1. SELECTING PROJECTS FOR VENTURE
CAPITAL FUNDING:
A MULTIPLE CRITERIA DECISION
APPROACH
Gina Beim, P.E.
MCDA Consulting LLC
gina.beim@mcdaconsulting.com
Moren Lévesque, PhD
Schulich School of Business
York University

2. Venture Capitalists & their Decisions
 Selecting businesses for investment
 3 broad criteria:
– quality of management
– unique product or market opportunity
– potential for capital appreciation
 Evaluation process:
– objective information gathering and analysis
– intuition, gut feeling and creative thinking

3. Modeling the VC Decision Process
 Direct criteria weighting with questionnaires
(MacMillian et al., 1985 and Fried et al.,
1993)
 Conjoint analysis (Muzyka et al., 1996,
Zacharakis and Meyer 1998, Shepherd,
1999, Riquelme and Rickards, 1992) -
 Actuarial models (Zacharakis and Meyer,
2000)
 UTA (Utilité Additive) models (Siskos and
Zopounidis, 1987)

4. Modeling the VC Decision Process
 Conjoint Analysis: acknowledges the multiplicity of
criteria; relative weights inferred; limited in criterion
rating; utilizes hypothetical evaluation as initial point
 Actuarial bootstrapping models and UTA: related to
Multi Attribute Value Theory (MAVT); utilize decision
maker’s real past evaluations as initial point.
 Shepherd and Zacharakis, 2002: A call for more than
reproducing the investment selection process, and
instead for the use of decision aids in the venture
capital world.

5. MCDA in Financial Decision Making
 Investment portfolio selection (Bouri, Martel
and Chabchoub, 2002),
 Extension of credit (Matsatsinis, 2002)
 Foreign direct investment (Doumpos,
Zanakis and Zopounidis, 2001)
 Several papers presented in this conference

6. Promising New Field of Application
for MCDA: VC Portfolio Selection
 Bridges gap between official and de facto policies:
helps VCs understand and express what policies are;
incorporates policies into decision model.
 Interactive sensitivity analysis: brings aspects not
previously considered to forefront.
 Belton and Stewart (2002: 283): “most memorable
interventions in organizations have been those in
which the multicriteria analysis has brought about a
strong challenge to the decision making group’s
intuition”.

7. The JumpStart Fund
 Created by business and academic leaders
to provide start-up capital to companies
headquartered in Northeast Ohio.
 $2.3 million fund
 Based at Case Western Reserve University
between 2001 and 2003. In 2004 became
part of a larger organization.
 Until 2003, a typical JumpStart investment
amount was in the range of $200,000.

8. 9 Business Plans in our Case Study
 Dental device
 E-commerce facilitation
 Human resources tool
 Management software
 Market research tool
 Media company
 Medical device
 Pharmaceutical
 Supply chain management software

9. Modeling and Analysis
 Multi Attribute Value Theory – Logical Decisions®
software.
 Criteria developed in interactions with JumpStart
fund manager.
 Combination of top-down and bottom-up structuring
techniques.
 Fund manager encouraged to avoid criteria
redundancy, lack of independence, and extreme
complexity while being comprehensive and sensitive
to criteria relevance.

10. Model Structure
 Overall goal: “Selecting the Best Businesses to
Fund”.
 4 sub-goals: “Management and Governance”,
“Feasibility of Proposition”, “Market Considerations”
and “Return on Investments”.
 10 lower level (measurable) criteria.
 Criteria critically evaluated against entrepreneurship
literature and practice.

11. Founder's track record
Measure
Quality of Board
Measure
Quality of Management
Measure
Management and Governance
Goal
Realistic Approach to Financing
Measure
Well thought out milestones
Measure
Feasibility of Proposition
Goal
First Mover?
Measure
Potential Market Size (billion US$)
Measure
Proprietary Techonology / Patent Protection
Measure
Market Considerations
Goal
Exit Opportunities
Measure
Time to Achieve Profitability
Measure
Return on Investment
Goal
Successful venture
Goal
Hierarchy
of Criteria
for
Business
Plan
Evaluation

12. Business Plan Ratings
 Ratings based on information contained in the
business plans.
 Performance assessed on an interval scale of
measurement containing minimum and maximum
local reference points.
 Group of business plans being analyzed was
representative of the universe of plans targeted by
JumpStart: global and local reference points
coincided.
 Fund manager had choice of categorical or ordinal
scales. Mostly chose a subjective categorical scale.

13. Business Plans Ratings
Business Plan
Exit
Opportunities
First
Mover?
Founder's
track
record
Potential
Market
Size
(billion
US$)
Proprietary
Techonology /
Patent
Protection
Quality of
Board
Quality of
Management
Realistic
Approach to
Financing
Time to
Achieve
Profitability
(years)
Well
thought out
milestones
dental device
Acquisition
likely yes High 1 Patent protected
No board
mentioned High
Highly
realistic 3
Well thought
out
e-commerce facilitation
No exit
opportunity yes Medium 0.5 patent pending Medium Medium
Financing not
mentioned 3.45
No
Milestones
mentioned
human resources tool
Acquisition
likely no Medium 3.3 No protection High High
Highly
realistic 1
Well thought
out
management software
Acquisition
likely no High 3.6 No mention
No board
mentioned Medium
Somewhat
realistic 0
Well thought
out
market research tool
Acquisition
likely no Medium 5.9 No protection High Medium
Highly
realistic 1.21
Well thought
out
media company
No exit
opportunity yes Low 0.1 No mention
No board
mentioned Low
Highly
realistic 1
Somewhat
realistic
medical device
Acquisition
likely yes Low 4.8 patent pending High Medium
Highly
realistic 5
Well thought
out
pharmaceutical
Acquisition
likely yes Medium 3.375 patent pending
No board
mentioned Medium
Financing not
mentioned 1
Somewhat
realistic
supply chain
management software
Acquisition
likely no Low 15 No mention High Medium
Somewhat
realistic 1
Well thought
out

14. Probabilistic Assessment
 Point estimates of discrete probabilities of each
event or expected values of uniform
distributions between the upper and lower
estimates as mentioned in the business plans.
 Probabilistic ratings incorporated in the
analysis.
 Subjective probability estimates. Elicitation
avoided cognitive biases.

15. Weight Elicitation
 Swing-weight for the lower level criteria.
 For higher level goals, the fund manager felt
very strongly that all goals should have equal
weights. We revisit this proposition in the
sensitivity analysis.

16. Value Function Elicitation
 Direct assessment for criteria with only a few
possible discrete values.
 Value functions for the two criteria modeled
by continuous variables were assessed with
the aid of software graphical tools.
 Additive value function to aggregate the
value functions for each criterion: very
intuitive, widely used in practice, and
mathematically sound.

17. Value Function for “Time to Achieve
Profitability”
Utility
Time to Achieve Profitability (years)
1
0
0. 5.
Selected Point -- Level: Utility:3.11111 0.886154

18. Alternative
supply chain management software
dental device
human resources tool
medical device
market research tool
management software
pharmaceutical
media company
e-commerce facilitation
Value
0.824
0.777
0.766
0.729
0.660
0.637
0.542
0.349
0.261
Ranking for “Successful Venture” Goal

19. Results and Sensitivity Analysis
 Sensitivity to outcome of probabilistic assessment.
 Sensitivity to weights.
 Ranking of top 5 alternatives very robust; rank
reversal only between “medical device” and “market
research tool”.
 Equal weights for the 4 higher level goals revisited.
Top ranked alternatives insensitive to weight
variation in those goals.

20. Discussion
 JumpStart fund manager selection corresponded to the
4 highest ranked businesses. These had exhibited
considerable robustness to variations in weights or
probabilistic ratings.
 Confidence of venture capitalists in the methodology
– Increased for JumpStart manager, but did not prompt
reconsidering the fund decision process.
– Consultations with other VCs revealed cautious interest.
– Zacharakis and Meyer’s (2000): VCs reluctant to use decision
aids.

21. Potential Contributions
 Addresses Zacharakis and Meyer (2000) suggestion that models
better reflect the “needs and beliefs” of each individual firm.
 Improves dichotomous attributes from conjoint analysis of
Shepherd et al (2000).
 Gives VCs feedback on decision processes called for by
Shepherd and Zacharakis (2002).
 Allows for greater flexibility than other models in scales choice.
 Captures a VC’s uncertainty.
 Minimizes cognitive biases of seasoned VCs.
 Encourages inexperienced VCs to engage in systematic rating
and critically examine results via sensitivity analysis.

22. Limitations
 Zacharakis and Meyer (2000): improvement = selecting higher %
of successful business plans than the VCs. We cannot make that
claim, but we can claim better educated, more transparent and
more thought out decisions.
 We cannot ascertain elimination of bias but we minimize them by
structuring the interview encouraging fund manager to think
carefully about each probabilistic estimate and conducting
sensitivity analysis
 Fund manager preferences may not be entirely consistent and
rational, but sensitivity analysis accounts for this and allows for a
reevaluation of preferences.
 VCs who report taking an average of only 8 to 12 minutes to
evaluate a business plan may resist MCDA, but our fund
manager did not share that evaluations could be so quick (12
minute is an average).

23. Conclusions
 MCDA: goal is not to replace or outperform VCs, but to improve
their decisions by shedding light into the complexities of the
choices they face and minimizing their cognitive biases. Better
results may be a natural consequence.
 Future research:
– Methodologies and processes that facilitate MCDA acceptance
by VC community.
– How to conduct interviews in a manner that at the same time
minimizes errors in judgment, maximizes the comfort level of
the VC, and retains all the necessary validity conditions for the
construction of a mathematically rigorous MCDA model.

24. Thank you.
Questions or
Comments?