Robert E Wiltbank, Ph.D.

wiltbank@willamette.edu

www.Willamette.edu/~Wiltbank

The Entrepreneurial Problem

For Profit / Social / Otherwise

• Goals are rarely well known & specified

• The future is extremely unpredictable

• People don’t ‘follow instructions’

• 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

The Angel Investing DataSet

-

10

20

30

40

50

60

<1X 1X to 5X 5X to 10X 10X to 30X >30X

Exit Multiple

Percent of Exits

Angel Investing Distribution of Returns

UK: Overall Multiple: 2.2X

US: Overall Multiple: 2.6X

0

10

20

30

40

50

60

< 1X 1X to 5X 5X to 10X 10X to 30X > 30X

Exit Multiples

Percent of Total Exits

Distribution of Returns by Venture Investment

Overall Multiple: 2.6X

Avg. Holding Period: 3.5 years

$20M

$40M

$60M

$80M

Blue bars: % of exits in that Category

Green Bars: $’s returned in that Category

Hold: 3.0 yrs.

Hold: 3.3 yrs.

Hold: 4.6 yrs.

Hold: 4.9 yrs.

Hold: 6.0 yrs.

Outcomes Split by Due Diligence

-

10.0

20.0

30.0

40.0

50.0

60.0

70.0

<1X 1X to 5X 5X to 10X 10X to 30X >30X

Multiple Category

Percent of Exits

Less Than 20 Hours 20+ Hours

2X better multiple

for 20+ due diligence

Outcomes Split by Industry Expertise

-

10.0

20.0

30.0

40.0

50.0

60.0

70.0

<1X 1X to 5X 5X to 10X 10X to 30X >30X

Multiple Category

Percent of Exits

No Industry Expertise Some Industry Expertise

60% better multiple for deals

related to industry expertise

-

10.0

20.0

30.0

40.0

50.0

60.0

70.0

<1X 1X to 5X 5X to 10X 10X to 30X >30X

Multiple Category

Percent of Exits

Yes Follow-On No Follow-On

Follow-On Investment from Same Angel Investor

3X better multiple in deals

where the investor did not make

a follow-on investment.

30% of deals had follow on

investments.

• 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?

Diligence in the Angel Fund: 1st screen

• 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

Diligence in the Angel Fund

• Transaction Economics vs. Macro EconomicsPrice Cost T.A.M.

Contribution Margin Predicted Market shares

Historical Data

Cost of Customer Acq Comparables

Customer ROI

Key Target List

• Cash to Cash cycles & Capital Intensity

Longest lead time supply Production cycle

Sales Cycle Order/Shipment gap

Days in AP

Diligence in the Angel Fund

Diminishing Marginal Returns to Raising Capital

Variable N= 539 Burners N=514 Earners

Test of

Significant

Difference

Inc Year 1994 1987 0.000

Revenue (M's) 16.84 18.33 0.449

Total Cashout 37,051,885 18,442,152 0.000

Deal Profit Dollars 22,236,724 18,347,816 0.064

Return on Capital 16.3% 53.8% 0.046

Paid in Capital 13,919,446 95,055 0.000

Years 6.5 12.2 0.000Acceleration!

Improved Wins

Pain of Capital Intensity

Diminishing Marginal Returns to Raising Capital

• Fail at 90% for earners vs. 10% for burners: equal ROIC

• Raise past $1.1M = marginal effect of dilution > acceleration

Diminishing Returns to Invested Capital

Private Acquisition Size from ‘96 to ‘06

Only 15%> $50M

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.

As you assemble information on this business, you would: 1.

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”.

As you develop a marketing approach for this product you will: 2.

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.

As you manage product development, you will be driven by: 3.

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.

If you were to look at predictions for where potential markets are heading you would: 4.

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.

As you learn about the expectations other people have for this industry, you: 5.

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.

Effectual vs. Predictive Logic

Given

Goals

M1

M2

M3

M4

M5

Distinguishing Characteristic Of Predictive Logic:

Selecting various means to achieve pre-determined goals

New means may be generated over time

Distinguishing Characteristic of Effectuation:

Imagining & Selecting various goals using a given set of means

E2

E3

E

En

Given Means

M1M2

M3

M4

M5

E1

Imagined

Ends

Effectual vs. Predictive Logic

What CAN we do, rather than what SHOULD we do.

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.

Predictive. The future is a reliable continuation of the past. Accurate prediction is possible and useful.

Transformative. The future as shaped (at least partially) by actions of all players. Prediction is neither easy nor useful.

5. Approach

Avoid Contingencies. Surprises are bad. Contingencies are managed by careful planning and focus on targets.

Leverage Contingencies. Surprises are good. New developments encourage imaginative re-thinking of possibilities and continual transformations of targets.

4. Contingency

Perform Competitive Analysis. Protect. Strategy is driven by potential competitive threats.

Form Partnerships. Grow. Strategy is created jointly through partnerships to create new opportunities.

3. Attitude Toward Outsiders

Calculate Expected Return. Pursue the (risk adjusted) largest opportunity and accumulate required resources. Maximize upside potential.

Set Affordable Loss. Pursue interesting opportunities without investing more resources than you can afford to lose. Set a limit on downside potential.

2. Risk, Return and Resources

Set a Goal. Goals determine actions. For example, the goal of achieving X, will dictate I need person A with skills matched to X.

Assess Your Means. Take action based on what you have available:

* Who I am* What I know* Whom I knowExample: I have person A, I can achieve X, Y, or Z

1. Where to Start

Tactics for PredictionTactics for Control

Predictive. The future is a reliable continuation of the past. Accurate prediction is possible and useful.

Transformative. The future as shaped (at least partially) by actions of all players. Prediction is neither easy nor useful.

5. Approach

Avoid Contingencies. Surprises are bad. Contingencies are managed by careful planning and focus on targets.

Leverage Contingencies. Surprises are good. New developments encourage imaginative re-thinking of possibilities and continual transformations of targets.

4. Contingency

Perform Competitive Analysis. Protect. Strategy is driven by potential competitive threats.

Form Partnerships. Grow. Strategy is created jointly through partnerships to create new opportunities.

3. Attitude Toward Outsiders

Calculate Expected Return. Pursue the (risk adjusted) largest opportunity and accumulate required resources. Maximize upside potential.

Set Affordable Loss. Pursue interesting opportunities without investing more resources than you can afford to lose. Set a limit on downside potential.

2. Risk, Return and Resources

Set a Goal. Goals determine actions. For example, the goal of achieving X, will dictate I need person A with skills matched to X.

Assess Your Means. Take action based on what you have available:

* Who I am* What I know* Whom I knowExample: I have person A, I can achieve X, Y, or Z

1. Where to Start

Tactics for PredictionTactics for Control

Non-Predictive Control: Effectuation

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 means

New goals

Who I amWhat I knowWhom I know

Expanding cycle of resources

Means

Converging cycle of constraints on goals

Goals

What canI do?

Call peopleI know

Stakeholder commitment

s

Your Effectual Process

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

– 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

Early stage investing perspectives

Cognitive Matching between VC’s and Entre’s

Conjoint analysis of VC investment evaluation.

Simultaneous manipulation of preferences

1. The match between VC’s and Entre’s significantly increased funding

2. Social Capital and Match were jointly as ‘powerful’ as the economics

Economics: Hi Potential vs. Moderate

Social Capital: Strong rep and Referrals vs. Moderate

Entre Mindset: Effectual vs. Causal

… But Trending Up at Year EndMedian Angel Round Size Reaches Five Quarter High in Q4 2012

$0.00

$0.50

$1.00

$1.50

Q4 2011 Q1 2012 Q2 2012 Q3 2012 Q4 2012

Median Round Size Mean Round Size

24

*Angel rounds include angels & angel groups only

$950K $950K $900K

$1.25M

$850K

$500K $550K $550K$620K $690K

$M

Median Early Stage Pre-Money Valuation Stays the Same in 2012

25

*Including all rounds with angel groups before Series A

$2.5MMedian

$1.5M1st Quartile

$3.7M3rd Quartile

$0.11M

$6.6M

Noteworthy

More Convertible Debt Deals in 201211% of 2012 Deals were Convertible Debt; up from 6% in 2011

Very Early Stage Investments in 201247% of 2012 angel group deals were in companies with no revenue

Significant 2nd round activity by Groups in 201256% of 2012 angel group deals are in new companies; stable with 55% in 2011

26

Most Active Angel Groups in Each Region 2012 - Deals

27

32.8% 31.9%

24.6%20.9%

9.3%13.3%

7.5% 6.0%

1.5% 3.8%3.5% 5.3%4.6% 4.5%

16.2% 14.2%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

2011 2012

Other

Software

Consumer Pdcts &Svcs

Electronics

Industrial

Mobile & Telecom

Healthcare

Internet

Share of Mobile Deals Grows, Healthcare ShrinksShare of Angel Group Deals by Sector 2012 vs 2011

28

Investors prefer opportunities:

in large and fast growing marketswith customers lined up waiting to repeatedly buy a high margin productwhere no powerful competitors existwith the potential to ‘keep others out’ of the marketled by experts in the field who have prior entrepreneurial success

The problem is the sequence; prioritizationi.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?

Selecting ventures for investment

Staged Decisions in Angel Investing

• Angels use predictive information more than they think– Especially early in the process

• This shifts as we approach actual investment decisions

• Investors with more Entre Experience Prefer Non-Predictive Info.

-2.16 0.00 -3.41 0.00 0.10 0.50 -4.07 0.00

0.52 0.00 0.42 0.00 0.09 0.00 0.05 0.05

0.17 0.00 0.21 0.00 0.16 0.00 0.17 0.00

are essentially identical between OLS and multinomial or binary logistic regression.

N = 2156

Adj R2 = .040Adj R2 = .455

N = 2383

Adj R2 = .409

N = 2109

Adj R2 = .046

N = 2283

DD1 DD3 DDP2 FundedDependent Variable

Constant

Inv Entre

Inv Angel

Eval Prediction

Eval Control

Prediction Emphasis

Control Emphasis

Robert E Wiltbank, Ph.D.

wiltbank@willamette.edu

www.Willamette.edu/~Wiltbank