This document discusses Lean Analytics and using data to build startups more effectively. It advocates for an iterative approach of "aim, fire, ready" over "ready, aim, fire" to quickly learn what works and adapt. Key principles discussed include focusing on metrics that are comparative over time, understandable, use ratios/rates not just numbers, improve predictions, and change behavior. The document outlines metrics to track depending on business model and startup stage, including qualitative customer feedback early on and metrics like activation, retention, referral rates, revenue, and customer acquisition costs later. It emphasizes choosing just one key metric to focus on at a time to avoid distraction and instill a culture of experimentation.

1. LEAN ANALYTICS
Using data to build a better startup faster
Ben Yoskovitz
@byosko

2. A little about me…
Acquihired Started writing Exited (but Big win!
instigator blog not a real
win)

3. YEAR ONE LABS

4. Wrong subtitle!
http://leananalyticsbook.com

5. PART
ONE
The future
of business
is lean.

6. Silicon Valley doesn’t
love failure.

7. It just hates it less
than the alternative:

8. Building something
nobody cares about.

9. ALL THAT MATTERS IS THE PROBLEM

11. The basic Lean message
is learn and adapt, fast.

12. READY, AIM, FIRE IS SO 1995.
Photo by US Army. Used under Creative Commons.
http://www.flickr.com/photos/soldiersmediacenter/4097699857

13. Instead: aim, fire, ready

14. Different things matter now.

15. Knowing
what’s not
Getting
working,
features right
quickly,
matters less.
matters
more.

16. Finding
Building known
product needs
matters less. matters
more.

17. “What if”
“We should”
matters
matters less.
more.

18. Parallel
Your
experiments
roadmap
matters
matters less.
more.

19. Business
Business
models
plans matter
matter
less.
more.

20. LEAN
ANALYTICS

21. WE’RE ALL LIARS

22. DON’T
IGNORE
YOUR GUT

23. Your Gut Increased
+ = likelihood of
Lean Analytics success

24. PART
TWO
The basics
of good
analytics.

25. Analytics is the measurement of
movement towards your business goals.

26. In a startup, the purpose
of analytics is to iterate to
product/market fit before
the money runs out.

27. Three categories of metrics to think about:
1.Qualitative and quantitative
2.Vanity and actionable
3.Exploratory and reporting

28. What makes a great metric?
1. Comparative over time, against a baseline
2. Understandable
3. Ratios and rates, not numbers
4. Improves the accuracy of your predictions
5. Your behavior changes when it does

29. PART
THREE
Key
principles
of Lean
Analytics

30. What you should track is dependent on:
Your basic business The stage your
model startup is at
How you make $$ Lifecycle
• E-commerce • Find a need
• SaaS • Find a solution
• Free mobile app • Find a product
• Media site • Find a business model
• Collaborative content site • Find an exit
• Two-sided marketplace

31. http://www.slideshare.net/dmc500hats/startup-metrics-for-
pirates-sept-2012?from=new_upload_email

32. Eric Ries’ Engines of Growth
1. Sticky engine
2. Viral engine
3. Price engine http://www.flickr.com/photos/toastforbrekkie/113065857/

33. The Long Funnel
• Understanding the
customer journey
• Exploratory & reporting
Get it here!
http://bit.ly/QTPPsq

34. THE GATES OF LEAN ANALYTICS
Area of focus “Gate” needed Key metrics for
Lean McClure this stage
at this stage to move forward
I’ve found a real, poorly-met Qualitative responses
Customer Attention Empathy need a reachable market Meetings held
development (of faces.
testers, prospe Scored qualitative
cts, etc.) Surveys, bulk feedback
I’ve figured out how to solve
MVP building the problem in a way they
Signup rates
Stickiness will adopt and pay for.
Activation Activation on invite/beta
MVP iteration,
Startup lifecycle stage
sticky engine Retention MVP adoption
I’ve built the right
Retention
product/features/functionality
that keeps users around. Message open rates
Organic Referral Virality Inherent sharing
growth, viral
Viral coefficient/cycle time
Growth rate
engine The users and features fuel
growth organically and Word of Mouth sharing
artificially.
Key goal conversion rates
Monetization, Revenue Revenue Customer lifetime value
price engine
I’ve found a sustainable, Customer acquisition cost
scalable business with the Margins
right margins in a healthy
Access to employees
ecosystem.
Inorganic Attention Attention Access to capital
growth, (at scale, of Competition
beyond Lean customers) I can achieve a successful
Visibility/SEO/SEM
exit for the right terms.
Channel health
Exit!

35. The One Metric That Matters
Or how to deal with your crippling ADD
http://www.flickr.com/photos/andrec/2893549851/

36. Choose only one metric.

37. Benefits of the OMTM:
1. It should answer the most important
question you have
2. There’s a clear goal
3. Focuses the entire company
4. Instills a culture of experimentation

38. METRICS ARE LIKE SQUEEZE TOYS

39. Lean Analytics example: SaaS
Area of focus
at this stage
Empathy Qualitative analysis (any interviewees writing checks?)
Scoring problem interviews
Survey results
Beta signup (how many people sign up?)
Stickiness
Enrollment (how many visitors become users?)
Daily, weekly, monthly active users
Churn (how many users leave in a given time period?)
Virality Virality (inherent – built into the product)
Virality (artificial – invite someone for a free month)
Growth rate
Conversion (how many users become paying customers?)
Revenue per Customer
Revenue Up-selling (what makes customers increase spending?)
Customer Acquisition Cost (CAC)
Customer Lifetime Value (CLV)
Attention Building sales channels
Site visitors (how many people visit with other #s maintained?)

40. Draw lines in the sand.

41. What’s ideal?
• ~2.5% churn
• Free to paid in freemium of 2%
• CLV is 3x CAC
• Time to recoup CAC of 6-12 months

42. The Problem-Solution Canvas
Or how to stay focused as much as possible

43. Problem-Solution Canvas #1

44. Problem-Solution Canvas #2

45. Sneak peek!
• Break down of 6 business models and key metrics
to focus on
• How to use Lean Analytics at every stage of your
startup & for big companies / intrapreneurs
• Examples of “what’s ideal” for each major metric
• The penny machine
• The three-threes model
• How crowdfunding fits in
• How to instill a culture of analytics
• Case studies from Circle of Moms, AirBnB, WP
Engine, SEOmoz, Backupify, Wooga and more!

46. Thank you!
@byosko & @acroll
leananalyticsbook.com
instigatorblog.com