of such data might be inaccessible for the company or
be hard to access at scale. These answers serve as the foundation for the second identified
theme, customer specifics. Previous research has highlighted several similar data types that can
be categorized as customer specifics, e.g. data on firm-customer transactions (Kumar et al., 2019;
Vesanen and Raulas, 2006) and the communication pattern about firm offerings to customers
(Kumar et al., 2019).
In addition to data about industry specifics, several interviewees (9, 15 & 18) discussed the
utilization of engagement data from e-commerce and digital channels such as social media and
the company website which led to the identification of the third theme digital customer behavior.
This theme reinforces previous findings by Montgomery & Smith (2009) who highlighted the
use of clickstreams.
Learning
Learning is described as the second process step of marketing automation and occurs as a result
of data analysis. The aim of the step is to understand the customers’ needs and allow for
matching the customer to a personalized marketing output. One theme, improved accuracy of
personas, has been identified relating to learning.
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The answers relating to the subject revolve around what the marketing professionals wish to
understand about the customers to create more advanced and accurate personas. Personas are a
common tool among marketing professionals at the case company and were discussed by several
interviewees (9-11, 17 & 18). The concept of personas is similar to the concept customer profile,
which was identified as a basic element of the personalization process (Vesanen and Raulas,
2006). Personas can be used for matching customers to specific content. The answers indicate
that the concept of AI can drive the granularity of personalization from mass personalization
towards segment level personalization (Wedel and Kannan, 2016).
The ability to generate customer insights to use as a foundation for personas is something that
has been found to be a challenge for many marketers (Leeflang et al., 2014). One digital
marketing manager, interviewee 10, stated that "a quality engineer is going to have different
challenges then a purchasing manager versus like the you know a plant manager”, illustrating the
granularity of the persona which could be achieved. A complementary perspective was
mentioned by interviewee 13 who brought up how personas could be extended to include
stakeholder role.
Another aspect of the personas that was discussed is activity level. Interviewee 15 discussed the
idea of trying to understand when a customer has an upcoming project and adapt marketing to it.
This illustrates the complexities of B2B processes where the buying processes are long (Schmitz
et al., 2014) and the importance of using marketing at the right time when it is the most relevant
for the customer. Additionally, the possibility to predict the appropriate time for a marketing
actions using AI was highlighted by Kumar et al. (2019) and Murthi and Sarkar (2003).
Personalized Marketing Output - Channel
The questions relating to the delivery of personalized marketing where centered around two main
sections; content and channel. These sections were adapted from the process proposed by
Heimbach et al. (2015) where the delivery of personalized marketing is separated into a content
stage and a channel stage.
The channels mentioned as suitable were indistinguishable between the use cases which has led
to common themes in the answers. The first theme is social media channels, namely 1) social
media, 2) website and 3) direct marketing
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Almost all interviewees discussed social media as a complement to the company website to reach
new audiences. Among the major social media channels LinkedIn was mentioned most
frequently, followed by Facebook. Interviewee 11 described that very few marketing activities
were conducted on Instagram.
Moreover, they described that the aim of social media is often to bring visitors into the website
for more educational content and further engagement (interviewee 9, 13,16, 17 & 19). Therefore,
the website is our second theme relating to channel.
Besides social media and the company website several interviewees (13-19) discussed the use of
direct marketing via email as a channel for more personalized marketing. Their answers served
as the foundation for the third theme regarding channel which is direct marketing. A marketing
manager, interviewee 13, described the motivation behind the current use in the following way,
“contacting customer directly with information that is tailored to them via email rather than
pumping info on the website”. Previous work by Strycharz et al (2019) indicates that marketing
professionals regard email personalization as standard procedure. However, the degree to which
the emails are personalized vary. Some emails only use personalization in the form of adding the
name of the respondent while others personalize the content of the email. The more extensive
forms of personalization have been shown to have a more significantly more positive effects on
click-through rates (Strycharz et al., 2019). All of the identified themes have previously been
recognized by Järvinen and Taiminen (2016) and Heimbach et al. (2015) as prominent channels.
Personalized Marketing Output - Content
In the answers relating to suitable marketing content one theme have been identified which is
educational content. Interviewees (9-11, 13, 15-19) mentioned content types such as webinars,
text descriptions, white papers, videos, newsletter and Q&A which are educational in their
nature. All mentioned content types were recognized by Järvinen and Taiminen (2016).
Additionally, usage of interactive content was proposed by interviewees 15 & 19. Inclusion of
business impact for the specific customer provided with personalized marketing was also
suggested (interviewee 13). Furthermore, many interviewees (9 & 17) discussed the importance
to find a balance between written and video content to make it comprehensible for different
audiences.
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Evaluation
Three themes regarding evaluation of the uses cases has been revealed through the coding
procedure, namely 1) evaluation through the sales funnel, 2) digital engagement and 3)
benchmarking.
The first one is evaluation through the sales funnel. The sales funnel is an established tool to
evaluate marketing efforts at the case company and was brought up by interviewee 13. Actions
can be evaluated by their ability to convert a customer in the funnel. Using the sales funnel as a
part of the marketing automation process has previously been used in literature (Järvinen and
Taiminen, 2016). This reinforces the findings by Strycharz et al. (2019) who also concluded that
marketing professionals are comfortable using and often use the sales and marketing funnel as an
evaluation tool. Furthermore, the use of the sales funnel for evaluation is compatible with the
prerequisite for successful personalization listed by Kaptein and Parvinen (2015) who state that
successful personalization needs to affect the company’s profits.
Secondly, KPIs based on engagement data has been mentioned in a varied number of ways. One
marketing manager, interviewee 9, described that KPI should capture the amount of attention a
marketing action receives. Another, interviewee 12, discussed that in addition to click-rates KPIs
should encapsulate how long a customer is engaging with content as a complementary measure
of marketing relevance. The effect of personalization on click-through rates have been well
researched with mixed results (Strycharz et al., 2019) indicating that it is a suitable metric to
monitor.
A less frequently mentioned evaluation method that was nevertheless discussed is the third
theme, benchmarking. Interviewee 9 discussed the importance of the situational component to
evaluation and acknowledge that the organization had the potential to improve their capabilities
relating to it. Furthermore, interviewee 15 described that it would be important to first find the
benchmark performance of the marketing actions relating to both revenue and customer attention
and then use it to evaluate new AI initiatives.
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4.2.2. Value Types
In this section the results from the thematic analysis of the interview data relating to value is
presented. A pilot interview deemed which value types were relevant for the case study and in
Table 11 the relevant values and identified themes are presented. The answers regarding each
value type are discussed more in depth in this section, as well as the interviewees general view
on value.
Table 11. Themes identified relating to value including value description
Value types Brief description Themes
Benefits The perceived advantages
Efficiency/
Convenience
The extent to which an object makes life
easier for the customer. E.g. increased output
of products from a given time
- Easily accessible information
- Relevance of content
Status The extent to which an object enhances a
positive impression on others
- Customer centricity
- Performance in relation to
competition
Excellence The extent to which an object is of high
quality. Can both be product and service
excellence. Includes reliability and
responsiveness
- Understanding the customer
Aesthetics The extent to which an object is appealing.
Relates to sensory appreciation - Visually appealing
Relational The extent to which an object improves the
relationship with the service provider
- Catering to customer needs
- Changing role of the
salespeople
Epistemic The extent to which an object provides
novelty, arouse curiosity or satisfy a desire for
knowledge
- Novelty
Sacrifices The perceived loss for the sake of other
considerations
Time The extent an object requires time to prepare,
use, understand, etc.
- Advertising skepticism
- Interaction time
Effort The extent an object requires effort or energy
to prepare, use, understand, etc. - Understanding the products
Privacy The extent an object can result in loss of
privacy
- Transparency in data usage
- Clear policies
Security The extent an object can result in security
issues, e.g. being more vulnerable for hacking - General security fears
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Furthermore, the pilot interview helped to determine where in the marketing automation process
different value types arise and are perceived by the customer, which is in line with foundational
characteristic 3. Value types are only prevalent when there is an interaction with an object,
resulting in an experience for the customer. All value types can be perceived in the output stage,
when the customer interacts with the personalized output. Additionally, privacy and security can
be felt due to data collection in the data and evaluation stage, where a data collection interaction
takes place. However, the learning stage itself does not include an interaction with the customer
and therefore customer value is not believed to be created in this stage.
Privacy
Multiple interviewees (9, 13 & 15) emphasize privacy as a sensitive subject where the user needs
to understand what the collected data is used for and that is used legitimately. If the customer is
aware that the data is collected, they need to be reassured that it is done in order to help them and
not be sold to a third party. If the customers feel that it is done in a way to better help them with
their challenges, they are more likely to feel comfortable with the personalized marketing.
Murthi and Sarkar (2003) suggest directly asking the customer for data as a possible method of
overt data collection and emphasize that it requires the customer to understand the benefits of
personalization. If the customer is not aware of the data collection it has a higher chance of being
perceived as intrusive. These findings are in line with previous work which has indicated that
covert data collection increase customers sense of vulnerability and can have a negative effect on
click-through rates (Aguirre et al., 2015; Xu et al., 2011).
The second theme, is the usage of policies to disclose some or all the ways that the company
gathers, uses, and handles customer data (interviewee 9, 15 & 18). Policies should prescribe and
make sure that data is used in an agreed way. One way to communicate data policy is by using
web cookies and in EU, users must give their consent to allow cookie usage as a part of GDPR.
Another way to communicate policies is by using a privacy notice on the website as in the case
of Atlas Copco.
Interviewee 1 highlight privacy challenges as a “fine balance”. Depending on sensitiveness of
data, customer trust in data controller, how marketing based on the data is presented, different
feelings of privacy will be perceived. Interviewees 13, 15 & 18 describe that most users don’t
think about privacy as an issue, especially if they gave away basic information on the website,
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such as email, name and title. One interviewee (16) emphasized that customers want to be
understood, but they don’t want you to know everything about them. Asking for a lot of data that
is not justified, risk to raise feelings of privacy concerns (interviewee 16). Importance of trust is
also highlighted (interviewee 13). If the user recognizes the data controller as a well-established
business, they are less likely to perceive the personalized marketing as intrusive (interviewee
11). Being a previous customer also adds to the level of trust.
Furthermore, one interviewee (14) expressed that as long as the marketing is not too intrusive,
they will perceive it as positive. This sentiment reinforces the findings by (Strycharz et al., 2019)
whose research indicate that marketing professionals see privacy as a boundary condition for
personalization success.
Security
Security is a sacrifice dimension of value and is described as the extent to which an object can
result in security issues. The privacy domain partially overlaps with security and therefore some
of the answers were similar. In the context of personalized marketing, security issues are related
to the handling of data. Most interviewees acknowledged that there is a general fear that
unauthorized persons will in some way access the data. The exception was some interviewees
(13, 15 & 16), who felt that if the data that is being collected is not sensitive, then security issues
are less prevalent.
Customers need to know that the data controller is protective of their data and be clear about how
data is handled. Suggestions to better protect data is to avoid linking data to personal information
such as names as much as possible and instead use IDs (interviewee 14). Customers will avoid
giving away sensitive data if there is a lack of trust and if they are not reassured that it is well
protected, also indicated by (Lapierre, 2000). Other concerns when giving away data includes
being spammed (interviewee 11). To mitigate security concerns, it was suggested that only data
that is meant to be used for a certain purpose should be collected and kept (interviewee 16).
Excellence
Excellence is a benefit or a positive dimension of value and describes the extent to which a
product or service is of high quality. The answers from the interviewees contained one theme
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which is about understanding the customer. The interviewees all expressed positive sentiments
regarding how the customers would perceive the effort undertaken to understand them.
Some interviewees (9, 10 & 15) expressed that high relevance of marketing would serve as
precondition to a positive customer perception of the case company’s effort. The perception of
the effort depends on relevance, tone of voice and use of channel (interviewee 9 & 16). The
combination of being understood and given the right content at the right time at right place were
considered as particularly valuable, which is an increased sense of service (Vesanen, 2007).
Interviewee 13 highlighted that this only applies if the customer values being understood and if
so, to what extent.
Furthermore, predicting behavior and presenting something the customer did not think about is
what creates superior value (interviewee 9, 15 & 18). In the case of cross-selling, being able to
provide the right combinations to satisfy customer needs in an efficient way, where fetching and
searching through an abundance of information is bypassed, increased the sense of quality
(interviewee 15).
It was also highlighted that from a customer perspective, it can be clear that a company wants to
understand them, but that they do not necessarily care about the customer (interviewee 16).
Rather it is all about selling products. On the contrary, it was proved in a physical exhibition by
the case company that customers appreciate the effort of understanding their needs when
presented with personalized solutions (interviewee 15). In this example, the case company had
analyzed what was valuable for the customer prior to physically meeting them.
Convenience/Efficiency
Convenience is a positive dimension of value which describes the extent to which an object
makes life easier for the customer. Two themes have been identified related to convenience,
namely 1) easily accessible information and 2) improved content relevance.
The first theme, easily accessible information, was derived from several of the interviewees’
answers regarding a quick and easy interaction. Several interviewees (9, 13, 15, 17 & 18)
discussed how accurate cross-selling recommendation would enable the customer to easier get
information about necessary products and thereby save time. Other answers (13 & 17) discussed
the currently used marketing activities that often are very time-consuming for both the company
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and the customer. The most frequently mentioned example of such an activity is called walk the
line and refers to when sales professionals visit production facilities and identify needs at the
customer site. By faster providing the customers with the information they need, sales cycles
could be shortened. When the customer trusts the digitally proposed solutions, then that is
described as a great improvement in speed.
Secondly, a theme about content relevance was derived from the responses. Several of the
interviewees (10, 13, 17, 19) expressed that the customer’s interaction with the case company
would be easier if they gained more relevant content, for instance marketing based on
segmentation or cross selling recommendations. This is line with the recognized benefit of
personalization ‘better preference match’ as presented by Vesanen (2007). One digital marketing
specialist (interviewee 11) stated that “catering to an individual's needs is going to make things
go a lot smoother, especially if a certain person is not used to doing something in a way that we
provide. If we are not, then they're going to get frustrated”.
The relevance of content also depends on the user at hand, their level of expertise and what they
are looking for (interviewee 10). From the same customer, different people would want
information presented in different ways according to their role. Business managers might want
information about how it is beneficial for them, while process engineers want information in
more technical detail. The importance of this is highlighted in foundational characteristic 4, that
the perception of value is subjective and based on personal characteristics.
Relational
Relational benefits are described as the extent to which an object improves the relationship with
the service provider. In the interviews there are two themes that are clearly distinguishable, first
one being catering to customer needs and the second one, the changing role of the salespeople.
When it comes to both cross-selling and segmentation, if the provider understands the customers’
needs and suggests the right products, the relationship is likely to be perceived as enhanced.
Most interviewees think that personalized marketing itself will have a positive impact on the
perception of the relationship. One interviewee (11) describes this as: “I think they'll believe that
it's a more personal company and that we're catering to their needs. We're keeping the customer
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as our first priority”. Being able to be proactive in the product lifecycle can build closer
relationships (interviewee 9).
If the company could understand the needs of the customers and recommend products in an
automized way, it changes the role of the salespeople, which was brought up as a challenge
(interviewee 13 & 15). Much of the time that the salespeople spend is focused on understanding
customer needs and helping the customer to understand technical solutions (interviewee 9, 13 &
19). If that were done in automized way, salespeople would have to find new ways to engage
with the customers. This new way of interacting with the customer concerned interviewees (13 &
14) and they described that it could impact the relationship in a negative way, where the human
interaction risks to be lost. On the other hand, smart product recommendations could help to
leverage the salesforce and better help them to provide the customer with what they need
(interviewee 13). In addition, rather than building relations with salespeople, the personalized
digital interaction is argued to build relationships between the customer and marketer (Wind and
Rangaswamy, 2001). Either way personalized marketing is described as a good thing for the
customer if done right, but the impact on the relationship is uncertain.
Aesthetics
Aesthetics is a benefit or positive dimension of value that describes the extent to which an object
is appealing. One theme has been derived from the collected data which is that marketers believe
that personalized marketing is visually appealing to the customer. All interviewees, apart from
interviewee 16, expressed unwavering positive opinions regarding the customers’ perception of
the aesthetics. The interviewee that brought up a conflicting opinion discussed the operational
issues related to creating to a large amount of appealing personalized content.
Furthermore, interviewee 9 described the importance of an appealing personalized website as “a
look and feel that I’m using the right site”. This is comparable to a contribution by Miceli et al.
(2007), where value of personalization is the site- and content specific attributes expected by the
customer, such as graphics.
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Epistemic
The epistemic value type aims to describe the extent to which an object provides novelty, arouse
curiosity or satisfy a desire for knowledge.
Most interviewees expressed the view that the marketing originating from the use cases would
spark the customer’s curiosity. Hesitation regarding the ability of the personalized marketing to
spark curiosity was centered around the accuracy of the personalization and the novelty of the
products marketed (interviewee 13 & 19). One interviewee (16) expressed that the marketing
output cannot just show exactly what the customer needs at the moment, but also has to show
novel products that the customer does not know about.
Several interviewees (9, 10 & 15) discussed the concept of thought leadership which reflects a
company ambition of being able to guide their customers through novel ideas. Additionally, one
interviewee (17) discussed the use of success stories to spark the curiosity of the customers.
To conclude, one overarching theme regarding epistemic value was identified which is that
marketers believe that personalization of high-quality will make the marketing feel novel.
Status
Status is a positive dimension of value and described as the extent to which an object enhances a
positive impression on others. If the perception of a company is increased, then that will likely
take shape in the products and services they sell and vice versa. In the context of status two
themes were recognized, 1) customer centricity and 2) performance in relation to competition.
Most interviewees think that personalized marketing for cross-selling purposes will positively
impact the perception of the company by being more customer-centric. With segmentation and
cross-selling, it is thought that better understanding the customers’ issues and providing them
with content accordingly will make the customers happier, thus increasing the perception of the
case company. Status will be increased in the sense that the case company better and faster solve
the customers’ issues. Good use of personalized marketing can reinforce the image of being a
thought leader and being top of mind. However, several interviewees (9, 12, 15, 16 & 19)
emphasize that it must be done in the right way and not to be felt as they are trying to push one
option or sell everything to the customer. Then it risks to negatively impact the status. The
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personalization should take place in the background and be perceived as a seamless experience
(interviewee 15). If the customer know that they are segmented in some way, they can feel that
their labeled and counterintuitively, that the personalized marketing then feels less personal
(interviewee 16).
The second theme is that status is perceived in relation to alternatives, the competition.
Competition within the case company’s market varies depending on region. If they can show
customers that they understand them in a way the competition doesn’t then that will positively
impact status (interviewee 9, 13 & 17). The interviewees describe that there are not many
technical savvy competitors with the capability to provide good personalized marketing and that
“it sets a brand aside from other brands” (interviewee 9). Vesanen (2007) recognized this benefit
of personalization as a way to differentiate a company from its competition.
Time
Time is a negative dimension of value, a sacrifice, and refers to the extent an object requires time
to prepare, use, understand, etc. Two themes have been identified in relation to the value type
time, interaction time and advertising skepticism.
Most answers presented contained nuances regarding what they expected the customer’s
perceptions of the required time to be and included both positive and negative aspects. Many
interviewees (9, 14-16 & 19) discussed the possibility that the customers would perceive the
personalized marketing as a time saver, as it would require them to spend less time interacting
with Atlas Copco which served as the foundation for the first theme. In contrast, in Vesanen
(2007) ‘time’ was mostly seen as a cost (sacrifice) in the shapes of ‘spent time’ and ‘waiting
time’ of personalized output. Several answers (interviewees 10, 11, 13 & 17) also touch upon
how some customers are negative to all marketing and perceive it as a waste of time. One
interviewee (17) discussed how it can originate from negative experiences with other companies.
Another interviewee (10) brought up was that many customers feel that they are spammed with
inaccurate personalized marketing from several companies and are therefore negative to most
marketing. This is a potential drawback with personalized marketing, that it risk to irritate
customers, seen in Figure 2 (Vesanen, 2007). These views are also similar to the concept of
advertising skepticism as a boundary condition of personalization success found among
practitioners by Strycharz et al (2019) and is the basis for the second theme.
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Effort
Effort is a negative dimension of value and described as the extent to which an object requires
effort or energy to prepare, use, understand, etc.
The theme that was derived from the answers is understanding of the products. Personalized
marketing itself is not likely to impact the effort required to use or understand the products. Even
though relevant marketing is provided, the technical complexity of many products at the case
company may require more in-depth communication (13 & 15). Suggesting relevant product
combinations to a customer as in cross-selling can pose a headache to understand, because they
might be technically complex. However, personalized marketing can help to present the most
relevant information with the highest chance of being understood (interviewee 12 & 16), which
also helps to mitigate information overload as presented by Strycharz et al. (2019).
General view on value
When the interviewees were asked specifically what value personalization based on
segmentation or cross-selling bring, they had differing perspectives on value. Both firm-specific
and customer-specific values was mentioned, similar to the case of Vesanen (2007) in Figure 2.
Interviewees 10, 13, 14 & 16 described efficiency as an important value driver. By using a high
level of personalization customers need to spend less time to find the right
content/solution/object and can instead use the saved time to consume more information or spend
their time elsewhere.
Interviewees 9, 12-14 & 19 described the value as a better preference match, where
understanding the customer is key and presenting the customers with what they really need,
which is comparable to the excellence dimension of value. However, content mismatch
moderates the effectiveness of personalization in such a way that the benefits of personalization
diminishes (Strycharz et al., 2019).
In comparison interviewee 18 described that the value is that “customers will feel like they are
taken care of and that we are thinking of them and their business”, thus reinforcing the
relationship. On a similar note interviewee 15 & 18 emphasized the improved image of the
company, indicating improved status.
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The ability to provide tailored advertising and promotion according to customer needs was seen
as a way to differentiate the company from its competition (interviewee 9 & 10), which is a more
firm-specific benefit rather than a customer benefit. Other mentioned firm-specific benefits
include increased lead generation, identification of key customers and more qualified leads
(interviewee 9 & 18). This contradicts foundational characteristic 3 and S-D logic described in
section 2.2.2., where value should be in the eyes of the customer rather than the supplier.
All answers show a positive perception on value of personalization and an example being “the
value would be tremendous” (interviewee 13). However, the answers solely addressed the
benefits of value and neglected the trade-off with potential sacrifices as a part of value
(foundational characteristic 2). The difference in value propositions concur with foundational
characteristic 4 & 6, showing that value truly is multidimensional and based on subjective
perceptions.
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5. Conclusion
This chapter summaries the most important findings to answer the research question. In
addition, directions for future research are suggested.
5.1. How Can AI be Used in the Marketing Automation Process to Create Value in a B2B
Context?
The contribution of answering the research question is threefold. Firstly, we address how AI fits
into personalization and then secondly, how it takes shape in the marketing automation process.
Thirdly, the study address if and how it creates value.
Based on the literature review, personalization is anticipated to be significantly impacted by AI,
which is also argued to be a major driver behind the popularity of AI. In personalization, AI can
enable more tailored personalization, moving from basing the personalization on preferences of
the mass, to narrowed segments or even to an individual level. On an individual level, AI can
achieve dynamic personalization, using a specific customer’s preferences by observing the
customer’s behavior over the time, rather than relying on cross-sectional customer data from
similar customers. AI can enhance the speed, precision and effectiveness compared to human
efforts, a perception our findings show.
Previous marketing automation processes have been reviewed, compared to personalization
processes and then boiled down into a process consisting of four steps: data, learning,
personalized marketing output and evaluation. What is required in the process was investigated
by exemplifying with two prominent use cases of AI in the studied context, namely cross-selling
and segmentation. The findings reveal different types of relevant data and that the use cases can
help the company to learn more about its customers, mainly to create personas. Personas is used
for segmentation, therefore, the current belief is to improve personalization on a segment level.
Proposed channels for providing personalized marketing content vary. In B2B, social media is on
an up rise, especially LinkedIn is emphasized as a great channel for providing content. In
addition, the traditional channels email and website are frequently mentioned sources for
personalized marketing content. Content should be of an educational character, providing
customers with marketing relevant for their needs and educating them how their needs can be
solved, with quantified potential impact. The personalized marketing action is proposed to be
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measured according to purpose of marketing actions (e.g. by converting customer in the sales
funnel), by measuring digital engagement and by benchmarking different actions.
In terms of value; definitions, dimensions and typologies have reviewed, which resulted in seven
foundational characteristics of value (adapted from Leroi-Werelds, 2019) and a revised value
typology that are used in the study. We’ve investigated dimensions of value arising in the
marketing automation process and our findings clearly suggest that AI in marketing automation
has tremendous potential to create value if done correctly. All studied value types can be
perceived in the personalized marketing output stage of the marketing automation process.
Additionally, during data collection for the data and evaluation stage, perceptions of privacy and
security can arise.
Depending on relevance, tone of voice, time and use of channel, different feelings of value are
perceived, which are factors that AI can help to determine. The benefits of personalized content
are highlighted in our interviews, especially in regards of better service and preference match,
which takes the shape of the value type ‘excellence’, also found by Vesanen (2007). By using a
high level of personalization, customers need to spend less time to find the right
content/solution/object, making efficient use of their time, implying the value type ‘efficiency’.
This in turn will lead to higher status and an improved relationship between supplier and
customer. Other positive value types that did not show as strong indications are ‘epistemic’ and
‘aesthetics’.
A foundational characteristic is that value is defined as a trade-off between benefits and
sacrifices, meaning that the benefits must exceed the sacrifices in order for value to be created.
Both previous research and our findings show a drawback of personalization being loss of
privacy, with a risk of personalized marketing being felt as intrusive. In addition, there are
general fears regarding data handling. To mitigate privacy and security issues, transparency in
data usages and having policies in place are emphasized. Personalized marketing in the studied
context was not believed to negatively impact time usage or effort required by the customer.
5.2. Limitations and Suggestions for Future Research
Due to the complexity of the studied topic this research has several limitations. The research
approach has been qualitative, which limits the generalizability of our findings. To explore the
topic further and get a more general understanding future studies should use a quantitative
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approach which could allow for statistical generalizations. Additionally, a quantitative approach
could, through structural models, serve as a research instrument to investigate the importance of
different value types.
The contemporary nature of the topic under investigation made it suitable to use a case study,
however, the research has only been conducted within the context of one company. Further
research could take a wider approach and conduct a case study of several large B2B companies.
Moreover, this research solely focuses on the perspective of the B2B marketing professionals. As
the view of value includes the perspective that customer value is not inherent in an object, but in
the customers’ experiences derived from the object (foundational characteristic 3), future
research could include interviews with B2B customers.
This research has been limited to research the impact of eleven value types. Future research
could explore the extent to which the relevance value types depends on the B2B context. This
could be achieved through multiple case studies. Additionally, our study has only addressed the
components of value and future research could delve into the processes of co-creating value
between customer and suppliers in the case of personalization, indicated by foundational
characteristic 7.
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Appendix
A. Value measurements
Value types Measurement Paper
Efficiency/
Convenice
“efficient way to manage my time”
“This X is easily bought”
“It is quick and easy”
(Mathwick et al., 2001)
(Petrick, 2002)
(Lin et al., 2005)
Status
“would improve the way I am perceived”
“would help me to feel acceptable”
“This X has status”
(Sweeney and Soutar, 2001)
(Sweeney and Soutar, 2001)
(Petrick, 2002)
Excellence
“has consistent quality”
“This X is very reliable”
“The employees make the effort to understand my
needs”
“The company is willing to respond to customer needs”
“I would say that this company provides superior
service”
(Sweeney and Soutar, 2001)
(Petrick, 2002)
(Gallarza et al., 2017)
(Lin et al., 2005)
(Ruiz-Martinez et al., 2018)
Aesthetics “The products are presented in an appealing way”
“XYZ’s internet site is aesthetically appealing”
(Willems et al., 2016)
(Mathwick et al., 2001)
Escapism/
Spirituality
“Shopping from XYZ’s internet site ‘gets me away from
it all’”
(Mathwick et al., 2001)
Self-esteem “increases my sense of self-worth” (Gallarza et al., 2017)
Relational “My participation helps me build a better relationship
with the service provider”
(Chan et al., 2010)
Social “is important to your social relationships” (Sánchez-Fernández et al., 2009)
Epistemic “I used […] out of curiosity” (Pihlström and Brush, 2008)
Ecological “is friendly to the environment”
“is environmentally friendly”
(Gallarza et al., 2017)
(Koller et al., 2011)
Societal
“is a socially responsible company” (Willems et al., 2016)
Price
“is reasonably priced”
“offers value for money”
“feel that the product you purchased is expensive”
(Sweeney and Soutar, 2001)
(Sweeney and Soutar, 2001)
(Lin et al., 2005)
Time
“does not waste your time”
“The time you have waited to […] has not been
excessive
“The waiting time […] is long”
(Lin et al., 2005)
(Sánchez-Fernández et al., 2009)
(Willems et al., 2016)
Effort “The mental effort made for […] was very high”
“It will take a lot of effort to understand how to use”
(Gallarza et al., 2017)
(Kleijnen et al., 2007)
Privacy “feel like your privacy is protected” (Lin et al., 2005)
Security “I am worried that information will be delivered to
wrong persons”
(Kleijnen et al., 2007)
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B. Interview guide
Introduction
We would like to start by introducing ourselves and explain the purpose of the project further.
We are studying industrial engineering and management at the Royal Institute of Technology in
Stockholm and we are now doing our master thesis at Atlas Copco. The aim is to investigate how
AI can be used in marketing to create customer value. We’ve been focusing on personalization,
where AI is seen as an enabler for more advanced and scalable solutions.
Interview structure:
To give you an idea of the structure of the interview, we will present two marketing use cases of
AI; cross-selling and customer segmentation. For each use case, we will ask you questions about
the digital marketing process and what the potential impact on customer value could be.
Documentation
Before we start, we would like to know whether it is okay that we record the interview. For our
research report we will not use your name, instead we will use a general title when referring to
the interview.
There are no right or wrong answers, all perspectives are appreciated and do not hesitate to ask if
a question is unclear.
Description of use cases
1. Cross-selling - the practice of selling an additional product or service to an existing
customer. E.g. suggesting a buyer of a phone to also buy a phone cover.
2. Customer segmentation based on technological maturity - identifying distinct customer
groups based on technological maturity levels to provide information of the next
generation products. E.g. maturity levels of a phone: landline, cordless phone and
smartphone.
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Introductory Questions
Cross selling
- How would you describe cross-selling at Atlas Copco?
- What kind of cross-selling opportunities do you see at Atlas Copco?
Segmentation
- How would you define customer maturity at Atlas Copco?
- What could be some distinct levels of maturity?
Questions are repeated for each use case:
Step Process questions Value questions
Data - In an automated process,
what data do you think
would be useful for this use
case?
- Regarding data for use case, do
you think customers feel that
their privacy and data are
protected?
- Do you think that customers will
be worried that information could
be accessed by unauthorized
persons?
Learning - What insights do you think
data can bring for the use
case?
- Do you think that by using
insights for (from) use case that
customers will feel that Atlas
Copco makes an effort to
understand their needs?
Personalized
marketing
output
- What channels do you
think are relevant for
sending content to
customers regarding (based
on) the use case?
- What could the content be?
- What value do you think
personalized marketing based
on use case will bring?
- In what way do you think that the
use case will make it feel quick
and easy for the customer?
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- Do you think the customer will
feel that the personalized
marketing message based on use
case is presented in an appealing
way?
- How do you think that
personalized marketing based on
use case will impact the mental
effort required by the customer?
- Do you think customers will feel
that personalized marketing for
(based on) use case is a waste of
their time?
- In what way do you think use
case will improve the customers’
perception of Atlas Copco?
- In what way do you think use
case will help to build a better
relationship between the
customers and Atlas Copco?
- Do you think personalized
marketing based on use case will
spark curiosity about the product
offering at Atlas Copco?
Evaluation - How would you evaluate
personalized marketing
based on use case?
TRITA TRITA-ITM-EX 2020:138
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