Returns to Angel Investors in Groups Presented by Prof. Robert Wiltbank to the Berkeley Angel Network group in Fall 2013. For more information on Prof. Wiltbank, please visit his website: http://www.willamette.edu/~wiltbank/

1. Robert E Wiltbank, Ph.D.
wiltbank@willamette.edu
www.Willamette.edu/~Wiltbank

2. The Entrepreneurial Problem
For Profit / Social / Otherwise
• Goals are rarely well known & specified
• The future is extremely unpredictable
• People don’t ‘follow instructions’

3. The Angel Investing DataSet
• Sample: publicly seeking angel groups in North America & UK
-Data from 125 different groups out 330 groups (38% participation)
-Data from 818 different investors (approx 13% participation)
-Approx $500M of investment (<1% of overall activity)
-1,957 exited investments (includes closures)
-95% of investments made after 1994, 75% made after 1999
-Only 6% of the exits occurred prior to 2000
22% of exits occurred 2000-2003, 72% occurred 2004 to present
• No Significant Self Selection Biases
– Outcomes are uncorrelated to the response rate of a group.
• 2.6X for 7 high response rate groups vs. 2.4X for low rate groups
• Median multiple was 1.2 for Hi rate groups, 1.4 for low rate groups

4. Angel Investing Distribution of Returns
60
50
40
Percent of Exits
US: Overall Multiple: 2.6X
UK: Overall Multiple: 2.2X
30
20
10
<1X
1X to 5X
5X to 10X
Exit Multiple
10X to 30X
>30X

5. 60
Distribution of Returns by Venture Investment
Hold: 3.0 yrs.
50
Hold: 6.0 yrs.
$80M
Percent of Total Exits
40
Hold: 3.3 yrs.
Overall Multiple: 2.6X
Avg. Holding Period: 3.5 years
$60M
30
20
$40M
Hold: 4.9 yrs.
Hold: 4.6 yrs.
$20M
10
0
< 1X
1X to 5X
Blue bars: % of exits in that Category
Green Bars: $’s returned in that Category
5X to 10X
Exit Multiples
10X to 30X
> 30X

6. Outcomes Split by Due Diligence
70.0
60.0
2X better multiple
for 20+ due diligence
Percent of Exits
50.0
40.0
30.0
20.0
10.0
<1X
1X to 5X
5X to 10X
10X to 30X
Multiple Category
Less Than 20 Hours
20+ Hours
>30X

7. Outcomes Split by Industry Expertise
70.0
60.0
60% better multiple for deals
related to industry expertise
Percent of Exits
50.0
40.0
30.0
20.0
10.0
<1X
1X to 5X
5X to 10X
10X to 30X
Multiple Category
No Industry Expertise
Some Industry Expertise
>30X

8. Follow-On Investment from Same Angel Investor
70.0
60.0
30% of deals had follow on
investments.
Percent of Exits
50.0
40.0
3X better multiple in deals
where the investor did not make
a follow-on investment.
30.0
20.0
10.0
<1X
1X to 5X
5X to 10X
Multiple Category
Yes Follow-On
No Follow-On
10X to 30X
>30X

9. Diligence in the Angel Fund: 1st screen
•
Entrepreneurial Expertise
Have they done new venture work before? Deep relationships in this industry?
Does this opportunity seem deeply connected to what they know/who they are?
•
Affordable Loss
Uncanny ability to a ton of stuff done practically no money. Are they good at
this? Why do you think so? Do they appear committed to staying good at this?
•
Early yeses
Evidence of commitments earned from outsiders? Have they won yeses for their
most important needs? It’s Subjective: some yeses mean A LOT more than others.
•
Reachable Milestones
Critical goals that must be reached that make it EASY to raise money if needed.
Can they realistically be reached with the capital being raised in this round?

10. Diligence in the Angel Fund
• Start with the KEY uncertainties
– don’t just work a long list. Look for the things that could kill the opportunity the fastest.
• If those are clear, relatively speaking, work your rolodex.
–
–
Talk to people with more industry expertise/insight than you.
If you can’t build that list, that might be a negative indicator for you.
• Switch to assertion checking mode
– Are the things that you’re being told, that most excite you about the opportunity, actually true?
How do you know?
• Strategy considerations:
– Going Forward: 1 / 3 / 5 Investment Thesis
– Build our own model: identify the relative drivers of the business.
You’re trying to determine which risks you are actually buying

11. Diligence in the Angel Fund
• Transaction Economics vs. Macro Economics
Price
Cost
Contribution Margin
Cost of Customer Acq
Customer ROI
Key Target List
T.A.M.
Predicted Market shares
Historical Data
Comparables
• Cash to Cash cycles & Capital Intensity
Longest lead time supply
Sales Cycle
Days in AP
Production cycle
Order/Shipment gap

12. Diminishing Marginal Returns to Raising Capital

13. Diminishing Marginal Returns to Raising Capital
Variable
Inc Year
Paid in Capital
N= 539 Burners
1994
13,919,446
Revenue (M's)
Years
16.84
6.5
Test of
Significant
N=514 Earners Difference
1987
0.000
95,055
0.000
18.33
12.2
0.449
0.000
Acceleration!
Total Cashout
37,051,885
18,442,152
0.000
Improved Wins
Deal Profit Dollars
Return on Capital
22,236,724
16.3%
18,347,816
53.8%
0.064
0.046
Pain of Capital Intensity
• Fail at 90% for earners vs. 10% for burners: equal ROIC
• Raise past $1.1M = marginal effect of dilution > acceleration

14. Diminishing Returns to Invested Capital
Private Acquisition Size from ‘96 to ‘06
Only 15%> $50M

15. Wearable Computing
During your 12-year tenure as an engineer at a major computer manufacturer, you work on your own time to invent a
computer device that recognizes and responds to eye movements. You imagine it might make a great alternative to the
computer mouse. You can make it rest on the user’s head much like headphones and set it up so that point-and-click
navigation is accomplished with even the most minor head and eye movements. You are convinced that there is a huge
potential for change in the way things are currently done. But when you attempt to interest your current company in
licensing the idea from you, they are uninterested. There are no firms currently offering anything close to this, and you
possess all the technical skills to create the product effectively and efficiently. You quit your job to further develop this idea.
1. As you assemble information on this business, you would:
Disagree
Indifferent
Agree
1
2
3
4
5
6
7
Talk with people you know to enlist their support in making this become a reality.
1
2
3
4
5
6
7
Study expert predictions of where the market is “heading”.
2. As you develop a marketing approach for this product you will:
1
2
3
4
5
6
7
Research the competitors’ approaches.
1
2
3
4
5
6
7
Imagine possible courses of action based on your prior experience.
3. As you manage product development, you will be driven by:
1
2
3
4
5
6
7
Comparing your progress against the development of competitors.
1
2
3
4
5
6
7
Creating new solutions on your own terms, any competitors will have to keep up.
4. If you were to look at predictions for where potential markets are heading you would:
1
2
3
4
5
6
7
Use them to create forecasts of what your business might accomplish over time.
1
2
3
4
5
6
7
Discount them as they do not incorporate the impact of your innovation.
5. As you learn about the expectations other people have for this industry, you:
1
2
3
4
5
6
7
Imagine ways your venture will change aspects of the situation they are forecasting.
1
2
3
4
5
6
7
Form updated predictions of likely outcomes for the business.

16. Effectual vs. Predictive Logic
Distinguishing Characteristic Of Predictive Logic:
Selecting various means to achieve pre-determined goals
M1
M2
M3
Given
Goals
M4
M5
New means may be generated over time

17. Effectual vs. Predictive Logic
Distinguishing Characteristic of Effectuation:
Imagining & Selecting various goals using a given set of means
Imagined
Ends
Given Means
M1
M2
E1
E2
M3
M5
E3
M4
E
En
What CAN we do, rather than what SHOULD we do.

18. Prediction vs. Control
Prediction: To the extent that I can predict the future, I can control my outcomes.
efforts to insightfully position for success based on expectations/forecasts for the
development of important market elements. This often includes modeling event spaces, estimating
probabilities and consequences, and forming sophisticated portfolio strategies with multiple options.
Assumes that market elements are predominantly independent of the organization.
Control: To the extent that I can control the future, I do not need to predict it.
efforts to deliberately construct/create market elements, such as defined products, articulated
demand preferences, and market structures (i.e. channels, technical standards, common practices).
Assumes either the non-existence of some key elements, or the organization’s ability to significantly
affect the evolution of those elements.
Prediction is uniquely difficult with new ventures,
while efforts to directly construct markets may be particularly effective.

19. Non-Predictive Control: Effectuation
Tactics for Control
Tactics for Prediction
1. Where to
Start
Assess Your Means. Take action based on what
you have available:
* Who I am
* What I know
* Whom I know
Example: I have person A, I can achieve X, Y, or Z
Set a Goal. Goals determine actions. For
example, the goal of achieving X, will
dictate I need person A with skills
matched to X.
2. Risk, Return
and
Resources
Set Affordable Loss. Pursue interesting
opportunities without investing more
resources than you can afford to lose. Set a
limit on downside potential.
Calculate Expected Return. Pursue the
(risk adjusted) largest opportunity
and accumulate required resources.
Maximize upside potential.
3. Attitude
Toward
Outsiders
Form Partnerships. Grow. Strategy is created
jointly through partnerships to create new
opportunities.
Perform Competitive Analysis. Protect.
Strategy is driven by potential
competitive threats.
4. Contingency
Leverage Contingencies. Surprises are good.
New developments encourage imaginative rethinking of possibilities and continual
transformations of targets.
Avoid Contingencies. Surprises are
bad. Contingencies are managed by
careful planning and focus on
targets.
5. Approach
Transformative. The future as shaped (at least
Predictive. The future is a reliable
partially) by actions of all players. Prediction is
continuation of the past. Accurate
neither easy nor useful.
prediction is possible and useful.

20. Your Effectual Process
Expanding cycle of resources
New
means
Goals
Who I am
What I know
Whom I know
What can
I do?
Call people
I know
Stakeholder
commitment
s
Means
Converging cycle of constraints on goals
Take Stock of your means: who, whom, what.
What can you do for near zero; Or where you can afford to lose?
What commitments have you attracted and followed?
What surprises are you taking advantage of so far?
New
goals

21. Yan Cheung, ACN to Nine Dragons, transformation, $3,800, 10 years.

22. Early stage investing perspectives
– Select ventures that appear most capable of influencing critical market elements.
Create and influence localized markets OR
Compete in large growing markets
– Emphasize the current means and capabilities of the venture rather than on plans for
acquiring the “best” means to reach their original goals.
Adjust goals to use current means OR
Acquire means critical to insightful goals
– Encourage the venture to make smaller investments that get to cash flow positive
rather than investing in the resources suggested by market research to “hit plan.”
Overhead trails growth OR
Pre-position assets to time great opportunity
– Avoid prediction as the basis for investment decisions.
Emphasize affordable loss OR
Maximize expected values

23. Cognitive Matching between VC’s and Entre’s
Conjoint analysis of VC investment evaluation.
Simultaneous manipulation of preferences
Economics:
Hi Potential vs. Moderate
Social Capital: Strong rep and Referrals vs. Moderate
Entre Mindset: Effectual vs. Causal
1. The match between VC’s and Entre’s significantly increased funding
2. Social Capital and Match were jointly as ‘powerful’ as the economics

24. … But Trending Up at Year End
Median Angel Round Size Reaches Five Quarter High in Q4 2012
$M
$1.50
$1.25M
$1.00
$950K
$950K
$900K
$500K
$550K
$550K
Q4 2011
Q1 2012
Q2 2012
$850K
$620K
$690K
Q3 2012
Q4 2012
$0.50
$0.00
Median Round Size
Mean Round Size
*Angel rounds include angels & angel groups only
24

25. Median Early Stage Pre-Money Valuation Stays the Same in 2012
$2.5M
$1.5M
Median
$3.7M
3rd Quartile
$6.6M
1st Quartile
$0.11M
*Including all rounds with angel groups before Series A
25

26. Noteworthy
More Convertible Debt Deals in 2012
11% of 2012 Deals were Convertible Debt; up from 6% in 2011
Very Early Stage Investments in 2012
47% of 2012 angel group deals were in companies with no revenue
Significant 2nd round activity by Groups in 2012
56% of 2012 angel group deals are in new companies; stable with 55% in 2011
26

27. Most Active Angel Groups in Each Region 2012 - Deals
27

28. Share of Mobile Deals Grows, Healthcare Shrinks
Share of Angel Group Deals by Sector 2012 vs 2011
100%
16.2%
14.2%
70%
4.6%
3.5%
1.5%
7.5%
4.5%
5.3%
3.8%
6.0%
60%
9.3%
90%
80%
13.3%
Other
Software
Consumer Pdcts &
Svcs
Electronics
50%
24.6%
40%
20.9%
Mobile & Telecom
30%
20%
Industrial
32.8%
Healthcare
31.9%
10%
Internet
0%
2011
2012
28

29. Selecting ventures for investment
Investors prefer opportunities:
in large and fast growing markets
with customers lined up waiting to repeatedly buy a high margin product
where no powerful competitors exist
with the potential to ‘keep others out’ of the market
led by experts in the field who have prior entrepreneurial success
The problem is the sequence; prioritization
i.e. insightful market research to demonstrate market potential,
or win a great beta customer?
i.e. win a new great team member
or finish the prototype to demonstrate claims?

30. Staged Decisions in Angel Investing
Dependent Variable
Constant
DD1
DD3
DDP2
Funded
-2.16
0.00
-3.41
0.00
0.10
0.50
-4.07
0.00
0.52
0.17
0.00
0.00
0.42
0.21
0.00
0.00
0.09
0.16
0.00
0.00
0.05
0.17
0.05
0.00
Inv Entre
Inv Angel
Eval Prediction
Eval Control
Prediction Emphasis
Control Emphasis
N = 2283
Adj R2 = .455
N = 2383
Adj R2 = .409
N = 2109
Adj R2 = .046
N = 2156
Adj R2 = .040
• Angels use predictive information more than they think
S and multinomial or binary logistic regression.
– Especially early in the process
• This shifts as we approach actual investment decisions
• Investors with more Entre Experience Prefer Non-Predictive Info.

31. Robert E Wiltbank, Ph.D.
wiltbank@willamette.edu
www.Willamette.edu/~Wiltbank