Analysing and Improving the Stagend.com Services Platform · Analysing and Improving the...

University of FribourgDepartment of Informatics

Information Systems Research Group

Master Thesis in Information Management

Analysing and Improving theStagend.com Services Platform

Author:Lorenzo CimasoniVia Lucomagno 46500 Bellinzona

[email protected]

Examiner:Prof. Dr. Andreas Meier

Supervisor:Darius Zumstein

Fribourg, 31 August 2011

PUBLIC VERSION

Abstract

Abstract

The companies that offer Web services nowadays require an effective tool for controllingtheir Internet activities. The Web Controlling is a term which contains within it severaltools designed to understand the website-related aspects of a webcompany. Especiallyallow to put into practice, from the strategy outlined by the business plan, a series oftools which are used to control the progress of the eBusiness, such as the Web Scorecard,Web metrics and Key Performance Indicators (KPIs).To enable these tools to work properly they should be able to collect website usage

data and information, and turn them into measurements which are then used in severaldecision-making processes of the company.This paper aims is to explain initially, at a theoretical level, the functioning of Web

analytics tools in the theory of Web Controlling and then expand the application of someof them to the Web companies. Secondly to describe the practical implementation of thisstructure in a startup (Stagend.com), in order to compare with the real needs if the toolsproposed can be implemented or should be adapted to the specific situation.

Keywords: Web Controlling, Web analytics, Web Scorecard, Web metrics, KPIs,eCRM, Startup, Stagend.com, Google Analytics, AWStats, Database.

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Contents

Contents

1. Introduction 11.1. Problem Statement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.2. Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.3. Outline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

2. Web Analytics and Web Controlling 42.1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42.2. The Web Controlling Strategy . . . . . . . . . . . . . . . . . . . . . . . . 62.3. Strategic Level . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

2.3.1. Web Metrics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92.3.2. Key Performance Indicators . . . . . . . . . . . . . . . . . . . . . 102.3.3. From Balanced to Web Scorecard . . . . . . . . . . . . . . . . . . 12

2.4. Analytical Level . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 182.4.1. Data Collection Merge and Preparation For Analysis . . . . . . . 192.4.2. Data Interpretation and Act Proactively to Changes . . . . . . . . 20

2.5. Operative Level . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 212.5.1. Client-side . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 222.5.2. Server-side . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 252.5.3. Database . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

3. Stagend.com Case Study 293.1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 293.2. Stagend.com Business Plan . . . . . . . . . . . . . . . . . . . . . . . . . . 30

3.2.1. Products and Services . . . . . . . . . . . . . . . . . . . . . . . . 303.2.2. Markets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 313.2.3. Marketing Plan . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

3.3. Preparation for Web Controlling Implementation . . . . . . . . . . . . . 33

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Contents

3.4. Implementing Web Scorecards . . . . . . . . . . . . . . . . . . . . . . . . 343.5. From Strategy to Web Metrics . . . . . . . . . . . . . . . . . . . . . . . . 38

3.5.1. Google Analytics Metrics . . . . . . . . . . . . . . . . . . . . . . . 403.5.2. AWStats Metrics . . . . . . . . . . . . . . . . . . . . . . . . . . . 443.5.3. Database Metrics . . . . . . . . . . . . . . . . . . . . . . . . . . . 453.5.4. Sectorial Subdivision . . . . . . . . . . . . . . . . . . . . . . . . . 47

3.6. From Web Metrics to KPIs . . . . . . . . . . . . . . . . . . . . . . . . . . 493.6.1. KPIs for Platform Release . . . . . . . . . . . . . . . . . . . . . . 503.6.2. KPIs for Future Modules and Features . . . . . . . . . . . . . . . 54

3.7. Implementing Methods of Data Collection . . . . . . . . . . . . . . . . . 553.7.1. Google Analytics (Client-side) . . . . . . . . . . . . . . . . . . . . 553.7.2. AWStats (Server-side) . . . . . . . . . . . . . . . . . . . . . . . . 573.7.3. DataBase . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57

3.8. Reporting and Analyzing Collected Data . . . . . . . . . . . . . . . . . . 583.9. Future Improvements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62

4. Conclusion 64

Bibliography 66

Referenced web resources 68

A. Appendix: Web Scorecard Views Surveys 73A.1. Customer Survey . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73A.2. Organization Survey . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75A.3. Partners Survey . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76A.4. eBusiness Survey . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77

B. Appendix: Database Queries 78B.1. Web Metrics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78B.2. KPIs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79

C. Appendix: Data Collection Tools Implementation 80C.1. GA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80C.2. AWStats . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80

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List of Figures

List of Figures

2.1. The Web Controlling Strategy . . . . . . . . . . . . . . . . . . . . . . . . 72.2. Example of KPI on Visitors Entrances . . . . . . . . . . . . . . . . . . . 112.3. Balanced Scorecard View . . . . . . . . . . . . . . . . . . . . . . . . . . . 122.4. Web Scorecard schema . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152.5. Client-side Data Collection Method . . . . . . . . . . . . . . . . . . . . . 232.6. Google Analytics Processing Flow . . . . . . . . . . . . . . . . . . . . . . 242.7. Server-side Data Collection Method . . . . . . . . . . . . . . . . . . . . . 262.8. AWStats Robots and Spiders Visits Table . . . . . . . . . . . . . . . . . 27

3.1. Stagend.com’s Business Idea . . . . . . . . . . . . . . . . . . . . . . . . . 303.2. Stagend.com Syncronization Module Example . . . . . . . . . . . . . . . 323.3. Stagend.com’s Online Competitors . . . . . . . . . . . . . . . . . . . . . 323.4. Market Entry Strategy . . . . . . . . . . . . . . . . . . . . . . . . . . . . 333.5. Monetization Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 333.6. Web Scorecard Surveys Direct Metrics Subdivision . . . . . . . . . . . . 353.7. Example of pop-up question (Web Scorecard) . . . . . . . . . . . . . . . 363.8. Google Analytics Dashboard . . . . . . . . . . . . . . . . . . . . . . . . . 413.9. Profile Database Relational Schema . . . . . . . . . . . . . . . . . . . . . 463.10. Event Database Relational Schema . . . . . . . . . . . . . . . . . . . . . 463.11. Distribution of Metrics Direct Impact on Stagend.com Activity Fields . . 483.12. KPIs Distribution over Market Entry Strategy . . . . . . . . . . . . . . . 493.13. Absolute Unique Visitors KPI Example . . . . . . . . . . . . . . . . . . . 503.14. Example of Google Adsense Report . . . . . . . . . . . . . . . . . . . . . 533.15. GA Website Search Settings . . . . . . . . . . . . . . . . . . . . . . . . . 563.16. iReport Plugin for NetBeans . . . . . . . . . . . . . . . . . . . . . . . . . 593.17. LimeSurvey Question Report Example . . . . . . . . . . . . . . . . . . . 59

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List of Tables

List of Tables

2.1. Stategic Questions and Correlated Web Metrics . . . . . . . . . . . . . . 102.2. Web Scorecard Example of a Survey Form . . . . . . . . . . . . . . . . . 17

3.1. Google Analytics and Piwik Comparison . . . . . . . . . . . . . . . . . . 393.2. Browsers’ JavaScript Support Statistics . . . . . . . . . . . . . . . . . . . 433.3. Example Table of User Profiles KPI Calculation . . . . . . . . . . . . . . 513.4. Example Table of Advertising KPI Calculation . . . . . . . . . . . . . . . 543.5. This Table was removed from the public version . . . . . . . . . . . . . . 543.6. Metrics and KPIs Overview over Web Scorecard Views and Company Ac-

tivity Fields . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60

A.1. Customer Survey - Content . . . . . . . . . . . . . . . . . . . . . . . . . 73A.2. Customer Survey - Usability . . . . . . . . . . . . . . . . . . . . . . . . . 74A.3. Customer Survey - Interaction . . . . . . . . . . . . . . . . . . . . . . . . 74A.4. Organization Survey Section One: Content . . . . . . . . . . . . . . . . . 75A.5. Organization Survey Section Two: Technology . . . . . . . . . . . . . . . 75A.6. Organization Survey Section Three: Performance . . . . . . . . . . . . . 76A.7. Partners Survey Section One: Sponsoring and Advertising . . . . . . . . 76A.8. eBusiness Survey Section One: Financial and Growth . . . . . . . . . . . 77A.9. eBusiness Survey Section Two: ROI . . . . . . . . . . . . . . . . . . . . . 77

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List of Codes

List of Codes

2.1. Example of Apache Server’s Log Entry . . . . . . . . . . . . . . . . . . . 25

3.1. GA Onclick Event Personalization in Symfony Framework . . . . . . . . 56

B.1. Accounts Creation Query . . . . . . . . . . . . . . . . . . . . . . . . . . . 78B.2. Accounts Inactive Query . . . . . . . . . . . . . . . . . . . . . . . . . . . 78B.3. Bands and Clubs Query . . . . . . . . . . . . . . . . . . . . . . . . . . . 78B.4. Created Events Query . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78B.5. Next Events Query . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78B.6. KPI Accounts Creation Query . . . . . . . . . . . . . . . . . . . . . . . . 79B.7. KPI Events Creation Query . . . . . . . . . . . . . . . . . . . . . . . . . 79B.8. KPI Synch Usage Query . . . . . . . . . . . . . . . . . . . . . . . . . . . 79

C.1. Google Analytics Tracking Code (GATC) . . . . . . . . . . . . . . . . . . 80

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List of Abbreviations

List of Abbreviations

API Application Programming InterfaceCEO Chief Executive OfficerCIO Chief Information OfficerCMO Chief Marketing OfficerCRM Customer Relationship ManagementCTO Chief Technical OfficerCTI (Swiss) Commission for Technology and InnovationGA Google AnalyticsGATC Google Analytics Tracking CodeHTML HyperText Markup LanguageHTTP Hypertext Transfer ProtocolKBS Knowledge-Based SystemKPIs Key Performance IndicatorsIT Information TechnologyOS Operating SystemROI Return on InvestmentSaaS Software as a ServiceSEO Search Engine OptimizationSQL Structured Query LanguageSUPSI University of Applied Sciences and Arts of Southern SwitzerlandURL Uniform Resource LocatorUSI University of Lugano

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Acknowledgement

Acknowledgement

I want to thankDarius who followed and helped me throughout this work,

the Stagend.com team for giving me this great opportunity,my parents and Wendy for their constant help and support.

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1. Introduction

1.1. Problem Statement

Nowadays the need to use performance data to monitor the progress of an organization oran individual project is a need which is almost everywhere in modern society. More theactivities becomes complex and more the need for reference values becomes important.This also includes all of the modern companies and projects that base a part of the entirebusiness on the Internet.The expansion of this sector is getting increasingly important in economic terms, and

this leads to adapt some tools of control from traditional sectors such as industry, andcreate new ones, in order to control main aspects of a modern Web company.For this reason, terms like Web analytics and Web Controlling for some years now

became topics for discussion and development more and more interesting in the areasrelated to the Internet.Using these tools, if not well planned may become chaotic, and do not lead to particular

advantages to the company which uses them. For this reason it is necessary to defineclearly and schematically which tools to use and in what way.The fact of having an implementazion pattern the strategy of Web Controlling can

reduce the problems of applying the techniques and thereby reduce costs. This is espe-cially useful in companies that are emerging (startup), and which do not have significantfinancial resources available, and therefore the optimization of all processes is not just aconvenience but a necessity.

1.2. Objectives

The main research questions in this thesis are:

• What are the ideal instruments for measuring the success of the eBusiness strategy?

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1.3 Outline

With this question will be analyzed at a theoretical level all the instruments cur-rently used in companies with business focused on the Internet. Using as a startingpoint the theory of Web Controlling cycle, and analyzing techniques such as Webmetrics, KPIs and Web Scorecards. Will be used as main instruments to work theexisting literature and best practices that address the current state of art in thisfield.

• What tools can be used to collect information about platform visitors? This ques-tion seeks to analyze the best technical means available to achieve the aim of col-lecting as much high quality information about visitors to a particular Internetplatform. In particular, systems called client and server side, and also the instru-ments that allow to act on a corporate database which is often the most importantsource of information.

• How the data collected can be presented and used in the eBusiness? With thisquestion will be analyzed the tools and the techniques available for representing theresults from the various systems of Web analysis. Will be used as main instrumentsto answer this question some best practices.

• How all these techniques can be adapted to a startup company? This questionhas the intention to report the results of the practical work performed within thestartup Stagend.com in the form of a case study. In practice the answers to thequestions above will be used as a starting point to implement in an optimal way allWeb Analytics and Web Controlling techniques.

These questions are important in order to define a theoretical concept to be appliedinto the Stagend.com startup organization and the Web platform in development.All these questions are addressed in part by theoretical analysis and discussions based

on existing literature, and partly put into practice by creating the Stagend.com casestudy.

1.3. Outline

The main objectives of this thesis is to create a concrete value for the Stagend.com startup,a base for future traffic analysis, Web site optimisation and advertising campaigns, and

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1.3 Outline

realise a theoretical and scientific research about some possible alternative to reach itsgoals.In order to give a wider perspective on the use of possible best practices within an

emerging company, this work will be subdivided mainly into two parts.The first part will be more theoretical and based on the existent literature and best

practices, and it will resume and extend some important concepts which compose the WebControlling strategy. This part will begin with an introduction on the Web Controllingtheory, afterwards the main components of the theory will be analyzed individually beforebeing used in the case study.The second part concerns a more practical view on the subject with the introduction

of the Stagend.com case study. It will describe the work done within the company todefine the entire eBusiness, and from this to describe how all the techniques described inthe theory part can be put into practice.At the end of the work will be presented some final thoughts about future improve-

ments.

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2. Web Analytics and WebControlling

In this chapter will be introduced the concepts of Web analytics and Web Controllingtreated in [MZ10, pp.9-12] in order to examine thoroughly theirs components. In partic-ular, what are the systems of data collection for Web platforms, how the goals of a Webplatform can be defined and developed, and how the data collected must be use withinan eBusiness. Afterwards, this theoretical part will be used in the next chapter for thecase study of the Stagend.com startup.

2.1. Introduction

The necessity to collect and analyze information has become a vital process within organi-zations faced with fast rate of changes in their business sector. This information becomesessential to allow the company to adapt its strategies and to continue its activities.Within the so-called Web companies is possible to find a multitude of tools for the

purpose described above, but it is still necessary to create an organizational schema sothat the implementation of these tools will be effective and targeted.One of the theories applicable within a company is the Web Controlling. In large

companies with only part of the business centered around Internet, the Web Controllingis wrongly seen as a part of accountancy [YM08, p.261-263]. But in reality is much more,especially in what concerns the company marketing section which nowadays needs tofocus on using the Internet as a communication tool. All this has led to the developmentof a well-defined sector of marketing: Online Marketing.On the other hand in Web companies, Web Controlling has proved from the outset

to be a fundamental tool which allows to control and regulate the everyday activities.Not only for marketing, but since almost all the activity takes place on Internet, for allbusiness segments.

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2.1 Introduction

The terms Web Controlling and Web analytics are sometimes used as synonymous butthey are not. The term Web Controlling is used to describe a broader set of instrumentsused to monitor and control the company activities which concerns Internet. On the otherhand, the term Web analytics is much more accurate and focuses only on the activity ofcollect and turn data into information. , then it can be said that the Web analytics is anessential component of the Web Controlling strategy.The entire Web Controlling system must be able to manage and control many aspects

of a Web company, starting from the general business strategy (business plan) going fromthe more concrete strategies in different areas from marketing to customer relationshipmanagement.The term Web Controlling still remains very general and allows to wrap within it

techniques and tools of any kind, from management to marketing through InformationTechnology (IT) tools. Among all tools Web analytics are the base of the entire processand the concept of Web Controlling. Web Analytics are IT softwares that allow to gatherdata, which at a later stage are transformed into information that can be treated in turnby other instruments.The use of the Web analytics tools nowadays is widespread, any owner of a website

or a blog can have access to a wide range of data which describe quantitatively differentvalues, such as the number of visitors or the traffic generated by visits.

Most people are using web analytics as a benchmark: how did we do yes-terday, and how are we doing today? Smart people are actually analyzing tooptimize their website. The advanced people are using Web data to optimizeall of their marketing.

Jim Sterne (Web Analytics Association) [Cli08, p.300]

From Jim Sterne’s statement it is possible to deduce that the application of Webanalytics techniques may be different, and that is not enough to observe and collect datato improve the entire business, but the company must adopt a working method whichallow the collection of data, and a system of analysis that makes possible to continuoslyenhance every aspect of the online platform.Next in this chapter will be presented and extended all the different components of

the proposed Web Controlling schema, after which they will be applied to a startup Webcompany in the case study chapter.

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2.2 The Web Controlling Strategy

2.2. The Web Controlling Strategy

What is represented in the Figure 2.1 is an outline of how to set the entire Web Controllingstrategy within a company.The diagram is divided horizontally into three levels for each of which there are several

actions and a person, or more, responsible inside the company, specifically:

• Strategic level: is the management overview of the Web Controlling process andincludes all the strategic and business related decisions for the company. At thislevel are also defined all the tools that have to be implemented for analytic purposes.The Web Controlling Cycle is one of them, and it allows, after a planning phase(Step 3), to iteratively analyze and control (Steps 5-6) the information collected,which eventually will lead to modify some aspects of the website (Measures Step 8).The people responsible for the management of this level are usually senior managers(CEO, CIO, CMO).

• Analytic level: concerns the overall view of the data collected, and it manageseverything concerning the tools for information collection and integration into re-ports, that are then used in the above-described Web Controlling cycle. Among themost important steps of this level there are the collection, the storage, the treate-ment and the integration of all data from different sources. The person in chargeof this phase is typically a Web or data analyst.

• Operative level: regards the perspective of the website and the actions taken byvisitors. At this level are implemented all the techniques and tools of Web analyticsin the code of the website and in the hosting server. All actions which are performedon the platform by visitors are logged by the tools set up. The people responsibleat this level are the people directly in charge for the content of the website.

Based on the strategies, specific choices are made across the company and are imple-mented on the Web platform.

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2.2The

Web

Controlling

StrategyFigure 2.1.: The Web Controlling Strategy [MZ10, p.10]

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2.3 Strategic Level

Before being able to analyze some first results should be defined and implemented allnecessary tools (Web metrics, Web Scorecards and KPIs) that will be used to establishqualitatively and quantitatively whether the results obtained are consistent with expecta-tions, and also will, through an ongoing decision cycle, enhance and modify every aspectof the website.To ensure that the whole Web Controlling system is going to proprely work, it must

be able to draw upon a series of raw data that, once transformed into measurements, canbe used in different decision-making processes.Among the data collected the most important are those which come from the Web

platform and its users, to these can be added all company internal data and moreover,these from the different partners, including financial and economic data.Next in this chapter will be analyzed different elements which make up the three levels

of the Web Controlling strategy schema.

2.3. Strategic Level

At the strategic level (Figure 2.1) are placed all those elements that define the mainobjectives of the eBusiness, and allow to manage it as a whole. The main element fromwhich is possible then to start building an eBusiness is certainly the business plan whichsummarizes all the ideas and strategies to bring in a given market. Generally, the businessplan contains several elements in it, including the strategy of the website, the goals andthe business model. The main section of the business plan should contain at least thefollowing elements: [Adm]

• The description of the eBusiness

• The marketing plan

• The financial management plan

• The management plan

Following these four points will be realized additional items, such as the executivesummary, supporting documents and other financial projections. All these documentsallow to have a solid base from which is possible to start the implementation of theconcept. Without a clear strategy and a solid business plan the risk is to fail, and the

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2.3.1 Web Metrics

reasons may differ, for example if have not been taken into account elements of technicalfeasibility or financial commitment.Once the strategy is in place in the field of Web companies it is possible to deploy certain

tools for what concerns the strategies of Web analysis and Web Controlling. These toolsallow to manage the strategy within the company and at the same time to verify theperformance. The main goal is thus to bring continuous improvements to the companyto be successful on the market.In particular, as instruments there are Web metrics and KPIs that contextualize and

give precise values to the different strategic points of eBusiness, and Web Scorecard whichdefine in a more subjective way all the usefull values collected usually involving a Webplatform.As will be described hereafter the KPIs are few specific measures and essential for

the whole eBusiness, while metrics provide more data to allow to control every detail.In addition, these tools are supported by Web Scorecard to reach more abstract andsubjective information from all stakeholders of the Web platform.

2.3.1. Web Metrics

The term Web metric is very extensive and includes several different elements that com-pose a Web platform: transactions, site performance, user supplied data, Web usage andpatterns, usability and financial analysis. Thus all information which are potentiallymeasurable and analyzable.The Web metrics are distinguished from simple data due to the fact that already

represent a preliminary analysis. They are generally aggregated and processed datawhich provides usable information.They are numerous and concern many aspects of the entire eBusiness and can answer,

as shown in the Table 2.1, to different questions about the Web platform. The tableshows in particular a series of questions that specifically relate to the origin and customsof visitors of a website. They can also be several Web metrics that can respond in differentways to a single question.Among all existing metrics, only few of them are used during the phase of analysis,

this selection is made in order to have different levels of metrics. This classification wouldform a pyramidal structure where there are few metrics at the top, but very generic, andmore the level is lower more the metrics increases and becomes gradually more specific.

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2.3.2 Key Performance Indicators

Question Web metricsWho visited the web site? Unique visitor, Countries, Lan-

guagesFrom where did they come? Referring URLs, Referring Search

PhrasesWhich pages did they view? Entry/Exit pages, Average Time

on Pages, Page Views per Visitor,Bounce Rate

Did they have any problem on theweb site?

Browser Versions, Flash Versions,Java Support, Operating System(OS) Versions

What did they buy? Goals and Funnel

Table 2.1.: Stategic Questions and Correlated Web Metrics

Among all the available Web metrics, as will be explained next, only a small part hasthe necessary requirements to become a KPI.

2.3.2. Key Performance Indicators

The term KPI comes from the industrial sector and indicates a system for measuringthe performance of any process [FG99]. The KPIs are usually used to evaluate theperformance and the level of success of an entire organization or specific activities withinit. The data required for the establishment of KPIs are generated directly from industrialprocesses in progress.Bringing the term in the Web environment, KPIs are basically less than ten measure-

ments usually taken among all available Web metrics, which are defined as a vital turningpoint for the business. As in industrial sector, in the field of Web companies KPIs aredirectly generated by the business processes.A good definition given by [Par10, p.4] is that KPIs represent a set of measures focusing

on those aspects of organizational performance that are the most critical for the currentand future success of the organization.Mainly for this characteristic, the KPIs differ from Web metrics which instead may

represent any type of statistics, although not essential or critical. Basically the resultsof a well-defined KPIs must lead to some important actions, as outlined in the followingquote:

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2.3.2 Key Performance Indicators

If a 10 percent change positive or negative in a KPI doesn’t make you situp and call someone to find out what happened, then it is not well defined.[Cut10, p.304]

The main difference between simple measurements and KPIs can be summarized inthe example shown in Figure 2.2 in which a measurement can provide the followingresult: "we had 242 visitors in January", while a KPI can be set to compare the variousmeasurements and represent the variation, and therefore the result in this case is: "thenumber of visitors in January increased by 25.88% compared to the average of the monthsrepresented".

Figure 2.2.: Example of KPI on Visitors Entrances [Gooc]

All KPIs are represented by percentages, ratios or averages and are also strongly linkedto specific time periods. The KPIs diverge from other kinds of indicators because they arefrequently nonfinancial measures (even if certain best practices recommend turning theminto monetary values to be better represented [Cut10, p.305]) and are measured frequently(on a daily or weekly basis). This allows to use them within a dynamic organization thatfocuses most of their activities around a Web platform.Briefly KPIs must have the following characteristics:

• Should be few (less than ten).

• Must be linked to periods of time.

• Must be represented by percentages, ratios or average, usually by variations.

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2.3.3 From Balanced to Web Scorecard

• A variation in a KPI should lead to take decisions within the eBusiness.

The KPIs therefore become an essential tool for the daily management of an eBusiness,if well implemented allows to act quickly to any change taking place.

2.3.3. From Balanced to Web Scorecard

The Web Scorecard come directly from the theory of Balanced Scorecard developed byRobert Kaplan and David Norton in 1992 [KN96]. The Balanced Scorecard is a man-agement tool used to monitor the performance of all sectors of an organization, usuallytowards strategic goals. The first generation of Balanced Scorecard measures suggestthat a series of performance measures are spread among four perspectives: Financial,Customer, Internal Processes, and Learning & Growth (Figure 2.3), which are all aroundthe core business and the strategy. For each perspective five or six performance measuresare defined.

Figure 2.3.: Balanced Scorecard View [Ins]

From best practices [2GC] of those who have tried to use the first generation of thistool result many failures. One of the main reason is attributed to the lack of ability oforganization to really adapt the theory to different practical needs.From the first generation were then developed a number of changes in order to make

this tool more stable and easy to apply. This led to the mid 90’s to the second generation

12


of the Balanced Scorecard, which is composed of a set of limited priority goals to beachieved. These main goals are controlled by a series of measures which are linkedtogether with the aim of creating a strategy map.This improvement process continues and led to a third, and at the current state, a

fourth generation of Balanced Scorecard.With the development of the Internet and the consequent growth of companies based

wholly on the Web has grown also a strong need to adapt the tools used in traditionalorganizations to new requirements of Web companies.The Web Scorecard must adapt to a different environment from that of industry for

which the Balanced Scorecard have been created. Within the Web companies all businessprocesses orbit around a Web product that has been developped. For this reason, WebScorecard should achieve the same goal of translating the strategy into action, but in adifferent environment.The Balanced Scorecard are applied in practice with different methods of management.

The main effects produced by the Balanced Scorecard are: [Per09]

• Clarifying strategy: Through an in-depth internal discussion during their cre-ation it allows everyone involved to improve the understanding of corporate strategy.

• Translating strategy into action and executing it: all different managementtools deal with defining and implementing the plan and the roadmap, while theBalanced Scorecard is used to track and monitor the execution.

• Aligning business units around the strategy: create permanent channels ofcommunication between different business units to unify the efforts around thestrategy.

• Communicating the strategy to all levels: expand the strategy at all levels ofhierarchical organization by spreading the Balanced Scorecard.

• Monitoring and managing strategic execution: support during the meetingsto focus on strategy, used like an agenda.

From the points above, it can be deduced that the Balanced Scorecard seek to providethe largest support possible to the implementation of the strategy. The size of the com-pany in term of human resources is also a key factor in order to adapt this tool, in fact

13


in small businesses such as startups and Web companies not all concepts brought by thismethodology are valid, but must be modified to make the instrument lighter. The riskotherwise is to have a tool which become time consuming compared with the long-termbenefits which brings.The first advantage of the application of Web Scorecard in small companies lies in the

communication between the different units, since initially the number of people workingon the project is limited, it is also much easier to manage verbally and clarify the companystrategy at all levels. This however requires that all members are apt to actively seekcommunication.So starting from the five points of the Balanced Scorecard, for Web Scorecard are taken

into account two of them:

• Translating strategy into action and executing it.

• Monitoring and managing strategic execution.

At this point must be considered also the other instruments used to manage the strategyand the business plan: the Web metrics and KPIs.While Web metrics and KPIs provide accurate data on theWeb product, Web Scorecard

deal with all those aspects which cannot be directly translated into concrete values. Inpractice, in addition to the quantitative data supplied by the Web metrics and KPIs thereare also the qualitative data of Web Scorecard.One method used to implement the Web Scorecard, and therefore to retrieve all im-

portant information, are the evaluation forms (or surveys), which differ based on whoresponds to them and to which area of the organization are addressed.The evaluation forms allow to collect information which otherwise would not be pos-

sible to gather. A practical example concerns the subjective opinion of the users whenevaluating several aspects of the platform, from graphics to contents. For this reason theforms after having identified the recipients should be divided into categories to evaluateall the important elements.The recipients of these surveys may vary among all stakeholders, for example:

• Customer: all users of the website or, in the case of a project in development, asmall circle of them who experiment the platform or some new functions in a betaphase.

14


• Organization: company founders and employees at all levels and in all sectors.

• Partner: all of the external partners who collaborate with the company but donot deal with the daily work. In particular, sponsors, consultants, private andinstitutional funders.

An additional element of assessment within the company covers the whole eBusiness,small business as startups can try, through the Web Scorecard, to evaluate whether allthe primary strategic goals, from financial to technical ones, have been achieved at whichlevel of completion they are. Among these evaluations the Return on Investment (ROI)and the evaluation of the growth of company are certainly two elements of this category.The Figure 2.4 shows the four perspective described above, together with the Strategy

and the Business plan which creates the Web Scorecard View.

Figure 2.4.: Web Scorecard schema

The questions contained in the individual survey are different for each recipient, forwhat concerns the users the categories are:

• Content: questions about the content of the platform, its clarity and quality ofinformation provided. In practice, how the communication is managed, textly andgraphically.

• Usability: questions about how the use of the platform is evaluated on a practicallevel. How the experience of browsing is evaluated and whether the instrumentsavailable meet the all users needs, and if they are adequate.

15


• Interaction: similar to usability but with questions that concern the daily use ofthe platform. Especially how the interaction between the various actors is managedand which degree are they encouraged to use the tools provided.

Together with the assessment of the users of the platform is also important to haveinternal reviews of the company, from who is directly and indirectly involved in thedevelopment and maintenance of the project.Within the organization the evaluation questions concern the internal processes of the

company. The survey are organized as follows:

• Content: includes evaluations of how the content is handled at the level of admin-istration, and how the communication between users and administrators of platformis managed.

• Technology: concerns questions about the current technical status of the platform,in particular is evaluated how the bugs, and the implementation of new featuresare managed.

• Technical Performance: brings together questions about the different types ofperformance related to the platform, from hardware and software security to opti-mization of different aspects on the Web interface.

As for the sponsors and investors, the communication channel which is probably moreappropriate is direct communication, and not the use of forms which can however beused as supports. The questions in this case are used to assess whether the partnershipcomplies with the agreements reached, and if both sides are satisfied or if some changes tothe platform or to the agreement have to be done. A typical example is the placement of asponsor’s banner into the website, or with institutional investors the assessment of projectdevelopment, which must follow the milestones agreed at the beginning of cooperation.What is missing now to the questionnaire is an efficient scoring system which meets

the needs of Web Scorecard. The construction of possible answers methodologies maybe:

• A simple answer "yes or no" allows to immediately assess whether a response ispositive or negative, but it is not always possible to give an immediate and preciseanswer to every question, it is often necessary to add intermediate values. [Par]

16


• Introduce a numerical scale of values, for example to evaluate between one and five,so from poor (1) to very good (5). This allows to add the intermediate values, butrequires that all questions are correctly formulated so that it is possible to providean answer in this way.

• Create a slightly more advanced version than the previous one, again with a numer-ical rating system (1 to 5) but with the addition of a specific value for each question,for example from 1 to 100. In this way it is possible to get for each question andeach category the maximum value reachable and the value actually achieved. Thegap between the two is what is lacking in order to reach the goals set by the WebScorecard. [Com][McG]

The last point introduced is represented in Table 2.2 which is a sketched example ofhow a form can be structured. In addition to the scoring system it is important to leavespace for comments which provide more details to answers.

Category After-survey evaluationQnb Qval Question Evaluation

(Ev)Comment (MaxPt) (EvPt)

1 1-100 How...? 1 2 3 4 5 Qval1 ∗ 5 Qval1 ∗ Ev...

...

j 1-100 How...? 1 2 3 4 5 Qvalj ∗ 5 Qvalj ∗ EvMaxPtCat =j∑

i=1

Qvali ∗ 5

EvPtCat =j∑

i=1

Qvali ∗Evi

Qnb: Question Number MaxPt: Maximum points achievable by questionQval: Question Value EvPt: Points achieved by questionEv: Rating points MaxPtCat: Maximum points achievable by category

EvPtCat: Points achieved by category

Table 2.2.: Web Scorecard Example of a Survey Form

Whatever evaluation methodology is chosen it is important that questions are correctlyformulated, and to the right target in order to maximize results and get information whichcan be used at the strategic level.

17

2.4 Analytical Level

As previously mentioned the use of any instrument, including Web Scorecard must becompatible and proportional with the size of the company and the project itself. Thegoal is to have a usefull tool in terms of resources, and that do not provides vague andunnecessary information.An important perspective that is missing under the current view in Figure 2.4 is one

which takes into account competitors, but since all the perspectives currently consideredevaluate from different points the development of the company, the assessment of what isdone by the competitors remains an internal-based research, and recovered informationnot always is reliable. For this reason the analysis of competitors, which usually takesplace at an early stage in terms of strategy and business plan, is usually performed as anindependent activity of the internal Web Controlling tools.The next section will discuss the subject of how all gathered information through the

Web metrics, KPIs and the Web Scorecard can be used at the analytical level and willthen kick off of some concrete actions that will affect the eBusiness.

2.4. Analytical Level

At this level will be presented how the information collected can be used.The view presented by the theory on Web Scorecard (Figure 2.4) allows to add a variety

of direct metrics that do not otherwise be available by using only Web analytics tools.Therefore the metrics gathered by the different tools can be divided into two main

categories:

• Indirect (also called implicit): mesures and KPIs collected using Web analyticstools. They are categorized as indirect because there is no direct intervention ofusers, and the metrics are created from browsing data.

• Direct (also called explicit): mesures and KPIs collected using tools which allow todirectly ask any question and opinion to the stakeholders (like forms and surveys).

The combination of these two categories of metrics allows to obtain a dashboard whichis the most complete, with both, objective and subjective information.The possibilities to gather and present collected information are several, and should

bound to the needs of the company for what concerns the flow of information. In practice,if the company needs daily data, it is preferable to choose an online dashboard accessible

18

2.4.1 Data Collection Merge and Preparation For Analysis

at any time and updated frequently. On the other hand, if the data is to be presented,for example, in monthly reports it is possible to use software such as spreadsheets, whichcan be easily modified and used to create presentations.The main challenge is to gather all information which come from different sources

(introduced in Section 2.5).All collected data must be transformed into useful measurements to take strategic de-

cisions. These measurements, for a company based on Web services, are key instrumentsto operational and strategic decisions on what changes need to be undertaken, and howwill be developed the eBusiness in the future.In the next two points are presented some tools which can be used to collect metrics

and KPIs, represent them, and then interpret them in order to be able to take concreteactions concerning the Web activities.

2.4.1. Data Collection Merge and Preparation For Analysis

First of all, for the analysis, the data have to be represented in the form of tables orgraphs, the raw data is not enought to analyze immediately the results, so there is theneed for some dedicated software which can turn raw data into metrics.There are several softwares which model the data and present them according to the

needs, some of them are:

• Internet and Intranet dashboards: usually based on Web technologies suchas html, php, javascript, mysql. Most Web analytics softwares in addition to datacollection provides also a dashboard in order to present them.

– Google Analytics (GA), Piwik, AWStats dashboards: three free softwares forInternet data collection which have a online dashboard that permits to browseamong the metrics.

– LimeSurvey Reports [Lim]: example of free software for creating surveys usedto collect direct metrics. It has a system of automatic reports with charts andgraphs for each survey question.

• Desktop software: installed directly over a PC or company server, and allowto perform several tasks, from creating presentations to questioning databases fordata.

19

2.4.2 Data Interpretation and Act Proactively to Changes

– Microsoft Excel (or OpenOffice.org Calc): extremely versatile spreadsheet usedto import data and represent them graphically.

– Microsoft Powerpoint (or OpenOffice.org Impress): software used for creatingslides presentations.

– JasperForge iReport [Jas]: is a software that allows to perform queries on adatabase and create reports with charts and graphs.

These are just some examples of softwares used for the process of collection and prepa-ration of metrics. More tools are used and more is the risk of dispersion of collectedinformation.One of the primary objective of the analytical level is to aggregate the majority of

information regardless their origin. In order to do this, the data interoperability throughthe use of compatible formats for data exchange is essential, or by using some ApplicationProgramming Interface (API) to retrieve the information.Thus, for the analysis it would be very useful having a single instrument able to gather

and represent information. Another possibility could be use only the KPIs, therebyreducing the work required. The KPIs would therefore represented all together and usedas main indicators, while for more detailed analysis the access of the metrics associatedto KPIs would be guaranteed by the different softwares used.Once determined what has to be represented and where, it is necessary to define how

represent it, for example in table and graphic form, specifying the timeline, in order tobetter observe the variations.Further arrangements for representing in an optimal way the metrics are provided by

the softwares themselves. In partcular, GA enables the use of segmentation and filtersthat improve the selection of the metrics according to the different needs, for exampleselecting a geographical location of visitors.Once all the metrics and KPIs are packed and available in dashboards and in presen-

tations, it comes the time to analyze and interpret them.

2.4.2. Data Interpretation and Act Proactively to Changes

One of the key factors in the Internet world is the speed of changes, that is much morerapid than in any other sector. It is therefore essential to have a very active approach forwhat concerns the variations which may affect the eBusiness. Changes that can range

20

2.5 Operative Level

from simple bug fixes or usability improvements of the platform, to eBusiness strategyshift towards the market.Starting from the instruments previously described, the collected data must be ana-

lyzed, interpreted and contextualised with the eBusiness strategy. Then it is possible toadopt certain measures to improve each platform aspect.The KPIs alone are not enough to make decisions, it is important to interact with

information in a completely and in depth way: example, the KPI number of registeredusers (in a Web platform) is not enough to know if the platform is successful or not, therecould be spam-bots and inactive accounts. For this reason, the secondary metrics areimportant to inform about the interaction between the users and the website.The KPIs serve as an alarm bells, but to make decisions is essential to interpret all the

metrics which lead to take appropriate actions (such as CAPTCHA test to verify if theuser is a human).This phase represents a continuous cycle of analysis and control which constitutes the

core of the Web Controlling technique. In addition this cycle has to be keept open alsoto further information and data not related directly to Web Controlling techniques: thenews and insides from competitors, technological developments in IT sector, as well asinternal company factors.In the next section will be presented some softwares and proceeding used to collect the

information which allows the strategic and the analytic level to work.

2.5. Operative Level

At this level, are implemented all the techniques and instruments of Web analytics directyin the platform code or in the Web server.One of the most comprehensive and wide range between the definitions of Web analytics

is the one proposed by [MZ10, p.5]:

Web analytics is the measurement and analysis of Web data in order to betterunderstand site usage and visitors behavior. The definition and analysis ofKPIs should help to verify achieved website-related objectives and to optimizethe web site and the eBusiness, in particular the eMarketing and eCustomerRelationship Manager (eCRM).

21

2.5.1 Client-side

This definition has the advantage of summarizing all fields affected from the Webanalytics techniques, and create a framework of discussion within the eBusiness.For further analysis there are three most commonly used methods of data collection,

which are:

• Client-side: includes all methods of data collection that are usually carried outby scripts contained in the platform code and executed by users while browsing.

• Server-side: refers to a precise methodology for analyzing log files from the plat-form’s hosting Web server.

• Database: Web platforms typically store a large amount of data in dedicateddatabases which can be very valuable source of information.

These three methodologies will be briefly described and will be presented some practicalexamples of suitable tools available on the market.

2.5.1. Client-side

The client-side data collection method interact directly with the visitor browser in orderto collect all necessary information.The most popular system currently consists of inserting a tag, usually some JavaScript

code, into the page that has to be controlled. An example is the Google AnalyticsTracking Code (GATC) represented in Code C.1, which calls the script directly hostedon Google servers and sends all the collected information to a specific account with areference code (line 3: UA-XXXXXX-X).As shown in Figure 2.5, the visitor initially enter the website URL address that wants

to visit. The browser takes care of asking all necessary information from the Web serverwhich answers by sending the content to display the page, usually with the JavaScripttag inside the header of the requested page.Once the tag is executed it calls other scripts which allow to collect every browsing

information and send them to the designated tracking server, including the informationcontained in browser cookies.Usually while surfing the tracking servers send additional scripts that add additional

capacity for data collection.

22

2.5.1 Client-side

Figure 2.5.: Client-side Data Collection Method [Gro]

There are many different products available on the market, some are free and otherswhich requires a paid subscription, and have different characteristics: some are opensource, or proprietary, some can be installed on personal server, other are hosted onthird parties servers, and some provide professional support services like in Software asa Service model (SaaS).The choice of product must be made according to the needs and resources of the

company. For what concerns Stagend.com, as will be described later in the case study,the choice has been given to a free software.

Google Analytics

GA [Gooc] is currently one of the most popular free products for Web analytics. Aspreviously explained all the information collected through the use of page tags (CodeC.1) are sent and processed directly on Google’s servers to be displayed on the onlinedashboard.In the Figure 2.6 is represented the data collection process of GA. Once the tag is exe-

cuted, it actively seeks all the characteristics of the visitor and its browsing environment.

23

2.5.1 Client-side

It also takes care of the cookies set and update with the latest information about thevisit.The script ga.js (line 8, Code C.1) is loaded, and the data collected is sent to the server

ready to be treated.

Figure 2.6.: Google Analytics Processing Flow [Cut10, p.14]

In a second stage starts the phase of data processing in which each line of data collectedis analyzed as happens to the log files for server-side methodologies. The extracted datais then stored in a centralized database ready to be represented within the dashboard.GA allows to track any of the most common and well known website metrics, but

especially allows a great variety of customization including the ability to track specificgoals which are very important for specific eBusiness related results.In fact, the breadth of data collected by GA according to [Cut10, p.13] allow to define

it as an information aggregation system rather than a simple hit collector.Another advantage of GA is the ease with which it could be connected with other tools

such as Google AdWords and AdSense. That allow to have a variety of very useful toolsfor the different goals of eBusiness.A strength is the ability to make extensive changes in the GATC in order to be able to

adapt it to the needs of the platform. In particular, the event tracking which can measurecertain actions on individual objects on the platform, for example clicking on pictures,selecting objects in forms or downloading a document. The events can be sorted intospecific categories with personalized labels and monetary values.

24

2.5.2 Server-side

However, the major disadvantage is certainly that all information are stored on third-party servers, that creates some problems related to their security and privacy. In addi-tion, the script ga.js is continuously modified and the work to keep everything up to datemust be done within the company [Good].

Piwik

Piwik aims to be an open source alternative to Google Analytics [Piw].One of the initial considerations for using Piwik as a tool for Web analytics was that it

allows to have direct control and all the data collected, which are stored in an companydatabase. Also being an open source product is possible to imagine a customizationproject in order to better to adapt it to the needs of the business.The functioning of Piwik very similar to that of GA, is always based on a JavaScript

tag placed on the page to be controlled.The dashboard reminds that of GA is also configurable and the metrics are represented

both graphically and numerically, with the ability to define custom goals.In the case study will be analyzed in more details the characteristics of these instru-

ments to choose the most suitable for the company needs.

2.5.2. Server-side

The tools that are based on the technique of data collection called server-side differ fromclient-side especially for what concerns the modality of data collection. In fact, they takeall information directly from the Web server log files (Figure 2.7).During normal running the Web server, in this example is the Apache [Apa], saves into

single lines all requests that are resolved. In details in Code 2.1 is possible to observethe following information [Doc]: client IP address, server date and time of the request,request method used (in this example GET method), requested file (ated.gif), transferprotocol (HTTP), response message (200 that means OK), quantity of bytes transferred,referrer and client browser type and version.

1 85.2.173.227 - - [21/ Oct /2010:17:22:57 +0200] "GET /ated.gif HTTP /1.1"200 4366 "http ://www.stagend.com/home/" "Mozilla /5.0 (Windows; U;Windows NT 6.1; it; rv :1.9.2.11) Gecko /20101012 Firefox /3.6.11"

Code 2.1: Example of Apache Server’s Log Entry

25

2.5.2 Server-side

In some aspects the analysis of log files can work around to certain problems encoun-tered with the methods based on tags. An example is the ability to record bots andspiders coming from the various search engines looking for information on the site andwith the objective to index it.On the other hand, stored information is not very customizable, and every change must

always respect the parameters of communication between Web servers and clients, thismakes server-side tools not flexible nor adaptable to specific needs.

Figure 2.7.: Server-side Data Collection Method [Gro]

There are several other technical problems related to server-side data collection method,mainly caused by proxy and caching systems that do not call resources from the Webserver and therefore does not produce any logs about. Another problem arises from theimpossibility of tracking events (JavaScript, Flash, and othes technologies) that severelyrestricts the collection of data for applications of the so called Web 2.0.For these reasons it is currently imaginable the use of tools based on the methodology

server-side only side by side to other methods, in order to achieve specific measurementswhich are not otherwise available.

26

2.5.3 Database

AWStats

AWStats [AWS] is one of the most used server-side tools which has the characteristics ofbeing open-source and free.As mentioned earlier one of the advantages of server-side is the possibility of tracking

some visitors not detectable by the client-side technique, robots and spiders in particular.In the Figure 2.8 is represented an example of summary of requests and traffic generatedby bots.

Figure 2.8.: AWStats Robots and Spiders Visits Table [AWS]

AWStats should be used along with other systems of data collection and as a sourceof control and comparison of these data. Into practice it can become a system whichallows to verify quantitatively the differences between the data collected from differentmethodologies and then be able to take measures to solve potential problems.

2.5.3. Database

In addition to data that may be collected directly during browsing of users, there arealso all the other information which, depending on how it was structured website, aretypically stored in one or more databases. The simplest example is the case that thevisitor in order to perform certain actions on the website needs to create an account.Any information that will be prompted during the account creation procedure will bestored within the database.

27

2.5.3 Database

Most of this information are used for operational functions of the website, but thedatabase and the website itself could be modified to store additional information that arenot only needed to normal operation, but can be used as sources of information withinthe Web Controlling system.Taking the example of the user who register his account, once he browses within the

website every action that performs, interacting with the objects of the different Webpages, leaves records in the database. The objective of the tools that will take careof extracting useful information from the database is to optimize the collection of thisinformation in a way to make work easier and to make information useful with the goalof creating Web metrics and KPIs.All the technique which allows to extract valuable and significant data from a database

are based on queries that allow to retrieve and organize content.

28

3. Stagend.com Case Study

In this chapter it is treated the main topic of the thesis. How the strategy of WebControlling described in the previous chapter have been implemented and also adaptedfor Stagend.com startup, which deals with building a services Web platform for peopleinvolved in the music business.

3.1. Introduction

The case study has the objective of trying to understand how a system of Web Controllingcan be applied in practice in a startup company. The goal is to analyze if all the toolsavailable are useful and necessary in carrying out business activities; especially thesewhich need to be adapted or entirely modified to be used effectively.This chapter want to go over the theory of the previous chapter adapting it step by

step with the work carried out at Stagend.com. First of all all startup objectives will bedescribed and the context in which they are located, then will be introduced the businessplan [Tea10] and strategy applied to them.The ultimate goal of implementing a system of Web Controlling within the startup is

that of having a comprehensive tool that can help to take important strategic decisionsbased on accurate information. Moreover, it is important to have real measurements ofsuccess and competitiveness, as well as to define what measures to consider in marketingfor increasing the attractiveness of the services offered by the platform.Stagend.com venture starts when Marco Alberti is selected by Venturelab [fTC] to

participate to its entrepreneurial courses. Then other three members join the project.This entrepreneurial project is selected among many other to enter in the CP Start-up[US], the technological park in Lugano; so this allows to have the nencessary logisticsupport (inclusing an own office).The Start-up Promotion Center is a service promoted by the Foundation for the Lugano

Faculties and set up in collaboration with the University of Lugano (USI) and with

29

3.2 Stagend.com Business Plan

the University of Applied Sciences and Arts of Southern Switzerland (SUPSI), in orderto assist Swiss and foreign graduates who plan to start a company in Canton Ticino(Switzerland).In march 2010, although competing with some multi-awarded projects, Stagend.com is

awarded with the ATED Award as best IT business idea of the year (2010) in Ticino. Atthe end of June, the project is also accepted by the Swiss Commission for Technology andInnovation (CTI) startup coaching phase for the next 2 years. Currently, six volunteerpeople are working to the realization of this business idea. At the end of 2010 Stagend.comisfounded as Ltd by Marco Alberti (CEO), Oscar Mora (CIO), Mattia Minotti (CTO)and Martino Piccioli (CMO).

3.2. Stagend.com Business Plan

This part was removed from the public version.

Strategy of Stagend.com


Market of Stagend.com

Data on the music industry are promising. Live events have been steadily increasing since2005 (+33%) and online platforms in the Swiss music business show strong growth rates(for example, 10 new users per day on mx3.ch). Moreover, in the last years Europe haveassisted to an increased interest in local music and in independent bands, meaning thatindependent music is attracting ever more fans.

3.2.1. Products and Services


This Figure was removed from the public version

Figure 3.1.: Stagend.com’s Business Idea [Tea10, p.3]

30

3.2.2 Markets

This title was removed from the public version.






Online Platforms Syncronization (Sync)

• What: This service offers the possibility to update the several web-pages managedby a music industry actor, all at the same time, directly from Stagend.com.

• How: This is granted by an informatics technology that pushes all the informationrelative to user status and newly scheduled events on existing online platforms andwebsites.

• Why: In the attempt to be as visible as possible, live scene actors (especially clubsand bands) manage their profiles on at least three social networks at a time. Keep-ing up-to-date all pages involves a series of repetitive update actions performedseparately. By the Sync, updating Stagend.com immediately brings the other de-sired profiles to date, saving much time on an ongoing basis. As is shown in Figure3.2 in (a) the interface which allows to perform the events synchronization and in(b) the result presented on a social network.

3.2.2. Markets


3.2.3. Marketing Plan


31

3.2.3 Marketing Plan

(a) Stagend.com Sync Interface

(b) Facebook Result of Sync Action

Figure 3.2.: Stagend.com Syncronization Module Example


Figure 3.3.: Stagend.com’s Online Competitors [Tea10, p.10]

32

3.3 Preparation for Web Controlling Implementation

Market Entry Strategy



Figure 3.4.: Market Entry Strategy [Tea10, p.13]


Figure 3.5.: Monetization Model [Tea10, p.15]

3.3. Preparation for Web Controlling Implementation

One of the first steps it is to analyze the set of potentially useful information which couldresults from different techniques of Web Controlling. In particular, this concerns all theinformation which can provide an overview of the eBusiness, and that also allow detailedanalysis of each feature.Later, it is necessary to set some categories into which are listed all information. This

must be collected in order to correct and improve the implementation of business plans.The first categorization of necessary information, created after a first meeting, has giventhis list as a result:

• Visitors: it contains all information related to visitors (customers) of the platform,from their frequency of visits to their origin.

• Platform content: category closely linked to the previous and includes all infor-mation available about the contents of the platform, from the frequency of publi-cation of new content to the quality of the content itself.

• Technical Performance: everything about the performance of the platform, fromhardware to software level.

• Marketing: all that concerns marketing campaigns of partners on the platform,and of company on external media.

33

3.4 Implementing Web Scorecards

• Financial: everything concerning the financial side of platform such as the statis-tics on paid features or related to the preceeding paragraph, the revenue from themarketing campaigns of the partners.

This initial categorization process is then used in the following sections as a startingpoint to better define all the Web Controlling tools starting from Web Scorecards.

3.4. Implementing Web Scorecards

The Web Scorecards can be implemented within the company as a primary tool whichsupport the use of Web analytic metrics and KPIs. The description provided in thefirst part of this work suggests a direct use of Web Scorecards which provide additionalelements of assessment that are different from the information provided by KPIs and Webanalytic. In fact, the Web analytics metrics generally return only numeric values thathas to be interpreted, while the Web Scorecard can provide additional metrics related tohow are perceived and evaluated each single component of the eBusiness. These metricsare called direct (or explicit) since it is necessary the direct involvement of stakeholders(especially for surveys).In practical the implementation of the Web Scorecard in the Stagend.com startup is

done in parallel with the implementation of indirect metrics from Web analytic tools. Ithas decided to bring these proceedings on the same level to fill up the gaps in informationavailable whithin indirect metrics with the tools offered by Web Scorecard, in particularwith the use of surveys.These surveys are divided into four categories, as described in the theoretical section

in Figure 2.4 (User, Organization, Patner, eBusiness), and the scheme used is shown inTable 2.2. The result is presented in Figure 3.6 with all direct metrics listed according tothe categories and subjects. Then these metrics will be integrated in a next stage withthe indirect metrics from the Web analytics tools.The first category is addressed to the users of the platform (User Perspective in Figure

3.6), also known as clients, and is divided into three main subjects:

• Content: questions about all information within the platform, evaluating theirquality, and ease of finding the one is needed.

• Usability: unlike the previous point that is based mainly on textual content,usability evaluate the ease of use of the platform and the quality of the layout. It

34


Figure 3.6.: Web Scorecard Surveys Direct Metrics Subdivision

35


also deals with the first interactions with the platform, including the creation of themain account.

• Interaction: deals with user interactions with the platform after he created hismain account, is evaluated how much the user is stimulated and encouraged tointeract with the different features present in the platform.

The first version of the form for users is presented in the Appendix A.1 and includes18 questions divided into the three subject just introduced.Another method during the preliminary stages of development of the platform is to

ask potential users, who have never worked with the platform previously, to try to use itunder the supervision of a member of the startup, and comment freely on the activitiesdone on the website. This methodology allows to assess which are the main elementsthat a user checks and which ones are ignored completely; this also allows to improve theScorecard Web surveys in order to ask more targeted and effective questions to a largernumber of potential users.

Figure 3.7.: Example of pop-up question (Web Scorecard)

Another implementation of Web Scorecard that has been taken into consideration isthe use of lightweight and not invasive pop-ups (Figure 3.7) used on the platform, whichappear to the user at the end of certain processes (like account registration), asking foran evaluation of the process itself. An example related to the live music world is whena fan on an event page marks that is going to participate, the day after the event if theuser logs onto the platform a pop-up form will ask him to give an assessment about theevent that went to see.While asking to answer to a survey to tester users does not represent a problem, gather

information from regular users is more difficult since these surveys are often see as a wasteof time.

36


These pop-ups can be a viable alternative to the surveys but do not allow to collectthe same amount of information, especially for what concerns the personal comments.The second category covered by the surveys is addressed to the members of the company

(Organization Perspective in Figure 3.6), concerns the company itself (or Organization),and is also divided in three main subjects:

• Content: questions about the content of the platform but from the standpointof the organization, especially on the quality control of information and behaviorsthat violate the policy.

• Technology: brings together questions about the technologies used to build theplatform, including bug fixes, platform compatibility with all browsers and devel-opment of external applications. It also assesses the optimal performance of datacollection method both client and server-side programs.

• Technical Performance: assesses conditions at the hardware level with checkingthe Web server and software with the control of the platform.

The first version of the form for users is presented in the Appendix A.2 and includes13 questions. Additional tools used at company level to evaluate the work done on theplatform, are internal documents and reports which allow to add additional informationto those collected by the surveys. More precisely, using a document management system(in this specific case: Nuxeo DM [Nux]) a platform for members of the company is createdand it allows to share any kind of document and create internal discussion on them.The third category of surveys concerns the company’s external partners (Partner Per-

spective in Figure 3.6), including sponsors and advertisers which through agreements withthe company want to publish their products or services on the platform, mostly throughthe display of banner ads. For this reason, the prepared questions concern almost allaspects previously presented, the version of this survey is presented in the Appendix A.3.The fourth and final category covered by the Scorecard Web concern the overall assess-

ment of the entire eBusiness (eBusiness Perspective in Figure 3.6), the questions relatedto this topic were divided into two subjects (Appendix A.4):

• Financial and Growth: brings together questions about the company’s financialmodel and the model of growth. In particular, if what has been forecast in thebusiness plan is fulfilled or not.

37

3.5 From Strategy to Web Metrics

• Return of Investment (ROI): introduce some general questions about whatconcerns the financial return of investments made by both founding members ofthe company and by third parties.

Although the surveys for partners and for the evaluation of the eBusiness is created,it is much more likely that will be used only at the conceptual level and in their placewill be used other tools to achieve the same purposes. In particular for what concernsthe partners, it is better to do direct questions during some scheduled meetings, ratherthan submit a form to fill. In some special cases, such as for automated banner offeredby third party companies, the contact with the partners is missing completely.The same shall be applied to the eBusiness perspective which needs instead some more

targeted tools, especially for what concerns financial assessments some tools must be morerelated to economic values, such as calculations of economic profitability and quarterlyfinancial statements. The surveys nevertheless are used to provide an overall view ofeBusiness although they must be integrated with the other tools (Web metrics and KPIs)which will be discussed in the following sections.

3.5. From Strategy to Web Metrics

Parallel to the development of Web Scorecard (and direct metrics), it is necessary toidentify all key elements that constitute the eBusiness, always using as a starting pointthe strategy and the business plan. Are then retrieved all potential Web metric whichcould be used during the daily work within the company.The use of Web metrics arises from the need to collect the largest possible amount of

information on platform’s users, from their behaviors to their activities, in order to, aftera phase of analysis, improve the services offered or to add new ones.Another important element in the selection and implementation of Web metrics con-

cerns the tools needed to collect all this information, in particular Web analytics software.According to the tools chosen the amount of available Web metrics can vary substantially.For this reason, the metrics are closely related to the chosen tool which allows to retrieveall necessary values.The first decision is to use as much as possible free and open source softwares to reduce

costs which represent the first challenge for a startup. This reduces the possible choiceswithin software for Web analytics (both client and server side).

38

3.5 From Strategy to Web Metrics

The final choice for what concerns the cliend-side is: GA [Gooc] and Piwik [Piw]. Theyare both tested and analyzed according to the needs of the company as it is shown in theTable 3.1 based on ten points among the most important.

Google Analytics Piwik (v.1.4)Open Source No YesDirect data control No YesCustomizable Dashboard Yes YesSoftware customization Yes (GATC) YesAdsense/Adwords support Yes Yes (Partially)Goals tracking Yes YesFunnel analytics Yes NoEvents tracking Yes NoAPI data support Yes YesCustom reports Yes No

Table 3.1.: Google Analytics and Piwik Comparison

The decision taken after this comparison is that despite the concerns of security andprivacy of data collected and stored by GA in their server, the software itself is more de-velopped than Piwik. GA is close to professional paying systems while still remaining free.In addition, the full integration with other Google tools (AdWords and AdSense) makesit a very powerful instrument that allows to manage several aspects of Web analytics andonline advertising on a single platform.If all the data is stored and managed by third-party servers, it does not only creates

disadvantages, if is considered that the platform generates a large number of visitor’straffic, having a server that manages all information externally bring less work for theWeb server, and consequently reduces the hosting costs of services that are based onperformance.The decision to use GA, however, is not definitive, if the development of Piwik or other

similar tools should improve, it is always possible to change gradually from one systemto another by overlapping two systems for a given period.For what concerns the server-side software for Web analytics, the choice was easier

since the competition in this area is lower and that few software in this field have reacheda level of sufficient quality to ensure that the data collected is the most close to realityas possible. For this reason, after a brief investigation, AWStats [AWS] is chosen as

39

3.5.1 Google Analytics Metrics

server-side software.The last system of Web analytics is provided directly by the platform itself, it is the

internal database that gathers and stores all data. This powerful source of data will beused in practice to provide Web metrics. The following sections will explain how at atechnical level the database is used for this and which are the limitations.After all the practical considerations regarding the software to be used, the initial

subdivision of Web metrics is made according to their origin, in this case:

• GA metrics: includes all the metrics (Section 3.5.1) that the algorithm of GA isable to collect and the results are represented in the online dashboard. Furthermore,as it will be described below, it allows to partially customize the GATC to fit withsome precise requirements of the platform.

• AWStats metrics: includes all the metrics (Section 3.5.2) that this server-sidesoftware is able to retrieve analyzing the Web server’s log file. As presented in thetheoretical part some of these metrics are similar to those found in GA, only re-garding the definition but not for what concerns the values that will be represented.

• Database metrics: the data for the creation of these metrics (Section 3.5.3) willbe recovered through the establishment of queries and views within the database,which will allow to automate this task.

Not all the metrics presented by AWStats and Google Analytics are critical to the dailywork, it is therefore necessary to reduce their number with an accurate selection so thatin the analytical phase can be presented only the most important metrics.In the following sections are described the metrics coming from the three instruments

mentioned and are subsequently analyzed as a whole.

3.5.1. Google Analytics Metrics

Google Analytics provides a large amount of basic metrics and also allows to extend theircapabilities through customization.The Figure 3.8 shows a page of the Google Analytics dashboard, the Web interface to

navigate through the metrics collected by the GATC.Following the main menu metrics, there are four categories: visitors, traffic sources,

content and conversions.

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Figure 3.8.: Google Analytics Dashboard [Gooc]

41


• Visitors: contains all the metrics that relate to the origin and visitors’ behaviorson the platform. The most important metric in this category concerns the uniquevisitors which allow to have essential information on the diffusion of the website.

– Demographics: metrics that define the geographical origin and language of thevisitors.

– Behaviour: contains the most important metrics to define the type of visitorand his loyalty to the platform.

– Technology: metrics that relate to the software and infrastructure used by thevisitor, especially the browser, operating system, the provider used and if it isa mobile user.

• Traffic sources: metrics that allow to identify the origin of visitors at the browsinglevel.

– Incoming Sources: all sources from which came the users, in practice what theywere doing just before arriving on Stagend.com including referrals websites.

– AdWords: shows statistics of AdWords campaigns in GA.

• Content: contains all the metrics that affect the content of the platform, particu-larly how the user benefits from the content and from the specific tools provided.The most important metrics are the events which are configured and deployed asneeded.

– Site Content: contains metrics that refer to page views and in particular whatare the most used entry and exit pages.

– Site Speed: evaluates the speed of loading of individual pages to find items tolighten to make navigation smoother.

– Site Search: allows to track the internal search of the platform in order toevaluate usage and keywords searched.

– Events: are metrics defined in the platform code, especially to collect data onspecific events such as clicks made on an image or a link.

– AdSense: shows statistics of Adsense campaigns in GA.

• Conversions: represent the most important metrics for the company because theyare customizable and adaptable to the needs. In particular, the goals and funnels

42


allow to have detailed information on specific user behavior on the platform. Whileat the moment for what regards the eCommerce metrics are not used since thereare no products for the market.

– Goals: allow to define particular transaction and browsing goals, for examplethe average number of pages viewed by users or the average time spent on site,and representing the number of users reaching the goals set as a percentageof overall visitors. Another important tool is the funnel which sequentiallyrepresents the percentage of people who follow a particular path, an examplethe registration process of the user on the platform, the funnel is used to assessthe achievement of the registration procedure or where the largest number ofusers stops the process.

– E-commerce: as specified above, it contains some tools for websites which sellproducts online, from website subscriptions to industrial items, in order tomonitor and evaluate all the performance related to customers purchasing.

The main problem, presented also previously, of Google Analytics is the tag systemwhich uses JavaScript to collect information. In all modern browsers JavaScript is inte-grated and it works well, but also in some cases it can be purposly disabled depending onthe browsers preferences. As it is possible to see from the data collected by w2schools.com[w3s] in Table 3.2 a minority of browsers do not support or have disabled JavaScript.

Date JavaScript On JavaScript OffJanuary 2008 95% 5%January 2007 94% 6%January 2006 90% 10%

Table 3.2.: Browsers’ JavaScript Support Statistics

Addons have been developed for browsers which have the task of blocking flash andJavaScript scripts during the normal browsing with the intent to limit the display ofadvertising, but as a side effect it compromises the data collection of Google Analytics.An example application for the browser is AdBlock [Pal] developed with the aim to

remove or minimize all advertisement, and optionally (not set by default) allows to blockeven the GATC.

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3.5.2 AWStats Metrics

Therefore, the results of the statistics will be lower than actual values and for this reasonthat the integration of another tool such as AWStats server-side should allow to evaluatethese differences and take into account when analyzing the data collected. Although itshould be considered that the amount of information gathered is nevertheless statisticallysignificant in order to assess the average behavior of visitors.Further details on the implementation and customization of Google Analytics will be

presented in Section 3.7.

3.5.2. AWStats Metrics

The application AWStats is part of the scheme of data collection as a complement toGoogle Analytics, basically it allows to gather information that otherwise would be lost.It permits to have a comparison with the values measured by GA, in order to find possibleproblems that concern every system of data collection in the event that data, such as thevalues for a period of unique visitors, become too divergent (+/-20%) between the twotools.While the data from GA will tend downwards, showing fewer visitors than what ac-

tually happens, AWStats has the tendency to show more visits, because of incorrectidentifications of some spiders and crawlers that intentionally disguise themselves to realvisitors.Most of the stats provided are purely meant to inform about website functionality and

finding out more about the technologies used. [Edw]Among all the metrics found in AWStats those that will be considered the most for a

preliminary analysis are the following:

• Bandwith: allows to have precise values on the amount of data transferred, in orderto control the use of available resources and also to monitor individual componentsof the platform.

• Robots/Spiders: check the presence and browsing of these automated programsmainly used by search engines for indexing every website. These metrics allow toget an idea on the actual indexing of the platform and the refresh rate of the inputdata. Some of these bots are dedicated to retrieve critical information and they arenot recognized and incorrectly increase the number of visitors, such as bots that

44

3.5.3 Database Metrics

collect e-mail addresses in order to use them for different purposes (specially spamand fishing).

• HTTP Status codes: returns all the values of hits on pages with special statuscode, such as pages 404 (not found), or 401 pages (unauthorized). These values areused to identify possible browsing errors encoutered by users. In addition will beimplemented an automated ticketing system [osT] in order to better identify bugsand other problems connected to the platform, and to create a sort of helpdesk forcustomers.

• Files type: rankings for file extension every request made to the Web server, tobetter identify the sources of greater data traffic.

The other metrics provided by this tool are similar to those that can be found in GAand therefore, as mentioned above will only be used as a yardstick.As a final evaluation AWStats is a program that is executed and maintain all data

within the company’s production server, this ensures that data is protected and not usedby third parties if not explicitly allowed. In the case of Google Analytics the assurancesprovided about confidentiality of data collected can not always be guaranteed, and thereis the possibility of the use of sensitive data made anonymous. This is granted by theUS law which result to be outdated about IT data ownership problems, that is an areawhich in recent years (with the advent of so-called digital age) it is at the center of largedebates. A long story short, actually for the US law the act of collecting and analyzingmassive amounts of public and private data actually generates more data, which is oftenas useful as the original information, and belongs to whomever performed the analysis.[Cro]

3.5.3. Database Metrics

Compared to the two tools described above the database enables to define any metric in amore customized way through the use of Structured Query Language (SQL) instruments.Basically any information that has to be retrieved from the database should be subjectto a specific request called query.Before begining to collect data and analyze them, it is necessary to examine the entire

structure of the database in order to identify if there are all the data sources necessaryfor achieving the goals set or if it is necessary to make some changes.

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3.5.3 Database Metrics

This part was removed from the public version


Figure 3.9.: Profile Database Relational Schema

The next step after defining some basic metrics is to check if the database includes allnecessary information for the implementation of targeted queries to retrieve the data. Inparticular, if all affected fields have all time references required by the metrics which areessentially based on precise periods of time, this allowing to trace back the past data incase of problems.


Figure 3.10.: Event Database Relational Schema

A first proposal of queries for each metric is presented in the Appendix B, and theyare passed through the test phase of the platform in order to refine them.This part was removed from the public versionWhile datamining processes can be defined as a process of extracting patterns from

large data sets by combining methods from statistics and artificial intelligence withdatabase management [Fra], the technique to retrieve the necessary data to create metricsrelate almost exclusively on the field of statistics.This part was removed from the public versionThe metrics obtained through queries on the database, as well as other metrics, must be

able to provide more accurate data on the main actors which use the platform daily. Theymust be able to show behaviors and habits of users, or at least provide sufficient data tointerpret them, with the objective of adapting the platform to their needs and enhanceunderperforming parts. In addition, some metrics collected may be made available tousers of the platform.This part was removed from the public versionAt the technical level to ensure the security and integrity of the database most of the

queries are performed on a cloned database builds on daily backups of the database’sproduction server. This creates a database exclusively for analytical purposes.

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3.5.4 Sectorial Subdivision

3.5.4. Sectorial Subdivision

Before being able to define and choose which metrics have enough characteristics tobecome KPI, it is necessary to try to sort them according to the company needs.An initial assessment serves to subdivide the metric based on the corporate sector

mainly involved from their results, so it is used the classical subdivision schema [LEA]with its four main components:

• Finance: includes all monetary aspects, from revenues to costs.

• Marketing: includes all, internal and external, aspects of communication anddistribution of the platform.

• IT: includes all the technical aspects and research and development related to theoperation of the platform and the creation of new features.

• Human Resources: includes all aspects directly related to the effort and employ-ment of staff.

As represented in Figure 3.11 considering the four areas described above it is possibleto create a graphically subdivision by placing the various metrics available in intersectedsets.In the schema presented, if the metrics are divided into four business units it can be

seen that practically there are few metrics that have a direct impact on human resources.It should be considered that this area is touched only at a later stage since a metric hasan impact on another sector. An example is the Bandwidth metric that measures theamount of data traffic on the platform, if this quantity reaches levels too high the ITdepartment will have to arrange new hardware or software solutions to meet the trafficgenerated, this will affect later the human resources with higher work demand withinthe company, and also the finance department which shall be prepared for the additionalcosts. Thus, using the field for the division of human resource metrics might be droppedin favor of a three-sectors schema (IT, marketing and finance).This subdivision done within the startup is needed mainly to define which Web metrics

would the potential to be defined as KPIs. In practice this concerned all metrics thatit is more hard to position because they had a potential direct influence on almost allbusiness sectors. In fact, these metrics are positioned towards the center of the diagramwhere all four sectors schema converge.

47

3.5.4 Sectorial Subdivision

◦ AwStats metrics • GA metrics � Database metricsThis Figure was modified for the public version

Figure 3.11.: Distribution of Metrics Direct Impact on Stagend.com Activity Fields

48

3.6 From Web Metrics to KPIs

Another subdivision that is taken into account, but it is not fully implemented, concernsdividing all metrics over the Web Scorecard view (seen in Figure 2.4, but the resultgenerats by a first consideration do not lead to a clear definition of potential KPIs.After the implementation of Web metrics and Web Scorecard, the following section will

define and implement KPIs.

3.6. From Web Metrics to KPIs

As explained in the theoretical part of KPIs, from the set of all available metrics a dozenof them are chosen to represent the essential measures for the eBusiness.The KPIs chosen creates an evaluation and alarm system of the main core activities of

eBusiness, through consistent performance and accurate measurements.The main features which a KPI must have are summarized in the following points:

• Represents a sector or a vital feature.

• The resulting values are expressed as rations, percentage or averages.

• Represents the change of value over time.

As a matter of accuracy and objectivity of the data, KPIs are chosen among indirectmetrics from Web analytics tools.Starting from these initial conditions the KPIs are identified and classified from all

strateg information supplied by the eBusiness. The research begins by analyzing eachplatform module separately (seen in Figure 3.4), and later as a whole.This part was removed from the public version


Figure 3.12.: KPIs Distribution over Market Entry Strategy

All main features of the new modules are analyzed simultaneously with the vision ofthe possible evolution of the eBusiness.These KPIs are provisionally defined to plan in advance the work required for their

implementation.All KPIs derive from metrics or variations of them. Each of them is also closely

associated with a group of similar metrics that allow, in the analysis phase of the results,

49

3.6.1 KPIs for Platform Release

to have all the tools needed to try to answer the main question: Why is there a variationin the KPI?

3.6.1. KPIs for Platform Release

KPI 1: Unique Visitors

This first KPI shows the percentage growth in unique visitors of the platform using datafrom Google Analytics. This allows as a primary indicator to analyze the usage andspread of the platform over the Internet.In the example shown in Figure 3.13 monthly unique visitors are represented as a

percentage of total visitors in the selected period.

Figure 3.13.: Absolute Unique Visitors KPI Example [Gooc]

The representations are based primarily in weekly and monthly changes. Annual valuescan also be used for long-term statistics, and specific days of week or hourly statistics forshowing major traffic peaks.But despite the different time scales for this KPI, the outcome focuses on weekly and

monthly base. All others metrics serve to identify specific problems, and are not directlyused as KPI.

50


t Profiles (Fan) ∆Profiles KPI (M) Band Profiles Club Profiles0 680 81 181 750 +70 +10.29% 95 +17.28% 22 +22.22%2 815 +65 +8.67% 112 +17.89% 31 +40.90%3 1042 +227 +27.85% 142 +26.78% 39 +25.80%4 1230 +188 +18.04% 182 +28.16% 48 +23.07%5 1160 -70 -5.69% 248 +36.26% 56 +16.67%6 1315 +155 +13.36% 260 +4.83% 61 +8.92%7 1630 +315 +23.95% 298 +14.61% 64 +4.91%8 1940 +310 +19.02% 314 +5.36% 78 +21.87%9 2500 +560 +28.87% 461 +46.81% 86 +10.25%10 3768 +1268 +50.72% 502 +8.89% 98 +13.95%11 4589 +821 +21.79% 585 +16.53% 113 +15.30%12 5830 +1241 +27.04% 630 +7.69% 128 +13.27%

t: time Profiles: number of profiles M: monthly

Table 3.3.: Example Table of User Profiles KPI Calculation

KPI 2: User Profiles

The second KPI has the aim to represent the percentage variation in profiles (or accounts)on the platform.Starting from the fact that there are three different kind of profiles (Fan, Band and

Club), the platform user creates his personal account as if it were a fan, then later he isgoing to have the ability to create a new account depending on whether he is the managerof a club or he is in a band.This KPI then, with the help of queries on the database (Appendix B, Code B.6), will

represent the growth in the number of registered basic profile (Fan) and later also ofsub-profiles Club and Band (Appendix B, Code B.3).Following the example of Table 3.3 at the end of each time period t (in this example are

months) the query performed on the database retrieves the number of registered profiles,and then using the data collected, the variation is calculated by the following formula:

ProfilesTrend =FanProfilestFanProfilest−1

− 1 (Percentage)

There could be another version of the previously KPI described. It represents thevariation, not on the basis of the amount of accounts in the previous period, but the

51


average of the variation in the number of accounts created between one period to ananother (period between i and j). The formula used to calculate is the following:

ProfileTrendAvg =

∑jt=i ∆Accountst

j(Average)

Using this pattern, the Profile Trend KPI represents the real variation of users of theplatform while the Profile Trend Avg KPI represents the rate of growth or decrease.

KPI 3: Events

The third KPI concerns the creation of events by using the platform. The creation ofevents (or concert) is the most important feature in the first release of the platform andallows all the actors involved (fans, club and band) to use a tool for organizing andsearching for events.The number of events created for different time periods t (weeks and months) are

retrieved via an SQL query executed on the database (Appendix B, Code B.7), and thetrend is calculated as follow:

EventsTrend =EventstEventst−1

− 1 (Percentage)

By assessing in advance some possible results of this KPI, it is conceivable that fluc-tuations will be observed in the creation of events based on seasonal factors, such as thesummer eases the organization of outdoor events in addition to those proposed in clubs.All this, however, could be normalized to the growth of users of the platform.

KPI 4: Sync Module

The fourth KPI goes hand in hand with the third, the module Sync allows to synchronizeevery events on different media, such as socialnetworks and events-agenda. This featureis available to key participants (club and band) to a specific event.The essential value for the eBusiness, and for evaluating the Sync Module, is to measure

and determine the effective usage of this tool. The maximum potential number of useis calculated with the number of events created during the period t multiplied by thenumber of participants (bands and club). The result of the potential is used to obtainthe ratio of the effective usage of Sync, following the formula:

SyncUsage =Synct

Eventst × Partecipants(Ratio)

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The more the ratio approaches 1, more the Sync feature is used as tool of eventspromotion (Code B.8).

KPI 5: Advertising

The fifth KPI deals with Advertising Banners that are on the platform at the launchphase and are part of the financial income of the company.The implementation of the KPI should consider various alternatives for what concerns

the type of advertising on the site. The advertising banners can be automated basedon services like Google Adsense [Goob], or through the direct sponsorship and manualplacement in specific platform locations, or even done by external partners involved inonline advertising.

Figure 3.14.: Example of Google Adsense Report [Goob]

In the first case it is possible to directly use data from Google AdSense to determinethe KPI, which is based on the percentage of clicks per impressions of the banner, as

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3.6.2 KPIs for Future Modules and Features

t Impressions Clicks Page CTR KPI0 674 16 2.37%1 453 6 1.32% -1.05%2 575 3 0.52% -0.8%3 374 4 1.07% +0.55%4 361 3 0.83% -0.24%

t: time period CTR: clickthrough rate

Table 3.4.: Example Table of Advertising KPI Calculation

This Table was removed from the public version

Table 3.5.: This Table was removed from the public version

shown in the example in Figure 3.14 is possible to extract all the necessary data from thereports interface or via the Google API system.In all cases involving an advertising banner the KPIs will be defined on the basis of the

absolute change in the percentage of clicks per impression, so with the following formula:

Advertising =Clickst

Impressionst− Advertisingt−1 (Percentage)

Following the example in Figure 3.14 the application of the KPI described above willresult in the data represented in Table 3.4.

3.6.2. KPIs for Future Modules and Features

This title was removed from the public version






54

3.7 Implementing Methods of Data Collection



3.7. Implementing Methods of Data Collection

This section will describe the procedures used to implement and customize the dataacquisition systems at the operative level of the Web Controlling strategy. As describedpreviously the systems used will be three:

• Google Analytics as client-side system.

• AWStats as server-side system.

• Database queries and views.

The deployment of the first two is carried out according to the present the best prac-tices, while for what concerns the establishment of a system of data collection from thedatabase are used techniques that are close to the datamining proceedings. This last partrequires a much more precise and continuous work, as the database structure is subjectto change during the development of the platform.

3.7.1. Google Analytics (Client-side)

The decision to use GA as a client-side system of data collection is taken after evaluatingthe strengths of the service, including the gratuity, the ease of implementation and use,and at the same time the good results during test phase of the data collected. The highlevel of customization and the ability to export data and reports which can be easilyintegrated with other sources of measurements also play a role in the choice.On the other hand, some concernments arise in terms of security of the data collected,

since they are hosted on external servers not owned by the company.About the GATC, since the web platform is composed by php dynamic pages (defined

by the framework symfony [Sen]) the script is placed in the two main php files which loadall the content pages of the platform, and the resulting collected data are tested duringthe closed alpha.The GATC is customized by adding the tracking functions of the events [Cli08] (as it

is possible to see in the Code 3.1), so that it can easily track a clickable element of the

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3.7.1 Google Analytics (Client-side)

platform according to the needs. The simple implementation for tracking visitor actions,or clicks, involves adding the _trackPageview() to the onclick event of that element.

1 /* Example of tracking click on advertising picture item with_trackEvent */

23 <?php echo link_to(image_tag (’/img/b105.png ’, ’width =200 ’), ’http ://www

.stagend.com ’, array(’class ’=>’text ’,’onclick ’=>"javascript:_gaq.push([’_trackEvent ’,’AdvertisingBanner ’,’EnergyDrink ’, ’energy drinkwhite banner ’,1]);",’target ’=>’_blank ’)) ?>

Code 3.1: GA Onclick Event Personalization in Symfony Framework [Cut10, p.142]

This change is used mainly at the level of sponsorship to provide to the sponsorsaccurate data on the amount of clicks on specific banners which are not handled by thirdparties, and therefore do not have tracking systems already implemented.Currently no further changes to the GATC are necessary since the e-commerce side of

the platform is still in development, and the alpha test will show eventually more changesto be done.

Figure 3.15.: GA Website Search Settings [Gooc]

All other settings are made directly on the online dashboard. One in particular concernsthe tracking of the Search engine inside the platform, the parameters that must be savedmust be defined in the settings as shown in Figure 3.15. To collect the information ofsearch performed GA analyzes the URL and extracts the search terms that are set.More advanced settings like defining specific goals, advanced segments, filters and cus-

toms alerts will be implemented during the alpha testing phase. The essential part is toensure that no potential useful information could be lost, and that performance of GA isguaranteed.

56

3.7.2 AWStats (Server-side)

3.7.2. AWStats (Server-side)

As described above, it is decided to use an analytics server-side program in order toovercome the major problems of client-site systems, and also to have a second source ofdata for comparing.In Appendix C.2 there is the full description of the procedure of AWStats setup [ttUdw]

on Stagend.com Web server.Currently, there are no further changes to customize the information collected server-

side, as AWStats already offers almost everything needed.

3.7.3. DataBase

The main effort at the operational level is to identify the best solutions at the technicallevel in order to use safely, and without compromising the reliability of the database.The currently applicable options are two:

• Create every query as a database view, or a script executed daily with the crontool, directly on the production web server.

• Create a clone database from daily backups where the same tecniques describedabove could be applied.

The second option is more useful, as suggested by best practices, because it enablesgreater security and creates a database exclusively for analysis.The tool to perform this analysis will be described in the next section, it is a software

which allows the querying and the representation of the results graphically.One of the main drivers of change is the potential growth of users and so consequently

the size of the database. This growth will bring in a second stage to select individualtables involved in the creation of metrics instead of the dump of the entire database.The evolution of the platform itself is a factor of change which will lead to the creation

of new metrics and the adaptation of the existing tools.The next section describes the work done at analytical level with the data collected by

deployed instruments, in particular how they are gathered and presented.

57

3.8 Reporting and Analyzing Collected Data

3.8. Reporting and Analyzing Collected Data

The last phase of the implementation of Web Controlling system concerns how to reportand analyze the collected information and how to use it as a decision-making instrumentin the eBusiness.At the present stage the metrics, both direct and indirect, are enclosed and available

from four different systems:

• GA online dashboard (Figure 3.8): as previously described this tools provides,in addition to displaying metrics, several instruments in order to filter, segmentthem and create automated reports sent by email. Furthermore, the use of the APIallows the export of metrics for external use.

• AWStats online dashboard: a simpler tool than that one provided by GA,it does not allow particular actions on the metric which play a less relevant rolecompared to those from the other softwares.

• iReport reports: desktop software used to automate the reporting of metricsstarting from the database queries. These metrics have a major importance alongwith those supplied by GA. In the Figure 3.16 iReport is represented as a plug-inof NetBeans desktop platform [Net] which is already being used for Stagend.comdevelopment together with the symfony framework [Sen].

• LimeSurvey reports: is an online platform that is used for surveys accordingto the Web Scorecard views. The reports allow to analyze the answers to thequestionnaires, and therefore provide an analysis tool for direct metric as shown inFigure 3.17.

Therefore at the current stage, according to the Web Controlling schema (Figure 2.1),the point four (Analyze) receives all the information submitted by these four instruments.This information is constantly collected, analyzed and controlled, and when it is neces-sary will be decided and implemented changes in the web platform or about strategiesemployed.The ideal situation would be not to have four different interfaces to access data, but

to create an unique dashboard with all the metrics available.

58


Figure 3.16.: iReport Plugin for NetBeans [Jas]

Figure 3.17.: LimeSurvey Question Report Example [Lim]

59

3.8Reporting

andAnalyzing

Collected

Data

This Figure was modified for the public version

Im: Implicit Metrics (Indirect) Ex: Explicit Metrics (Direct) KPIs

Table 3.6.: Metrics and KPIs Overview over Web Scorecard Views and Company Activity Fields

60


In order to do this, the first step necessary is to merge the direct and indirect metricstoghether, and then divide them into categories and views represented in the metrics(Figure 3.11) and the Web Scorecard distributions (Figure 3.6).The combination of these two classifications is summarized in the Table 3.6, vertically

are represented the fields of business activities according to the economic theory, whilethe horizontal subdivision is made according to the views of the Web Scorecard. Afurther horizontal subdivision is applied between the direct and indirect metrics. The finaloutcome is a table that summarizes the available metrics and KPIs positioned accordingto their main influences over the two views.To facilitate the work of analysis and control, at this stage of Web Controlling imple-

mentation in Stagend.com company, rather than trying to aggregate all the metrics, onlythe KPIs are collected in a single presentation. This acts as an alarm bell that allowsto have control over the situation. In the event of variation of a KPI is then possible totrace and control the metrics related to it.An example is that of Unique Visitor KPI, if there is a significant percentage change

through other metrics such as location, language and traffic sources it is possible to havemore accurate data to analyze the reason for this change. Another example, the variationmay be due to the expansion into new regions, or the publication of review (with directlinks) by other web platforms.In the early stages of alpha testing of the platform were many problems encountered and

solved through the use of the Web Controlling strategy, among many others is interestingto mention two of them:

• Profiles settings: the survey conducted among tester users showed many difficul-ties in setting personal data. In particular for what concerns editing the data inthe personal profile and in secondary profiles. It was not clear to the user that thebasic profile (profile fan) is managed separately from the secondary profiles (bandsand clubs).

– Action taken: improving communication about how user profiles are managed,and improve graphically their representation in the profiles menu.

• Site Content: the GA metrics Page and Title Page, which list the most visitedpages of the platform, revealed shortcomings in the implementation of Search En-gine Optimization (SEO) techniques. Both the URLs and the page titles consist ofwords and numbers with no connection to the pages content.

61

3.9 Future Improvements

– Action taken: apply SEO techniques as recommended in best practices, anexample of event page title is "Stagend.com Agenda: Soulline Live Concert,Lausanne, Les Docks".

The cycle of analysis and control must also monitor the implementation of metrics andKPIs which have to be continually challenged to ensure their accuracy and precision.The work carried out during the development phase and the alpha test will be taken for-

ward during the launch of public beta, which will also show the first concrete assessmentson the Web Controlling techniques applied.As will be explained in the next section, together with the development of the platform

some aspects of the Web Controlling system will be also improved.

3.9. Future Improvements

For what concerns the future development, with the establishment of new platform mod-ules, it is important that before the release all necessary changes on Web Controllinginstruments has already implemented to avoid losing vital information on the activitiesof stakeholders.One of the most important activities will be the development of the database, in par-

ticular the creation of new metrics starting from queries and structural changes, makingpossible to increase the quality and the amount of information gathered.For what concerns the surveys, after the test phase, it will be important to decide how

to take forward this source of subjective information, how to engage ordinary users of theplatform and not just the testers. A practice already used for surveys addressed to thebands and clubs, used to obtain an initial market analysis, has been to give some awardsprovided by sponsors (like concerts tickets) in order to encourage more people to fill outforms.Another aspect to improve concerns the management of internal reports on KPIs and

metrics collected. It is possible to develop through the available techniques an aggregationsystem of information which is made available within the company. One possibility maybe to establish an analytical database with a Web dashboard, or simply using the API ofthe different softwares, to create a common interface that deals with taking informationfrom all sources.This part was removed from the public version

62

3.9 Future Improvements

The work done so far has established the initial Web Controlling structure, but thisis just the starting point because the Stagend.com platform and analysis tools will con-stantly evolve. This requires active actions to ensure that the instruments used areupdated and lined up to company needs.

63

4. Conclusion

The use of detailed information within Web companies allows to better manage, to im-prove the strategy, and to monitor the progress of activities over the Internet. This thesisfocuses in describing, both at theoretical and practical level, the techniques of Web Con-trolling which can be used and adapted to the needs of a startup company, in this caseStagend.com.Chapter 2 introduces the different components of the Web Controlling strategy, going

over its three levels. Starting from the strategy and business model at the strategic level,it is possible to implement different monitoring instruments such as Web metrics, WebScorecards and KPIs.Then the analytical section describes how information collected may be represented so

that can be used in an optimal way in the cycle of analysis and control. In particular,how the metrics can be divided into several categories.At the operational level, the implementation of an available software used to collect

data it is compulsory. This data is provided by platform stakeholders either directly orindirectly.Chapter 3 deals with the implementation of the Web Controlling strategy described in

the previous chapter. The starting point is the business plan which describes Stagend.comideas and strategy regarding the creation of the eBusiness. Later, step by step, aredescribed which tools provided are chosen and how they are implemented.First, at the strategic level, Web Scorecard, Web metrics and KPIs are adapted to the

structure and needs of Stagend.com, also by creating new views for each of them.Later, the work done is to implement and adapt the chosen software according to the

needs outlined in the strategy.The last part of the work concerns the description of how the information gathered is

at first presented and especially how it will be used once the platform will become public.A special focus is given to a possible new organization of all the metrics collected, which

were initially separated into different categories, direct and indirect metrics, according to

64

the company activity fields and Web Scorecard view.Among the positive points of this experience there is a constructive approach to work,

everything that is done on a theoretical level should have a practical application withinthe company in order to create real benefits.However not every instrument described in the Web Controlling theory and best prac-

tices has the same efficacy in a startup company. In fact, by the whole of developedarguments it was compulsory a tailoring work to create a customized and comprehensiveschema (Table 3.6) for future developments. The main limitation of this approach isgiven by excessive customization of these techniques, which then cannot easily be ex-ported outside Stagend.com context.The work done at Stagend.com has been highly diversified and often crossed over the

arguments presented in this thesis, this situation sometimes led to the work itself out fromthe main subject. The small number of people in the team makes a necessary flexibleapproach to activities, so each member must be able to work in different fields of theeBusiness every day, from accounting to server maintenance.Another great advantage it is to collaborate with a startup which focuses on innovation,

thanks to this I could try to conceive and test new concepts starting from the bestpractices.Among the negative points of having done a thesis directly connected with the de-

velopment of the platform I can mention the problems about time planning, especiallythose involving the development and market launch, which had an impact on this thesisoccasionally delaying its development.In conclusion, this experience led me to better understand the details of the Web

Controlling mechanisms and verifying its usefulness and its potential in the companyenvironment. Moreover I could live the experience of assisting and helping to the growthof a new company day by day, starting from its initial idea to implementing it, and finallyto its launch on the market.

65

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A. Appendix: Web Scorecard ViewsSurveys

In this appendix the first versions of surveys for Stagend.com explicit metrics are pre-sented, they are divided according to the schema of Web Scorecard views (Figure 3.6),and the survey is structured as presented in Table 2.2. These forms allow to evaluateaspects and goals of the company strategy which are not possible to obtain with Webanalytics techniques, and create the base for explicit metrics.

A.1. Customer Survey

This first survey is for the platform’s customers (or users).

Table A.1.: Customer Survey - Content

73

A.1 Customer Survey

Table A.2.: Customer Survey - Usability

Table A.3.: Customer Survey - Interaction

74

A.2 Organization Survey

A.2. Organization Survey

Table A.4.: Organization Survey Section One: Content

Table A.5.: Organization Survey Section Two: Technology

75

A.3 Partners Survey

Table A.6.: Organization Survey Section Three: Performance

A.3. Partners Survey

Table A.7.: Partners Survey Section One: Sponsoring and Advertising

76

A.4 eBusiness Survey

A.4. eBusiness Survey

Table A.8.: eBusiness Survey Section One: Financial and Growth

Table A.9.: eBusiness Survey Section Two: ROI

77

B. Appendix: Database Queries

In this appendix are presented some of the queries implemented in order to retrieve usefuldata for metrics and KPIs from the platform database.

B.1. Web Metrics


1 This Code was removed from the public version

Code B.1: Accounts Creation Query



Code B.2: Accounts Inactive Query



Code B.3: Bands and Clubs Query



Code B.4: Created Events Query



Code B.5: Next Events Query

78

B.2 KPIs

B.2. KPIs



Code B.6: KPI Accounts Creation Query


Code B.7: KPI Events Creation Query


Code B.8: KPI Synch Usage Query

79

C. Appendix: Data Collection ToolsImplementation

This appendix shows the implementations of software for data collection. In particular,the GATC and the setup of AWStats.

C.1. GA

1 <script type="text/javascript">2 var _gaq = _gaq || [];3 _gaq.push([’_setAccount ’, ’UA-XXXXXX -X’]);4 _gaq.push([’_trackPageview ’]);56 (function () {7 var ga = document.createElement(’script ’); ga.type = ’text/

javascript ’; ga.async = true;8 ga.src = (’https:’ == document.location.protocol ? ’https ://ssl’ :

’http ://www’) + ’.google -analytics.com/ga.js’;9 var s = document.getElementsByTagName(’script ’)[0]; s.parentNode.

insertBefore(ga , s);10 })();11 </script >

Code C.1: Google Analytics Tracking Code (GATC) [Gooc]

C.2. AWStats

The system initial configuration is:

Ubuntu 11.04 (Natty Narwhal) Server edition 32bitApache /2.2.17 (Ubuntu)PHP 5.3.5 -1 ubuntu7 .2Mysql 5.1.54

80

C.2 AWStats

The first step is to retrieve and install AWStats from the repository:

# apt -get install awstats

Create a copy of the original configuration file for the domain stagend.com:

# cp /etc/awstats/awstats.conf /etc/awstats/awstats.stagend.com.conf

Modify the new configuration file for stagend.com:

# nano /etc/awstats/awstats.stagend.com.conf

Tell where the log files (Code) is, define the domain name and also others host aliases:

LogFile="/var/log/apache2/access.log"SiteDomain="stagend.com"HostAliases="localhost 127.0.0.1 stagend.com"

Generate the initial stats for AWStats based on existing var/log/apache2/access.log:

# /usr/lib/cgi -bin/awstats.pl -config=stagend.com -update

Add the command to cron for daily (1 am) execution:

# crontab -e

0 1 * * * root /usr/lib/cgi -bin/awstats.pl -config=stagend.com -update

Modify Apache server configuration:

# nano /etc/apache2/sites -available/default

Alias /awstatsclasses "/usr/share/awstats/lib/"Alias /awstats -icon/ "/usr/share/awstats/icon/"Alias /awstatscss "/usr/share/doc/awstats/examples/css"ScriptAlias /awstats/ /usr/lib/cgi -bin/Options ExecCGI -MultiViews +SymLinksIfOwnerMatch

Add some security for data access from the web, in this case password protection:

# nano /etc/apache2/apache2.conf

<Files "awstats.pl">AuthUserFile /groups/dev/data/. htpasswdAuthName "Restricted Area"AuthType Basicrequire valid -user</Files >


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D E C L A R A T I O N

Je, soussigné(e), déclare sur mon honneur, que j'ai personnellement préparé le travail qui précède et que celui-ci est conforme aux normes de l'honnêteté scientifique. J'ai pris connaissance de la décision du Conseil de Faculté du 09.11.2004 l'autorisant à me retirer le titre conféré sur la base du présent travail dans le cas où ma déclaration ne correspondrait pas à la vérité. ……………………………..…………, le ……………….………………20…….

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FACULTE DES SCIENCES ECONOMIQUES ET SOCIALES / WIRTSCHAFTS- UND SOZIALWISSENSCHAFTLICHE FAKULTÄT

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