Mobile App Analytics

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Master thesis exploring the emerging field of Mobile App Analytics. We explore the potentials of the mobile app as a data source and the current stage within mobile app analytics

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Mobile App Analytics

  1. 1. mobile app analytics nynne silding + sidsel baggesen
  2. 2. Executive summaryMassive quantities of data produced by and about people, things and theirinteractions is changing the way businesses operate .The smartphone and itsaccompanying apps is a new source of digital footprints, which due to its uniquecharacteristics 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 appanalytics and thus fill a void in the academic literature by identifying valuepropositions for mobile app data and explore the maturity level of mobile appanalytics.We explore the field of mobile app analytics from three perspectives – the mobileapp 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 chapterscorresponding to three research questions that collectively will allow us toexplore 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 multiplecase study. Our case study involves the retail chain Føtex, the financial enterpriseNykredit and the wholesaler and distributer AO. All three companies havedeveloped an app to support their existing business areas, and recently initiatedtracking by implementing analytics tools.The research design involves two rounds of qualitative interviews with caserepresentatives and a series of systematic feature inspections of the apps andtools currently employed by our case companies. The empirical analysis issupplemented by a comprehensive literature and industry review and theoreticaldiscussion, enabling us to build a body of knowledge for this emerging field thatpreviously 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 1
  3. 3. particular app specific characteristics, this leads us to conclude that the mobileapp as a data source is distinguished from other digital data sources by its abilityto add a contextual layer to the data, enabled by sensor technology. We find thatthe ability to add a time/place dimension to other data types is a unique valueproposition. The data comes in various types and structures while the digitalnature 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 personalizationand message tailoring.Our empirical analysis identifies gaps between the value propositions of mobileapp data, and the data that the case tools currently provide. This is particularlyapparent when it comes to sensor input such as location awareness and the factthat 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 formobile app analytics, otherwise proscribed by the theory. Based on app analytics,they currently take decisions regarding app optimization, and report top-linemetrics to management to ensure resources for future projects. Hence, we findthat decision-making on the basis of mobile app analytics currently revolvesaround the app itself or the department on which it is placed. Due to lack ofsystem integration, data complexity and validity, our case companies arecurrently not able to base strategic or automated decisions on the data that theycan derive from their apps.We thus conclude that although mobile app data presents a series of promisingvalue propositions, the current stage of mobile app analytics tools leaves roomfor further development and investment. A series of initiatives will have to beundertaken before the app data potentials can unfold: The analytics tools mustimprove their ability to utilize the app specific value potentials, allow for systemintegration and enhance the granularity levels of the data. If the overall maturitylevel is leveraged on both the organizational level and in the tool capabilities,mobile app analytics will be a valuable data source suitable for gaining insightsinto user behavior. 2
  4. 4. Index1 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 ............................................................. 92 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 ............................................................................ 293 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 3
  5. 5. 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 ............................................................................ 435 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 ................................................................................... 636 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 4
  6. 6. 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 ........................................................................... 947 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 ............................................................................................................... 1198 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 ..................... 1279 LIMITATIONS .................................................... 12910 PERSPECTIVE AND FUTURE WORK ......................... 131REFERENCES ......................................................... 134 5
  7. 7. 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 6
  8. 8. 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: 1. What characterizes mobile app data and what are its value propositions? 7
  9. 9. 2. What is the current stage of mobile app analytics? 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 8
  10. 10. 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, 9
  11. 11. philosophy and methods. Chapter three will provide a presentation of our threecase companies and their respective mobile apps. Chapter four consists of amacro environment analysis with the aim of identifying the essential elements ofthe ecosystem surrounding mobile app analytics. In chapter five, six and sevenwe will present our theoretical framework and the analysis that enables us toanswer our three research questions as described above.In chapter five the characteristics of mobile app data and its value propositionswill be examined by a thorough literature review, focusing on mobilespecifications and data value. Chapter six is an empirical outline of the currentcapabilities of the mobile app analytics tools and their ability to capture the valueof mobile app data. In chapter seven we construct a theoretical frameworkaround organizational data usage and examine how mobile app analytics isemployed in our three case companies. Each chapter in the analysis will closewith a discussion of relevant findings and a preliminary conclusion.These three main chapters will set the scene for chapter eight, where we compilethe various findings and arguments leading to our conclusions of thecharacteristics 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 furtherperspectives and possibilities for future examination. 10
  12. 12. 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 11
  13. 13. 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 12
  14. 14. and tool and app inspections. The qualitative interviews and the coding andanalysis 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) 13
  15. 15. 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). !"#$%&()(*+! !"#$%#&()*+,#%-,#(&.#! !"#$%&%%()! !"#$%&! 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 14
  16. 16. 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, 15
  17. 17. 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. 16
  18. 18. 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.). "#$%&! !"!#$%&(! !"#$%&!"! $./,&,0+1! #/%"#$)$) 2$-$&+1,3+",%-) %()$&*+",%-)! 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. 17
  19. 19. 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: 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? 18
  20. 20. As mentioned the three questions each contribute with a different perspective ofmobile app analytics. The model demonstrates specifically what methods areapplied in each question and their relative weight. As can be seen, the firstquestion is addressed by reviewing previous literature, while the two subsequentquestions are more empirically founded.Figure 5: Research design overview 19
  21. 21. 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
  22. 22. variables of mobile app analytics and deliver more consolidated results, usingmultiple cases, than we would be using just one case.A single case approach can be appropriate when examining an extreme or rarecase, with limited access to data, while a multiple case approach is considered todeliver more robust results (Blumberg et al., 2005). Hence while a study of thecircumstances surrounding mobile app analytics in one organizationalenvironment can be said to deliver context dependent results, the substance ofour findings increase as several cases are examined. Within the time frame andscope of this project we have found it appropriate to examine three cases, as thisallows us to broaden our empirical analysis while still being able go into depthwith each case.Traditionally in a multiple case study approach, the cases will be selected on thebasis of some predefined criteria of commonalities or differences between thecompanies. Our case company selection process, however, has taken its point ofdeparture in the apps rather than the companies.In line with our scope, the primary criterion is that the app is created as a supportfunction to existing business processes, not as the central element in a businessmodel. This is important since we want to examine how mobile app analytics canadd value to an organization, not how a mobile app company can measureperformance. Furthermore within our scope we want to examine different typesof apps in order to map as many input types as possible. A final criterion is thatthe apps must be connected to an analytics tool. Hence our three case companieswere chosen on the basis of their apps, and how these apps complement eachother. The exploratory and inductive nature of our study means that no criteriabesides the app were put forward, as no theory, hypothesis or prior studies couldinform such criteria.In each case company we have sought to find the person with most knowledgeabout our topic. Therefore, each case representative has been chosen according totheir level of contribution to, and knowledge about, the app development process- from idea to implementation - and their practical involvement in thecompanies’ use of mobile and other digital media. 21
  23. 23. 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
  24. 24. 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
  25. 25. our exploratory approach, we aim at discovering the present conditions insteadof looking for errors, inconsistencies or lack of user-friendliness. Rather thanmaking a predefined checklist of what we expect to find, we list the features wecome across, and map them according to functionality and data type. Thepurpose of mapping all features of the apps and the tools is to be able to comparethe app input with the tool output, and thereby be in a position to assess whatdata our case companies are currently gaining from their apps. We further aim togain 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 toolsaccordingly.App inspection: We conduct a systematic feature inspection of the three appsusing the exact same approach in all three cases. We inspect the applicationaccording to the ‘natural’ order of features. Starting from the landing page, goingfrom left to right, and from the top and downwards. From each page we navigateas ‘deep’ as we can, repeating this path for each feature. This way wesystematically go through all features in order to map all functionalities and datatypes (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 24
  26. 26. 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
  27. 27. 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. 26
  28. 28. 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. 27
  29. 29. 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 28
  30. 30. 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. 29
  31. 31. 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
  32. 32. 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) 31
  33. 33. 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 Indkøbshjælp3.2 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
  34. 34. 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 33
  35. 35. 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
  36. 36. 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
  37. 37. The purpose of this section has been to give an introduction to our casecompanies, while a more detailed elaboration of how our case companies workwith app analytics will follow in our analysis.We have been trusted with access to information and data that is sensitive to thecompanies and have therefore agreed to sign a Non-Disclosure Agreement,promising that we will safeguard the information obtained and not reveal anyknowledge that could be considered sensitive. 36
  38. 38. 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
  39. 39. 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
  40. 40. 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
  41. 41. 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, 20114.3.1 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
  42. 42. 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 dont 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
  43. 43. 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
  44. 44. 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

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