Dietmar Jannach
Institute of Applied Informatics, Klagenfurt University, Universitätsstraße 65, Klagenfurt, Austria
Uffe Bundgaard-Joergensen
InvestorNet & Mermaid Venture, Scion-DTU, Diplomvej 381, Kongens Lyngby, Denmark
Keywords: Self-service applications, e-services, knowledge-based systems.
Abstract: The business plan is one of the essential tools for companies to attract investors and raise venture capital.
Getting a business plan "investor ready" in that context means that it has to be professionally prepared such
that it answers all questions of potential investors. This, however, requires in-depth knowledge of a typical
investor's expectations and viewpoints, a type of knowledge which in particular first-time entrepreneurs or
small companies do not dispose of. As a consequence, significant amounts of consulting hours are required
to get the business plan ready to be presented to investors.
The goal of the SAT project presented in this paper is to provide an in-depth business plan advisory service
over the Web. While current approaches in that area rely on static fill-out forms or checklists, the SAT tool is
based on personalized interactive dialogs, knowledge-based input analysis and feedback generation, as well
as on detailed financial calculations. Within the paper, we thus show how knowledge-based approaches can
serve as a technological foundation for such next-generation electronic services and how the corresponding
development and maintenance efforts can be minimized. The paper gives an overview on the general
knowledge-based architecture of the system, discusses the integrated graphical modelling environment, and
finally reports on experiences gained from the practical use of the tool.
Getting a business plan "investor ready" is a
challenging task in particular for young entre-
preneurs or smaller companies with limited
experience and background in acquiring investor
capital. Although the importance of a business plan
as one of the essential tools for attracting investors is
widely known and lots of guides and further
literature are available, our year-long experience
shows that typical mistakes are still common. In the
context of the commercialization of e-Business
related innovations for instance, technological
aspects are in many cases overly emphasized. At the
same time, investors often complain that the
investor's view is not properly addressed in the
business plans they receive. Much too often aspects
like experience of the management team, the internal
return rate and exit scenarios for the investor, or
detailed statements on financials and uncertainty
factors are neglected. This has even lead to an EU
sponsored training initiative called "Master Classes"
where innovation professionals and entrepreneurs
are introduced to the world of "investor thinking".
Thus, these entrepreneurs or less-experienced
companies often chose to utilise professional advice
to increase their chances of raising capital for their
business ideas. Professional counselling however
requires highly-experienced advisors and hence
further increases the amount of in-advance
investments required for the planned commercializa-
The herein described S
AT (self assessment tool)
project was initiated with the goal to provide a new,
high-quality advisory service on the Web. The
resulting software solution should consequently be
deployed in the context of an EU-funded Web portal
for Business and Innovation Financing
(Gate2Growth), which aims at bringing together
entrepreneurs, innovation professionals and
When examining the existing sources of
information for entrepreneurs who search for
Jannach D. and Bundgaard-Joergensen U. (2007).
In Proceedings of the Second International Conference on e-Business, pages 99-106
DOI: 10.5220/0002109500990106
innovation financing advice on the Web, one can
observe that these sources mostly exist in the form
of static fill-out forms or checklists. In contrast to
such static approaches, the S
AT tool in its vision
aims at providing a virtual advisor that simulates the
behaviour of an experienced business consultant in
different dimensions. This includes for instance that
both the characteristics of the entrepreneur as well as
the key aspects of the business idea are elicited in a
highly personalized interactive dialog or interview.
Technically, this means for instance that the
sequence of questions can not be a static one and the
system has to continuously react to the current user's
inputs. In addition, such a system should also
immediately react once it detects inconsistencies in
the user's answers or identifies situations, in which
additional hints or cross-references appear to be
useful. For a general overview of these
personalization opportunities in such e-service
applications, see also (Jannach & Kreutler, 2005).
As the provision of highly personalized
applications is known to be a knowledge-intensive
task (Kobsa et al. 2001), we have decided to follow
a knowledge-based software development approach
and base the S
AT tool on the ADVISOR SUITE
framework described in (Felfernig et al. 2007). The
main advantages that we expected from the decision
were as follows. First, the efforts for development
and maintenance should be minimized as domain
experts are given the opportunity to add and modify
individual pieces of knowledge by themselves. In
addition, by choosing a more general and domain-
independent framework as a basis, future
extensibility toward other types of online advisory
services on the Web portal should be guaranteed.
Based on an analysis of the particular challenges
and opportunities of a fully knowledge-based
approach the paper shall thus demonstrate how – on
the basis of a concrete example – these techniques
can serve as a basis for next-generation intelligent
electronic online services.
The rest of the paper is organized as follows. In
the next section, we will sketch how the end user
perceives the virtual advisor application and what
aspects are covered during the business plan analysis
phase. Next, we give an overview on technical
aspects, in particular how the different required
pieces of knowledge can be modelled within the
graphical S
AT knowledge acquisition environment.
The paper ends with a discussion of first experiences
both with respect to system development as well as
end user feedback and a comparison to similar
Fig.1 depicts how the profile of the online user is
interactively elicited. The system, which is
optionally personificated by an animated avatar,
guides the entrepreneur through a series of
personalized questions. Throughout this system-
driven dialog, the user inputs are constantly
monitored and the virtual advisor chooses the next
questions to be asked based on the current state of
the dialog. Depending on the current context, for
Figure 1: An interactive dialog page.
ICE-B 2007 - International Conference on e-Business
instance, unnecessary questions may also be fully
left out or specific textual variant might be asked
when the user seeks for investors in a certain
industry sector. When the system identifies
(obviously) conflicting user statements, the question
and answer dialog is interrupted and opportunistic
hints and explanations are provided.
In order to give the user appropriate feedback
about the progress, the dialog is structured into
phases, see also the right hand side of the screen.
Note that not all these phases are mandatory, i.e.,
when no detailed financial analysis is desired,
individual parts of the dialog are automatically
skipped (in contrast to more static approaches).
Once enough details have been provided, the
system generates different types of feedback for the
entrepreneur. The integrated analysis of the business
plan given by the system has various facets (Figure
2). First, the completeness of the business plan
content is evaluated based on a scoring mechanism
along twelve different dimensions like management
team, products and services, sales and marketing and
so forth. The results of this scoring are visualized
and summarized in terms of a "completeness radar"
as shown in Figure 2. In the detailed feedback
section, a comprehensive review of the user's
statements and characteristics is given. For each of
the twelve different subsections, about five to ten
comments, explanations, or suggestions are
proposed to the end user. Next, the "investor's view"
is summarized, which is based on an in-depth
analysis of various factors. The influence factors
include the user's statements about financial aspects
like expected profit and loss within the next years,
an estimate of internal return rates, or funding-
related issues like the percentage of ownership
offered to the investor. The feedback given is split
into a section in which key indicators for investors
are calculated and a section which explains and
comments how these indicators will be interpreted
by the investors. The individual statements are then
also condensed into compact aggregated advices
(see also Figure 2).
Finally, the S
AT system provides a detailed
statement about the entrepreneur's attitude toward
time preferences and uncertainty aspects: In this
section, the given financial numbers are compared
with the user's feedback on his estimate of future
developments and preferred investment scenarios.
One of the goals of this feedback section is thus to
cross-check the plausibility of user responses.
As can be seen from the short system description in
the previous section, the application is knowledge-
intensive, i.e., significant amounts of domain
expertise have to be encoded in the background,
from business plan analytics, over dialog
management, to investor-specific financial
calculation schemes. Consequently, aspects of
knowledge acquisition and maintenance have to be
in the centre of system design considerations: On the
one hand the knowledge in the field constantly
evolves while at the same time on the other hand the
Figure 2: In-depth investor readiness analysis.
system should be continuously improved based on
usage experiences or user feedback. In the
subsequent sections, we will thus discuss how the
different pieces of knowledge can be captured with
the help of the S
AT Modeling Environment and how
knowledge acquisition and maintenance efforts can
therefore be kept at a manageable level.
3.1 The Business Plan Profile
The business plan and entrepreneur profile is the
central element in the system's knowledge base and
it is used to capture the key characteristics of the
entrepreneur and the corresponding business plan to
be analyzed. Technically, the profile consists of a set
of variables V, each of them with a defined domain,
which can be a finite enumeration of values or a
scalar data type like integer or real. Each variable
can also be set-valued, i.e., multiple values can be
assigned to a variable at the same time. The actual
values for a business plan analysis session are
determined either by directly questioning the user
(Figure 1) or by internally deriving the value based
on defined business rules, for instance based on
scoring functions.
Figure 3 sketches how the business plan profile (BP)
is related to the other pieces of knowledge in the
AT tool. When values for BP should be directly
acquired, the variables are referenced from the
dialog model, in which the interactive elicitation
process is defined.
The variables of BP are of course also the
starting point for defining the feedback generation
process. First, they serve as an input to the internal
derivation of additional values based on financial
calculations or scoring rules. In addition, the
variables of BP are to be used in the definition of
feedback rules that determine the set of analytical
statements and hints to be displayed to the
entrepreneur (see Figure 2).
3.2 Feedback Rules
The knowledge base of feedback rules (KBFR) about
the various aspects of the business plan to be
analysed is the largest piece of domain expertise
encoded in the system. Currently, the S
AT tool
comprises more than 450 of these statements.
In the S
AT system, a feedback rule FR is represented
as a tuple
<ID, Group, Text, Order, Expression, Lang>.
Each feedback rule has a unique ID, belongs to a
group, and has an associated multi-lingual,
parameterizable textual statement as well as an
optional order of display. The selection of
appropriate feedback statements for a given business
plan profile is determined by an expression over the
variables of BP. An example for such a feedback
rule that also includes derived variables from the
financials module could be as follows.
"The discount rate of $
proposed_discount_rate$ you
have proposed to be used for the 'net present value'
calculations is higher than the "time preference
rate" you have revealed from answering other
questions during the test (…)."
The corresponding rule could be for instance
Display when
proposed_discount_rate >
The symbols proposed_discount_rate and
computed_time_preference_rate are variables from
the Business plan Profile BP; the expression
language used in the S
AT tool comprises standard
arithmetic and relational operators as well as logical
operators like and, or, and not. The definition of
feedback rules including the display conditions can
be fully done with the help of graphical editors (see
later sections). At run-time, the conditions are
automatically evaluated by the system in the context
of the current user session as to determine the subset
of feedback texts to be displayed.
3.3 The Dialog Model
In contrast to the above-mentioned feedback rules,
the definition of the dialog model describing for
instance, which questions have to be displayed under
what circumstances, is more complex.
In the S
AT system, the possible flow of the system-
user dialog is modelled as a predicate-based finite
state automaton (van Noord & Gerdemann, 2001).
The main advantage of this common dialog
modelling approach is mainly that the resulting
models can be easily understood and modified by
using a corresponding graphical editor. Note that
this aspect is of particular importance in the context
Figure 3: Knowledge chunks in the SAT tool.
ICE-B 2007 - International Conference on e-Business
of our work, in which we aim at enabling the domain
expert himself to update the knowledge base as far
as possible.
Within the described S
AT system, the nodes and
edges of graph can be annotated as follows (see
Figure 4). As the nodes of the automation
correspond to dialog pages (Figure 1), we define the
set of questions to be displayed on that page and also
specify how each question should be rendered.
Furthermore, we can make the display of a particular
question (or answer option) dependent on previous
user inputs in order to reach a more dynamic
behaviour. The questions themselves again
correspond to variables from the central Business
plan Profile (BP), i.e., when variables from BP
should be directly elicited by asking the user, these
variables are annotated beforehand with appropriate
text fragments, both for the question itself, the
answer options, and optional explanations.
The second type of annotation is that for transition
conditions which specify how the dialog shall
proceed. Syntactically, these conditions are again
expressions over variables of BP which we
consistently use in the modelling environment. Note
that the system does not allow for indeterminism in
the dialog graph.
In order to support the user when modelling the
dialog, the underlying A
comprises a novel, built-in diagnosis mechanism
(Felfernig et al., 2006) for finding inconsistencies or
unreachable paths. Also in the dialog model, the
domain expert can specify when conditional hints
should be displayed, if for instance the user has
given inconsistent answers during the dialog. A
detailed description of these features is omitted in
this paper for sake of brevity. The development
cycle of the S
AT tool with regard to the dialog model
is as follows. Once the dialog model has been
completed or modified, a user interface generation
task is initiated in which dynamic HTML pages
(Java Server Pages in our case) are generated based
on the definitions in the knowledge base. Thus, also
the maintenance process for the personalized
graphical user interface is supported in the
modelling environment. Further details on this
specific code generation process can be found in
(Jannach & Kreutler, 2004).
3.4 Scoring Schemes
The evaluation of "investor readiness" of the
business plan along different dimensions is based on
a standard scoring mechanism and visualized by
means of a completeness diagram (see Figure 2).
Thus, for each of the twelve dimensions (like
Marketing & Sales, Technology and so forth) a
scoring function has to be defined. In the S
modelling tool, each dimension is therefore
associated with a set of variables from the business
plan profile BP and for each possible value of a
variable a numerical and normalized score from 0 to
10 is defined. The definition of this score value can
be either done by assigning concrete numbers for
enumerated domains as well as by means of a
mapping function for variables with continuous
At run time, the actual values in the BP are
evaluated and an overall cumulative scoring value
per dimension is determined based on a Multi-
Attribute-Utility-Theory calculation (von Winterfeld
et al, 1986). Note that the scoring dimensions
themselves are again treated as variables in BP,
which means that one can also define expressions in
personalization or feedback rules that take the
current value of the scoring result into account.
3.5 Financial Calculations
An integrated evaluation of a business plan with
respect to attractiveness to potential investors
requires an in-depth analysis of financial aspects and
accompanying uncertainty factors. Such calculations
are based on various inputs such as estimated profit
and loss figures for the upcoming years, a projection
of these numbers for subsequent years, or time
points and amounts of future investments. From
these and other inputs, the "investor's view", i.e., an
assessment of the financial opportunities and risks of
the investment, is derived. The typical figures on
which investors’ base their decisions with respect to
financials are for instance Internal Return Rate,
Price Earnings Ratio, or the Net Present Value of
investments. The values need to be above a certain
Figure 4: Annotated state diagram.
"hurdle rate" which depends on risk and maturity of
the case in hand.
When such calculation schemes are to be made
explicit and formalized in some software system, we
observe that for many domain experts the preferred
way of doing this is by means of a spreadsheet
program. As the opportunities of this form of end
user programming for specific tasks are well known
(see for instance Nardi & Miller's paper from 1990),
we decided to adopt such a knowledge acquisition
approach also in the S
AT system. Thus, the domain
expert can formulate and update the calculation
schemes with a standard spreadsheet program. At
run time, the S
AT system directly interacts with this
spreadsheet service: The values of the variables in
BP serve as inputs to the financial calculations and
computed values are automatically transferred back
to standard BP variables, on which further
expressions - like in feedback rules - can be defined.
The only prerequisite for this tight integration of
external spreadsheet logic is a defined mapping
between BP variables and spreadsheet cells.
3.6 Overall Architecture &
Development Cycle
Figure 5 summarizes the overall architecture,
stakeholder roles, and process flow of the SAT tool,
see also (Felfernig et al. 2007) and (Jannach &
Kreutler, 2004) for technical details of the
underlying A
DVISOR SUITE framework. During
design time, the domain expert – optionally together
with a knowledge engineer – constructs or modifies
the required business logic like the feedback rules or
financial calculation schemes. All definitions except
for the spreadsheet calculations are stored in a
central knowledge repository. In parallel, a Web
developer defines and maintains the HTML
templates, which the framework uses for the
generation of a corresponding Web application in an
automated application assembly process. Once the
resulting Web application is deployed on a Web
server, it is ready for use for the entrepreneurs. The
run-time components of the framework comprise
among others a personalization agent which handles
the interaction with the end user based on the
definitions in the central knowledge repository, i.e.,
it for instance evaluates the current state of the
interaction and determines the further flow of the
At the moment, the definitions in the knowledge
base are static in a sense that they do not change
automatically over time based, e.g., on the
experiences gained from previous advisory sessions.
The incorporation of such learning techniques is
however part of our current and future work.
4.1 Modelling and Technology Aspects
For the SAT tool, a knowledge-based, integrated-
modelling approach has been chosen due to the
anticipated complexity of the business logic to be
implemented. Currently, there are for instance more
than 450 feedback fragments and accompanying
selection rules stored in the knowledge base. The
business plan profile comprises more than 120
possible direct questions, of which of course only a
part is actually asked in one single dialog as all those
questions are left out that are not relevant in the
current context. The dialog model describing the
different pages and the interaction flow (see Figure
4) is comparably small and contains about 40
different interaction states. Note, that one such node
can comprise several questions; in addition,
individual questions are dynamically dimmed out
based on personalization rules. Thus, the graphical
dialog model remains at a manageable size.
Compared to other ways of interaction modelling
like dialog grammars (see, e.g., Bridge 2002),
however, our state diagram approach is somewhat
limited and static as all possible dialog paths have to
be explicitly modelled. Still, from the perspective of
Figure 5: Architecture and development, stakeholders and development cycle.
ICE-B 2007 - International Conference on e-Business
modelling complexity, we claim that state diagrams
are more comprehensible to users with limited IT
background, given the declarative and implicit
nature of grammar-based approaches. In future
releases of the S
AT modelling environment,
however, we plan to incorporate the possibility of
modelling sub-graphs in order to provide additional
structuring mechanisms for complex graphs.
In general, the task of selecting appropriate
feedback texts based on an interactively acquired
user model can be accomplished by a conversational
recommender system (see, e.g., Thompson et al.,
2004) and a rule-based filtering approach. In
contrast to previous work in this area, however, the
AT tool and the underlying ADVISOR SUITE system
aim at providing not only algorithms for problem
solving but also a comprehensive modelling
environment which also includes domain-specific
extensions like financial calculation schemes.
To the best of our knowledge, Ernst & Young's
IPO Navigator (see a report by Quittner, 1999) is the
only other approach toward providing a comparable
web-based electronic advisory service for
entrepreneurs. While there exist some similarities
between the IPO Navigator and the S
AT tool from
the end-user perspective, no reports on the internal
implementation and the technological foundations of
this tool are available.
Ernst & Young's IPO Navigator is part of a
larger set of electronic self-service tools developed
within the company's broader e-service initiative.
Comparably, the S
AT modelling environment aims at
providing a common technological basis for such
types of applications as the internal mechanisms
like, e.g., scoring or rule-based feedback, can be also
applied to various other types of decision support
and advisory problems. Up to now, two further
related advisory applications not described in the
paper (an entrepreneur personality check and
another financials tool) have been built based on
AT technology.
4.2 First Practical Results
Currently, there are two versions of the SAT tool are
in productive use. SAT LIGHT is a free, smaller
version of the business plan advisory system which
mainly covers the aspect of feedback rules and
scoring. Furthermore, the questionnaire and the
evaluation are not as detailed as in the full S
version, which also includes the detailed financial
calculations. The S
AT framework itself comprises a
comprehensive logging component in which all user
interactions are monitored and stored in the central
repository. Thus, from the light version, which is
online since spring 2005, first empirical results
about the usage of the system are available. Up to
now, about one thousand successful advisory
sessions have been registered, in which an online
user has fully stepped through the series of about 25
questions. From the overall usage statistics we see
that from all users that go through the first five
questions, about 50 percent make it to the final
recommendation. We interpret this as a promising
sign with respect to the end user acceptance and
usability of the system, given the fact that online
users in our opinion not easily accept such long
"click-distances" on the Web, see also the study of
(Smyth and Cotter, 2002) in the context of mobile
portals. From our point of view, the more interesting
type of information contained in the usage logs is the
knowledge about common shortcomings of business
plans or the typical characteristics of the
entrepreneurs. On the importance of such aspects in
the domain see for instance (Carswell & Asoka
Gunaratne, 2005). Although a variety of books and
online guides exist about how to write effective
business plans, to the best of our knowledge no
statistical evidence is yet available about common
mistakes and in particular about correlations along
the different dimensions. Given such information,
we hope that an in-depth analysis, which is part of
our current research, can be a valuable contribution
in particular in the area of entrepreneurship
At the moment, such an evaluation has not been
done yet, mainly because the sample size of the
more detailed S
AT PRO tool is not yet sufficient.
What can be reported from the S
AT LIGHT tool yet
are basic figures on individual numbers in the
profile. So for instance more than a half of the online
users had a combined practical management
experience of less than four years, which indicates
that most users of the tool are first-time
entrepreneurs. Still, more than 70 percent are sure at
the beginning of the analysis that their business plan
is very clear about how the company will make
money with its products and services. Even more,
users also stated it would be easy to understand for
investors why customers will pay for exactly their
products, which indicates a common trend toward
overestimation of the advantages and marketability
of the new product. Another example would be the
description of investor exit opportunities in the
business plan, which is fully missing in 40 percent
of the cases and which thus indicates that the
"investor's view" is commonly not properly taken
into account. Compared with our own practical
experience, the examples nicely reflect our
observations of entrepreneurial attitude and investor
perception during individual coaching sessions. The
findings area also in line with the feed back received
from more than 400 participants in the 30 Master
Class seminars held throughout Europe during 2006
and 2007. Although it is too early yet to make well-
founded statements based on these statistics, the
examples above should give a first impression about
possible types of information contained in the
interaction logs.
In this paper we have presented SAT, an interactive
online advisory system in the area of technology and
innovation commercialisation. The challenges with
respect to the formalization of comprehensive
domain expertise and the need for personalized user
interactions have been discussed and we have shown
how knowledge-based approaches can be exploited
to minimize system development and maintenance
efforts. In a broader view, we thus see our work also
as a real-world case study of how such technologies
can serve as an enabler of new e-business self-
service applications.
A first usage analysis indicate that such a system
will be accepted by end users and as an alternative to
classical means of information gathering and
business plan evaluation like possibly expensive
expert counselling. The “How to attract investors”
Master Classes are all structured around the S
tool. More than 30 Master Classes have been held in
12 European countries. Practical experiences from
contacts with more than 400 participants in these
classes indicate that the tools also have a strong
potential user base among innovation professionals.
The tools thus provide an adequate structured
approach to advising and coaching services offered
to entrepreneurs by innovation professionals.
The work presented herein has been developed as
part of the Gate2Growth Initiative, in particular the
InvestorNet activities. It as been supported by the
European Commission, D.G.Enterprise, see also
InvestorNet and the development of the SAT-tool
have been supported by the European Commission,
DG Enterprise.
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