Mobile App Analytics

mobile app analytics

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Executive summary
Massive quantities of data produced by and about people, things and their
interactions is changing the way businesses operate .The smartphone and its
accompanying apps is a new source of digital footprints, which due to its unique
characteristics is gaining increased attention from managers and decision-
makers. Hence the analysis of mobile app data is a field of growing interest,
although it remains to be thoroughly studied academically.

The purpose of this thesis is to examine the emerging field of mobile app
analytics and thus fill a void in the academic literature by identifying value
propositions for mobile app data and explore the maturity level of mobile app
analytics.

We explore the field of mobile app analytics from three perspectives – the mobile
app as a data source, the analytics tools needed to turn the data into insights,
and the organizational aspect of using actionable insights to inform decision-
making. Accordingly this thesis’ main analysis is divided into three chapters
corresponding to three research questions that collectively will allow us to
explore the field of mobile app analytics:

1. What characterizes mobile app data and what are its value propositions?
2. What is the current stage of mobile app analytics?
3. How are mobile app analytics used to support organizational decision-
making?

We undertake a combination of methodological inquiries, centered on a multiple
case study. Our case study involves the retail chain Føtex, the financial enterprise
Nykredit and the wholesaler and distributer AO. All three companies have
developed an app to support their existing business areas, and recently initiated
tracking by implementing analytics tools.

The research design involves two rounds of qualitative interviews with case
representatives and a series of systematic feature inspections of the apps and
tools currently employed by our case companies. The empirical analysis is
supplemented by a comprehensive literature and industry review and theoretical
discussion, enabling us to build a body of knowledge for this emerging field that
previously have not been covered in academic literature.

Our analysis shows that the mobile platform is characterized by being personal,
interactive, ubiquitous, location aware, and multimodal. With reference to

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particular app specific characteristics, this leads us to conclude that the mobile
app as a data source is distinguished from other digital data sources by its ability
to add a contextual layer to the data, enabled by sensor technology. We find that
the ability to add a time/place dimension to other data types is a unique value
proposition. The data comes in various types and structures while the digital
nature of the app means that interactions can be tracked instantly as they occur.
The app as a data source thereby allow for holistic analysis and timely insights.
The app data is furthermore personal and behavioral, enabling personalization
and message tailoring.

Our empirical analysis identifies gaps between the value propositions of mobile
app data, and the data that the case tools currently provide. This is particularly
apparent when it comes to sensor input such as location awareness and the fact
that the tools are only able to report data on an aggregated level. Additionally,
the tools lack transparency, why trust and validity issues arise. On the other hand,
the tools provide detailed records of usage, interaction and navigational patterns.

In our case companies, we find that there is a general lack of strategic targets for
mobile app analytics, otherwise proscribed by the theory. Based on app analytics,
they currently take decisions regarding app optimization, and report top-line
metrics to management to ensure resources for future projects. Hence, we find
that decision-making on the basis of mobile app analytics currently revolves
around the app itself or the department on which it is placed. Due to lack of
system integration, data complexity and validity, our case companies are
currently not able to base strategic or automated decisions on the data that they
can derive from their apps.

We thus conclude that although mobile app data presents a series of promising
value propositions, the current stage of mobile app analytics tools leaves room
for further development and investment. A series of initiatives will have to be
undertaken before the app data potentials can unfold: The analytics tools must
improve their ability to utilize the app specific value potentials, allow for system
integration and enhance the granularity levels of the data. If the overall maturity
level is leveraged on both the organizational level and in the tool capabilities,
mobile app analytics will be a valuable data source suitable for gaining insights
into user behavior.

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Index
1 INTRODUCTION .................................................... 6
1.2 Purpose and Objective .......................................................... 7
1.2.1 Clarification of concepts ................................................................................................. 8
1.3 Demarcation and Scope ....................................................... 8
1.4 Structure of Thesis ............................................................. 9

2 METHODOLOGY................................................... 11
2.1 Research Approach .............................................................. 11
2.1.1 Research process model ................................................................................................. 12
2.2 Research Paradigm s ........................................................... 14
2.2.1 Epistemology and theoretical perspective ................................................................ 15
2.2.2 Reasoning and theory construction ........................................................................... 16
2.3 Research Design ................................................................. 17
2.3.1 Research model ............................................................................................................... 18
2.4 Research M ethods .............................................................. 20
2.4.1 Case studies .................................................................................................................... 20
2.4.2 Semi-structured interviews ........................................................................................ 22
2.4.3 Feature inspections ...................................................................................................... 24
2.4.4 Literature review ...........................................................................................................25
2.4.5 Online sources ............................................................................................................... 26
2.5 Data Analysis ..................................................................... 27
2.5.1 Data coding ......................................................................................................................27
2.6 Research Quality ................................................................ 28
2.7 M ethodological Limitations .............................................. 28
2.8 Summary ............................................................................ 29

3 CASE DESCRIPTION ..............................................30
3.1 Føtex .................................................................................. 31
3.1.1 Indkøbshjælp ....................................................................................................................32
3.2 Nykredit ............................................................................. 32
3.2.1 Mobilbank ........................................................................................................................ 33
3.3 AO ....................................................................................... 34
3.3.1 AO.dk mobil..................................................................................................................... 34
3.4 Summary ............................................................................ 35

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4 MACRO ENVIRONMENT ANALYSIS .......................... 37
4.1 The Smartphone ................................................................. 37
4.2 M obile Apps ........................................................................ 38
4.3 The App Analytics Ecosystem .............................................. 39
4.3.1 The app users ................................................................................................................. 40
4.3.2 The app owners .............................................................................................................. 41
4.3.3 Tool providers ................................................................................................................ 42
4.4 Summary ............................................................................ 43

5 THE VALUE OF MOBILE DATA ............................... 44
5.1 Characterizing M obile Footprints ..................................... 45
5.1.1 The mobile platform ...................................................................................................... 46
5.1.2 Advanced sensor technology ....................................................................................... 48
5.1.3 Sensor networks and the internet of things............................................................. 49
5.2 M obile Data and Research Value ........................................ 50
5.2.1 Reality mining and urban planning ........................................................................... 51
5.2.2 Mobile phone sensing ................................................................................................... 53
5.2.3 Summary ........................................................................................................................ 54
5.3 Data Value Propositions .................................................... 55
5.3.1 Personal data ................................................................................................................... 55
5.3.2 Big data ........................................................................................................................... 56
5.3.3 Data through a critical lens .........................................................................................60
5.3.4 Summary ......................................................................................................................... 61
5.4 Discussing M obile App Data Value ...................................... 62
5.4.1 Mobile app data and volume, variety and velocity ................................................ 62
5.4.2 Particulars of mobile app data ................................................................................... 63

6 CURRENT STAGE OF APP ANALYTICS ......................67
6.1 Tool Industry Analysis ...................................................... 68
6.1.1 Analytics tool market .................................................................................................... 68
6.1.2 Mobile app analytics strategy .....................................................................................72
6.1.3 Strategy and tool selection ...........................................................................................72
6.1.4 App analytics value propositions .............................................................................. 74
6.2 App Developm ent and Tool Im plem entation ....................... 75
6.2.1 Føtex ................................................................................................................................. 75
6.2.2 Nykredit ........................................................................................................................... 77
6.2.3 AO .................................................................................................................................... 78
6.2.4 Summary ........................................................................................................................ 79

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6.3 App Input and Tool Output Analysis ................................. 80
6.3.1 App input types ............................................................................................................. 80
6.3.2 App analytics tools ....................................................................................................... 83
6.3.3 Summary ........................................................................................................................ 89
6.4 Discussing Data Value and Tool Output ............................ 90
6.4.1 App analytics tool maturity ........................................................................................ 93
6.5 Summary ........................................................................... 94

7 ORGANIZATIONAL DECISION-MAKING ....................95
7.1 Analytics and Decision-M aking ......................................... 96
7.1.1 Analytics defined ............................................................................................................ 96
7.1.2 Data driven decision-making ...................................................................................... 98
7.1.3 Analytics maturity level.............................................................................................. 100
7.1.4 Real-time business intelligence ................................................................................. 101
7.2 M obile App Analytics at W ork .......................................... 103
7.2.1 The analytical culture ................................................................................................. 104
7.2.2 Resources and skills .................................................................................................... 106
7.2.3 App data reporting ..................................................................................................... 108
7.2.4 Data integration ........................................................................................................... 111
7.2.5 Decision levels and latency ......................................................................................... 112
7.2.6 App analytics maturity level...................................................................................... 114
7.3 Discussing App Analytics ................................................... 116
7.3.1 Analytics resources ........................................................................................................ 117
7.3.2 Tool and system integration ...................................................................................... 118
7.3.3 Decision levels ............................................................................................................... 119

8 CONCLUSION .................................................... 122
8.1 Characteristics and Value Propositions .......................... 123
8.2 Current Stage of M obile App Analytics ........................... 124
8.3 M obile App Analytics and Decision-M aking .................... 126
8.4 The M aturity Level of M obile App Analytics ..................... 127

9 LIMITATIONS .................................................... 129

10 PERSPECTIVE AND FUTURE WORK ......................... 131

REFERENCES ......................................................... 134

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1 INTRODUCTION
As we continue the journey from analog to digital, we are entering a new era - an
era where data is everywhere, across sectors, economies and organizations.
Massive quantities of digital data are being produced by and about people,
things, and their interactions with websites, social media, mobile devices and a
growing number of sensors embedded into our surroundings. This steady stream
of digital footprints is causing a new data hype. Since there are documented
performance gaps between companies that effectively make use of data and
those who do not, data value propositions have gained attention from many
researchers and business executives.

Data becomes valuable when meaningful patterns are extracted from it; hence
activities concerned with obtaining insights from data are gaining impetus as the
data awareness increases. Analytics is one of the activities, which recently has
become synonymous with the measurement of digital data sources such as
websites, social media and most recently mobile phones.

Mobile services are ubiquitously available, and in Europe the mobile penetration
has long surpassed 100 percent. We tend to carry the mobile with us everywhere
we go, and as the phones get ‘smarter’ they fulfill a growing number of our daily
information and communication needs. Hence increasing volumes and varieties
of data will come from our interactions with mobile devices. The realization that

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the mobile platform is becoming an increasingly important touch point between
companies and their customers, means that an increasing number of
organizations move services unto the mobile platform. For many companies this
entails the development of an app that can be used as a new communication
touch point, branding tool or e-commerce channel.

A part of the recent data hype is associated with the mobile footprints that are
created when users interact with smartphones and apps. This has resulted in an
emphasized focus on how companies can measure their mobile activities and
utilize this potentially rich data source to reach their business goals. A recent
addition to the analytics field is thus measurement of data from mobile apps.

Mobile app analytics is therefore a field of increasing awareness in the blogosphere,
and though the field is still young, a wide range of tools is being developed with the
purpose of helping companies turn their mobile app data into actionable insights.
The novelty of the field, however, means that the topic of mobile app analytics has
yet to be covered scholastically why we believe there is a need for concrete
knowledge on the matter. The aim of our thesis will thus be to fill this academic
void by providing an exploratory study of the emerging field of mobile app
analytics.

1.2 Purpose and Objective
This thesis presents an exploration of the field of mobile app analytics by
enlightening relevant themes and exploring its current stage of development, as
well as future potentials. Hence the purpose of this thesis is to identify value
propositions for mobile app data and explore the maturity level of mobile app
analytics.

We find that the novelty of the notion calls for an exploratory and descriptive
research design, which is appropriate when uncovering potentials of an
uncharted and complex field. In order to fulfill our research purpose we find it
vital to focus on its three constituent elements – the mobile app as a data source,
the analytics tools needed to turn the data into insights, and finally the
organizational aspect of using actionable insights to inform decision-making. For
this reason we have developed the following three research questions:


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3. How are mobile app analytics used to support organizational decision-making?

These questions will constitute the three main chapters of our thesis, and will each
contain a critical analysis and a thorough discussion before concluding on our
findings and answering the different perspectives of our research purpose.

1.2.1 Clarification of concepts
In this section we will account for the concepts that constitute our research
questions. We will further provide a clarification of the deliberations and
delineations inherent in these notions.

We use the word value propositions to describe a promise or potential of value.
We intent to evaluate value potentials by reviewing and analyzing what benefit
mobile app data can bring to organizations and in some cases to research. Since
little academic writing has been devoted to the app as a data source, we analyze
and discuss related data areas, and hence borrow value potentials from the more
established fields, to create new value propositions for the emerging field of
mobile app analytics.

With the current stage of mobile app analytics, we refer to the developmental
stage of the analytics market, and the analytics practices that are carried out in
companies. This entails an analysis of the current tool capabilities and the
analytics processes that our cases currently undertake.

When asking the question of how app analytics is used to support decision-making,
we intent to analyze how our case companies utilize the data that their current
analytics tools provide to inform decisions. This entails a discussion of which types
of decisions mobile app analytics is suited for, and how the analytics tools are
currently supporting their decision processes.

1.3 Demarcation and Scope
The following will account for the deliberate choices we have made regarding the
scope of our research. Since our aim is to explore and define an emerging
research field with no predefined boundaries or scope, our research purpose
encompasses a broad perspective on mobile app analytics, its potentials and

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challenges.

Our thesis employs a case study approach in order to examine and illustrate the
current stage of mobile app analytics in a real-life organizational context. Even
though an important constituent of this case study approach has been to access
the case companies’ mobile app data in order to uncover its potentials, we do not
undertake specific data processing.

Since the field of mobile app analytics concerns topics such as personal data
collection and analysis, a relevant component is the ethical and privacy
precautions that should be considered under such circumstances. While we
realize that privacy is an important concern, we will not engage further in this
discussion. This is a deliberate choice since our focus is on the value propositions
of mobile app analytics and not its constituent ethical dilemmas.

Within the field of mobile app analytics many different types of apps create a
multitude of deviating value propositions. Accordingly we have chosen to narrow
the scope to include applications where the main value creating activities lie
beyond the apps. This means that we exclude mobile games, mobile social
networks or other mobile services where the organizations’ business model
revolves around their activities on the mobile platform. Hence, this thesis focuses
on the type of companies and apps, where the app is created as a support
function to the existing business processes and hence adds value to these.

Finally this thesis will not account for technical specifications of mobile app
analytics, even though the term does entail many technical aspects. The technical
analysis undertaken in this thesis is limited to a data perspective on our case
companies’ apps and their affiliated analytics tools.

We find it relevant to emphasize that this exploratory part of our thesis is to be seen
as a snapshot of the current stage of development, as we realize that new
technology and media are fast moving objects, where trends and tendencies quickly
develop and dissolve.

1.4 Structure of Thesis
This section will provide an overview of the structure of the present thesis.
Following the introduction, chapter two will present our research design,

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philosophy and methods. Chapter three will provide a presentation of our three
case companies and their respective mobile apps. Chapter four consists of a
macro environment analysis with the aim of identifying the essential elements of
the ecosystem surrounding mobile app analytics. In chapter five, six and seven
we will present our theoretical framework and the analysis that enables us to
answer our three research questions as described above.

In chapter five the characteristics of mobile app data and its value propositions
will be examined by a thorough literature review, focusing on mobile
specifications and data value. Chapter six is an empirical outline of the current
capabilities of the mobile app analytics tools and their ability to capture the value
of mobile app data. In chapter seven we construct a theoretical framework
around organizational data usage and examine how mobile app analytics is
employed in our three case companies. Each chapter in the analysis will close
with a discussion of relevant findings and a preliminary conclusion.

These three main chapters will set the scene for chapter eight, where we compile
the various findings and arguments leading to our conclusions of the
characteristics of mobile app data and the maturity level of mobile app analytics.
Chapter nine and ten will outline the limitations of this thesis and its further
perspectives and possibilities for future examination.

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2 METHODOLOGY
The following chapter will outline our research strategy and the plan by which we
intend to carry out this strategy. In line with the definition provided by Blumberg
et al. (2005) our research design includes the overall approach to research, the
methods used for data collection and analysis, as well as the theoretical
perspective and epistemological considerations underlying our study. How a
research project is designed is of great importance for the establishment of new
knowledge. The purpose of the present chapter is thus to account for our
deliberations regarding the theoretical, empirical and analytical approach of this
thesis.

The chapter will open with an account for our overall research approach, before the
research philosophy is outlined in terms of our epistemological stance and
theoretical perspective. Subsequently, we will argue for our choice of methods and
describe in detail how each is carried out. The chapter will conclude with a section
on the methodological limitations that our research design presents.

2.1 Research Approach
The novelty of the topic we address in this thesis calls for exploration, which
Blumberg et al. (2005) points to as the suitable research approach when little
information about a particular topic is known and relevant variables must be
identified in order to advance the study. The purpose is to learn from our
experiences of the current investigation and refrain from being biased by any

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preconceived notions. On an operational level this means that we, with the
exploratory approach, are concerned with hypothesis generation rather than
hypothesis testing (ibid.). Consequently this stage of our research process allows
for an open-minded, flexible and unstructured approach.

In most research projects exploration comprises the first stage of a study, before a
more exhaustive approach, such as description, is undertaken. Description is
relevant when the purpose is to describe and encapsulate variables or
phenomena without the intent to unveil causal links between them (ibid.). This
approach is preferred when the researcher attempts to answer ‘what’-questions
and when the amount of available information is relatively low. Compared to the
exploratory approach descriptive studies are characterized by a higher level of
structure and formality, and often take their point of origin in research questions
or hypotheses.

The research approach undertaken in this thesis is an interchangeable process of
both exploration and description. The early stages of our project are highly
characterized by exploration when the objective is to discover and establish an
overview of our problem area and to identify relevant literature, sources and
themes. Furthermore, the subsequent phases of our research process have an
exploratory starting point, especially when conducting the literature review
aiming at uncovering the current field of mobile app analytics. The latter part of
this project has been approached descriptively since we aim to describe the
significance and interconnections of the variables we have identified in the
exploratory phases.

2.1.1 Research process model
The model below provides an illustration of our research design and the general
research approach that underpins this study. As mentioned above, our research
process has had an exploratory starting point where preliminary unstructured
interviews were conducted and relevant literature and themes were identified.
As the amount of information increased, our broad problem area narrowed
resulting in the formulation of our research purpose and the three research
questions.

The subsequent process involving the developing of our research design,
collecting empirical data, and coding and analysis also had an exploratory
starting point, that particularly reflected our approach to the literature review

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and tool and app inspections. The qualitative interviews and the coding and
analysis process were in greater extend characterized by a descriptive approach.
A more detailed account of each method will be provided in a subsequent section.

Figure 2: Research Process Model, inspired by Blumberg et al., (2005)

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2.2 Research Paradigms
In line with Crotty (1998), we find that any research process entails four basic
elements: Epistemology, Theoretical perspective, Methodology and Methods. These
four elements present the interrelated levels of decisions that go into the process of
planning and designing research (Crotty, 1998).

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Figure 3: The research process, Crotty, 1998.

Central to any research process is the methodology and methods used to conduct
a study. We understand methodology as the underlying strategy used to arrive at
a desired outcome, while methods are the tools and techniques applied to realize
it (ibid.). According to Crotty (1998) the justification behind choosing a particular
methodology and an accompanying set of methods lies in the purpose, or the
research question, of a study. How these methods are applied, and how the
results are viewed, are determined by the theoretical perspective - the
philosophical stance we bring to our methodology. Inherent in the theoretical
perspective is the epistemological preposition. We define epistemology as the
theory of knowledge, which involves the meaning ascribed to knowledge and its
creation (ibid.). We introduce Crotty’s four interrelated paradigms here with the
aim of describing our research process in terms of these elements in the coming
sections. Outlining the research process in terms of these four basic paradigms
will account for our rationales and choices regarding the structure of our
research design.

Quite a substantial number of models have been developed with the attempt to
describe the research- process and design. We have chosen to draw on Crotty
mainly because we find his problem-oriented approach a natural and logic way
to go about the research process. According to Crotty (1998), a research process
should always take its origin in the problem or question it is trying to solve, even

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though the influences of method, methodology, theoretical perspective and
epistemology can work in several directions, and from different starting points.

To avoid confusion it is worth noting that when Crotty refers to methodology, the
concept is similar to what other scholars call research design (Blumberg et al., 2005;
Babbie, 2007). For the sake of clarification, we use the term research design to refer
to the overall plan or strategy from which we carry out our research.

2.2.1 Epistemology and theoretical perspective
In the following we will account for the epistemology underpinning the present
study. Epistemological considerations is the theory of science concerned with the
nature of knowledge, its creation, and the definition of what can be conceived as
‘true knowledge’ (Gilje & Grimen, 2002). When we consider the epistemological
stance, we are not concerned with the empirical collection of data about the real
world but rather with philosophical concerns regarding the procedures and
approaches used when generating knowledge about reality. Epistemology is thus
not science, but reflection on scientific activities and knowledge creation (ibid.).
Historically epistemology has been divided into two main groups, objectivism and
constructivism, and a range of variations of the two. In the following section we
will discuss the opposing perspectives seen in the two epistemological stances.

“Objectivist epistemology holds that meaning, and therefore meaningful reality,
exists as such apart from the operation of any consciousness” (Crotty, 1998, p. 8).
As this quote illustrates, we see that objectivists believe that objective truth
exists, and that it is therefore possible for a researcher to encapsulate true
knowledge about the world using scientific methods (Crotty, 1998). This is a
typical way of considering knowledge within the natural sciences and a
supposition tightly linked to the theoretical perspective of positivism.

We find that constructivism on the other hand, deals with the notion of truth in a
noticeably different way. From this perspective, knowledge and truth is internally
constructed, and what is true is ‘socially negotiated’ (Blumberg et al., 2005). The
constructivist standpoint is often seen in the theoretical perspective
interpretivism (Crotty, 1998). If we use the widely cherished metaphor: ‘if a tree
falls in the forest, and no one sees it, did it really fall?’ An objectivist will say that
the truth is objective and therefore independent of the subject, meaning that the
tree falls regardless of anyone watching. A pure constructivist, on the other hand,

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will argue that there is no meaning without the mind and therefore contest the
notion.

We find that a general difference between positivism and interpretivism can be
found in how knowledge is developed (Blumberg et al., 2005). Positivism is
concerned with reducing phenomena to simple elements, which is to represent
general laws. On the contrary, the interpretivism that we see in the humanities
and in some branches of social science, seeks a broad and total view on the
phenomena, with the intent to find explanations that goes beyond the current
knowledge (ibid.). An interpretivist approach therefore typically results in a
qualitative research approach, rather than the quantitative that is most
commonly used in positivism.

In line with Crotty (1998), we believe that our methodological standpoint should
spring from the questions that we are trying to answer. Due to the exploratory
nature of our research purpose we have chosen a qualitative research design, which
will provide us with new and valuable insights and explanations to a phenomenon
that goes beyond existing knowledge.

2.2.2 Reasoning and theory construction
In the literature the relationship between theory and research are mainly
described in philosophical terms, but we find that their interconnectedness have
operational implications for our study (Blumberg et al., 2005). The literature
describes two main models for reasoning and logic: Deduction and induction.

Deductive reasoning moves from the general to the specific. That is, from “(1) a
pattern that might be logically or theoretically expected to (2) observations that
test whether the expected pattern actually occurs” (Babbie, 2007, p. 22).
Deduction has traditionally been the preferred method of reasoning in the
positivistic perspective as it is based on ‘proof’ and validation.

In contrast, inductive reasoning moves from the specific to the general - “from a
set of specific observations to the discovery of a pattern that represents some
degree of order among all the given events” (ibid., p. 22). In the inductive model
conclusions are viewed more as hypotheses, and are thus not presented as the
only possible truth.

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The practical implication of using either induction or deduction lies in the way
arguments are constructed. In inductive reasoning arguments are built around
previous experience or observations, while deductive reasoning bases arguments
on theory and other accepted principles (ibid.).

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Figure 4: Wheel of Science, inspired by Adeline de Gruyter (1971).

Since this study is concerned with uncovering a new field of research, and hence do
not have previously stated hypotheses, it is mainly based on inductive reasoning.
However, in practice, most research projects sequentially make use of both
reasoning approaches in the interplay between empirical data and theory
(Blumberg et al., 2005). Even though we do not explicitly work with hypotheses,
deduction occurs when we use theoretical sources to explain certain empirical
phenomena.

2.3 Research Design
By taking its point of departure in our research purpose the following paragraph
will account for the choices made regarding our methodological combination of a
multiple case study and a literature review. A more detailed account of the
methods will follow in the subsequent sections.

The overall purpose of this thesis is to identify value propositions for mobile app
data and explore the maturity level of mobile app analytics.

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In order to adequately fulfill the overall purpose, we find it necessary to break
down the main problem into sub-questions, which allows us to focus on three
particular areas of mobile app analytics. Figure 5 illustrates the three questions
and the research methods applied in each question.

Since few prior studies have been engaged with identifying the value potentials
of mobile app data in particular, we will employ a literature review synthesizing
sources from related fields such as mobile communication, digital footprints, Big
data and the personal data ecosystem. By combining these areas, we illustrate
unique characteristics of mobile app data and develop a set of value propositions
for the data source. This will describe the insights that potentially can be derived
from the analysis of mobile app data, which we will use to compare with the
current performance of the app analytics tools in our case companies.

Mobile app analytics happen in an organizational context. Therefore we have
chosen to employ a multiple case study approach where we can study the topic in
its natural setting. The case study approach allows us to study the entire
ecosystem of mobile app analytics in an organization, while the multiplicity
increases our ability to identify relevant elements and deliver robust results. This
approach allows us access to three mobile apps and three analytics tools, on
which we will base a large part of our analysis. By systematically inspecting the
three apps and their related analytics tools we can identify the available data
types from each platform.

Our analysis of the three case companies furthermore entails qualitative semi-
structured interviews with the app responsible in each company. From this we
gain access into the strategic considerations and particular app analytics
activities and discover how each company generally works with data and
decision-making.

2.3.1 Research model
The following model illustrates how we fulfill our overall research purpose by
addressing the three research questions:

3. How are mobile app analytics used to support organizational decision-making?

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As mentioned the three questions each contribute with a different perspective of
mobile app analytics. The model demonstrates specifically what methods are
applied in each question and their relative weight. As can be seen, the first
question is addressed by reviewing previous literature, while the two subsequent
questions are more empirically founded.

Figure 5: Research design overview

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2.4 Research Methods
The aim of this section is to elaborate on the particular methods that we have
chosen to collect empirical data from the ‘real word’. As illustrated above our
research design consists of a multiple case study, a series of qualitative research
interviews, a feature inspection of the related apps and tools, and finally an
extensive literature review. These methods will be presented individually in the
following sections.

2.4.1 Case studies
The case study specialist Robert Yin provides a frequently cited definition of the
case study as “an empirical inquiry that investigates a contemporary
phenomenon within its real context; when the boundaries between phenomenon
and context are not clearly evident; and in which multiple sources of evidence are
used” (Yin, 1984, p. 23). The case study approach is rooted in the social sciences
and is typically descriptive or exploratory in nature, as the purpose of a case
study is to identify relevant phenomena and clarify how they unfold in a
particular context (Yin, 1984).

As the quote above describes, the case study approach is relevant when the object of
study is embedded into its context to such an extent, that it becomes meaningless
to separate the two. We find that evaluating how mobile app analytics is used in an
organizational context is as crucial to the study as the examination of the data
source itself. This means that the boundaries are blurred between phenomenon
and context, and that separating the two disputes the goal of our entire study.
Furthermore the case study approach is appropriate for holistic analysis, for
instance when an object of study entails multiple elements and processes (ibid.).
The value propositions of mobile app analytics are dependent on both the app as a
data source, the analytics tools, and the organizational context, why the
phenomenon must be studied holistically. For this purpose we employ several
methods to gather empirical data, as Yin suggests in his quote above. These will be
outlined subsequently.

Multiple case studies: In order to examine the field of mobile app analytics in its
organizational context, we have chosen a multiple case study approach. The
rationalization behind this choice is that we are able to discover more relevant

20

variables of mobile app analytics and deliver more consolidated results, using
multiple cases, than we would be using just one case.

A single case approach can be appropriate when examining an extreme or rare
case, with limited access to data, while a multiple case approach is considered to
deliver more robust results (Blumberg et al., 2005). Hence while a study of the
circumstances surrounding mobile app analytics in one organizational
environment can be said to deliver context dependent results, the substance of
our findings increase as several cases are examined. Within the time frame and
scope of this project we have found it appropriate to examine three cases, as this
allows us to broaden our empirical analysis while still being able go into depth
with each case.

Traditionally in a multiple case study approach, the cases will be selected on the
basis of some predefined criteria of commonalities or differences between the
companies. Our case company selection process, however, has taken its point of
departure in the apps rather than the companies.

In line with our scope, the primary criterion is that the app is created as a support
function to existing business processes, not as the central element in a business
model. This is important since we want to examine how mobile app analytics can
add value to an organization, not how a mobile app company can measure
performance. Furthermore within our scope we want to examine different types
of apps in order to map as many input types as possible. A final criterion is that
the apps must be connected to an analytics tool. Hence our three case companies
were chosen on the basis of their apps, and how these apps complement each
other. The exploratory and inductive nature of our study means that no criteria
besides the app were put forward, as no theory, hypothesis or prior studies could
inform such criteria.

In each case company we have sought to find the person with most knowledge
about our topic. Therefore, each case representative has been chosen according to
their level of contribution to, and knowledge about, the app development process
- from idea to implementation - and their practical involvement in the
companies’ use of mobile and other digital media.

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2.4.2 Semi-structured interviews
As part of our case study framework we have chosen to conduct a series of
qualitative research interviews. This section will therefore account for our
particular take on the qualitative research interview, which we will use to gain
insights into the current stage of analytics work within our case companies. In
line with our constructivist standpoint we find qualitative interviews to be a
valuable method for gaining insights into the views and opinions of individuals
involved with our subject of study.

When conducting interviews the main goal for us as researchers is to obtain
knowledge about the so-called ‘life world’ of the interviewee by interpreting the
meaning of the described situations (Kvale, 2007). We use qualitative research
interviews in order to obtain a holistic understanding of the organizational
attributes surrounding the three companies, how they generally work with data,
the strategies behind their app and the analytics initiatives, and specifically how
they use the data generated from their apps.

According to Blumberg et al. (2005) there are three essential forms of scientific
interviews: structured, semi-structured and unstructured. Determining the type of
interview to conduct is often based on the fundamental research philosophy, the
data collection strategy and the problem at hand (Blumberg et al., 2005). In each
case we have chosen to conduct an interview with the company representatives
that were chosen on the basis of their knowledge about the mobile app analytics
processes. They are not to be treated as experts, but as subjects with key
knowledge and insights into the practical and strategic aspects of our research
area (Kvale, 2007).

We conduct two rounds of interviews; a preliminary unstructured interview and
a second round of in-depth semi-structured interviews. In the exploratory phase
of our studies we employ an unstructured interview approach, where the
researcher only keeps a mental list of what questions to ask, and apart from that,
the format is very loosely defined (Blumberg et al., 2005). We approach these
initial interviews with no predetermined expectations of what to find, as the aim
is to gain a broad understanding of the field and life world of the interviewees.
The findings from these interviews subsequently guide the design of a more
structured and formal set of interviews.

22

For the second round of interviews we use a semi-structured interview approach,
where a more carefully designed interview guide frames the interview. We do not
incorporate specific questions, as we want the conversation to flow freely, but
include topics and areas that are necessary to answer our research questions (see
Appendix I). Furthermore we do not want to induce any specific terminology on
the interviewees, why we make sure to use ‘neutral wording’ and open-ended
questions. The goal of the interviews is to mimic a conversation between two
parties on a particular topic (Kvale, 1997). We aim to make the interviewee feel
free to bring up any topic he or she finds relevant, allowing us to maximize the
outcome (Kvale, 2007). Throughout the entire design process we make a point of
asking questions that lead to dialogue and discussion and also consider how to
make the interviewee ‘open up’ and feel secure. As part of the conversational
form of the interview we specifically attempt to prompt the atmosphere in the
order and formulation of topics. We start with general, factual, ‘non sensitive’
questions and slowly progress to more specific and knowledge intensive ones
(Kvale, 1997).

All interviews are recorded and transcribed in order to enhance the further
processing of the data, which we will elaborate on in the section on coding and data
analysis.

2.4.3 Feature inspection method
Since mobile apps and analytics tools present a new object of study, no
established research tradition exists to guide this part of our empirical research.
We therefore examine the three apps and their associated tools by performing a
variation of a usability method called Feature Inspection (Nielsen, 1994). The
original aim of this method is to describe the technical features of a program, or a
piece of software, as detailed as possible often with the aim of making
comparative analysis (ibid.). We use the inspection method to examine the more
data oriented aspects of mobile app analytics with the objective to map the three
apps and tools systematically and in as much detail as possible.

We find that the feature inspection method sets a meaningful frame for our
systematic review, since the goal is to conduct a manual examination of the
technical aspects of a program (application or tool), which serves the purpose of
our study well. The original feature inspection method relies on a predefined
checklist of expected features from which the walkthrough is carried out. Due to

23

our exploratory approach, we aim at discovering the present conditions instead
of looking for errors, inconsistencies or lack of user-friendliness. Rather than
making a predefined checklist of what we expect to find, we list the features we
come across, and map them according to functionality and data type. The
purpose of mapping all features of the apps and the tools is to be able to compare
the app input with the tool output, and thereby be in a position to assess what
data our case companies are currently gaining from their apps. We further aim to
gain insights into the current stage of the tools involved in our study.

In the following we will briefly describe the method applied on the apps and tools
accordingly.

App inspection: We conduct a systematic feature inspection of the three apps
using the exact same approach in all three cases. We inspect the application
according to the ‘natural’ order of features. Starting from the landing page, going
from left to right, and from the top and downwards. From each page we navigate
as ‘deep’ as we can, repeating this path for each feature. This way we
systematically go through all features in order to map all functionalities and data
types (see Appendix V, VI, VII). The paths of each app are visualized in a tree-
model as shown in Figure 6.

Figur 6: Mapping of app input types

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Tool inspection: Much like the method applied to the apps, we systematically
inspect the features and data types of the three analytics tools. Again we follow
the most ‘natural’ paths within the system and navigate as deep as possible.
Similar to the app inspection the findings are mapped, although the complexity
of the tools requires a more systematic approach. In order to get a thorough
understanding of how the tools function, a series of data extractions are made in
this inspection process.

2.4.4 Literature review
Literature reviews can serve a multitude of purposes ranging from complex
theory building to theory evaluation or accounts of the entire ‘body of
knowledge’ of a particular topic. The method is often referred to as a separate
methodological discipline, with the objective of researching a topic or problem by
reviewing previously written literature. However, in practice, literature reviews
are often applied as part of a research design along with other data collection
methods (Baumeister & Leary, 1997).

According to Baumeister & Leary (1997) a literature review differs from empirical
research in the questions it can address, and the conclusions it is able to draw. By
focusing on patterns and connections among several empirical findings, a
literature review can address theoretical questions that are beyond the scope of
any one empirical study (Baumeister & Leary, 1997, p. 313). This is relevant for our
study since we apply a literature review to examine the maturity level of the
mobile app analytics field, which is a problem area with a relatively broad scope.
Due to the novelty of this topic we simultaneously suggest a body of knowledge
for the emerging research field, where no prior theoretical framework exists.

Conducting literature reviews involves three main activities; searching for
relevant sources, reading and evaluating the literature, and finally synthesizing
the findings (Blumberg et al., 2005). Due to the novelty of our problem area, the
literature included in this thesis is primarily found through online academic
databases. Our searches are directed by the problem formulation as well as
related variables identified in the initial stages of our exploratory research.
Furthermore we use a method informally referred to as ‘snowball literature
search’. This includes finding an article on a given topic, and browsing through its
literature references to find supplementing sources among these. When
repeating this activity with several papers within a given subject, the chance of

25

discovering a large portion of the relevant and authoritative sources is high
(ibid.).

As research projects often benefit from methodological diversity, so can a
literature review profit from an assortment or sources (ibid.). As mentioned the
third activity when conducting a literature review is to combine and synthesize
the various pieces of literature into a new whole. We will do this by bridging and
illustrating the interrelatedness of disparate sources, which have not previously
been connected. These sources can be distinct in terms of the topics they cover, in
the methods they apply, or the literary genre. The breadth and diversity of
sources lies naturally in our problem area, as the novelty of the field calls for a
combination of scholarly sources, business white papers and online sources.

2.4.5 Online sources
Specifically for this thesis, when attempting to undertake a new area of study
within the digital realm, online literature becomes an important source of
information. Various blogs and websites concerned with data, analytics and the
mobile platform have become central in order to uncover relevant variables and
tendencies within our subject area. These sources will often be the first to detect
and formulate certain trends and interesting cases about a subject, long before
the scholarly literature can generate articles about it. However such blog entries
and websites are rarely based on a scientific foundation, seldom refer to scholarly
sources in their proclamations, and rarely construct scientific argumentation.
Furthermore many of the available online sources concerned with mobile app
analytics have some kind of affiliation with the field and can therefore lack
objectivity.

Consequently, in order to make the most of these, online sources should be
approached critically and used for what they are good at. Namely uncovering
current movements and orientations within the field, providing real life examples
and linking to other interesting sources. We find that the proclamations made in
online literary sources must often be supported by empirical findings or scholarly
literature. This has been an important guideline for us during the entire research
process, both in our exploratory research and especially when conducting our
literature reviews.

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2.5 Data Analysis
After all empirical data has been successfully collected, we begin to systematize and
categorize the findings before our analytical process begins. This following section
is devoted to the analytical methods applied to the collected data, and hence the
process of turning data into insights for further analysis and discussion.

2.5.1 Data coding
Having transcribed all the recorded interviews and mapped out all features and
data types of the involved apps and tools, we initiate our coding process. Coding
is a pre-analysis method that can be seen as the transitional phase between data
collection and the more extensive analysis. The coding process enables us to
group and organize data into categories through which patterns appear and
themes and concepts arise (Saldaña, 2009).

A code is typically a word or phrase assigned to a section of text in order to
capture the underlying meaning. The exact wording depends on the coding-
paradigm, which include descriptive-, value-, theme- and in-vivo- coding, among
others. The distinction is basically between using objective descriptive wording or
more value-laden categories, while the in-vivo method is characterized by using
the interviewee’s own words as codes (ibid.).

Coding the interview data is basically carried out using pen and paper. We print
the interview transcripts, read through the material and write the words in the
margin that captures the meaning of the data. We use an objective descriptive
coding method, trying to capture the essence of the statements without making
any premature interpretations or analysis.

The data from the feature inspections consists of a large set of variables in form of
descriptive feature labels using the terminology of the apps and tools in question.
From the application inspections a set of input categories are created containing
related features, while redundancies are erased after systematic evaluations. The
input types will thus refer to the type of data that the users create when interacting
with a specific feature in the app. A set of common categories is determined, but
the input types from the different apps are kept separately for further analysis. A
similar approach is carried out turning the tool variables into output categories.
This coding process demands a more thorough investigation of the functionalities
of the different tool features, before placing them in common categories.

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2.6 Research Quality
In qualitative research the findings will be reliant on the interpretations of the
researcher, meaning that the ‘truth’ will be somewhat normative (Blumberg et
al., 2005). For this reason we find it necessary to establish quality measures in
order to strengthen the soundness of our findings.

Discussing quality in terms of validity and reliability belongs to the positivistic
research approach, and is less applicable for this type of qualitative study (ibid.).
In their pure form, quantitative and qualitative methods are opposing
paradigms, with fundamentally different understandings of ‘true knowledge’,
why we find it necessary to use other types of assessment tools when evaluating
the quality of our research. A main focus for us will be to eliminate biases,
present our finding as unambiguously as possible and establish trustworthiness
and transparency around our results (Golafshani, 2003). This will enable other
researchers to evaluate our methods and thus judge the robustness of our
results, which is one of the main purposes of clear and precise reporting
(Blumberg et al., 2005).

Finally we find it crucial that we as researchers frankly reveal the limitations of the
conducted study. This is not to undermine the results, but on the contrary to build
trust around the proclaimed quality of our study. The specific methodological
limitations will be discussed in the following section, equally with the intention to
enhance transparency and trust. The general limitations of our results will be
presented after our conclusions, in a closing chapter of this thesis.

2.7 Methodological Limitations
We realize that with a qualitative research approach results are more easily
influenced by our own personal biases and idiosyncrasies. The inherent strength
of qualitative research is to be able to obtain a deep understanding of a
phenomenon, but the focus on detail, subjectivity and context conversely
becomes a limitation as it prevents the more objective view.

In case studies the research subject is embedded into its context to such an
extent, that it can be difficult to isolate the phenomena and distinguish between
cause and effect. Furthermore the case study setting presents an uncontrollable
and complex environment where many factors might affect the given study

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objects. The current stage of app analytics, within our case companies, consists of
a series of different factors, which can be difficult to separate. One is the design
of the app; another is the selection of tool, and the way the tool is set up.
Additionally we have to take into account how the company is organized, their
general workflow, processes, priorities, and management style. These conditions
present a limitation since it will be difficult to avoid ambiguities - something we
will try to overcome by ensuring transparency as mentioned in the previous
section.

Conducting literature reviews is equally dependent on the judgment calls of the
researcher performing the searches and reviews. Choosing to follow one path will
include leaving others behind, and every selection contains interpretations of the
importance and relevancy with a certain degree of subjectivity. With an exploratory
approach we try to exhaust a new field of all relevant resources, which presents
limitations due to the scope and resources of this project.

2.8 Summary
As we have illustrated in this chapter, our research purpose requires a
combination of methodological inquiries. Since we aim to uncover and identify
the constituting elements of a new research field, our research approach is a
reciprocal process between exploration and description. Due to the novelty of the
field we have no prior hypotheses and are therefore highly driven by inductive
reasoning. Our methodology is based on an interpretivist view on knowledge
creation, and thus a qualitative approach to data collection.

Our qualitative research design consists of a case study, which provides the frame
for our empirical inquires. Within each case company we conduct two rounds of
qualitative interviews with the case representative, as well as a series of
systematic feature inspections of the apps and tools that our case companies
currently employ. The empirical analysis is supplemented by a comprehensive
literature review, enabling us to build a body of knowledge for an emerging field
that previously has not been covered in academic literature.

Our empirical data is coded in order to deduce the patterns, themes and
characteristics, which is to underpin our analysis. Throughout this thesis we will
continuously present results, analyze findings and accordingly discuss their
implications for our research field.

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3 CASE DESCRIPTION
This thesis bases its empirical analysis on a multiple case study approach where
each case contains three objects of study: the app, the app analytics tool and the
organization.

We have chosen three case companies that are characterized by a prominent
position within the Danish marked, or by their innovative use of mobile apps.
Føtex presents one of the largest retail chains in Denmark, with more than 88
stores across the country. Nykredit is the first financial institute in Denmark to
develop a mobile app, while AO has achieved industry awards for their innovative
take on a business-to-business application.

All three companies have developed an app that contributes to their core
business, while central activities lie beyond the mere app functionalities.
Common for all three apps is that they are used as a customer touch point, as
opposed to an internal service or tool. The apps represent three different services
and each entail a variety of functionalities. The three apps therefore complement
each other well and thus allow us to explore a broad array of features and data
types.

In the following section we will present our three case companies, each case
representative, as well as briefly account for the main ideas and functionalities
behind their apps. Throughout our analysis we will dive further into the specific
deliberations, strategic considerations and particular analytics processes carried
out in each case.

30

3.1 Føtex
Føtex is part of the business concern Dansk Supermarked - a large corporation
consisting of 1300 large retail stores in Denmark and other European countries.
Dansk Supermarked employs 43.000 people in Denmark while Føtex alone has
15.000 employees dispersed between the headquarter in Brabrand and their
retail stores (Dansk Supermarked1).

Dansk Supermarked has recently been subject to a great deal of organizational
change and restructuring. Where each subsidiary traditionally was managed
disassociated from each other, many alignment initiatives have been
implemented since 2011. For instance, the communication departments,
procurement departments and e-commerce departments for each subsidiary
have been consolidated and will hereafter be centralized to achieve economy of
scale. In 2012 the organization got a new CEO; Per Bank, after the previous CEO
had been occupying the position since 1999.

According to Per Bank the financial crisis has changed the way consumers buy
their everyday groceries and non-food articles (Dansk Supermarked2). His goal
with the various consolidations is a more agile and flexible organization that
prospectively can respond to changes in consumer patterns in a more efficient
manner. In order to detect such changes, the organization will focus on getting in
closer contact with their customers by making customer analysis through
surveys, interviews and workshops.

Our contact in Føtex is Kristine Salmonsen, a project manager in charge of digital
and mobile marketing. She has been responsible for the Føtex app, its
development and maintenance, since the decision to spread onto the mobile
platform was made in 2010. She is therefore well acquainted with the entire
process of developing and implementing the app, as well as the thoughts behind
their analytics activities.

Our contact with the company also includes Thomas Nielsen, who is a system
lead consultant for the mobile platform across the entire group. Thomas Nielsen
is responsible for reporting on performance of each app in the organization.

www.dsg.dk (a)
1

2
www.dsg.dk (b)

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3.1.1 Indkøbshjælp
Føtex’s app Indkøbshjælp (Shopping Aid) is designed to assist the customer in the
shopping situation. Its main feature is a shopping list where users can add and
remove items and share the list with other Føtex app users by SMS or e-mail. The
app also consists of Føtex’s weekly leaflet, a recipe directory, and a service that
helps the users locate the nearest physical store. From both the leaflet and the
recipes, items can be directly added to the shopping list. Hence the main objective
for the app is to be a helpful tool to Føtex’s costumers in their everyday shopping
activities.

Image 1: Screenshots from Indkøbshjælp

3.2 Nykredit
Nykredit is a leading Danish finance institution with 4100 employees between its
headquarter in Copenhagen and the many subdivisions all around Denmark.
They have commercial and mortgage banking as their cornerstones, but also have
activities within insurance, leasing, pension and real estate (Nykredit3). The
company is structured around four integrated business units: Customers,
Products, Operations, and Support. Each unit is supported by a number of
specialized competence centers and central staff functions. The responsibility for
the digital activities lies in Operations, which comprises the company’s
production- and administrative functions, IT operations, digital channels and
customer service.

3
www.nykredit.dk

32

Nykredit’s case representative, Thomas Clausen, is a senior project manager in
the digital media department, responsible for all of the digital customer touch
points such as the website, home banking, mobile banking, and banner adds.

Nykredit was the first financial institution in Denmark to launch a mobile app
and has since developed quite a large portfolio of products for the mobile
platform. Thomas Clausen is responsible for the development and maintenance
of the various apps, their mobile analytics and reporting activities. He has been
close to the entire app development process and is daily engaged in the
performance and utility of the app.

3.2.1 Mobilbank
In Nykredit our case app is a mobile banking service with many of the
functionalities known from home banking services tailored to the mobile
platform. The main features are centered on banking services such as checking
account balance, paying bills, transferring money etc. There are also several ways
in which the user can visualize and categorize their spendings to get an overview
of their expenditures. Apart from this, the app contains numerous support
functions such as a currency converter and directions to the nearest Nykredit
branch or ATM. The app also includes several ways to get in contact with Nykredit
employees.

Image 2: Screenshots from MitNykredit

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3.3 AO
Our third case company AO is a wholesaler and distributer specialized in products
for the plumbing industry. AO is a business-to-business enterprise with a
customer base consisting mainly of small plumbing firms with 1-10 employees.
The company employs 700 people, has 48 wholesale stores all around Denmark,
and a fully automated warehouse from which their products are distributed.
When customers place an order either from their e-commerce platforms or over
the phone, the items are sent to the stores where it can be picked up shortly after.
In addition a large portion of the revenue is made directly in the stores where
customers can purchase their products on site (AO4).

AO is structured with central staff functions such as marketing and HR referring
directly to top management, and underlying divisions such as Sales, Procurement
and Inventory. The app responsibility is placed in the department of development
and marketing, headed by Søren Thingholm - our contact person in AO. Søren
Thingholm is titled Development Manager and is in charge of business
development, e-commerce and marketing. As head of these departments he has a
central position and refers directly to top management.

Søren Thingholm is involved with all of the company’s e-commerce platforms,
which currently counts a web shop, and apps for the mobile and tablet platforms.
He is involved with the internal information systems, such as the inventory and
distribution systems and CRM system. He is additionally responsible for analytics
activities and reporting of the performance measures for each of the company’s
digital products.

3.3.1 AO.dk mobil
AO’s app is centered on supporting customers when they are buying construction
and plumbing items. Its main feature is a large product catalogue from which
users can check prices and place orders. All products are categorized and listed so
that the users can search for products, check inventory and add them to an order
list. Users can also find product information or add items to their order list, by
using a scanner function when in the physical stores. Hence, the app can function
as a mobile product catalogue, or as an actual m-commerce app.

www.ao.dk
4

34

Image 3: Screenshots from Ao.dk mobil

Besides these main functionalities, the app entails several other features. The
users can locate the nearest store, read news about AO and the industry, update
their customer profiles and check-in to let colleagues know their location.

3.4 Summary
Our three cases present obvious similarities, as well as differences. Both Nykredit
and AO have designed their app so that it is directly integrated into their central
business activities and is a natural addition to their other digital platforms and
services. Føtex’s app functions more as a support service that can help their
customers in shopping situations, but does not allow for any transactions as is
the case with AO and Nykredit.

Our cases represent three very different organizations in three distinctive
industries. Føtex and AO can be said to be product oriented, while Nykredit offers
a service. AO stands out since it is the only business-to-business company where
the other two can be described as business-to-consumer enterprises. Although
they all have a relatively high number of employees and thus qualify as ‘large
enterprises’5, we see a large variation between 700 employees in AO, 4100 in
Nykredit and 15.000 in Føtex. In all three cases, we see a tendency to move core
business activities online, though they all have a physical location where they
meet and interact with their customers on a daily basis.

5
According to the European Commission companies with less than 250 employees qualify as small and medium-
sized enterprises (SMEs) and those above qualify as large enterprises. (www.wikipedia.com: Small and medium
enterprises)

35

The purpose of this section has been to give an introduction to our case
companies, while a more detailed elaboration of how our case companies work
with app analytics will follow in our analysis.

We have been trusted with access to information and data that is sensitive to the
companies and have therefore agreed to sign a Non-Disclosure Agreement,
promising that we will safeguard the information obtained and not reveal any
knowledge that could be considered sensitive.

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4 MACRO ENVIRONMENT ANALYSIS
The aim of this chapter is to provide a scan of the macro environment that
surrounds the field of mobile app analytics. This macro environment analysis will
focus on the constituting elements of the app analytics ecosystem, which we find
is determinative of the value propositions that we can ascribe to mobile app
analytics.

Smartphones and mobile apps are prerequisites for the app analytics ecosystem.
We will therefore initiate this inquiry by outlining these technological entities.
Subsequently we focus on three main stakeholders in the app analytics
ecosystem: the app owners, which are the companies that develop the apps, the
app users, and finally the mobile app analytics providers. When exploring the
characteristics of mobile app data, and determining the maturity level of the
field, these prerequisites and main stakeholders make out the foundation for our
analysis. The aim of this chapter is thus to provide an introduction to the field,
and furthermore provide insights and definitions that we will draw on
throughout our thesis.

4.1 The Smartphone
The mobile revolution has changed the way we communicate, carry out our daily
routines and navigate in urban spaces. Our conception of time and space has
changed immensely, since we became able to communicate and access

37

information, more or less, anytime – anywhere (Ling, 2008). Communication
functionalities have progressively migrated to the mobile platform, transforming
it from a portable telephone, to a personal mobile computer and information
repository, typically referred to as smartphones.

The boundaries between the Smartphone and the less advanced feature phone
are often blurred and unspecified. However some technical attributes are
typically ascribed to the smartphones such as Internet and wireless access, built-
in sensors, touchscreen and increased processing power (Ling & Svanæs, 2011).
Furthermore smartphones are often characterized by their ability to download
and install apps that extend their functionalities. As their desktop counterparts,
smartphones are run on different operating systems, mainly dominated by
Apple’s iOS and Google’s Android platform.

We expect that the term ‘smartphone’ will eventually die out, as all mobile
phones at one point will have adapted smartphone functionalities, and other
more advanced phones will take over the position in the market. But for the time
being, we will be using this definition as it illustrates the current stage of
technological development.

4.2 Mobile Apps
As explained above, smartphones are characterized by their ability to install apps.
This section will bring a definition of apps and furthermore highlight its main
characteristics.

Apps are the small programs, which we access from our smartphone ‘desktops’.
Although there is no industry-wide definition, Pew Internet provides a
description, that we find adequately sums up the term. Apps are “[...] end-user
software applications that are designed for a cell phone operating system and
which extend the phone’s capabilities by enabling users to perform particular
tasks” (Pew Internet, 2011, p. 2). They further draw a clear distinction between
‘applications’ and ‘functions’, namely that apps are software-based, while
functions are “hardware enabled activities such as taking pictures and recording
video” (Pew Internet, 2011, p. 2). To complete its purposes, many apps will activate
hardware-functions such as the camera, GPS or accelerometer within the device,
which is one of the features that distinguish the app from regular mobile
browsing.

38

Besides the integration with the phones’ features and services, apps have a
particular characteristic that distinguishes it from the general capabilities of the
smartphone. Compared to many other online activities, apps are usually single
purposed and focused on one particular service or function (Ling & Svanæs, 2011).
‘Normal’ Internet browsing activities are characterized by short attention spans
and a multitude of alternatives and links, which lead the user to other sites. On
the contrary, once a user enters an app it usually provides several navigation
options within the app itself, but rarely let the user move into another portion of
the web. This cultivates the app as a ‘silo’ for individual attention, which has
become a rare occurrence in a media landscape characterized by endless
alternatives and information ‘overload’ (ibid.). The siloed nature of apps is seen in
regard to the practical use of it, as it is difficult to navigate away from the app, as
well as in regard to the advantage of having undivided attention from the user.

Apps are distributed through sales portals provided by the smartphone suppliers,
with the two market dominators being Apples App Store and Google Play.
According to Datatilsynet (2011) these suppliers have quite different profiles.
Apples iPhone and its associated operating system iOS is a closed system, only
running on iPhones. Apple preapproves content and software in all the apps that
are offered in App Store, which according to a recent press release counts 775,000
apps available for download (Apple6).

The Android platform is a more liberal system with a variety of hand set providers
and an open-source developing environment (Datatilsynet, 2011). The Android
platform has previously been lagging behind Apple in regards to the volume of
apps, but several sources report that Google Play has now surpassed Apple and as
of January 2013 has approximately 800,000 apps in store. (McCarra, 2013).
A post in the popular media blog The Sociable estimates that, by June 2013,
Google Play will reach their one million app milestone, and continue to increase
their market share in a steady curve (ibid.).

4.3 The App Analytics Ecosystem
In a report from Datatilsynet, the authors provide an overview of the app
ecosystem that illustrates the various stakeholders and parties constituting the
6
www.apple.com

39

application market. Inspired by their model we identify three players we find of
particular importance to the app analytics ecosystem; the app users, the app
owners and the analytics tool providers.

Figure 7: ‘Players in the app ecosystem’, inspired by Datatilsynet, 2011

4.3.1 The app users
Though this thesis is focused on the analytics field from an organizational
perspective, the app user plays a central role in the app analytics ecosystem. Since
a smartphone ownership is a premise for the access to apps, this section will
examine the diffusion of smartphones in a Danish context.

According to a study performed by Index Danmark/Gallup, the number of people
in Denmark who owns a smartphone has increased significantly through the last
years. The study shows that the number has grown from 1.5 million to 2.1 million
in just one year (Danske Medier7). These findings are supported by the mobile
consumer study Our Mobile Planet, which Google conducted for the same time
period. This survey shows that the smartphone penetration in Denmark has
increased from 30 percent of the population in the first quarter of 2011, to 45 %
the following year (Our Mobile Planet: Denmark, 2012).

7
www.danskemedier.dk

40

According to the Google survey the smartphone penetration rate is highest
among the 18-29 year olds where approximately two thirds own a smartphone.
This number drops as the age increases till about 22 percent for the age group of
50 and above. Another indicative finding shows that the smartphone owners are
becoming increasingly reliant on their devices, with 86 percent using their
phones when ‘on the go’ (ibid.).

We find that as the use of apps increases, so does the concern for privacy
violations. Several projects and studies are launched with the aim of discovering
how the collection and transfer of app data might breach the individual’s right to
privacy (Datatilsynet, 2011). In an experiment carried out by a Wall Street Journal
investigator, 50 of the most popular apps from Apple’s App Store and Google Play
were investigated in order to reveal the data streams from the apps send to third
parties (Wall Street Journal8). According to the project initiators; “These phones
don't keep secrets. They are sharing this personal data widely and regularly”
(ibid.). While the quantities and personal nature of the data raise privacy for the
app user, they simultaneously present a valuable asset for companies that seek
knowledge about their customers and are thus a prerequisite for mobile app
analytics.

4.3.2 The app owners
From an enterprise point of view the mobile platform offers a new
communication touch point, increasingly used for marketing or branding
purposes. Hence in recent years there has been an explosion in the number of
commercial apps covering a wide range of services.

The Networked Business Factbook, which examines how Danish businesses make
use of social media, mobile services and cloud technology, illustrates the novelty
of the app market. The report is based on a wide-ranging survey conducted on
2742 participants, from a wide selection of Danish companies and public
institutions. According to their study, mobile services are a fairly new area of
interest for most Danish businesses. Nearly half of the recipients are currently
present on the mobile platform, and the area is continuing to show rapid growth.
Of the companies that are present on the mobile platform, more than half have
engaged in their mobile activities within the last year. 23 % have been active
between one and two years, while only 8% have worked with mobile solutions for

8
www.online.wsj.com

41

more than three years. Many companies see great potentials in the mobile arena
and have therefore prioritized the matter and placed the mobile responsibility on
a high organizational level. Of the companies that are not present on the mobile
platform, 38% answer that the reason is that they are currently planning their
mobile initiatives (Social Semantics.eu, 2012). The study furthermore shows that
the branding value is seen as the number one reason why companies invest in
mobile solutions, while a large part are also motivated by the business
development benefits that comes with an increased customer loyalty, improved
services and added value in terms of mobility.

These numbers clearly illustrates the novelty of the mobile field for organizations
and the interest it is receiving among Danish companies. The study by Social
Semantics hereby emphasize how mobile app analytics can be expected to be
object of increased attention in the coming years, as the companies grow interest
in documenting and rationalizing their mobile investments.

4.3.3 Tool providers
The third major player in the app analytics ecosystem is the analytics tools,
often referred to as the third parties. While the mobile app industry is
relatively young, there are already a variety of platforms competing to provide
companies with good app analytics such as Flurry, Google Analytics,
SiteCatalyst, Localytics, WebTrends and many more. These tools serve to help
app owners obtain data on how their costumers interact with the content they
are presented to, as well as gain knowledge about their behavior.

The app analytics tools are based on a similar tradition to web analytics and social
media analytics. Each tool functions by offering the companies a software
development kit (SDK) that is to be installed in the apps code source. These tags
track user-interactions and subsequently send the captured data to the tool in
question. There is typically a standard code package, but custom tracking is also
available for most tools, allowing the companies to keep track of events and
occurrences of particular interest (Apptamin9 ).

In large, the tool interfaces offer a compilation of graphs and data visualizations,
where a selection of metrics can be selected and combined to illustrate the
performance measures of interest. The visualizations often consist of a certain

9
www.apptamin.com

42

variable and its occurrence over time, with an adjustable time span. The market
already counts many players with different specializations such as games or
location, and many with supporting services such as cross promotions, add
management etc. (ibid.).

4.4 Summary
The aim of this chapter is to introduce the field of mobile app analytics, identify
main players in the macro environment surrounding the field and provide
explanations and definitions to important concepts. The chapter has thereby
delivered characteristics of smartphones and apps, entities that are prerequisites
for app analytics. Furthermore we have accounted for three main players in the
app analytics ecosystem, namely the app users, the app owners and the tool
providers. We will draw on the definitions and descriptions outlined in this
chapter for the remaining of our thesis.

43

5 THE VALUE OF MOBILE DATA
As the first part of a trinity aimed at uncovering the field of mobile app analytics,
we intend to explore the medium from which the data is collected. The purpose of
this chapter is thus to analyze the characteristics of mobile app data and its
inherent value propositions, in order to answer our first research question.

Since few previous studies have been devoted to examine the value created from
mobile app data, we turn our attention to more established research areas. This
part of our study has consequently been carried out as an extensive literature
review and discussion, where we combine and synthesize sources that are relevant
for our particular research purpose. We will thereby draw on previous findings in
order to construct a body of knowledge related specifically to mobile app data.

From our exploratory research we find that mobile app data contains two core value
potentials - the value propositions of the mobile platform to which it is naturally
affiliated, and value propositions belonging to digital data sources in general.
Accordingly this chapter is divided into two main sections; the first part will outline
the specific mobile attributes and the particular value propositions that can be
ascribed to the mobile platform. The second section will take a broader view on the
current data tendencies that we associate with the value of new digital data
sources. Finally, a concluding section will provide a discussion of the literature
presented in the two previous parts. This discussion allows us to identify and
outline the potential value propositions specifically for mobile app data, which we
apply in the subsequent parts of this thesis.

44

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