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A framework for how to approach and analyze tech startups in a world that is full of them
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(and what to do about it)
What to expect from
investing in startups
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This world is full of shiny objects.
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These shiny objects were
created with wizardry.
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These shiny objects were NOT
created with wizardry.
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These shiny objects were NOT
created with wizardry.
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We hope to provide some
coaching on what goes
on behind the curtain.
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First: Let’s put shiny
objects in context
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Shiny objects are more powerful
than they’ve ever been before.
2001 2011
100
2,000 • Always On
• Higher Speed
• Payment Ready
• Socially Linked
Millions Online
Source: Marc Suster, GRP Partners
Shiny objects are more powerful
than they’ve ever been before.
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Shiny objects are also cheaper to
make than they have ever been.
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Open source
Cloud Developers Start
Companies
2000
$5m
$500k
2005
$50k
2009
$5k
2011 Source: Marc Suster, GRP Partners
“It is now usually cheaper to just try something than to sit
around and try to figure out whether to try something.”
– Joi Ito
Shiny objects are also cheaper to
make than they have ever been.
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Knight is putting big
bets on shiny objects.
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BUT, most shiny objects
crash and burn.
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• 70-90% of startups fail within 3-5 years.
•Even professional venture capitalists have a hard time.
•Top tier early stage venture capitalists look for 30% of the companies they invest in to
do well (5-10X capital invested), 30% to break even and the balance to return less
than 1/3 of capital invested**.
• Bad VCs have a success rate of less than 10%
Avg Returns by Asset Class as of 3Q 2010*
1 Year 3 Year 5 Year 10 Year 15 Year
US Private Equity 17.70% 1.30% 9.10% 8.10% 12.10%
US Venture Capital 8.20% -2.10% 4.20% -4.60% 36.90%
DJIA 14.10% -5.40% 3.10% 2.50% 7.90%
*Source: Cambridge Associates
** Source: Union Square Ventures, Fred Wilson Blog
BUT, most shiny objects
crash and burn.
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70-90% failure rate, means you
need shiny object roadmap.
70-90% failure rate, means you
need shiny object roadmap.
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Best way to avoid failure is to:
-Plan for it, and
-Don’t take unnecessary risks.
70-90% failure rate, means you
need shiny object roadmap.
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In order to plan/avoid risks
here, ask 3 key questions.
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• Problem?
• Solution?
• Why will solution work?
In order to plan/avoid risks
here, ask 3 key questions.
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What Is The Problem?
• Be sure to include:
• Whose problem is it?
• How big is it?
• How painful is it?
• How much is solving it worth?
• How do you know it’s a real problem? • (Hint: This information should be coming from potential or actual customers)
What Is The Problem?
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What is the Solution?
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Solutions have multiple parts.
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Solution Part 1: Value
We will fix the problem like this.
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Solution Part 1: Value
We will fix the problem like this:
How will the product or service deliver value to
customers once they are using it?
Sample Hypotheses:
+ Lower customer cost/time
+ Better experience (increase willingness to pay)
+ Improve supply chain for customers (B2B)
+ Increase volume for customers (B2B, B2C)
Understand Value Hypothesis:
Source: Value Hypothesis concepts comes from Eric Ries, The Lean Startup
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Solutions Part 2: Growth
People will find out about us like this.
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Solutions Part 2: Growth
People will find out about us like this:
How new customers will discover product or service
Sample Hypotheses:
+ People will tell their friends (viral)
+ Via advertisement (paid)
+ Marketed directly to enterprise customers (sales)
+ Established partners will distribute product.
Source: Value, Growth Hypothesis concepts come from Eric Ries, The Lean Startup
Understand Growth Hypothesis:
Problem: People are less socially conscious consumers than they want to be.
Hypotheses:
-People would be more socially conscious consumers if they got recognition from their
friends for making socially conscious purchases.
-If there were a social network for people who cared about causes to share what they
were buying, people would (1) get value from it and (2) get their friends to join
-Linking credit card purchases to user accounts automates this process.
-Friends would encourage each other to consume more responsibly.
Test: Early focus groups prove unwilling to share credit card data.
Pivot: Build social site around gift card purchases not credit card access.
Problem/Solution Fit
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Why should you believe it will work?
Pre-Minimum Viable Product:
•Do customers recognize that
they have the problem? (market)
•If there was a solution would
they use it? (market)
•Would they use a product built
by this team? (Team)
•Can team build the solution
they are proposing? (Team)
•Are they capable of iterating?
(Team)
•Will they be able to market the
solution? (Team)
After Minimum Viable Product:
Traction vs. $/Spent in terms of:
•Engagement (Value)
•Conversion/Activation (Value)
•Retention (Value)
•Churn (Value)
•User Growth (Growth)
•Marketing Effectiveness
(Growth)
•Payment (Value)
•Customer Lifetime Value (Value)
•Cost of User Acquisition
(Growth)
Why should you believe it will work?
• Primarily, MVP is a way to test your value hypothesis
• Focus on CUSTOMER – Qualitative Discovery, Quantitative Validation
• Get to know habits, problems, desires (FUN MATTERS) – what causes pain? what causes pleasure?
• Define 1-5 TESTABLE Conversion Metrics of Value – Attention/Usage (session time, clicks)
– Customer Data (email, connect, profile)
- Revenue (direct or indirect)
- Retention (visits over time, cohort behavior)
- Referral (users evangelize to other users)
• Note: Paid Solutions drive FOCUS (& pay rent)
SOURCE: DAVE MCCLURE, 500 Startups, Metrics4Pirates Presentation, June 2011
= F(Customer, Problem, Time or $$$)
What is Minimum Viable Product? MVP
1. Accept that startups initially don’t know much.
2. Test assumptions via continuous customer interaction
(before and after building something).
3. When you get significant agreement on problem, build MVP.
4. Stay lean.
“A startup is an
organization formed to
search for a repeatable
and scalable business
model.”
-Steve Blank.
Lean Startup = (Platonic) method of
learning by anticipating (initial) failure
Today
Current Funding Horizon
Launch
Current Funding Horizon
Alpha 1st Iteration 2nd Iteration etc
Expect to Solve Challenges through
Testing & Iteration (like Aristotle).
• Many times informed recommendation = NO
• Saying no is good, it will allow you to
process a lot more shiny objects …
• And a chance to add value through feedback
These questions should help you
make an informed recommendation.
• Wrong Problem (should be identified): – One we are not interested in solving
– One that does not really exist • Solution is already out there
• No evidence that target customers view problem as real
• Performance risk is too high (should be identified): – No Problem/Solution fit
– Team unlikely to deliver/learn
– Not enough traction
• Wrong Solution/Approach (may be identified): – Do not believe in value/growth hypothesis
*Using and sharing results of this framework can make a “no” more useful for prospective grantees
You should say no when:
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Outcomes (from Best to Worst)
1) Success
2) Failure (allows for pivot)
3) ??? Not Sure
Be sure you know what success & failure look like!
This is more difficult in a nonprofit context.
Outcomes (from Best to Worst)
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Key Takeaways:
1. Risk analysis can be used to parse investments that
are more likely to learn/succeed
2. For early stage investments, focus on Problem & Team
(since initial proposed solution will likely not work)
• One way to de-risk early stage investments is to invest
after MVP has been developed
• Cost of Tech is so cheap that most teams should be
able to develop MVP without outside funding.
• Once MVP has developed, can analyze performance
as well as potential
Key Takeaways:
