Determine a company’s
Software as a Service potential
The development of a perspicuous investment analysis model
from a venture capital perspective

Master’s thesis in Management and Economics of Innovation

ELIN SILVER
ANNA SUNDVALL

DEPARTMENT OF TECHNOLOGY MANAGEMENT AND ECONOMICS
DIVISION OF ENTREPRENEURSHIP AND STRATEGY

CHALMERS UNIVERSITY OF TECHNOLOGY
Gothenburg, Sweden 2021
www.chalmers.se
Report No. E2021:072

www.chalmers.se




REPORT NO. E2021:072

Determine a company’s
Software as a Service potential

The development of a perspicuous investment analysis model
from a venture capital perspective

ELIN SILVER
ANNA SUNDVALL

Department of Technology Management and Economics
Division of Entrepreneurship and Strategy

Chalmers University of Technology
Gothenburg, Sweden 2021



Determine a company’s Software as a Service potential
The development of a perspicuous investment analysis model
from a venture capital perspective
ELIN SILVER
ANNA SUNDVALL

© ELIN SILVER, 2021.
© ANNA SUNDVALL, 2021.

Report No. E2021:072
Department of Technology Management and Economics
Division of Entrepreneurship and Strategy
Chalmers University of Technology
SE-412 96 Gothenburg
Telephone +46 31 772 1000

Gothenburg, Sweden 2021

iv



Determine a company’s Software as a Service potential
The development of a perspicuous investment analysis model from a
venture capital perspective

ELIN SILVER
ANNA SUNDVALL

Department of Technology Management and Economics
Chalmers University of Technology

Abstract
The software industry is facing significant changes, and new business models are
emerging. Instead of providing traditional software, the industry focuses on provid-
ing Software as a Service rather than a software product. There is a need to inves-
tigate how these new business models can be categorised, evaluated, and analysed.
Therefore, this thesis aims to enhance the understanding of objectively evaluating
companies with the potential to transform into Software as a Service. The goal was
reached by identifying factors that need to be analysed and which conclusions can
be drawn by analysing those factors. That knowledge was used to create a robust
evaluation tool that can align investment decision-making processes with investment
strategies focusing on investing in potential Software as a Service companies, and
was performed using an iterative process consisting of three steps: data collection,
model visualisation, and validation. Data was gathered through mapping, a work-
shop, and two rounds of interviews. The data was used to develop models that were
validated in the workshop and the interview rounds. Several factors were identi-
fied as critical to evaluate when searching for companies with Software as a Service
potential. Many of those factors affecting the Software as a Service potential were
subjective and challenging to evaluate. Therefore, a more in-depth analysis was
made of these factors. The factors were divided into six comparison areas: product
strategy, revenue strategy, distribution, service and implementation, market, and or-
ganisation. A model was developed to analyse each comparison area systematically
by dividing the areas into comparison metrics. The comparison metrics facilitates
an objective analysis. The model created enables the user first to collect the infor-
mation, visualise it in the model, and then get a comprehensive summary of the
information, guiding an investment decision. The separation of tasks prevents the
user from getting influenced by one factor before information about all necessary
factors is gathered, improving objectivity. When a company’s analysis is visualised
in the model, an indication is given of the company’s Software as a Service potential.

Keywords: Software as a Service (SaaS), Investment analysis, Investment decision,
Venture capital firm, Objectivity

v





Acknowledgements
First of all, we want to thank our families for always being there for us, throughout
our upbringing and education. It is through you that we have learnt how to stay
true to ourselves and challenge set standards.

We would like to thank our supervisor Charlotta Kronblad at for guiding us through
our research. Continuously during the thesis, you have helped us stay on track, make
sure that we have stayed true to our research question, questioned us to make us do
better, and give support when we have needed it.

Lastly, we are grateful for the opportunity to collaborate with a great company
and inspiring people. The exchange of knowledge has been very much appreciated.
We want to give an extra thank you to the person that has been our main contact
person at Company X. You have been there from the beginning, answering all of
our questions, discussed dilemmas with us, boosted our morale when we questioned
our self, and making us feel like part of the team.

Elin Silver and Anna Sundvall, Gothenburg, May 2021

vii





Contents

List of Figures xiii

List of Tables xv

1 Introduction 1
1.1 The case . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

1.1.1 The company . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.1.2 Problem formulation . . . . . . . . . . . . . . . . . . . . . . . 3

1.2 Aim and research questions . . . . . . . . . . . . . . . . . . . . . . . 3
1.3 Delimitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

2 Theory 5
2.1 Business models and characteristic elements for software companies . 5

2.1.1 Product strategy and strategy . . . . . . . . . . . . . . . . . . 7
2.1.2 Distribution model and downstream . . . . . . . . . . . . . . . 7
2.1.3 Revenue logic and revenue . . . . . . . . . . . . . . . . . . . . 7
2.1.4 Service, implementation, and usage . . . . . . . . . . . . . . . 8

2.2 Software as a Service . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
2.3 Factors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

2.3.1 Key performance indicators . . . . . . . . . . . . . . . . . . . 9
2.3.2 Product offering . . . . . . . . . . . . . . . . . . . . . . . . . . 10
2.3.3 Add-ons and modularity . . . . . . . . . . . . . . . . . . . . . 10
2.3.4 Development responsibility . . . . . . . . . . . . . . . . . . . . 10
2.3.5 Horizontal niche . . . . . . . . . . . . . . . . . . . . . . . . . . 11
2.3.6 Revenue streams . . . . . . . . . . . . . . . . . . . . . . . . . 11
2.3.7 Recurring revenue . . . . . . . . . . . . . . . . . . . . . . . . . 12
2.3.8 Sales focus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
2.3.9 Customer concentration . . . . . . . . . . . . . . . . . . . . . 12
2.3.10 Customer acquisition cost . . . . . . . . . . . . . . . . . . . . 13
2.3.11 Implementation . . . . . . . . . . . . . . . . . . . . . . . . . . 13
2.3.12 Service . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
2.3.13 Vertical niche . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
2.3.14 Market share . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
2.3.15 Competitors . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
2.3.16 Barriers to entry . . . . . . . . . . . . . . . . . . . . . . . . . 15
2.3.17 Marketing strategy . . . . . . . . . . . . . . . . . . . . . . . . 16

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Contents

2.3.18 Sales strategy . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
2.3.19 Knowledge . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
2.3.20 Dependence . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
2.3.21 Maturity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

2.4 Analysing soft metrics . . . . . . . . . . . . . . . . . . . . . . . . . . 17

3 Method 19
3.1 Research design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
3.2 Applied methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

3.2.1 Gathering of secondary data . . . . . . . . . . . . . . . . . . . 20
3.2.1.1 Mapping . . . . . . . . . . . . . . . . . . . . . . . . . 20

3.2.2 Gathering of primary data . . . . . . . . . . . . . . . . . . . . 21
3.2.2.1 Workshop . . . . . . . . . . . . . . . . . . . . . . . . 21
3.2.2.2 Interviews . . . . . . . . . . . . . . . . . . . . . . . . 22

3.2.3 Data analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
3.2.3.1 Documentation and analysis of qualitative data . . . 23

3.3 Method process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
3.3.1 Initial interviews . . . . . . . . . . . . . . . . . . . . . . . . . 25
3.3.2 First iteration . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
3.3.3 Change of direction . . . . . . . . . . . . . . . . . . . . . . . . 27
3.3.4 Second iteration . . . . . . . . . . . . . . . . . . . . . . . . . . 28
3.3.5 Third iteration . . . . . . . . . . . . . . . . . . . . . . . . . . 29
3.3.6 Fourth iteration . . . . . . . . . . . . . . . . . . . . . . . . . . 29

3.4 Reliability and validity . . . . . . . . . . . . . . . . . . . . . . . . . . 29

4 Results and Analysis 31
4.1 Mapping . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
4.2 Understanding the foundation of an investment decision . . . . . . . . 33

4.2.1 Investment process . . . . . . . . . . . . . . . . . . . . . . . . 33
4.2.2 Areas affecting an investment decision in addition to factors

in mapping . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
4.2.2.1 Key performance indexes . . . . . . . . . . . . . . . 34
4.2.2.2 Product offering . . . . . . . . . . . . . . . . . . . . 35
4.2.2.3 Niche and complexity . . . . . . . . . . . . . . . . . 36
4.2.2.4 IT landscape . . . . . . . . . . . . . . . . . . . . . . 37

4.2.3 Specific terms and definitions used by Company X . . . . . . . 37
4.3 Model version 2 - after workshop . . . . . . . . . . . . . . . . . . . . 37

4.3.1 Model for internal use . . . . . . . . . . . . . . . . . . . . . . 38
4.3.1.1 Product strategy . . . . . . . . . . . . . . . . . . . . 39
4.3.1.2 Revenue strategy . . . . . . . . . . . . . . . . . . . . 40
4.3.1.3 Distribution . . . . . . . . . . . . . . . . . . . . . . . 40
4.3.1.4 Service and implementation . . . . . . . . . . . . . . 41
4.3.1.5 Market . . . . . . . . . . . . . . . . . . . . . . . . . 42

4.3.2 Model for external use . . . . . . . . . . . . . . . . . . . . . . 43
4.3.2.1 General characteristics . . . . . . . . . . . . . . . . . 45

4.4 Development of the model . . . . . . . . . . . . . . . . . . . . . . . . 45
4.4.1 The model for internal use . . . . . . . . . . . . . . . . . . . . 45

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Contents

4.4.1.1 Product strategy . . . . . . . . . . . . . . . . . . . . 45
4.4.1.2 Revenue strategy . . . . . . . . . . . . . . . . . . . . 46
4.4.1.3 Distribution . . . . . . . . . . . . . . . . . . . . . . . 46
4.4.1.4 Service and implementation . . . . . . . . . . . . . . 47
4.4.1.5 Market . . . . . . . . . . . . . . . . . . . . . . . . . 47
4.4.1.6 Organisation . . . . . . . . . . . . . . . . . . . . . . 48

4.4.2 Model for external use . . . . . . . . . . . . . . . . . . . . . . 50
4.5 Model version 4 - Final model . . . . . . . . . . . . . . . . . . . . . . 51

4.5.1 How to use . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
4.5.1.1 Model for internal use . . . . . . . . . . . . . . . . . 53
4.5.1.2 Model for external model . . . . . . . . . . . . . . . 53

5 Discussion 55
5.1 Assessing the final model - its structure and content . . . . . . . . . . 55

5.1.1 Product strategy . . . . . . . . . . . . . . . . . . . . . . . . . 57
5.1.2 Revenue strategy . . . . . . . . . . . . . . . . . . . . . . . . . 59
5.1.3 Distribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
5.1.4 Service and implementation . . . . . . . . . . . . . . . . . . . 62
5.1.5 Market . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
5.1.6 Organisation . . . . . . . . . . . . . . . . . . . . . . . . . . . 65

5.2 Reassuring proper use of the model . . . . . . . . . . . . . . . . . . . 67
5.3 Method reliability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68

6 Conclusion 69
6.1 Next steps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70

6.1.1 Future research and development of the model . . . . . . . . . 70
6.1.2 Practical implications . . . . . . . . . . . . . . . . . . . . . . . 70

6.1.2.1 For company X . . . . . . . . . . . . . . . . . . . . . 71
6.1.2.2 For venture capital firms in general . . . . . . . . . . 72

6.2 Value created for Company X . . . . . . . . . . . . . . . . . . . . . . 72

References 73

A Appendix I
A.1 Companies in mapping . . . . . . . . . . . . . . . . . . . . . . . . . . I
A.2 Factors identified . . . . . . . . . . . . . . . . . . . . . . . . . . . . . II
A.3 Descriptions of rankings used to rank SaaS companies . . . . . . . . . III
A.4 Definitions of metrics . . . . . . . . . . . . . . . . . . . . . . . . . . . III
A.5 Different versions of the model for internal use and how they have

changed . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . IV
A.6 Different versions of the model for external use and how they have

changed . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . VII
A.7 Questions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . IX

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Contents

xii



List of Figures

2.1 Business model for software companies by Schief and Buxmann, 2012 6

3.1 Research design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
3.2 Suggestion for grouping of factors . . . . . . . . . . . . . . . . . . . . 27

4.1 Model for internal use - version after workshop . . . . . . . . . . . . . 39
4.2 Clarifying table for analysing Marketing and sales strategy . . . . . . 41
4.3 Clarifying table for analysing Competition . . . . . . . . . . . . . . . 43
4.4 Clarifying table for analysing Entry barriers . . . . . . . . . . . . . . 43
4.5 Model for external use - version after workshop . . . . . . . . . . . . 44
4.6 New clarifying table for analysing Competition . . . . . . . . . . . . . 48
4.7 Clarifying table for analysing Structure and processes . . . . . . . . . 49
4.8 New clarifying table for analysing Marketing and sales strategy . . . . 50
4.9 Final model for internal use . . . . . . . . . . . . . . . . . . . . . . . 52
4.10 Final model for external use . . . . . . . . . . . . . . . . . . . . . . . 52

A.1 Model for internal use: Version 1 . . . . . . . . . . . . . . . . . . . . IV
A.2 Model for internal use: Version 2 . . . . . . . . . . . . . . . . . . . . IV
A.3 Model for internal use: Version 3 . . . . . . . . . . . . . . . . . . . . V
A.4 Model for internal use: Version 4 . . . . . . . . . . . . . . . . . . . . V
A.5 Model for internal use: Version 5 - Final version . . . . . . . . . . . . VI
A.6 Model for external use: Version 1 . . . . . . . . . . . . . . . . . . . . VII
A.7 Model for external use: Version 2 . . . . . . . . . . . . . . . . . . . . VIII
A.8 Model for external use: Version 3 - Final version . . . . . . . . . . . . VIII

xiii



List of Figures

xiv



List of Tables

3.1 Summary of the method process . . . . . . . . . . . . . . . . . . . . . 25

A.1 Companies in mapping . . . . . . . . . . . . . . . . . . . . . . . . . . I
A.2 Factors identified through mapping and Company X . . . . . . . . . . II
A.3 Descriptions of rankings used to rank SaaS companies . . . . . . . . . III

xv



List of Tables

xvi



1
Introduction

The origin of the software industry is in the early 1950s. At that time, software did
not exist as a separate product; it was always integrated into hardware. The industry
grew when IBM in 1969 started to offer software and hardware as separate parts.
From that point, many companies began to allocate resources to develop software.
One characteristic of software products is that they, due to being digital, can be
reproduced with almost no variable costs and without affecting quality. In addition,
software can relatively easily be adapted into different packages and versions to fit
different customer needs (Buxmann, Diefenbach, & Hess, 2012).

O’Grady (2014) highlights that the software industry is changing, and the term
"software company" may change with it. Mäkilä, Järvi, Rönkkö, and Nissilä (2010)
agree and argue that the industry is facing significant change which challenges tra-
ditional boundaries. Mainly, the industry focus on providing services instead of
software products. As new software businesses are introduced, such as "Software
as a Service" (SaaS) and open source, the traditional definition is becoming invalid
(Mäkilä et al., 2010). In the past, software was distributed physically through, for
instance, CD-ROM or floppy disk (O’Grady, 2014). Back then, the software business
operated similarly to a traditional manufacturing company; they produced software
and then distributed it to their customers to use in their own environment. As the
industry changes, new business models and new ways of distributing software in-
crease the competitive environment for traditional software companies. Dubey and
Wagle (2007), at McKinsey and Company, predicted that SaaS companies would
threaten traditional software companies as SaaS companies’ profit margins would
rise quickly with increased scale.

What differentiates SaaS companies from traditional software companies is the way
they sell and distribute their software. Ju, Wang, Fu, Wu, and Lin (2010) highlight
that since SaaS are delivered via the cloud, it is easy to install, require no high
upfront costs, and enable the customers to change provider easily. Furthermore,
SaaS is standardised and does not require complex infrastructure, making it easy
to scale. All these differences that SaaS offers are changing the software industry
and influence all software companies, from their organisational structure to financial
reporting (Ju et al., 2010).

1



1. Introduction

As new business models emerge, the software industry is changing. Mäkilä et al.
(2010) argue that when new ways of distributing, developing, and selling software
emerge, it complicates the process to categorise, evaluate and analyse software com-
panies. Consequently, resulting in problems within the empirical research of the
software area. Since the number of SaaS companies is increasing, there is a need to
investigate how these companies can be categorised, evaluated, and analysed.

As awareness grows about the potential of SaaS, venture capital firms are showing
an increased interest in companies offering SaaS (Dubey & Wagle, 2007). It is es-
sential for venture capital firms to categorise, evaluate, and analyse SaaS companies
effectively to make objective decisions aligned with their investment strategy. With
our interest in this subject, we identified a venture capital firm investing in SaaS
companies, and they were experiencing similar problems as described above. There-
fore, it was a suitable company to investigate and expand our understanding of the
problem area, identify challenges and potential areas of improvement.

1.1 The case
This thesis motive is to research the problems related to the changes in the software
industry as business models connected to SaaS emerge. It was valuable to collaborate
with a company with first-hand experiences of this problem area in the research
phase, and we identified a company that agreed to share its experiences. However,
they want to stay anonymous and are hereafter referred to as Company X. This
section describes Company X’s organisation, and the task is further explained using
the explicit problems expressed by Company X.

1.1.1 The company
Company X is a group investing in entrepreneurial companies offering software so-
lutions and niche IT services within business-critical areas across several industries.
They are a start-up that aims to invest and actively develop these companies. In ad-
dition to the invested capital, they contribute with expertise and support in certain
areas, focusing on helping them develop their business. Company X was established
when a business opportunity was identified within the software industry. They
discovered it is a plethora of promising entrepreneurs in small and medium-sized
software companies with SaaS potential. Utilising this opportunity was the begin-
ning of Company X, where the focus is to invest in entrepreneurs and letting them
have self-governance. Additionally, synergies can be achieved, such as competence
and resource sharing, by gathering many software companies into a group.

Company X is currently a small organisation with six employees but is in an expan-
sive phase. They have a flat hierarchy structure, and all employees are part of the
decision-making process whether to invest in a target company or not.

2



1. Introduction

1.1.2 Problem formulation
A vital part of Company X’s work is screening potential target companies and
decide whether they are interested to invest or not. They work with a one-pager to
collect necessary data to create a first perception of the investment’s potential. In
the second stage, they perform a more in-depth analysis and evaluation, a complex
process. At this stage, a question battery is sent to the target company to gather
all the necessary information to make an informed investment decision.

When Company X decides upon whether to invest in a particular company or not,
they have pre-defined requirements that the target company needs to fulfil. Some
requirements are non-negotiable, and some requirements can change depending on
the situation and how well the company performs in other areas. Due to the "depends
on"-situation, the evaluation process becomes more complicated since every analysis
is specific to that particular company. Additionally, even though a target company
may be an attractive investment, it is essential for Company X to only invest in
companies that are a good fit for them, their investment strategy, and their current
holdings. Therefore, it is challenging for the employees to make an objective decision
that takes into account both if it is an attractive investment and if it is a good fit for
Company X. This is in many aspects a subjective opinion that may differ between
employees. Therefore, there is a need to find an objective way for all employees to
evaluate target companies similarly and find companies aligned with Company X’s
strategy.

Within research, SaaS companies are an interesting subject. However, there is a
lack of detailed research on how venture capital firms investing in SaaS companies
evaluate target companies objectively. This thesis sets out to fill this gap.

1.2 Aim and research questions
The thesis aims to enhance the understanding of how to objectively evaluate software
companies and which factors need to be analysed to understand if a software com-
pany can be transformed into a SaaS company. The intention is that the enhanced
understanding will assist in creating a practical model for venture capital firms, us-
able in their investment process. The goal is to create a robust evaluation model
aligning an investment decision-making process with investment strategies focusing
on investing in companies with SaaS potential. The goal is that the decision-making
process will achieve a higher level of objectivity to reach a consensus internally about
a decision.

Therefore, the model should enable analysis and evaluation of SaaS companies and
communicate the results to reach consensus. The model should enable assessment
based on the investment strategy of a venture capital firm. This objective results
in one research question and two implementation missions. The implementation
missions will be based on the understanding gained from answering the research
question.

3



1. Introduction

Research question:

1. What attributes of software companies are essential to analyse when investing
in companies with SaaS potential?

Implementation missions:

1. Create an investment analysis model to guide venture capital firms to deter-
mine a company’s SaaS potential.

2. Develop a model to use for communication of a decision in a perspicuous way.

1.3 Delimitations
To be able to provide in-depth research, some limitations are necessary. The limi-
tations highlight what this investigation does not cover.

• First, since this study is conducted in collaboration with one company, Com-
pany X, the data gathered to create the model will be based on their insights.

• Second, when analysing business models, it is recognised that there are un-
countable numbers of business models. Therefore, the model should be used
with precaution when applied to other types of business than SaaS companies.

• Lastly, as the implementation mission of this research is to create a model to
enable making more objective investment decisions, it will consequently not
focus on the later stages of the investment process.

Further, the research will be focused on the in-depth evaluation of the investment
process, and therefore, not take into account the initial screening process.

4



2
Theory

In the following chapter, we present all relevant theory upon which this thesis is
based. The purpose of presenting selected theories is to give the reader an extensive
foundation to understand the research topic. The theory is divided into three parts:
business models and characteristic elements of software companies, factors, and
analysing soft metrics.

Business models and characteristic of software companies’ are included to deepen
the knowledge about business models for software companies developed in previous
research. The section describes which elements two earlier research papers analyse
when assessing software companies. This research is used to develop a framework
for evaluating SaaS companies.

The following section defines several factors that are important to consider when
analysing SaaS companies. These factors are used to develop the model described in
the implementation missions. It is essential to know these definitions to understand
the model and why each factor is included.

Analysis of soft metrics is included to highlight the complexity of analysing a com-
pany’s soft metrics and how it differs from analysing hard metrics. This complexity
is essential to have in mind since many of the identified factors require a more
subjective valuation, common for soft metrics.

2.1 Business models and characteristic elements
for software companies

Over the years, researchers have come up with different meanings to the concept of
Business models (Osterwalder, Pigneur, & Tucci, 2005). According to Osterwalder
et al. (2005), it is due to authors disagreeing about the meaning of a Business Model.
Different authors have tried to clarify the concept Business Model. Osterwalder et
al. (2005) define the concept using eight dimensions: Value Proposition, Target
Customers, Distribution Channels, Customer Relationships, Value Configuration,
Capability, Partnership, Cost Structure, and Revenue Model. Later, Gassmann,
Frankenberger, and Csik (2013) use a different definition to define the concept, using
four broad dimensions: Who, What, How, and Value. In an attempt to summarise,

5



2. Theory

Zott, Amit, and Massa (2011), reviewed the concept Business model, its definition,
and what it is used for. They concluded that a business model differs depending on
the context in which it is used and thus has no single definition. A business model
also seems to have multiple purposes, being (1) a unit for analysis, (2) explaining
how a firm does business, (3) seeks to explain how value is captured and how it is
created (Zott et al., 2011).

Much like with the general concept of business models, there is no single definition
of business models for software companies. An early structure was defined using
four factors: Product Strategy, Distribution Model, Revenue Logic, and Service and
Implementation (Rajala, Rossi, & Tuunainen, 2003). The structure was later devel-
oped by (Schief & Buxmann, 2012) into an extensive framework with five factors
and four elements under each factor. The factors were Strategy, Revenue, Upstream,
Downstream, and Usage. The business model is presented in Figure 2.1 where all
elements are listed. Several options for each element are suggested by the authors
and included in Figure 2.1 (Schief & Buxmann, 2012).

Table 2: Software industry business model framework with its elements and choice options 

Investment Horizon 1
Cross 

Finance

Unique Selling Proposition n
Intimate 

Cust. 
Relationship

Network 
Leverage

One Stop 
Shopping

Product Portfolio n
Softw. 

oriented 
Services

Value Chain Strategy n

License Model n

Pricing Model n

Sales Volumes 1

Operating Margins 1

Technical Platform n Java C C++ C# PhP Python
(Visual) 

Basic
Objective-C Perl JavaScript Others

Principles n
Cloud 

Computing
Lean & 
Scrum

Multi-
Tenancy

Mobile Security Web Services Web 2.0

Localization n

Degree of Standardisazion 1

Channel n

Target Industries n Mining Construction
Manu-

facturing

Trans/Comm/
Elect/Gas/Sa

nitary
Trade

Finance/ 
Insurance/Re

al Estate 
Services

Target Customer Size n

Target Customer Type 1

Operating Model n

Support Model 1

Maintenance Model
(release frequency)

1

Replacement Strategy
(avail. releases at a time)

1

Social Model

Quality Features Innovation Leadership Efficiency

Subsidence Model Income Model Growth Model Speculative Model

Mobile & Web 
Applications

Usage Based Usage Independent

Sell Rights Sell Right of License Usage Freeware
Open Source (w/o 

inheritance)
Viral Open Source

Make Buy Ally

Hardware Control System Software Middleware / Database Application Software 

Medium High

Low Medium High

Low

Batch Production Bulk Production

User Developer

Private Individuals Smal Organizations Medium Organisations Large Organisations

Sales Agents Events

Agri., Forestry, And 
Fishing

Public Administration 

Individual Production

TelesalesOnlineRetail

Many Releases

Monthly Quarterly Biyearly

One Release Few Releases

YearlyWeekly

Strategy

On Premise On Demand

Standard Support Few Support Options Customer Specific Support

Real-time

Local AMERICAS EMEA APJ

SOA

Revenue

Upstream

Downstream

Usage

 
 
 

3335

Authorized licensed use limited to: Chalmers University of Technology Sweden. Downloaded on February 02,2021 at 17:10:00 UTC from IEEE Xplore.  Restrictions apply. 

Figure 2.1: Business model for software companies by Schief and Buxmann, 2012

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2. Theory

The factors in the frameworks of Rajala et al. (2003) and Schief and Buxmann (2012)
are similar but expressed in different words or structure. The factors are presented
more closely in the following sections, below the heading named after the factors
presented by both authors.

2.1.1 Product strategy and strategy
Product strategy, presented by Rajala et al. (2003), refers to what strategy a com-
pany chooses when constructing its product and service proposition. It also describes
how the development of the product is done. Different product strategies are; cus-
tomised products, product platforms, uniform core products, modular product fam-
ily, and standardised online services. The examples go from customer-specific to
standardised in falling order (Rajala et al., 2003). The strategy described by Schief
and Buxmann (2012) differs from Rajala et al. (2003)’s product strategy in that the
authors, in addition to the product portfolio, include: investment decisions, unique
selling point, and value chain strategy. Rajala et al. (2003) do not explicitly mention
these elements. Instead, they include the development strategy of the product.

2.1.2 Distribution model and downstream
The factors distribution model and downstream are described similarly with key-
words like market, customers, and sales channels. The focus is on identifying a
profitable market, the right customers, and reaching those customers through an ef-
fective sales channel (Rajala et al., 2003; Schief & Buxmann, 2012). The distribution
model can be centralised or decentralised. An example of centralised distribution
is direct sales, and examples of increasingly decentralised distribution methods are;
reseller or agent model, OEM model, dealer model, and partner network (Rajala et
al., 2003). There are multiple options on how to target a customer. Schief and Bux-
mann (2012) suggest that target customers should be targeted based on customer
type. One way to classify customers to software companies is users or develop-
ers. Another alternative is private customers or businesses dividing the latter into
different business sizes (Schief & Buxmann, 2012).

2.1.3 Revenue logic and revenue
Revenue logic and revenue both focus on how a company generates revenue and
profit (Rajala et al., 2003; Schief & Buxmann, 2012). Schief and Buxmann (2012)
focus on different strategies for generating revenue, how to license out and price
the software, and connects that to the sales volume. The source of the revenue
needs to be taken into account in a business model according to Rajala et al. (2003).
Different revenue models are: effort-, cost- or value-based pricing, license sales and
royalties, revenue sharing, hybrid models, loss-leader pricing, and other models like
media model (Rajala et al., 2003). Costs are approached indirectly by focusing on
the margin that each product generates (Rajala et al., 2003; Schief & Buxmann,
2012). Comparing the fixed costs with the marginal costs can often be connected
to what type of product is sold. For example, information-intensive products often
have high fixed costs, in forms of competence and development, and low marginal

7



2. Theory

costs (Rajala et al., 2003).

Revenue logic and revenue both focus on how a company generates revenue and profit
(Rajala et al., 2003; Schief & Buxmann, 2012). Schief and Buxmann (2012) focus
on different strategies for generating revenue, license out and price the software, and
connect that to the sales volume. The source of the revenue needs to be taken into
account in a business model, according to Rajala et al. (2003). Different revenue
models are; effort-, cost- or value-based pricing, license sales and royalties, revenue
sharing, hybrid models, loss-leader pricing, and other models like the media model
(Rajala et al., 2003). Costs are approached indirectly by focusing on the margin that
each product generates (Rajala et al., 2003; Schief & Buxmann, 2012). Comparing
the fixed costs with the marginal costs can often be connected to what type of
product is sold. For example, information-intensive products often have high fixed
costs in the form of competence and development and low marginal costs (Rajala et
al., 2003).

2.1.4 Service, implementation, and usage
Service, implementation, and usage can be summed up as what happens after the
software is sold to a customer. The implementation can be done either on the
premises or through the cloud. Included in service are support and maintenance,
which can range from being more or less frequent and customer-specific. Depending
on how standardised versus tailored the software needs to be, different solutions
are preferable. Ranging from standardised to tailored solutions, some examples of
how service and implementation are done are: self-service, online service, software
deployment, system integration projects, and IT consulting and customer specific
system works (Rajala et al., 2003; Schief & Buxmann, 2012).

2.2 Software as a Service
During the last couple of years, it has become more common for software vendors to
change their software business model into delivering the software as a service to their
customers, Software as a Service (Stuckenberg, Fielt, & Loser, 2011; Ju et al., 2010).
SaaS buyers subscribe to an ongoing service, which provides software compared to
buying a perpetual software license. That service is paid for through recurring fees
(Ju et al., 2010).

Dubey and Wagle (2007) describe SaaS as software delivered and accessed online.
SaaS differs compared with traditional software offerings. SaaS vendors provide soft-
ware from their own data centres where the software is hosted. Further, the vendors
are responsible for delivering maintenance, support and upgrades. As a result, the
responsibilities described are transferred from the customers to the vendors (Ju et
al., 2010). A SaaS model is, according to Ju et al. (2010), generally characterised
by a number of areas:

8



2. Theory

• The software is accessed through the web.

• The software is hosted and managed by the vendor instead of being managed
by the customer’s internal IT department.

• There are usually no high up-front costs; the customer is instead, paying a
recurring fee.

• The software is highly standardised, and the customisation is minimised.

• The vendor exclusively performs upgrades.

This change has challenged the industry structure and put pressure on existing
business models. Companies are drawn between using SaaS or traditional software
business models (Stuckenberg et al., 2011). When having a SaaS, the vendor can
share one software with multiples companies in a cost-effective way (Dubey &Wagle,
2007). Many customers prefer the new way of getting software delivered since they
do not have to pay any expensive up-front costs or go through expensive and time-
consuming upgrades. Additionally, the customers have an advantageous position in
the relationship with the software supplier since they are paying a monthly fee, and
if they are not satisfied with the software, they can easily change to another vendor
(Dubey & Wagle, 2007).

Stuckenberg et al. (2011) summarise the differences into three main areas: service
property, deployment model, and pricing model. The first; service property is that
SaaS creates a continuous and ongoing relationship between the customer and ven-
dor. The second difference, the deployment model, is that the vendor retains the
responsibility of the software in terms of operating and maintaining it, which re-
duces the customers’ commitment. Further, the software is accessed through web
browsers. The last difference, the pricing model, is the way the customer pays for
the software. The customer pays for the usage, and the subscription fee includes
support and maintenance.

2.3 Factors
In the following section, the relevant factors that are introduced in this thesis are
described.

2.3.1 Key performance indicators
Key performance indicators (KPI) are quantifiable measures used to evaluate a
company’s performance. There are several KPIs to choose between when evaluating
companies. However, the suitability of each measure depends on the type of company
and which industry it operates within. One type of KPI often used for evaluating
companies within various industries is financial measures, focusing on revenue and
profit margin. KPIs can be used to compare companies within the same market and
understand changes for a specific company over a long-term time horizon (Twin,

9



2. Theory

2020).

2.3.2 Product offering
The variety of products offered to customers is something businesses have deliber-
ated over decades. To which extent the product offering should be standardised or
customised is related to differences in revenues and costs for the different options
(Dobson & Yano, 2002). What ultimately decides a company’s product offering is
who the potential customer is, the demand of that customer, and the customer’s
willingness to pay (Evans & Webster, 2007). Generally, more choices result in reach-
ing more customers, which potentially generates more revenue. Nevertheless, the
revenue needs to be weighed against the costs of producing the products, where a
wider variety requires more resources than a more restricted offering. This trade-off
between revenue and cost is altered by technological development. Software com-
panies do often not encounter the same amount of increase in costs with increased
variety since production and distribution are not limited to the same extent as in
manufacturing firms. It is further confirmed by the "long tail theory, which states
that internet-related innovations can serve narrow customer niches with customised
products more cheaply. However, aggregation strategies and creating economies of
scale with standardised products are also becoming cheaper with internet innova-
tions (Evans & Webster, 2007).

2.3.3 Add-ons and modularity
Within software, modularity is used to describe the software design and how the
code is structured. The way the code is structured has significant impacts on the
quality of the code and its maintainability, reusability, and understandability (Xiang,
Pan, Jiang, Zhu, & Li, 2019). Software modularity can also be used to supply
different parts of the software product to a customer. Bråtegren (n.d.) states that
developing products with add-ons and modules is the same for software companies
as for manufacturing companies. Modules can be developed to satisfy different
customer needs but use the same standardised interface. Modularity enables quicker
development of new software functions since the new code can be incorporated into
the old interface and does not need to be intertwined in a new complex code. The
keywords for add-ons and modularity are according to (Bråtegren, n.d.) fast and
flexible. In this thesis, using add-ons and modularity will refer to Bråtegren (n.d.)’s
definition of offering different functionalities to customers to satisfy their exact needs.

2.3.4 Development responsibility
Mital, Desai, Subramanian, and Mital (2008) define the process of product devel-
opment as "conceptualising a product, designing, producing, and selling it". It is
essential to know which features are valued by the customer to succeed with a prod-
uct development process, develop that product speedily and with high quality and
low costs, and maximise profits (Mital et al., 2008).

The development process of software products differs in some ways from the devel-
opment of traditional products. Digital transformation has resulted in a need to

10



2. Theory

increase the speed of the development process, altering the whole approach. Devel-
opers of software today often need to design a product without input from users,
creating uncertainty regarding the design requirements. It creates an additional
challenge for software developers (Langer, 2016). To tackle this challenge, compa-
nies have developed new ways of working. Working according to an agile approach
is beneficial when developing software (Kelly, 2008). Digital transformation, imple-
menting a more agile process, and competitive knowledge abroad has resulted in
virtual development teams and development outsourcing (Langer, 2016). Further-
more, new ways of developing software are emerging, like open-source, where the
source code is publicly available to alter and develop (Wikipedia, n.d.).

2.3.5 Horizontal niche
Companies having a horizontal niche specialise in a product, system, or function.
A product with a horizontal niche can be used for a specific purpose in a business
but is not limited to a specific industry. The focus is to sell a product with the
right features to perform well in a single function and be versatile across different
industries. A limitation of software with horizontal niches is that they lack the
holistic perspective of a company’s business, which vertical niched software often
provides (Omile, 2020). The advantage of horizontally niched products is that one
can sell the product to many different industries, potentially earning higher revenue
(Khurana, 2018).

2.3.6 Revenue streams
Revenue streams are the different sources that generate revenue for a business. It
includes earned revenue from products and services sold. Products and services can
be priced in different ways. For example, a product can be charged with a one-time
fee or rented out, then charged over a period of time. Revenue streams are often
categorised according to the types of pricing mechanisms that exist. There are some
common types of pricing mechanisms, which are used to categorise revenue streams.
Four types usually mentioned are transaction-based revenue, project revenue, recur-
ring revenue, and service revenue. Transaction-based revenue is generated through
one-time payments when goods are sold. Project revenue refers to payments from
one-time projects. Recurring revenue is generated on an ongoing basis through con-
tinuing services, subscription fees. Service revenue refers to payments made by the
customer for services, and it is paid based on the number of hours the service is
required. The different revenue streams enable different ways to predict the future
generated revenue. In a business with recurring revenue, the cash inflow is the most
predictable and remains stable within the existing customer base. Transaction-based
and service revenue is harder to foresee since the customers’ demand tends to change
over time. However, project revenue is the most difficult to predict since it is very
volatile due to its dependence on customer relationships. Therefore, some revenue
streams are more desired to predict future generated revenue (CFI, n.d.).

11



2. Theory

2.3.7 Recurring revenue
Recurring revenue refers to the revenue that a company generates that is expected
to proceed in the future. Instead of having one-time sales, periodic sales create
a continuous and constant revenue stream. It is a model that companies usually
desire as it creates stability and predictability (Liberto, 2020; CFI, n.d.). Recurring
revenue is not guaranteed to continue forever but creates a good foundation since it
lowers the risk of drastic turns in revenue. One way to generate recurring revenue
is to have automatically renewable subscriptions, which refers to the service being
delivered until it is cancelled. Companies with recurring revenue are often valued
higher by investors since their forecasts are more reliable than companies with non-
recurring fees (Liberto, 2020). CFI (n.d.) summarises the possible revenue streams
as; renting or leasing, subscription fees, advertising fees, and licensing.

2.3.8 Sales focus
There are two different approaches to make sure a company has customers: customer
retention or customer acquisitions. Customer retention refers to when companies
focus on the already existing customer and are allocating resources to create long-
term value for those customers. Customer acquisition is allocating resources to
finding new customers and making sure they become paying customers. A company
does not necessarily need to focus on one of these areas since they are not mutually
exclusive. However, they both require resources, and therefore companies need to
decide to what extent they want to focus on these areas (Arnold, Fang, & Palmatier,
2011).

The company’s focus on either developing new relationships or deepening the exist-
ing ones can potentially affect the company’s performance. Focusing on customer
retention and deepening the relationship with the existing customers can improve
the company’s performance in the short term. However, a consequence may be that
the customer base becomes more concentrated, which is a potential issue (Arnold et
al., 2011).

2.3.9 Customer concentration
The customer concentration metric visualises how a company’s total revenue is dis-
tributed among the customers. A low concentration refers to a large customer base,
where all the customers are purchasing small volumes (Marin, n.d.). If the concen-
tration is high, a smaller number of customers are making bigger purchases, leading
to more significant loss if an account is lost (Grant, 2016). When referring to high
customer concentration, the rule of thumb is that a single customer constitutes more
than 10 per cent of the total sales or the five largest customer generates 25 per cent
of the revenue (Marin, n.d.).

The level of preferred customer concentration is different depending on the indus-
try; some industries typically have lower concentrations; meanwhile, others are more
likely to have larger customers constituting a large proportion of the company’s sales
(Marin, n.d.). Depending on the industry and company, there are both benefits and

12



2. Theory

disadvantages with having low or high customer concentration. Generally, high con-
centration speaks in favour of developing a long-term relationship with the customers
due to fewer customers. The long-term relationship benefits both the company and
the customer. The company can get more information from the customer and de-
velop more suitable solutions, and the customer can get more customised offerings.
However, high concentration also creates a risk since the company is dependent on
its customers, and losing one may significantly impact the revenue and profit (Marin,
n.d.).

2.3.10 Customer acquisition cost
When talking about customer concentration, it is highly relevant to think about cus-
tomer acquisition cost (CAC), which is the total costs of acquiring a new customer.
If marketing and sales are efficient, it often results in a low CAC. It is commonly
acknowledged in the business environment that acquiring a new customer is con-
nected to higher costs than extending an existing customer. For investors, the CAC
is interesting to consider if it is surprisingly high or low. CAC gives insights into
how to plan for the future, make budgets, and allocate money. The CAC is often
higher in a highly competitive environment since customers have various companies
to choose between (Kenton, 2021).

2.3.11 Implementation
When implementing software, there are two main ways to approach it, on-premises
or through the cloud (Rajala et al., 2003; Schief & Buxmann, 2012). The first refers
to having everything in-house to install the software on the computer and access
it locally. Meanwhile, for the cloud solution, everything is hosted by a third-party
provider. What differentiates cloud solutions is that the computing services are
offered outside the firewall and delivered through the web browser. Cloud solutions
are beneficial as it does not require the same amount of up-front costs, it is easier to
do product upgrades, it reduces the need for internal IT support, and it is easy to
access the software (Fisher, 2018; Wei & Blake, 2010). When software is centrally
hosted, in other terms through the cloud, the users do not need to have licensed
ownership, instead, they pay for the right to use it (Fisher, 2018).

On-premises solutions require servers on-site, system administration labour, and
other infrastructure. Further, on-premises software is usually not maintained con-
tinuously, resulting in outdated software. However, on-premises are less exposed to
security threats, less affected by price increases, and the vendor does not have the
same responsibility after the installation (Fisher, 2018).

2.3.12 Service
After the customer has gained access to the software, the services delivered can
take many forms, but usually in the form of support and maintenance. These
can be both standardised or customised and be provided more or less frequently
(Rajala et al., 2003). Maintenance is an integral part of the after services that the
vendor can provide to its users. It refers to all modifications done to the software

13



2. Theory

after the software has been delivered. It can take the form of correcting faults,
adapt the software or improve it. Since software is part of a continuously changing
environment, the software is never finished; there is always a need to meet new
requirements (Grubb & Takang, 2003).

According to Wei and Blake (2010) customer maintenance is easier to perform when
the software is delivered through the cloud since one update can be shared with all
users simultaneously. Since it is impossible to upgrade all on-premises software si-
multaneously, all the distributed software may not have identical versions, requiring
additional and varied support (Wei & Blake, 2010). Further, the location where the
service is executed differs depending on how the software is distributed. The main-
tenance and upgrades can be delivered remotely when the software is distributed
through the cloud. However, an on-premises solution may require on-premises main-
tenance.

After services can differ in more ways than delivery, they can also be charged dif-
ferently. If the vendor is offering SaaS, the maintenance, upgrades, and support are
all included in the subscription fee (Ju et al., 2010). SaaS’s charging model differs
significantly from the traditional software model where the software is licensed, and
the business needs to operate and manage the software themselves. However, in
addition to the licensing fee, the customer can pay a recurring fee for additional
service, for example, maintenance and support. The additional maintenance fee is
then of a recurring nature. However, within the licensing model, it is more common
to have the responsibility of after services internally, which is not the case for SaaS,
where the vendor provides everything related to the software (Mäkilä et al., 2010).

2.3.13 Vertical niche
Companies within a vertical niche are specialised to serve that specific market’s
need and not a broader market. The niche consists of a group of customers and
companies that are all connected to that specific niche. The companies active in
a vertical market are offering specialised products to that market (Young, 2020).
Therefore, companies within vertical niches act as specialists and usually offer better
products for that market since they can create products based on the user experience
(Khurana, 2018).

Acting within a niche usually create higher entry barriers for new competitors, and
it can potentially lead to higher profits since the company is focusing on a smaller
customer base. Further, companies can often create a closer relationship with the
customer, offer more specialised products and get more targeted insights. More
specialised products allow for higher prices, according to Young (2020). Companies
within vertical niches become experts and therefore have deep knowledge of market
trends and regulations (Young, 2020). Grant (2016) argues that market leaders
often focus on the mass market, which creates an opportunity for new firms to
create niches within the market where the market leaders will not interfere (Young,
2020).

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2. Theory

2.3.14 Market share
Market share represents the percentage of total sales that a specific company gen-
erates within a market. It gives insight into a company’s size in comparison to
the total market. For investors, the metric can be used to observe how the mar-
ket shares fluctuate, as it reveals the competitiveness of the company’s products.
Further, market shares can indicate how a company maintains market shares if the
total market grows. If a company increases its market shares, it is an indication
that they are growing their revenue more rapidly than its competitors. Increasing
market shares in a mature industry with low growth is highly difficult. Further, it is
crucial to understand that in an industry where the market is growing, a company
can still increase its sales even if its market shares decrease (Hayes, 2021b).

2.3.15 Competitors
What determines the competitiveness and the profitability level within an industry
is the rivalry between companies. Competition can also be created by companies
having substitute products. However, the internal rivalry is more evident than
rivalry by substitutions. Internal rivalry can have different outcomes on the pricing
of the product depending on the competitive landscape. Therefore, it is important
to consider both how many competitors there are and how large market shares the
most prominent players have (Grant, 2016).

Within the software industry, especially amongst SaaS companies, it is difficult
to compete with large companies (Aley, 2018). The competitive advantages that
software companies possess (Aley, 2018) can be explained by both the nature of their
business (Aley, 2018) and the fact that they experience increasing returns (Arthur,
1996). Aley (2018) explains that software companies today gather a considerable
amount of data that they can use to elevate their businesses further. Arthur (1996)
further adds that increasing returns is why large software companies experience
increasing competitive advantages. Increasing returns means that companies that
are ahead, get even further ahead (Arthur, 1996).

2.3.16 Barriers to entry
Entry barriers can delay or prevent new companies from entering the market; in
other terms, they limit the competition in that specific market. The entry barriers
protect incumbent companies and improve their possibilities to generate profit and
retain market shares. If a company wishes to enter a new market, it will face several
barriers, which differs depending on the industry since each industry has a unique
collection of barriers (Hayes, 2021a). Grant (2016) mentions possible barriers to
entry as high up-front costs, which is the capital required to get established in the
market, governmental and legal barriers such as patents, regulations, brand loyalty,
high switching costs, and know-how (Hayes, 2021a).

15



2. Theory

2.3.17 Marketing strategy
Having a well-developed marketing strategy relates to how well a company succeeds
in creating a game plan for reaching the potential customer and getting them to buy
the product or service. The marketing strategy is focused on communicating the
company’s value proposition to potential customers. Further, the market strategy
should have a long-term perspective that communicates the company’s competitive
advantage over the competitors. Independent of how the message is communicated,
for example, on print, digitally, to the mass market, the assets can be valued based
on how effectively the strategy delivers the message (Barone, 2021).

2.3.18 Sales strategy
A sales strategy is a plan for selling products or services. It focuses on differentiating
the company’s offering from competitors’ offers to reach qualified buyers. The strat-
egy includes clear objectives, for example, KPIs, sales processes and methodologies,
product positioning, and competitive analysis. Furthermore, a sales strategy should
include goals for the sales organisation to work towards and monitor progress to
assure that quotas are met (Riesterer, 2019).

When transiting to software-based products from traditional products, some changes
in sales strategy can be observed, especially when software is sold as a service. Chung
(2021), this includes selling directly to customers instead of through partnerships.
The changes demand a restructuring of the sales department, where a new skill set
is demanded, including working closely with customers and having deep business
knowledge (Chung, 2021).

2.3.19 Knowledge
A commonly growing trend is the knowledge-based economy. Today knowledge
is a significant strategic asset. Knowledge is a resource that affects and shapes
the business. It creates high value since it is unique to each specific company,
and knowledge differentiates one company from another. However, knowledge is
connected to individuals and can easily be lost if an employee leaves (Invest Northern
Ireland, n.d.).

Even though today’s economy is knowledge-based, knowledge is still a relatively
scarce resource since the workers’ knowledge, expertise, and skills are limited. To
achieve long-term competitive advantages in a knowledge-intensive company is highly
important to sustain the expertise within the organisation (Horwitz, Heng, & Quazi,
2003).

2.3.20 Dependence
When measuring the operational risk of a business, it is common to look at what
different operations depend. An operation is most often dependent on either people
or processes; the first is called people dependent, and the latter process dependent.
There are advantages and disadvantages to both. People-dependent businesses,

16



2. Theory

where an operation is dependent on a specific person’s knowledge, are often more
flexible. The main disadvantage with people-dependent businesses is the risk of
losing the knowledge that a person has about a process if that person leaves without
passing their knowledge on to the next person. Process-dependent businesses, where
an operation is done following a pre-designed and standardised task, can be hard to
achieve, but if succeeded, anyone can perform the tasks by following a list of steps.
The main disadvantage of process dependency is that it is less flexible, has little
room for employee input, and resistance to change has been observed. The optimal
level of how much a business is dependent on either people, processes, or a mix of
them varies between different types of businesses (MassAnalytics, n.d.).

2.3.21 Maturity
An industry evolves during its lifetime, and it is referred to as an industry life cycle.
The life cycle is usually divided into four stages: introduction, growth, maturity,
and decline. When a company reaches the maturity stage, the industry’s market
has reached a saturation point. It results in the focus being moved from increasing
sales to create more efficient processes (Grant, 2016). Further, the markets are more
predictable, and companies have well-established connections to the customers and
other stakeholders (Audretsch & Feldman, 1996). A mature company is usually a
well-established actor within the specific industry possessing a known product and
brand (Grant, 2016).

Companies within this stage are often experiencing saturated sales, leading to steady
or slow revenue growth. Companies aim at keeping their market shares since the
market’s growth is limited (Audretsch & Feldman, 1996). Instead, the focus lies
on establishing strategies to achieve good levels of profitability, often by reducing
their costs as a result of making the organisation more efficient. However, mature
companies often have well-established competitors, creating high competition within
the industry ((Kenton, 2019).

According to Audretsch and Feldman (1996), the management, processes, and mar-
keting routines needs to be well developed in a company at this stage. Due to growth
being limited, the focus is on creating an efficient organisation, which can be in data
management, efficient planning, and resource processes. Typically, mature organi-
sations have established procedures to capture information to improve the processes
and technology on an enterprise-level (Kenton, 2019).

2.4 Analysing soft metrics
Soft metrics are indicators that are not regarded as traditional "hard" measures, such
as KPIs. Instead, they are measures related to a company’s value and performance
that can not be quantified (Fernando, 2021). Soft metrics are usually "hidden" and
remain intuitive since they hardly ever can be measured (Tosic, 2017). Soft metrics
are flexible to use and are intended to be adapted to the company. Therefore, there
is a wide range of possible metrics, and they are not standardised, and therefore,
free for the analyst to develop based on the company. The purpose of including soft

17



2. Theory

metrics when evaluating the characteristics of a company is that they are impor-
tant to understand a company, but they do not appear on the financial statement
(Fernando, 2021).

18



3
Method

This chapter describes the methods used in the study. The first section presents
the planned design of the study. The research design was used to plan the research
process and which methods are used.

After that, the applied methods are presented in the order they were used. The
methods presented include the gathering of primary and secondary data. Primary
data was gathered through a workshop and interviews. This section also includes
how the data were analysed. Applied methods are included to demonstrate why the
methods used are the most suitable approach to answer the research question.

Next, the method process is presented to enable the reader to follow the process as
it was carried out. It allows the reader to replicate the process, which is vital to
validate the findings further.

Lastly, reliability and validity are included to increase the trustworthiness of the
study.

3.1 Research design
The research is divided into three steps; data collection, model visualisation, and
validation. This process is iterated until the desired model is achieved, which is
illustrated in Figure Figure 3.1.

Figure 3.1: Research design
Initial interviews leading to data collection, model visualisation, and validation, rep-
resents each step of the iteration process resulting in a desirable model.

19



3. Method

First, initial interviews were conducted to gather information about Company X’s
current investment process, its advantages, and disadvantages. It is essential to
understand how an investment process is structured and how an investment model
should be constructed to be valuable. Thereafter, the iteration process started
with data collection, model visualisation and validation. Data collection was done
through the mapping (first iteration), workshop and interviews (following iterations).
The data type collected through the mapping was secondary and the data type
collected through the workshop and interviews was primary. This data was used to
first construct the model, and thereafter update the model according to gathered
input. During each update of the model, the model was tested to detect faults and
improvement potentials against theory and already gathered knowledge, represented
by "internal testing" in Appendix 3.1. The model was thereafter validated in each
iteration through the workshop and interviews. In total, 4 iterations was conducted,
which resulted in the final model.

3.2 Applied methods
Below, we present all methods used during the study and the reason they were
adopted.

3.2.1 Gathering of secondary data
Secondary data is data that has already been collected by someone else. Secondary
data is valuable when needing to collect a large amount of data in a short period
of time. Further advantages of using secondary data are that it is easy to access
and can be collected from multiple sources. Secondary data allows researchers to
draw new conclusions from previously collected data (Krishnaswami & Satyaprasad,
2010).

3.2.1.1 Mapping
The gathering of secondary data was done through mapping a selection of software
companies. The mapping was done to identify different business models and factors
present within the software industry. Information was gathered through the compa-
nies’ websites, annual reports, and other relevant business sources. To be relevant
for this study, the company needed to fulfil a few pre-specified requirements listed
below.

• The company is listed on Small, Mid, or Large-cap on the Swedish stock
exchange.

• The company’s focus should be to sell its products to other businesses (B2B).

• The company must produce software.

• The software should be or have the potential to be sold as a separate product.

• The software should be developed and owned by the company.

20



3. Method

The selection of companies was based on these requirements. However, since some of
the requirements are subjective, interpretations were necessary during the process.
The selected companies were analysed in-depth to identify the business models’
factors and understand what differentiates a business model.

3.2.2 Gathering of primary data
Information about what is important in an investment process was derived from a
workshop and interviews with Company X. Primary data refers to data that has
not been collected previously. This data can be adapted to fit the needs of the
research study (Krishnaswami & Satyaprasad, 2010). The primary data collected
was qualitative, and the purpose of collecting it was to gain a deeper understand-
ing of the behaviour, opinions, and attitude of a smaller number of people. This
type of study is often more time-consuming than quantitative studies and gathering
secondary data, where the focus is on gathering large amounts of data effectively
(Krishnaswami & Satyaprasad, 2010). The two methods, workshop and interviews,
are further explained in the following sections.

3.2.2.1 Workshop
A workshop was held with Company X to gain insights about what type of model
could create value in an investment process. An aspiration was to discuss different
alternatives. Therefore, a workshop was chosen as a method since it, according to
Steinert (1992), is an effective way to promote discussion.

According to Steinert (1992), a workshop, when held with a smaller number of par-
ticipants, allows everyone personal attention. A workshop is preferably constructed
for people working together or in the same field. Workshops are helpful to spur
participants to develop their ideas, which is suitable when developing new models
or concepts. When constructing a workshop, planning, preparation, and implemen-
tation are factors to take into account. When planning the workshop, it is essential
to consider the workshop’s purpose, how information is presented, the audience and
their previous knowledge. Preparation is essential, and all necessary materials and
equipment needed should be arranged beforehand. Implementation has three phases:
introduction, substance, and closure. During the introduction, the tone of the work-
shop is set, and the agenda is presented. The substance of the workshop could be
made more interesting by presenting information in different ways and being en-
thusiastic. When closing the workshop, it is essential to summarise the content,
leave time for reflection and discussion, and collect feedback from the participants.
To sum up, extensive planning and preparation are essential to arrange an effective
workshop implementation (Steinert, 1992).

In accordance with Steinert (1992), a plan for the workshop was set up beforehand,
including several aspects to discuss. A PowerPoint was constructed with the agenda,
slides that should be filled out, and other relevant information for the workshop.
However, the planning left room for changes in the agenda to allow for gathering
unexpected data. The workshop was performed with two employees at Company
X. The workshop duration was six hours, allowing for deep discussion of all points

21



3. Method

on the agenda and having time for unexpected discussions. Thorough notes were
taken during the workshop. In addition, a meeting between the authors took place
afterwards to discuss and document thoughts and impressions from the workshop.

3.2.2.2 Interviews
Interviews were conducted for three purposes.

• First, to get a deep understanding of how an investment process works.

• Second, to identify important factors to consider when making an investment
decision.

• Third, to gather feedback from people with experience in investment decisions.

The goal was to perform interviews with different employees within Company X
to get multiple perspectives. Interviews were held with four out of six employ-
ees; some employees were interviewed on multiple occasions. All of the interviews
were held individually to avoid interviewees influencing each other (Krishnaswami
& Satyaprasad, 2010). According to Krishnaswami and Satyaprasad (2010), inter-
views are suitable when the data gathered is about people’s attitudes and opinions.
If the interviews are correctly performed, a large amount of in-depth data and de-
tailed information can be gathered. However, interviews are very time-consuming
and require the interviewer to know how to design and perform them appropri-
ately. Further, the interviewee must understand which role the interview will play
in the research. That promotes the interviewee to answer the questions truthfully
(Krishnaswami & Satyaprasad, 2010).

The interviews were performed with a semi-structured interview approach, implying
that the interviews were conducted based on a list of pre-specified areas and ques-
tions. However, the questions were adapted to fit the situation, the interviewee, and
the answers; to promote a continuous conversation. New questions were formulated
during the interview to achieve the desired outcome. Semi-structured interviews are
both flexible and consistent (Dawson, 2002). However, the lack of standardisation
in semi-structured interviews can result in difficulties comparing answers from dif-
ferent interviews, leading to an inconclusive result (Saunders, Lewis, & Thornhill,
2009). It is also problematic to know to what extent the interviewee is affected
by the interviewer’s choice of word and formulation (Krishnaswami & Satyaprasad,
2010). Another aspect to consider is the interviewee’s perception of the interviewer
and vice versa (Saunders et al., 2009).

According to Gillham and Gromark (2008), the formulation of the interview ques-
tions is essential for the interview result, as the question shall be easy to understand
and not deceptive for the interviewee. Since the interviews were semi-structured,
the questions were open for the interviewee to interpret, but the questions were
formulated with accuracy and clarity to avoid misinterpretations, as suggested by
Gillham and Gromark (2008). To conduct the interviews more efficiently, they were
mainly held online since Company X is located in Stockholm. Krishnaswami and

22



3. Method

Satyaprasad (2010) argue that phone interviews, similar to online interviews, are
suitable for interviewees in different geographical places.

The interviews were not recorded to avoid interviewees becoming cautious in their
statements, especially when discussing potential company secrets, all per Krishnaswami
and Satyaprasad (2010). Instead, thorough notes were taken during the interview.
Between the interviews, discussions were held between the authors to make sure all
critical information was collected and noted.

3.2.3 Data analysis
All data gathered needs to be categorised, processed and compressed to help answer
the research questions. The method of processing data depends on the data gathered
(Patel & Davidson, 2003). In this thesis, the data collected is qualitative.

3.2.3.1 Documentation and analysis of qualitative data
When conducting the mapping, all information gathered was documented in a shared
excel document. The information was sorted based on different variables and was
divided into different sheets dependent on the nature of the information. Docu-
menting the gathered information in an excel document made it possible to get a
clear overview of the data. Furthermore, it enabled easy sorting of the data based
on different variables. To arrange collected data in a logical and clear order is by
Kothari (2004) called tabulation, enabling data to be effectively presented and com-
pared (Kothari, 2004). By comparing the data, correlations could be found, which
contributes to the research results.

During the workshop and interviews, detailed notes were taken since the interactions
were not recorded. To make sure comprehensive notes were taken, one of the authors
was responsible for taking notes during each interview. The notes were documented
in separate documents for each interaction which was labelled with date and name.
These documents were stored online to enable both authors to access them. Both
authors processed the notes after each interaction to make sure all essential parts
were included. If some parts had been left out, they were added. The main goal
of taking detailed notes was to enable processing the notes repeatedly during the
project. It enabled the authors to use the data continuously and draw additional
conclusions as they gained more knowledge of the area.

The notes were further processed to highlight the areas of interest to consider when
updating the model. The insights gained from the interaction were continuously
compared with the theory to confirm it or find additional ways to interpret it. If the
theory could confirm the insights it were added to the model, if the insights could
not be confirmed or additional ways to interpret the data came up, it was used as
an input to the next interaction.

23



3. Method

3.3 Method process
Early on, it was agreed upon with Company X that the focus would be to create
value in the investment process. Therefore, the research direction would change if
more value could be created differently. It has resulted in that the direction changed
during the research. However, the work performed before the change of direction
could not be excluded since it still provided essential insights to the thesis. Fur-
thermore, describing the insights and decisions that resulted in the change provides
additional insights and is vital to include in the thesis. The process has been com-
plex since this thesis has been deeply intertwined with Company X, an evolving
company. This section describes the process of evolvement, insights gained, and
what has laid the ground for the directions taken.

Part of the process was to anchor our findings in the literature continuously. Since
the aim changed during this thesis, the theory required to understand the area has
also changed. Therefore, the theory section in the thesis was an ongoing process,
where the theory of the new findings was added. Additionally, the reason is to enable
the reader to understand the concepts put forward.

In the following sections, the process of the initial interviews and the first, second,
third, and fourth iteration is presented. In between the first and second iteration,
the change of direction occurred. This change is described in a section between the
first and second iteration to follow a chronological order. The method process is
summarised in Table 3.1. The initial idea was to perform three iterations, but an
additional iteration was needed to achieve the desired model. The final model was
the result of the iterations of data collection, model visualisation, and validation.

24



3. Method

Iterations Data collection Model
visualisation

Validation

Iteration 1 Identifying factors
for SaaS companies
by mapping listed
software companies
and scanning
literature.

Creating a model
based on the factors
identified in the
mapping and
literature.
Model version 1

Presenting the first
version of the model
at the workshop, to
get feedback and
insights.

Change of direction
Iteration 2 Compiling all

feedback and insights
gained about the first
version of the model
from the workshop.

Creating a second
version of the model
based on the
feedback received
during the workshop.
Model version 2

Conducted the first
round of interviews,
to get feedback and
input.

Iteration 3 Compiling all
feedback and insights
gained about the
model from the first
round of interviews.

Creating a third
version of the model
based on feedback
and input received at
the first round of
interviews.
Model version 3

Conduct a second
round of interviews,
to get feedback and
input.

Iteration 4 Compiling all
feedback and insights
gained about the
model from the
second round of
interviews.

Creating a fourth
version of the model
based on feedback
and input received at
the second round of
interviews.
Model version 4

No additional
validation was
needed; the model is
complete. The final
model was achieved.

Table 3.1: Summary of the method process

3.3.1 Initial interviews
The first step was to understand Company X’s business, processes, and needs in sev-
eral interviews with different employees from Company X. During these interviews,
it was decided what the focus should be.

The initial idea was to create a tool that would assist venture capital companies
to better understand the software businesses they invest in. Discussions resulted in
that this would best be achieved by mapping software companies and their business
models. The information gathered in the initial discussion laid the ground for the
decision that an extensive mapping of listed software companies should be done.
Company X suggested that data be gathered from listed companies, making it easier
to access the information needed. The purpose of the mapping should be to identify
different factors that differ between software companies and how it is affecting their
business models.

Since the increased use of SaaS has changed business models, this needed to be
taken into consideration. Therefore, the different business models identified would

25



3. Method

be compared with whether or not the companies offered SaaS or were trying to
transform their offers from traditional software to SaaS.

3.3.2 First iteration
In the first iteration, several listed companies were analysed to understand which
factors are critical in software companies. Several factors were identified during the
mapping by reading annual reports, websites, and other information from the listed
software companies. According to areas in a business model, these factors were com-
piled and arranged and brought to the first workshop with Company X. A ranking
system was created to investigate how far a company had come in offering SaaS.
The idea was to understand if companies are offering SaaS or trying to transform
their offers to become SaaS.

The mapping process laid the ground for the continued work of constructing a model
to compare different business models with a company’s SaaS ranking. The mapping
process gave deep insight into different business models in software companies and
which factors were important to analyse. The factors were used to construct the
grounds for the workshop.

The mapping of companies and which factors had been identified was presented
and discussed, during which a few new factors were identified (see Appendix A.2).
The workshop aimed to rank the different factors according to relevance and im-
portance. Furthermore, suggestions were presented on how the factors could be
grouped (see Appendix 3.2) and how a SaaS ranking system could be constructed
(see Appendix A.3). Company X commented that the factors identified represented
what they are interested in when they assess target companies and that the factors
identified in the mapping were essential to analyse when making an investment de-
cision. However, Company X did not have any specific requirements for each factor.
There were no right or wrong answers to the questions asked regarding the factors;
they all depended on each other. When Company X analyse these factors, they
create a complete picture of the company using all of the factors. Therefore, it was
not possible to rank the factors in an easily. Ranking companies using factors would
result in too much of a simplification in analysing a target company. Therefore,
creating a model ranking business models and connecting it to SaaS ranking would
create little value.

26



3. Method

Figure 3.2: Suggestion for grouping of factors
A proposed grouping of factors, inspired by Rajala et al. (2003), were presented at
the workshop.

Company X was very interested in the grouping of the factors made during the
mapping. The reason is that the model included highly relevant factors for Com-
pany X, and they often talked about those factors. However, they did not have a
straightforward way to structure those factors. The grouping and the structure of
the factors realised during the mapping is presented in Figure 3.2. The suggested
SaaS ranking presented were also of interest and gave inspiration for other ways to
use it. A continued discussion followed about Company X’s other needs, how the
grouping and the SaaS ranking could create value in the investment process.

3.3.3 Change of direction
The new direction taken after the workshop was to develop a model to help Com-
pany X evaluate a target company, assess the target company’s SaaS potential, and
communicate the potential investments to external people of interest.

The change of direction was motivated by three aspects. First, when a target com-
pany has been internally labelled as attractive from the initial screening, Company
X sends out a question battery to further assess the target company. The purpose of
these questions is to gain a deeper understanding of the company and better assess
the company’s soft metrics. This process could be developed further to be more
objective and give a more comprehensive picture of the company.

Second, a profitable investment strategy is to find companies that have not yet
developed their offer to SaaS completely. However, it can be challenging to evaluate
if it is possible to transform a company to offer SaaS or not. If a model can assist
the investment process to align with that strategy, it would be valuable.

Third, the word "storytelling" is a word frequently used by Company X. What
Company X refers to is a story explaining what Company X does, their strategy, and
why they invest in a particular type of company. During the workshop, it became

27



3. Method

clear that Company X wants to develop their storytelling further. This storytelling
is important because it helps Company X motivate their investment decisions to
their investors. If the investors understand the advantages with an investment they
can allow Company X to offer a higher price for the shares of a company.

The idea was to create a model used during the evaluation process, from the initial
screening to the in-depth evaluation considering these three aspects. However, it
became clear when comparing the insights from the mapping with Company X’s
first screening that their initial evaluation was sufficient as an initial step. Instead,
the focus hereafter was to create a model for the later stage of the evaluation process.

To summarise, it was decided to focus on creating a model assisting Company X
in making their assessment of a target company more objective, evaluating its SaaS
potential, and their storytelling.

3.3.4 Second iteration
After the change of direction, the iterative process initially planned was still highly
relevant and therefore remained.

A model was created based on the factors gathered during the workshop, and the
factors were divided into comparison areas, comparison metrics, and dimensions.
During the following iterations, the model was updated and revised continuously by
adding, changing, and removing areas, metrics, and dimensions.

Initially, the plan was that one model would be sufficient to satisfy all objectives;
to objectively assess a company; to assess a company’s SaaS potential, and the
storytelling. However, the model was very information-intensive, which created the
concern that information would get lost when presenting it to the audience, thus,
losing the storytelling perspective. Therefore, a second model was developed that
would be better suited for external use, and it would be based on the more extensive
model best suited for internal use.

The next step was to test this version of the model in interviews with Company X
employees per the iterative process. In these interviews, the goal was to validate
the model and further identify possible improvements. The model that had been
created after the workshop was presented. Interviews were held with employees in
different positions to get input about the model from multiple perspectives. The first
round was with two employees at Company X, who had limited knowledge about the
purpose of the model. Therefore, a short introduction was given about the purpose of
constructing the model. Next, the constructed model was presented more generally,
and the interviewees were asked about their first impressions. After that, each
comparison area of the model was discussed, and questions were asked to identify
improvement potential and general thoughts. The focus was on identifying whether
the model included all relevant parts in terms of comparison areas, comparison
metrics, and dimensions, and further discussion was held regarding if the proper
definitions were used.

28



3. Method

During the whole process, any uncertainties about the model were noted to clar-
ify those parts further. Many inputs and impressions were gathered regarding all
aspects of the model: the design, comparison areas, comparison metrics, and dimen-
sions. Following the input, a new version of the model was created.

3.3.5 Third iteration
The next step was to include all information gathered during the first round of
interviews in the second iteration into the model. The new model was then presented
in the second interview round for validation. In this round, the model was presented
with one comparison area at a time, thus making the discussions more focused.

3.3.6 Fourth iteration
During the interviews, it became clear that there was a need to restructure some
parts of the model, include additional factors and change some of the existing factors.
The flaws identified were minor, easy to change and the overall reactions to the
model were positive. Therefore, it was decided that no further iteration was needed.
The model was updated based on the proposed changes from the second round of
interviews, resulting in the final model.

3.4 Reliability and validity
A critical perspective was applied to the method and the data collected to reach
a reliable and trustworthy result. To achieve this, it is essential to continuously
evaluate the reliability of the literature and the collected data to ensure high qual-
ity throughout the process. Reliability refers to examining what is intended, and
validity ensures that the examination is appropriately performed (Hallin & Helin,
2018).

When gathering secondary data, the data needed to be critically evaluated since
the information may not have been updated or gathered for the same purpose as in
this study, and according to Krishnaswami and Satyaprasad (2010), it is imperative
to take these risks into account and assess these circumstances to minimise the
effects on the investigation. The goal of gathering secondary data for this study
is to understand what is essential in the business model for software companies.
Therefore, the quality of the secondary data, in terms of it being current or collected
for the same purpose, was not as crucial for this study.

Saunders et al. (2009) highlight the difficulties with comparing results when using
semi-structured interviews. In this thesis, interviews are conducted with different
employees at Company X to enable insights from multiple perspectives. Since the
employees have different roles, the purpose was to gain insight based on their primary
area of knowledge. The purpose is not to compare the answers; instead, use each
answer separately to improve the model. Further, if interviewees give different input
within the same area, an informal discussion was held afterwards to identify why
the difference appeared. For example, was it because they have different opinions,

29



3. Method

the answers have been misinterpreted, or did the interviewees express themselves
differently. If there are different opinions, a discussion of the best alternatives was
held, and inputs from theory were updated.

Since Krishnaswami and Satyaprasad (2010) raise concerns about the interviewer’s
choice of word and formulation, extra attention was paid to ask the questions without
adding any value. Furthermore, during each interview, all feedback, both positive
and negative, is welcomed.

30



4
Results and Analysis

In this chapter, all empirical finding collected during the research is presented.

The results and analysis of the mapping are presented first, followed by the in-
formation gathered to understand investment processes and what areas influence
an investment decision; "Understanding the foundation of an investment decision"
creates a foundation for continued research and development of the model.

Next, a description of the model created is presented. Key versions of the model
are presented in more detail. These versions include version 2, the one constructed
after the workshop, and version 4, the final version. In between those sections, the
results and analysis that led to the final model are presented. Describing the critical
versions of the model highlights the significant changes done to it. Detailed progress
with all version of the model can be followed in Appendix A.5 and Appendix A.6.

4.1 Mapping
This section summarises the data collected about the 26 companies included in the
mapping, all of which are listed on the stock exchange. A list of all companies can be
found in Appendix A.1. 38 factors were identified during the mapping of companies;
all factors are presented in Appendix A.2. During the mapping, the factors were
divided into three groups. One group with KPIs, one with more extensive content,
and one group called "mention". ". The last one consisted of keywords that were
often incorporated in annual reports and websites of software and SaaS companies.
We observed that those words could indicate whether a company was working to-
wards offering SaaS or already offered SaaS. By compiling all the information in
the three groups, it was possible to observe how far a company had transformed its
solution to SaaS.

Important observations done during the mapping are listed below.

• Companies listed on the stock exchange have broad businesses with multiple
business areas focusing on different problems. A company usually has one
area equivalent to a typical software company, but another area focuses on
something else, such as hardware production.

31



4. Results and Analysis

• The companies already offering SaaS or companies expressing a clear focus
to transform their business into SaaS often used specific words to describe
their business. Some of these words include recurring revenue, cloud-based,
scalability, and "prefix"-aaS, where prefixes included software, cloud platform,
distribution, traffic enforcement, and risk assessment. All these prefixes refer
to software that solves problems within that area.

• Based on what the companies expressed in their annual reports regarding
SaaS and their future focus, a subjective ranking of how far they had come in
working towards becoming a SaaS company was done. This ranking had six
levels, where those in level six had come closest to offer complete SaaS, and
those in level one had not recognised the benefits with SaaS and did not seem to
be thinking about implementing SaaS as a way of offering software. A complete
list of the definitions of the different levels is presented in Appendix A.3.

• All companies in the mapping used direct sales as their primary sales method.
Eight companies complemented direct sales with sales through distributors,
agents, or similar to reach additional customers.

• Fourteen companies mentioned that parts of their revenue are recurring, and
eight of those companies specified how much of the revenue was generated
repeatedly.

• The number of customer industries a single company focused on is often broad.
The most significant number being ten industries. A majority of the companies
in the mapping focused on more than five different industries.

• Seven of the companies explicitly mention that they are operating in niche
markets.

• None of the companies in level six, referring to a high level of SaaS, mention
that they had high customer concentration. The first one to define the cus-
tomer concentration further was a company in level five, where the top ten
customers represent 43 per cent of the total revenue. In the lower levels, more
companies specified how much of their revenue came from their top customers.
In their annual report, the only company mentioned that they were dependent
on a low number of customers and had high customer concentration was in
level five.

• Many of the companies somehow mentioned they would focus on SaaS going
forward. According to the SaaS ranking of the 21 companies in level five
and lower, 12 companies mentioned that they want to explore SaaS. The six
companies in level six already had a functioning SaaS.

Factors included in the mapping not mentioned above did not provide any helpful
information since no conclusions could be drawn.

32



4. Results and Analysis

4.2 Understanding the foundation of an invest-
ment decision

The understanding of how an investment process is structured and how an invest-
ment decision is made is based on experiences from Company X. In this section,
information about the investment process, areas affecting an investment decision,
and specific terms and definitions used by Company X are presented.

It is essential to understand the investment process, how the process could be im-
proved, and the context of the model. Areas affecting an investment decision lists
different aspects to take into consideration when making an investment decision.
Specific terms and definitions used by Company X are included to show that com-
panies may define parts included in the investment decision differently. In this thesis,
definitions are aligned with Company X, which should be considered if applying the
model to other venture capital firms.

4.2.1 Investment process
In the evaluation process Company X, performs an initial screening, aiming at col-
lecting information necessary to decide whether a company is interesting or not. It
regards KPIs, such as revenue, growth, EBITDA, and general information about
the company, in terms of the number of employees, the target company’s offering,
services, products, and customers. All this information is compiled into a one-pager.
The one-pager does also include which part of the IT landscape the company is part
of and whether it has developed its own software or not. The initial screening of
target companies needs to be efficient. Therefore, all the necessary information to
make a decision needs to be accessible through Company X’s data collection sources:
the target company’s website, google retriever, Linkedin, and, if needed, annual re-
ports. The annual reports are only scanned if the necessary information cannot be
obtained from the other data sources.

Based on the compiled information, Company X decides if the target company is
attractive to invest in during an internal meeting. If a target company is approved at
the internal investment meeting, the second phase includes a more in-depth analysis
and evaluation. Company X approaches a target company at this stage, and the
in-depth analysis is based on information gathered from the question battery sent
to the target company. This analysis results in a conclusion whether Company X
wants to invest or not.

4.2.2 Areas affecting an investment decision in addition to
factors in mapping

This section summarises factors that an investment decision can be based on, iden-
tified through Company X. A critical thing to remember, according to Company X,
"Everything is important" because factors are assessed together. If one factor is be-
low standard, it could be offset by another factor being above standard. Therefore,

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4. Results and Analysis

it is very complicated and complex to create a model for assessing a company that
considers everything.

4.2.2.1 Key performance indexes
When assessing the KPIs of a target company, venture capital firms set up different
investment strategy requirements. Company X does not have many hard limits
that restrict a target company from continuing through the process. Company
X assesses every company individually and looks at the KPIs in context to get
a complete picture of the performance of a target company. The KPIs used in
the evaluation process are Revenue, Profit and Costs, Growth, Recurring revenue,
Customer concentration, and Employees per majority owner. Some of the KPIs are
more important than others, and all KPIs used by Company X is described in the
following sections.

First, revenue, profit, and costs are essential to assess. These metrics are closely
related and are therefore assessed together.

Company X targets a particular company size and looks at revenues ranging from 20
MSEK to 200 MSEK. However, the revenue is not the main focus, and they clearly
ex