Decision Analysis in Venture Capital Workshop, DAAG 2019

Ó 2019 Ulu Ventures. All rights reserved.
DA in VC Workshop
6 March 2019
Clint Korver
clint@uluventures.com

Agenda
Goals & Intros
VC Decision Challenges
Ulu’s VC Decision-Making Framework
Break
Power Law Portfolio Construction
Discussion

Workshop Goals
Better understand venture capital
Understand Ulu’s decision making framework
Understand Ulu’s design choices
Appreciate dynamics of Power Law in portfolio construction
Take away an insight or idea to apply to your business

Ulu Ventures Overview
Seed stage, enterprise IT focused, venture fund in Silicon Valley
2 general partners, 100+ investments, 10+ years in business
Built on shared values – e.g. relationships not transactions
Powered by decision analysis
Diversity as an investment thesis
Ulu goal: a venture industry which funds merit, not bias

Clint Korver Overview
Co-Founder, Ulu Ventures
Co-Founder, 4 Silicon Valley startups
25 years DA practitioner
Ron Howard PhD Student
Taught entrepreneurship @ Stanford

VC industry funds future, but operates in past
9% of VCs are women*
15% of VC $$ to teams with a female founder*
* www.allraise.org – Home Page
2% of VCs are Black or Hispanic**
1% of VC $$ to teams with a Black or Hispanic founder***
** techcrunch.com - “Here’s a Detailed Breakdown of Racial and Gender Diversity Data Across US Venture Capital Firms”
***www.forbes.com – “founders-and-venture-capital-racism-is-costing-us-billions”

Venture decisions are difficult
Challenges
Uncertainty
Ego
Biases

Decision best practices can help
Challenges Best Practices
Uncertainty Decision Analysis
Ego Double–loop Learning
Biases Diversity

Key drivers of venture returns
Sourcing
Selecting
Supporting
Portfolio Construction
*Based on Harvard, Stanford, U Chicago, & U British Colombia research which surveyed 885 institutional VCs at 681 firms. Findings published in 2016 research paper “How
Do Venture Capitalists Make Decisions?”
Most important
driver of returns*
23%
49%
27%
Not Asked

Ulu’s annual investing pipeline
Hit Ulu’s Radar
Trusted
Sources
Outreach
Demo Days
Unreferred
3,000
1st
Meeting
300
2nd
Meeting
60
DA
30
Fund
20

Early stage VC IRR highly variable
1 Yr 3 Yrs 5 yrs 15 Yrs 25 Yrs
US Early Stage VC 21% 9% 17% 11% 44%
US Late Stage VC 22% 10% 12% 12% 12%
S&P 500 18% 17% 14% 10% 10%
*Cambridge Associates, As of September 30, 2018

Goal: quality conversation leading to clarity of action
Conversations with entrepreneurs & investment partners
Across 300 1st meetings, 60 2nd meetings, …
Fast, principled ‘No’ 280/year
Robust, unbiased ‘Yes’ 20/year

Repeatable, turbocharged decision making process
1. Initial Rubric 2. Market Map 3. Risk Assessment
4. Uncertainty Ranges 5. Sensitivity Analysis 6. Risk/Return Calculation
TAMCust
Segment
Unit
Sales
Price
• Market
• Product
• Team
• Financial
High
Base
Low
No
No
Early
Stage
Success
Cross
Chasm
Team Market Fit
+ ++ +

Idea: Cheaper loans for students from low risk schools
Founding team: 5 Stanford GSB, 1 U of Michigan
Market Evidence: Stanford business school partnership
Revenue: none
Funding: none
Case study

Scores well on Rubric
Ulu Fit
Market
Oppt’y
Team /
Market Fit
Team
Values
Product
Financial
Viability
Super
Powers
+ ++ + + + 0 0
• Seed Stage
• Enterprise
IT
• Diversity
• Silicon
Valley /
Stanford
• TAM
• Traction
• Breaking in
• GTM
• Domain
expertise
• Complete
team
• Fit for
early
challenges
Scoring Key
++ outstanding
+ stands out
0 neutral
- poor
- - showstopper
• Authentic
Voice
• Ethics
• Character
• User
Engagement
• Learning
Orientation
• Enterprise
Appreciation
• Whole
Product
• Terms
• Capital
Needs
• Biz Model
• Exit
Possibilities
• Competition
• Network
Effects

Technology Adoption Life Cycle Model
VISIONARIES
PRAGMATISTS CONSERVATIVES SKEPTICS
(Early Majority) (Late Majority) (Laggards)
THE
CHASM
TECHIES
(Innovators)
(Early Adopters)
From Crossing the Chasm by Geoffrey Moore.

Simplified to key life stage transition points
Mass Market
Success
Cross
Chasm
Early Stage
Success
Adapted from Crossing the Chasm by Geoffrey Moore.

Life stage structure from technology adoption model
No
No
Early
Stage
Success
Cross
Chasm
Mass
Market
Niche
Only

Life stage risk assessment
Early
Stage
Success
Market
Product
Financial
Working Product
Core Tech Team
Team
Paying Visionary Customers
Compelling Reasons to Buy
Viable Business Model
Sufficient Runway
Capable CEO
Team Experience
100%
90%
100%
100%
90%
Cross
Chasm
Market
Product
Team
Financial
90%
70%
80%
45%
90%
Healthy Gross Margins
Sufficient Runway
Crit Mass Pragmatic Cust’s
Proven Value Prop
Predictable Alumni Sales Cycle
Whole Product
Complete Team
Healthy Team Dynamics
Mass
Market
Success
Team
Market
Product
Financial
Proven in Mult Niches
Marquee Customers
No Adverse Gov’t Regs
Scalable Product
Product Dev Continuity
Healthy Net Margins
Execution Track Record
Board of Directors
No Blow Ups
50%
80%
80%
26%
80%

Life stage success probabilities
0.26
0.74
No
0.45
0.550.9
0.1
10%
30%
50%
10%
100%
No
Early
Stage
Success
Cross
Chasm
Mass
Market
Niche
Only

Market position assessments
Distinction Assessment Notes
# Mass Market
Competitors
King
Prince
Serf
Adapted from The Gorilla Game: Picking Winners in High Technology by Geoffrey Moore.
10%
1 King
SoFi is no more or less likely to be king
1/10 = 10%
30%
3 Princes
SoFi is no more or less likely to be prince
3/10 = 10%
60% 60% = 100% - 10% - 30%
10 (Typical)
5 (Consolidated)
20 (Fragmented)

Life stage success probabilities
0.3
0.1
0.6
0.26
0.74
No
0.45
0.550.9
0.1
Market Share
3%
6%
1%
30%
50%
10%
100%
No
Early
Stage
Success
Cross
Chasm
Mass
Market
Niche
Only
Challenger
Market Leader
Also Ran
60% chance of failure
40% chance of success

Market Map
PWMOIC
Enterprise
Exit Value
Ownership
%
Investment
AmountDilution
Valuation
& Terms
Exit
Multiple
EarningsRevenue
Market
Share
Total
Addressable
Market
Net
Margin
A Tier TAM
(<1%)
A Loans
Addressable
$ (< 1%)
Gross
Margin
A Loan
Footprint $
% A Loans
Addressable
Origination
Fees
Gain on
Sales
%
Addressable
B Tier TAM
(1 – 1.5%)
Adjacent
Markets
Alumni
Acquisition
Costs
Tuition
Growth
Velocity
of Capital
Spread
Base
Spread
Community
Experience
Delta
Alumni
Participation
(% dollars)
B’s
Converted
to A’s
Social
Thesis
Delta
Coupon
vs <1%
B Loan
Footprint $
B Loans
Addressable
$
% B Loans
Addressable
Int’l TAM
Int’l Loans
($B)
Loan
Spread
Capital
Needs
Value
Decision
Uncertainty
Calculation
Influence
Key
Evocative

Tablet Adoption in Higher Education
by 2015
Ranges structure intuition
Precisely Wrong Approximately Right
High 90%
Base 75%
Low 60%
Dilution
70%

Market Share analysis
Distinctions Assessments Notes
# MM Competitors 10 (Typical) 5 (Consolidated) – 20 (Fragmented)
King share
Princes share
Serfs share
Share MM Players
Share Niche Players
SoFi Niche Share
Adapted from The Gorilla Game: Picking Winners in High Technology by Geoffrey Moore.
35% 1 * 35% = 35% of total share
10% 3 * 10% = 30% of total share
3% 6 * 3% = 18% of total share
83% 35% + 30% + 18% = 83%
17% 100% - 83% = 17%
5% Back calculate based on revenue

Sensitivity analysis
Probability Weighted MOIC
0 10 20 30 40 50
Base Value
Base Value = 16x
Mass Market Share 3% 30% 10%
Exit Multiple (Earnings) 5 20 10
Dilution 80% 50% 67%
Gain on Sales 2% 9% 6%
Conversion of B to A 0% 100% 30%
Int'l Addressable 0% 50% 25%
Net Margin 35% 65% 50%
Adjacent Markets 10% 80% 40%
Tuition Growth 0% 10% 5%
Int'l Loan Footpring $B 10 50 30
A Addressable 40% 100% 80%
Pre-money Valuation ($M) 5 3 4
Niche Market Share 3% 15% 5%
Velocity of Capital turns/yr 0 3 1
B Addressable as B 0% 40% 10%

passed Ulu’s 10x PWMOIC threshold
0.3
0.1
0.6
0.26
0.74
No
0.45
0.550.9
0.1
Market Share
3%
6%
1%
10%
50%
10%
100%
No
Early
Stage
Success
Cross
Chasm
Mass
Market
Niche
Only
Challenger
Market Leader
Also Ran
5.2
3.0
5.2
$3012
$9035
$904
Enterprise
Exit Value
($M) PWMOIC
2.4$75
$0
$0
0
0
$535
$1606
$161
Revenue
($M)
$27
$0
$0
15.8PWMOIC:
164
49
492
MOIC
8
0
0
*Probability Weighted Multiple on Invested Capital

Ulu invested
Raised over $2B
$547M Revenue, $126M Profit in 2017* – WSJ 2.9.18
$3B in refinancing / quarter*
Last financing valued company at > $4B
What happened
*Wall Street Journal: February 2, 2018.

Simplifications make analysis tractable
Snapshot in time (no time series)
Optimizing to understand outlier potential
Revenue only (no costs / profits)
With high margin business models, revenue is core challenge.
Capture time, cost, profit through life stage risk assessments
Timing: do you run out of money before accomplishing fundable milestones
Costs: ability to raise enough capital, dilution
Profits: unit economics / margin, robustness of business model

Why 10x PWMOIC?
Why Multiple on Invested Capital (MOIC) and Not IRR?
IRR expensive to assess and model
10x PWMOIC generates great IRR within reasonable time frames
Why Probability Weighted (PW)
Assessing, pricing, managing risk is core challenge of venture
Why 10x
Arbitrary. Lets the right number of companies through for Ulu.
Consistency in comparing investments is most important
PWMOIC does not capture risk aversion
Descriptively, most investors have a low tolerance for risk
A large portfolio allows Ulu to act as risk neutral decision maker

Ulu’s portfolio

• Seed
• Enterprise IT / Smart Data
• Silicon Valley / Stanford /
Diversity
• Top tier performance
• Seed to series A partner
• 66+
• Small
• n/a
•10x
PWMOIC
Portfolio decisions should be made in context
• Size (# of investments)
• Reserves ($’s held for follow-ons)
• Mix (stage, sector, geo, timing, risk)
Investments
• Initial investment decisions
• Follow-on investment decisions
• Stage
• Sector
• Geography
• LP Expectations
• Entrepreneur Brand
Promise
Strategy
Portfolio
Construction

Alternatives
(What you can do)
# of investments
Reserve Strategy
Mix - sector, stage...
Superior Returns
Serve Entrepreneurs
Operational Efficiency
Decision basis for VC portfolio construction
Estimated risk &
return of investments
Ecosystem Potential
Team Capabilities
Macroeconomic
Conditions
Information
(What you know)
Values
(What you want)

Portfolio Construction Decision #1: Portfolio Size
How many investments in your fund?
Venture firm typically raises a new fund every few years
Each fund has a 3-5 year investment window to make new investments
Funds last 10+ years
Just making follow-on investments in years 5 – 10+

Standard Micro VC
Ulu Portfolio Construction Difference
Ulu
I Can Pick Winners
I Can Make Winners
10-20 Investments
Normal Distribution
No one Can Pick Winners
I Can Help, Maybe
60+ investments
Power Law Distribution

Early stage returns follow a power law distribution
ChanceofOccurrence
Returns
Early Stage Returns
Power Law Distribution
Late Stage Returns
Normal (ish) Distribution

Dramatic Early Stage Mean / Median Gap
Mean
Returns
Median
Returns
Early Stage VC 22% 5.6%
Late Stage VC 12% 10.7%
Burgiss Data 1983 – 2Q2017 US
Courtesy of Virdis Investments
Early Stage = Series B and earlier

Outliers drive industry profitability
1Cambridge Associates Research Note: The 15% Frontier. 2016. Based on data from 2004 – 2012.
2Horsley Bridge data as reported via A16Z blog post: https://a16z.com/2015/06/08/performance-data-and-the-babe-ruth-effect-in-venture-capital/
2.5%
Of venture deals generate
nearly 100% of industry profits1
4.5%
Of capital generates 60% of
fund profits in top tier funds2
Average VC outlier rate: 2%
Top Tier VC outlier rate: 4.5%

VC Unicorn-Spotting Rate

0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
0 10 20 30 40 50 60 70 80
Chance of at Least One Oultier
Portfolio Size (# of Investments)
Good VC (2%)
Average VC outlier chance: 18% with 10, 64% with 50

0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
0 10 20 30 40 50 60 70 80
Chance of at Least One Oultier
Portfolio Size (# of Investments)
Top Tier VC (4.5%)
Good VC (2%)
Top tier VC outlier chance: 37% with 10, 90% with 50

Luck vs. skill
Average VC, 10 investments => 18% chance of at least one outlier
Out of 200 average VC firms with 10 investments in portfolio
– 29 will look top tier, with one outlier
– 6 will look like geniuses, with two outliers
– 1 will look like a super genius, with three outliers
They are equally average. Some just got lucky.
How can you tell the difference between luck and skill?

Smart portfolio construction
#
Investments
Skill Level
Chance of
Outlier /
Investment
1 Fund
with
Outlier*
3 Funds
with Outliers*
10 Top Tier 4.5% 37% 5%
50 Average 2% 63% 25%
Can help an average VC outperform a top tier VC

Smart portfolio construction also helps Top Tier VCs
#
Investments
Skill Level
Chance of
Outlier /
Investment
1 Fund
with
Outlier*
3 Funds
with Outliers*
10 Top Tier 4.5% 37% 5%
50 Top Tier 4.5% 90% 73%
Any outlier, regardless of fund size, is a winner

Portfolio Construction Decision #2: Reserve Strategy
How much of your fund do you reserve for follow-on investments?
Typical range: 1x – 3x initial investment
$100m fund with reserves of 2x initial investment
– $33m invested in initial round
– $67m reserved for follow-on investments in those same startups
Ulu’s reserves are 0.2x initial investment

Standard Micro VC
Ulu Portfolio Construction Difference
Ulu
Large Reserves: Double
down on winners
I Can Pick Winners
I Can Make Winners
10-20 Investments
No one Can Pick Winners
I Can Help, Maybe
60+ investments
Small Reserves: buy
max ownership upfront

Which reserve strategy will outperform?
Seed Series A Series B Series C
Funding Rounds 50 30 20 12
Invest / Co $m 0.26 0.26 0.66 1.32
Total Invest $m 13 8 13 16
Double
Down
All
Upfront
Funding Rounds 50 30 20 12
Invest / Co $m 1 0 0 0
Total Invest $m 50 0 0 0

Ownership dynamics for All Upfront strategy.
Seed
Investment $m 0.26
Pre Money $m 6
Raise $m 2
Post Money $m 8
Dilution
Ownership % 3.3%
Series A
0.26
20
8
28
0%
3.3%
Series B
0.66
75
20
95
0%
3.3%
Series C
1.32
250
40
290
0%
3.3%
8.9%
7.0%
6.1%
12.5%
COMPANYOWNERSHIP
3.3% 3.3%3.3% 3.3%

COMPANYOWNERSHIP
Ownership dynamics for All Upfront strategy.
Seed
Investment $m 1
Pre Money $m 6
Raise $m 2
Post Money $m 8
Dilution
Ownership % 12.5%
Series A
0
20
8
28
29%
8.9%
Series B
0
75
20
95
21%
7.0%
Series C
0
250
40
290
14%
6.1%
8.9%
7.0%
6.1%
12.5%

All Upfront doubles ownership vs. Double Down.
12.5%
8.9%
7.0%
6.1%
3.3% 3.3% 3.3% 3.3%
COMPANYOWNERSHIP
Investment / round ($k) Seed Series A Series B Series C Total
All Upfront 1,000 0 0 0 1
Double Down 260 260 660 1,320 2.5

Pre Money $m 6 80 900 4500
Raise $m 2 20 100 500
Post Money $m 8 100 1000 5000
Dilution 20% / 0% 10% / 0% 10% / 10%
All Upfront 1,000 0 0 0
Double Down 260 650 3,250 0
All Upfront is even better with home runs.
12.5%
10.0%
9.0%
8.1%
3.3% 3.3% 3.3% 2.9%
Vs. 6.1%
Vs. 3.3%

When “should” a VC make a follow-on investment
Mispriced follow-on round. Market undervalues progress.
Large fund unable to fit capital in early stage rounds
Your bad at picking - low follow-on rates
Your bad at pricing - small price increases

Think differently in a power law world
Normal Power Law
Upside Capped Uncapped
Diligence focus Fat part of distribution Upside tail
Key Indicator Loss Rate Chance of Outlier
Troubled company
Spend time to
minimize losses
Take losses to
minimize time
More investments Same mean Higher mean

Ulu’s template models on GitHub
https://github.com/ulu-ventures/VCDA
Truthy.xlsm
– Same template Ulu uses. Need to change Market Size variables.
– Built in tornado & decision tree functionality
VentureDeal.xlsm
– Simplified template. Market Size is one input with a range
– Built in tornado & decision tree functionality

Decision Analysis in Venture Capital Workshop, DAAG 2019

More Related Content

What's hot

Similar to Decision Analysis in Venture Capital Workshop, DAAG 2019

Recently uploaded

Decision Analysis in Venture Capital Workshop, DAAG 2019