4. Things that don’t matter
What technology you use How much money you raised
Your “development methodology” Having big wigs on your board of
Patents directors/advisors
Your business plan P&L & Pro forma budgets
Your marketing strategy How many Twitter followers you have
“Viral Anchors” Traction on your blog
Usability Articles about you in the NYTimes
Graphic Design Presenting at SXSW
Branding User signups
Working 80 hours a week Getting great response at
Flexible office hours Democamp
Free expresso & a foosball table How many employees you have
An Employee Stock Option Plan How much you pay them
Get Satisfaction/Uservoice How many features you build
Surveys If you are funded or who funded you
Winning Start-up awards Integrating Facebook Connect
Whether your friends like your idea Adding features
Whether anybody likes your idea Articles about you in TechCrunch
How robust your servers are Financial projections
Whether you like your idea
5. Things that do matter
Product/Market Fit
Traction
Running out of money
6. A Better Way
Problem/Solution Product/Market
Scale
Fit Fit
Finding a plan that works. Accelerating
that plan.
Build - Measure - Learn - Pivot Expend capital.
Grow.
Optimize.
7. Stage 1: Problem/Solution Fit
Do I have a problem worth solving?
1. Solve a problem that a lot of people have.
2. Solve problem that is close to large amounts of money.
3. Find a better way to do something that a lot of people
do.
4. Invent something new that a lot of people want.
Document your “Plan A”.
8. Stage 2: Product/Market Fit
Have I built something people want?
• The customer is willing to pay for the product.
• The cost of acquiring the customer is less than what they
pay for the product.
• There’s sufficient evidence indicating the market is large
enough to support the business.
* or *
40% of users saying they would be “very disappointed”
without your product.
9. The Goal: Minimum Value Product (MVP)
A product with the fewest number of features required to get
users to pay*.
*with some form of a scarce resource.
12. Building a validated learning loop
1. Formulate falsifiable hypotheses
2. Design a time-boxed experiment
3. Validate Qualitatively, Verify Quantitatively
BAD:
We want to become market leaders in mobile blah-blah-blah
for the blah-blah segment.
GOOD:
If we build a free iphone app that does blah, can we get 1
out of 10 people who download it to use it every day?
The goal here is clearly defining the conditions under which a hypothesis can be absolutely proved or disproved. Otherwise, you can easily fall into the trap of accumulating just enough evidence to convince yourself that your hypothesis is correct.\n\nReasonably smart people can rationalize anything and entrepreneurs are especially gifted at this.\n\nBefore Product/Market fit, you typically do not have enough traffic to afford waiting for statistically significant results. The good news is that if you are maximizing for learning and picking bold outcomes, that naturally works to your advantage. Your initial goal is getting a strong signal (positive or negative) which typically doesn’t require a large sample size. You might be able to do this with as few as 5 customer interviews1.\n
\n
In a pivot experiment, you attempt to validate parts of the business model hypotheses towards finding that plan that works. In an optimization experiment, you attempt to refine parts of the business model hypotheses towards accelerating a working plan. The goal of the first is a course correction (or a pivot). \n\nThis may sound like a subtle distinction but it has a significant impact on both strategy and tactical execution. Before Product/Market fit a startup needs to be architected for maximizing learning. In order to maximize learning, you have to pick bold outcomes versus chase incremental improvements. So, rather than changing the color of your call to action button, change the entire unique value proposition. Rather than experimenting with different prices, experiment with different pricing models.\n