Throughout history innovation has been the driving force behind
economic growth. Increased competition is fueling the explosive
demand for Innovation. Start-up companies and their new products
define and set new economic standards. With an ever-changing economic
environment, including globalization and competitive landscapes
stretched across countries, the rate of innovation and new business
ventures is increasing.
In the battle of ideas those who are well positioned
for creating financial gain will succeed. Whether those ventures
are created by entrepreneurs as independent companies or are set
up by intrapreneurs trying out new ideas inside a large corporation,
we will continue to witness an increase in Innovation.
Any new business venture needs financial support
to become successful and for the shrewd investor, new ventures can
become lucrative opportunities. To capitalize on these opportunities
and support the increases in innovation, it is important to improve
the efficiency of the investment decision-making processes and provide
a reliable and scalable methodology for investors to rely on.
Success is about wise investments and successful
investment requires impartiality, consistency, as well as superior
judgment and insight. Not only for investors, but also for entrepreneurs
it is important to view a venture directly as an investment opportunity
and consciously pursue it as such. Whether the resource is money,
time or talent that is contributed, every involvee is an investor.
This paper utilizes Knowledge Modeling concepts
to discuss the fundamental elements of a business venture and suggests
ways to systematize the screening and assessment process for investors
For all practical purposes an investment opportunity
can be viewed as a machine that consumes certain resources, and
produces a return within a timeline while carrying certain risk.
This general view of Investment Opportunity, as
shown in the above diagram, applies to all investments in the private
sector ventures. This concept is detailed throughout the paper.
An investment may be made with an expected return
of growth or cash flow. The valuation increase as shown above in
green symbolizes the growth of the investment.
The time lapse between resource consumption and
return will largely depend on the type of industry, company strategy,
success in execution and stage of development.
In a perfect world, an investor would ask only
three questions before making an investment decision:
|How much is needed?
|What is the payback?
||(Rate of Return)
|In what format and time frame?
||(Deal Structure, Exit)
However, in the real world, investors have experienced
or heard of cases where investments haven’t produced the promises.
So they now have to quantify the risk by asking questions such as:
|What do you want to do?
|How do you want to do it?
And that is where all the complexity of selection begins . . .
Risk/Reward model – all investment factors drive
this universal model.
The concept of investment is embedded within the
“Risk/Reward” model of wealth creation. It is commonly
accepted that a higher risk tolerance correlates to a higher potential
The following diagram illustrates possible opportunities in relation
to their Risk/Return ratio.
Of course any investment involves certain level
of risk. However a sound investment decision should incorporate
a calculated risk. As the level of risk increases, the range of
possible returns on the investment widens as shown in the diagram
Noteworthy, the quality of an investment opportunity
is not determined only by its level of return, but also by the certainty
of its risk level. In other words not every low yielding investment
is a bad investment and vice versa.
The calculated level of risk is usually the biggest
driver for an investment’s required rate of return. The riskier
the investment, the higher the rate will be in evaluating whether
the expected payoff will be sufficient. The table below shows what
one dollar of investment will need to grow to given the level of
risk and expected time to a liquidity event. For a highly risky
investment requiring a 60% annual rate of return, the payoff in
one year needs to be 23% higher than a less risky investment that
requires a 30% annual rate of return (1.6 / 1.3 - 1). Because the
payoff for investing in a private company often takes four or more
years to realize, it is important to recognize that as the riskiness
(required rate of return) of alternative investments increase, the
size of the payoff grows at an even faster rate for multi-year investments.
At four years, the payoff on an investment requiring a 60% rate
of return will need to be 129% higher than the investment that requires
a 30% rate of return.
Rate of Return
Time between investment
and payoff in years
return=investment * (1+IRR)^years
Ideally the risk/reward values are reasonably known
and predictable. However for early stage businesses that is usually
not the case.
Because of the impact risk has on the size of the
payoff over time, it is important to try to measure the uncertainty
of an investment outcome. How does one go about doing this? The
best way of answering this question is by using stochastic methods
for calculating expected return and distribution variance.
Expected return is defined as the sum of probability
of each possible outcome (probability distribution) multiplied by
its payoff. Expected return represents the average return one "expects"
to receive if investments with identical odds are repeated many
times. A situation with expected return of zero (no net gain nor
loss) is called a "fair game".
For example, assume that the potential return on
investment (ROI) in a Start-up company is predicted as followed:
|| -100% (lost of investment)
That will translate to an expected return of 15%
(0.1 * 5 + 0.15 * 1 + 0.25 * 0 + 0.5 * -1)
The variance and standard deviation describe the
dispersion (spread) of the potential outcomes around the expected
return, which correlates to degree of uncertainty or risk.
The larger the spread » The
higher the risk
Variance is defined as the sum of probability of
each possible outcome multiplied by squared deviations of each outcome
from expected return.
Usually no investment is undertaken unless the
expected rate of return is high enough to compensate investors for
the perceived risk of the investment.
The required return is composed of “risk-free”
rate of interest plus several premiums that reflect inflation, the
riskiness of the investment, and liquidity of the investment.
This relationship can be expressed as follows:
Required Return = Riskfree Interest Rate + Inflation
Premium + Risk Premium + Liquidity Premium
Riskfree Interest Rate = the real risk-free rate
of interest is the rate that would exist on a riskless investment
if zero inflation were expected.
Inflation Premium = IP is equal to the average
expected inflation rate over the life of the investment. The expected
future inflation rate is not necessarily equal to the current inflation
rate, so IP is not necessarily equal to current inflation.
Risk Premium = This premium reflects the possibility
that the investment will not pay interest or principal at the stated
time and in the stated amount. Risk Premium is zero for U.S. Treasury
Liquidity Premium = This is a premium charged by
investors to reflect the fact that some investments cannot be converted
to cash on short notice at a "reasonable" price. Liquidity
Premium is very low for Treasury securities but it is relatively
high on very small firms.
It is to consider that risky investments rarely
produce their expected return; they earn either more or less than
what was originally expected.
Investors' risk tolerance varies depending on their expected return
and size of investment compared to total wealth. Referred to as
“utility function”, this can be presented as a graph
similar to the ones shown below. Accordingly investors may leverage
syndication (pooled funds) to overcome constraints applied by their
In general assessing risk is a complex issue because
there is a myriad of factors involved. In the venture and corporate
investments the number of contributing attributes as well as the
complexity of risk assessment is much higher. There are factors
involved such as market needs, feasibility of the solution, management’s
ability to execute and barriers to entry. A careful examination
and breakdown of composite elements is the first step to gather
needed insight required for decision-making.
2. The Venture
- A Profit Machine
For purpose of this paper we break down a business
venture into seven fundamental elements contributing to its expected
A venture works just like a profit-making machine,
consuming various resources as input and producing a certain financial
profit as output; presented as “a function of time”.
Even though it may sound obvious, many still ignore the time dimension
and make investment decisions based on a two-dimensional snapshot
The time dimension and type of return vary depending
on the stage, type, and strategy of a venture. Investors should
always keep the timeframe and return type in focus. The return can
be immediate or delayed and manifested as growth or cash flow.
The venture’s management team is responsible to plan for
maximum profit and minimum risk and prove to Investors the plan’s
Investment decisions are based on risk/reward ratio.
However unless potential risk and reward can be accurately estimated
it is very hard to make a sound judgment.
The purpose of investment screening is to determine
the fitness of an investment opportunity with respect to specific
investment preferences. It helps to qualify the potential rate of
return and quantify the associated risks.
A systematic investment screening and assessment
methodology not only assists investors in making investment decisions,
it also helps entrepreneurs in their planning process.
Proper assessment is about moving from uncertainty
to certainty and likelihood and certainty.
To assess a venture with a systematic approach,
all contributing elements have to be examined independently; that
A heptagonal spider graph is used to visualize the results of the
venture’s assessment. Each axis in the graph represents one of the
The model will show how well each of the seven
elements contributes to the total success of the venture.
The set value along each axis represents the viability
of such element. A graph is constructed so that a perfect investment
opportunity corresponds to a fully symmetric heptagon as shown below
in yellow. However most investment opportunities will have a deformed
shape like the purple-shaded area.
The specific value of each contributing element
is produced by answering specific questions and to score the element’s
ability to fulfill the desired promises accordingly. These questions
are slightly different depending on industry and revenue stage of
the venture. For the purpose of calculation the elements are scored
in full if not applicable in specific cases.
In the following section each assessment element
is individually described and reviewed. For a more accurate reading
each element is broken down into sub components, which collectively
produce a judging of the main element.
The purpose of a mathematical model is to help both entrepreneurs
and investors measure and compare investment opportunities in
a consistent and systematic way.
Investment decisions are entirely subjective and cannot be generalized.
However, the proposed analytical models can help investors to
develop, test and standardize their own internal model.
For this reason, the assessment is best started by assigning
a numeric value to each element of the model. This will describe
how well each of the seven elements is positioned to contribute
to the total success of the venture. It has to describe the availability
or ability of the element to deliver the promises. The value is
selected from a range of 0 to 100, with 0 being "the worst"
and 100 being "the best".
Accordingly, the total score of a venture is calculated as follows:
Score = (O + S + E + H + F + R + P) / 7
O = Market Opportunity
S = Products/Solution
E = Execution Plan
H = Human Capital
F = Financial Engine
R = Potential Return
P = Margin of Safety
In addition the model is able to capture the confidence
level of assessment. In the following graph the blue line represents
the score of the elements, while the red line represents the confidence
level relative to each one. The yellow area represents the area
A maximum confidence level will cause the red line to overlay
with the blue line and minimize the yellow uncertainty area.
As such the total confidence level of a venture is calculated
Confidence = (CO*WO
+ CH*WH + CF*WF
C = Confidence Factor for each element as a percentage
W = Importance of each element as a percentage
WTotal = Total assigned weights
The weights of the criteria are usually determined on a subjective
basis. These weights represent the opinion of a single decision
maker or synthesize the opinions of a group of experts using a
group decision technique.
Furthermore, the model can capture the fitness of opportunities
in accordance with the importance of the elements to each investor
rather than treating them equally. While some investors might
be more intrigued by the market opportunity, others may put more
emphasize on the team.
Fitness = (O*CO*WO+S*CS*WS+E*CE*WE+H*CH*WH+F*CF*WF+R*CR*WR+P*CP*WP)/
To incorporate that aspect of an investor’s preferences the model
utilizes a weighting mechanism. The weights are captured and presented
in the graph as thickness of lines corresponding to each element.
Fitness is calculated as:
Accordingly, assessment can consistently result in a spider graph
as well as a short description detailed as:
$500K – Biotech – 30% Growth – 3Y Exit – Score:
65 – Confidence: 45 – Fitness: 78
$500K – Retail – 2% Growth + 10% Income – Score: 75 – Confidence:
55 – Fitness: 26
The above example includes the capital requirement, industry
and potential returns, along with a visual representation of potential
risks and weaknesses.
To increase the accuracy of assessment each element of the model
can be examined using the same heptalyzation model. As described
in chapter 4, each element is assessed using quantifying questions.
These sub-elements can be scored and weighted in the same manner
as in the core elements. This sublevel scoring can automate the
calculation of higher-level scores and confidence levels.
For example element "A" is examined using questions
A.Q1, A.Q2, A.Q3, … where each question is again represented with
a score, a confidence factor and a weight.
Accordingly the score and confidence level of "A" can
be calculated as:
SQ = Score of each quantifying question for element "A"
WQ = Weight assigned to each quantifying question for element
CQ = Confidence level of answers for each quantifying question
The sub-scoring concept can be applied to as many levels as needed.
While this framework is still vulnerable to judgment
errors, it provides a methodology that can be continuously improved
and adjusted in accordance to the user’s experience.
A working model of the heptalysis is available on www.heptalysis.com
5. Score, Confidence
and Weight Criteria
In its simplest form, a single user can derive
a Heptalysis score by rating each of the sub-factors. With each factor having 7 sub-factors of its
own, a user will rate 49 sub-factors in order to yield a score,
confidence and weight. But
how does a user score each sub-factor?
What constitutes a score of 100?
How do you put a numerical score on sub-factors such as Ethics
& Integrity or Vivid
Pain or Desire? All these questions are valid in that they
point to the ambiguity of an unbounded assessment methodology. This chapter will try to outline a standardized
quantification method, describe the drawbacks of a single user assessment
and explore alternative scoring methodologies to enhance the assessment’s
Scoring the Sub-Factors: A Standardized Approach
A wise person once said
“one man’s trash is another man’s treasure.”
In the case of Heptalysis, it should be “one person’s score
of 100 is another person’s score of 50.”
A major hurdle encountered by any assessment methodology
is that people conduct the assessment, and these people make judgments
based on their personal history, academic and professional background.
These judgments can vary widely from person to person. Therefore, the usefulness of Heptalysis is lost if one assessment
cannot be compared to another.
For an assessment to be repeatable and comparable across
all users, it must have a standardized input methodology.
A simple way to standardize
the input is to prevent the user from simply picking the sub-factor
score. This is usually accomplished by developing
a set of questions that, collectively, will produce a score for
the sub-factor. This methodology
controls the scope of the sub-factor, and ensures that the assessor
will take into consideration everything needed to accurately derive
a score. These questions can be thought of as sub-sub-factors
in that they are just derivatives of sub-factors.
The single user assessment is the simplest approach to
deriving a Heptalysis score. However, there are several issues that arise from an output derived
from a single user assessment:
- The output is
only as good as the input used to derive it
- What one user rates 100, another may rate 50.
- A good score to one user may be 90, while to an other
it may be 60.
- A user with 20 years of experience in venture capital
would probably provide a more meaningful assessment than an
entry level analyst.
- It requires the
user to have a broad knowledge of business fundamentals
- Considering that the Heptalysis model covers the entire
spectrum of what makes a business venture successful, the user
would need to be well versed in every facet of entrepreneurship
to adequately execute the model.
- Most people either know a lot about little, or a little
about lot, but rarely both.
- It is susceptible
to judgment error and systematic bias
- People tend to be more critical of factors within
their own area of expertise.
- For example, a user with a finance background might
put a higher emphasis on a venture’s revenue model and ROE than
its management team and marketing plan.
- Deriving a confidence
value can be difficult
- The single user assessment leaves no quantitative
way of deriving a confidence value.
- In this framework, a confidence value is basically
a judgment call determined by the user and is susceptible to
the same drawbacks as the user defined score.
The Panel of Experts Technique
The Panel of Experts
technique overcomes many of the downfalls presented by the single
user assessment. The process
is fairly simple: gather a group of industry experts and have them
conduct the same assessment independent of each other.
The result is a sample of scores for each sub-factor.
This technique is commonly
used in the aerospace and defense industries in conducting quantitative
risk assessments on complex systems involving multiple disciplines. For example, it would be nearly impossible
for one person to accurately assess the financial risk on the development
of a new surface-to-air missile.
To do this, the user would need in depth knowledge of aerodynamics,
electrical engineering, mechanical engineering, software design,
manufacturing, logistics, contract law, finance, etc.
Rather than searching the globe for this all knowing individual,
it is much easier to form a team of experts representing the various
Benefits of the Panel
of Experts technique:
- In theory, the more assessments conducted the higher
the confidence in the score
- Basic statistics tells us that the larger the sample
size, the higher the confidence that the sample reflects the
same attributes as the overall population
- It should “home in” on the true score
- It eliminates any systematic bias, as long as the panel
is sufficiently diverse
- By having a sample of scores for each sub-factor, a
confidence metric can be derived
- There are many ways to derive a confidence metric
from a sample, however the most common way is to subtract the
standardized distance between the 90th and 10th
percentile from one. The
result will be a decimal between zero and one and is usually
represented as a percentage.
- Subsequently, a weighted score can be derived that
accounts for a high or low confidence metric.
- It can be easily modeled via Monte Carlo simulation
- Monte Carlo simulation is a statistical technique
that can be used for, among many other things, modeling uncertainty. In this case, it allows the Heptalysis
model to have varying inputs based on the sub-factor score sample.
Drawbacks of the Panel
of Experts technique:
- It requires experts, which are sometimes hard to find. Finding a diverse panel may be even harder
- It exposes the venture and its idea(s) to outsiders,
who could potentially steal it
- If the same group of experts conduct every assessment,
the output could be biased
- Monte Carlo simulation can lead to deceiving output
if modeled incorrectly.
Overall, the Panel
of Experts technique is a great way to add certainty to a Heptalysis
assessment. Implementation could come in several forms. The panel of experts could be the general partners
of a venture capital firm, local business leaders paid for their
time, local MBA students, or some kind of Heptalysis member association.
Example: Panel of Experts
This technique can be
set up using the following table from Microsoft Excel©.
As shown in the table, each expert scores each sub-factor. In
this case, there are 5 experts and therefore each sub-factor has
a sample of 5 scores. From
this sample, various statistical measurements can be derived: Mean,
median, variance, standard deviation, kurtosis, and percentiles.
These measurements can be used to describe the sample distribution
and can also be used to derive a confidence value.
This particular example
has an overall score of 82.6, a derived confidence of 90%, and a
weighted score of 74.5.
to the single user assessment is benchmarking.
Benchmarking is the process of comparing the attributes of
a particular venture against the attributes of another venture,
known as the benchmark. Ideally, the benchmark venture has completed
its life cycle with relative success and can therefore be assessed
in hindsight. In hindsight,
one will likely have better judgment of which sub-factors the benchmark
excelled in as well as which sub-factors the venture lacked in. Once assessed, the benchmark can be used as a measuring stick to
rate new ventures.
For example, let us
assume the benchmark venture received considerable success in marketing
its product. The benchmark was rated, in hindsight, a score
85 in its sub-factor Marketing
and Promotion Plan. Let
us also assume the new venture has done everything the benchmark
did in terms of marketing and promotion, and has also developed
a new direct mailing system that is expected to have a significant
effect on sales. In theory, this new venture should be rated at least 85, if not higher
based on this comparison. This
process is then repeated for each sub-factor to yield an overall
Benefits of the Benchmarking
- Requires less subjectivity than the single user assessment
- Provides an idea of what is a good score
- Is widely acceptable to the layman
- Most people can easily understand better than, worse than scoring
- This benefit shouldn’t be taken lightly, considering
obtaining investor acceptance is key to any capital venture
- Can be easily quantifiable if implemented correctly
Drawbacks of the Benchmarking
- Availability of data on the benchmark
- Availability of benchmark ventures
- Newer VC firms may not have anything to compare to
- Some ventures are very unique, and should be treated
- Reliance on judgment is minimized, but not eliminated
- Confidence value not statistically derived
is a great substitute for the single user assessment. It is a more quantitative methodology, which leaves less room for
subjectivity and human judgment. It
also allows for continuity across multiple assessments. But, perhaps the biggest drawback of the benchmarking
technique is that it fails to quantify confidence, which is left
to the user to subjectively derive.
This technique can be
set up using the following table from Microsoft Excel.
As shown in the table, a benchmark is used to score the potential
venture, Start-Up.com. Each sub-factor is scored based on a comparison
to the benchmark company, Gurgle, Inc. This particular example has an overall score of 73.2, a derived confidence
of 68.8%, and a weighted score of 50.4. Based on this comparison, it can be inferred that Start-Up.com will
not do as well as Gurgle.
Both of the aforementioned
techniques increase the certainty and accuracy of a Heptalysis assessment. The Panel of Experts technique is a fundamentally
sound methodology of assessing a venture with implementation being
its only drawback. Benchmarking
is also a great method of deriving sub-factor scores; however, it
fails to sufficiently address confidence.
This paper does not provide investment advice,
and should not be relied on as such nor is it a substitute for investment
advice. The statements and opinions expressed here are solely those
of the author and are not intended to constitute professional advice.
Any reliance upon this paper shall be at the user's own risk, without
any recourse to the author and his associations.
The author would like to thank Nikolay Stanevski
who created the first heptalyzer engine, Jon Brager for his contribution
to criteria solutions and Prof. Mike Solt, Dr. Rolanda Pollard and
David Belgum for their support and contributions to the project.
Thanks are also due to the people who reviewed
the draft paper, and who gave feedback, which helped to correct
some errors and polish the final document. The author expresses
special appreciation to Silicon Valley Entrepreneurship Center and
other individuals who shared knowledge and wisdom with this study.
This paper is more comprehensive, and richer in its insights, thanks
to the willingness and generosity of many people to share their
knowledge and views.
Pejman Makhfi is a Silicon Valley technology veteran, serial
entrepreneur and angel investor in the high-tech industry. Pejman
has more than fifteen years of progressive experience in providing
consultancy services and best practices to entrepreneurs, technology
investors, and forward-thinking Startups.
Widely known as a leader in the field of Business Process Automation
and Knowledge Modeling, Pejman has an extensive background in
the software and financial industries and has been the key architect
for several award-winning industry leaders, such as FinancialCircuit
Today, Pejman is the Director of Venture Development at Singularity
Institute, Managing Director of a private
investment group and is a member of Venture Mentoring Team
where he provides assistance and guidance to several early-stage
His background includes executive position at TEN,
a top Silicon Valley technology incubator hosting more than fifty
Start-ups. Pejman managed TEN’s R&D as well as advised startups
on the issues and trends affecting early stage and emerging growth
companies. Since its inception, TEN has helped launch over sixty
Startups, including eBay, iPrint, Xros, Vertical Networks, Right
Works, and Intruvert Networks.
Mr. Makhfi holds a B.S./M.S. degree in Computer Science from
Dortmund University in Germany and is an internationally licensed
Project Manager (PMP) as well as a certified Lean Six Sigma Black
Belt (SSBB) in continuous business improvement. He has authored
multiple Patents and standards and is an active contributor to
organizations such as "IEEE Neural Networks Society",
"American Association for Artificial Intelligence" and
"American Society for Quality".
This paper is copyrighted by the author and distributed
but it may, by the author's permission, be freely downloaded, translated,
printed, copied, quoted, distributed in any appropriate media providing
only that it not be altered in any way in text or intent and the
author is properly credited.
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