29. Lean Startup: 3 engines of growth
Virality
Make people
invite friends.
How many they
tell, how fast
they tell them.
Stickiness
Keep people
coming back.Approach?
Get customers
faster than you
lose them.
Metrics?
30. Virality
Make people
invite friends.
How many they
tell, how fast
they tell them.
Price
Spend money to
get customers.
Customers are
worth more than
they cost.
Stickiness
Keep people
coming back.Approach?
Get customers
faster than you
lose them.
Metrics?
Lean Startup: 3 engines of growth
45. Make them
refer others
Retain them
Get revenue from them
Acquire
customers
Activate them
http://www.slideshare.net/dmc500hats/startup-metrics-for-pirates-long-version
46. Analytics Flow Chart
Customer Acquisition Cost
paid direct search wom
inherent
virality
VISITOR
Freemium/trial
Enrollment
User
Disengaged User
Cancel
Freemiu
m churn
Engaged User
Free user
disengagemen
t
Reactivate
Cancel
Trial
abandonment
rate
Invite Others
Paying Customer
Reactivation
rate
Paid
conversion
FORMER USERS
User Lifetime
Value
Reactivate
FORMER CUSTOMERS
Customer Lifetime
Value
Viral
coefficient
Viral rate
Resolution
Support
data
Account Billing Info Exp.
Paid Churn Rate
Tiering
Capacity Limit
Upselling
rate Upselling
Disengaged DissatisfiedTrial Over
48. Stage
EMPATHY
I’ve found a real, poorly-met need that a
reachable market faces.
STICKINESS
I’ve figured out how to solve the problem
in a way they will keep using and pay for.
VIRALITY
I’ve found ways to get them to tell their
friends, either intrinsically or through
REVENUE
The users and features fuel growth
organically and artificially.
SCALE
I’ve found a sustainable, scalable business
with the right margins in a healthy
Gate
Thefiveleanstages
54. E-Commerce SaaS Mobile
2-Sided
market
Media
User Generated
Content
Empathy
Stickiness
Virality
Revenue
Scale
Thestageyou’rein
The business you’re in?
One Metric That Matters
The power of Lean Analytics
69. Aunshul Rege of Rutgers University, USA in 2009
1000 emails
1-2 responses
1 fool and their money, parted.
Bad language (0.1% conversion)
Gullible (70% conversion)
1000 emails
100 responses
1 fool and their money, parted.
Good language (10% conversion)
Not-gullible (.07% conversion)
72. If a metric won’t change how you
behave, it’s a bad metric
73. A good metric is:
Understandable
If you’re busy
explaining the
data, you
won’t be busy
acting on it.
Comparative
Comparison is
context.
A ratio or rate
The only way
to measure
change and
roll up the
tension
between two
metrics (MPH)
Behavior
changing
What will you
do differently
based on the
results you
collect?
74. Now ask yourself:
How many of my current metrics are good metrics?
How many do you use to make business decisions
Are there others you’re not thinking of?
76. Which stage of the customer life cycle?
Acquisition
Revenue
Referral
Customers that buy
>1x in 90d
Once
2-2.5
per year
>2.5
per year
Your customers will
buy from you
Tell you which
stage you’re in
1-15%
15-30%
>30%
Lower acquisition
cost, high checkout
Increasing return
rates, market share
Loyalty, selection,
inventory size
What to
focus on
(From: Lean Analytics/Kevin Hillstrom)
79. “”...There are known knows; there are things
we know that we know. There are known
unknowns; that is to say, there are things
that we now know we don’t know. But there
are also unknown unknowns; there are
things we do not know we don’t know.
80. “”...There are known knows; there are things
we know that we know. There are known
unknowns; that is to say, there are things that
we now know we don’t know. But there are
also unknown unknowns; there are things we
do not know we don’t know.
81. Avinash Kaushik on Analytics
Things we
know
don’t
know
we know
Are facts which may be wrong
and should be checked against
data.
we don’t
know
Are questions we can answer by
reporting, which we should
baseline & automate.
we know
Are intuition which we should
quantify and teach to improve
effectiveness, efficiency.
we don’t
know
Are exploration which is where
unfair advantage and interesting
epiphanies live.
82. known knowns known unknowns unknown unknowns:: ::
(Vanity Metrics) (Actionable trends) (Leading Indicators)
84. Correlated
Two variables that are
related (but may be
dependent on
something else.)
Causal
An independent
variable that directly
impacts a dependent
one.
85. 1
10
100
1000
10000
Jan Feb Mar Apr May Jun Jul Aug Sept Oct Nov Dec
Relationship between Ice cream and drownings?
Ice cream consumption Drownings
88. Correlated
Two variables that are
related (but may be
dependent on
something else.)
Causal
An independent
variable that directly
impacts a dependent
one.
Ice cream &
drowning.
Summertime &
drowning.
90. ‣ A Facebook user reaching 7 friends within 10 days of signing up
(Chamath Palihapitiya)
‣ A Dropbox user who puts at least one file in one folder on one
device (ChenLi Wang)
‣ Twitter user following a certain number of people, and a certain
percentage of those people following the user back (Josh
Elman)
‣ A LinkedIn user getting to X connections in Y days (Elliot
Schmukler)
Some examples
(From the 2012 Growth Hacking conference. http://growthhackersconference.com/)
91. Picking your One Metric That Matters:
It answers the most important questions for your business
It puts focus into the entire company
It forces you to put a line in the sand
It inspires a culture of experimentation
97. A wealth of information creates a
poverty of attention...
(Computers, Communications and the Public Interest, pages 40-41,
Martin Greenberger, ed., The Johns Hopkins Press, 1971.)
98. Focus on the desired behavior, not
just the information.
http://www.psychologytoday.com/blog/yes/200808/changing-minds-and-changing-towels
26% increase in towel re-use with an appeal to social
norms; 33% increase when tied to the specific room.
100. ‣Buying
‣Signing up
‣Sharing
‣Etc
‣Brainstorming
‣Stolen from others
‣User feedback
‣Etc
One Metric That Matters:
Choose the KPI you want to improve
that represents the most fundamental
business risk
Did the KPI move past the
line in the send
Talk to customers and
draw a new line
Try again
Measure the effect the
change had on the KPI
Implement the best
experiment
Design A/B
experiments
Make changes to the
business that target this
commonality
Find an attribute the ‘best
customers’ share that’s
correlated with your KPI
What do these ‘best
customers’ have in
common?
Figure out how to improve that KPI?
Have a good idea you
want to try?
Draw a
line in the sand
Identify your ‘best
customers’
Pivot or give up?
Without data With data
Cautious testJust do it!
Yes!
101. Do AirBnB hosts get more business if
property is professionally
photographed?
102. Gut instinct (hypothesis)
Professional photography helps AirBnB’s business
Candidate solution (MVP)
20 field photographers posing as employees
Measure the results
Compare photographed listings to a control group
Make a decision
Launch photography as a new feature for all hosts
104. “Those houses with
high revenue look
really nice.”
Maybe it’s the
camera?
“What do all the
frequently rented
houses have in
common?”
Camera model?
With data:
find a commonality
Without data: make a
good guess
109. Blink Relevance Availability Scalability Sum
5-second rule
Gut feeling
Instinct score
Don’t overthink it
Do we have the
available
resources?
Do we have the
skills?
Do we have the
tools?
Is it expensive?
110. Blink Relevance Availability Scalability Sum
5-second rule
Gut feeling
Instinct score
Don’t overthink it
Is this Channel
relevant to your
product/service?
Does your
audience hang out
on this channel?
Can you target
them effectively?
Do we have the
available
resources?
Do we have the
skills?
Do we have the
tools?
Is it expensive?
111. Blink Relevance Availability Scalability Sum
5-second rule
Gut feeling
Instinct score
Don’t overthink it
Is this Channel
relevant to your
product/service?
Does your
audience hang out
on this channel?
Can you target
them effectively?
Do we have the
available
resources?
Do we have the
skills?
Do we have the
tools?
Is it expensive?
How scalable is
this channel?
Can we easily
increase it?
Law of diminishing
returns
112. Blink Relevance Availability Scalability Sum
5-second rule
Gut feeling
Instinct score
Don’t overthink it
Is this Channel
relevant to your
product/service?
Does your
audience hang out
on this channel?
Can you target
them effectively?
Do we have the
available
resources?
Do we have the
skills?
Do we have the
tools?
Is it expensive?
How scalable is
this channel?
Can we easily
increase it?
Law of diminishing
returns
1-5 1-5 1-5 1-5 BRASS score
121. Complete Web Monitoring
Web Analytics
(what did they
do on the site?)
Usability
(how did they
interact with it?)
122. Complete Web Monitoring
Web Analytics
(what did they
do on the site?)
Performance
(could they do
what they wanted
to?)
Usability
(how did they
interact with it?)
123. Complete Web Monitoring
Web Analytics
(what did they
do on the site?)
Performance
(could they do
what they wanted
to?)
VoC
(what were their
motivations?)
Usability
(how did they
interact with it?)
124. Complete Web Monitoring
Web Analytics
(what did they
do on the site?)
Competition
(what are they up
to?)
Performance
(could they do
what they wanted
to?)
VoC
(what were their
motivations?)
Usability
(how did they
interact with it?)
125. Complete Web Monitoring
Web Analytics
(what did they
do on the site?)
Competition
(what are they up
to?)
Performance
(could they do
what they wanted
to?)
VoC
(what were their
motivations?)
Usability
(how did they
interact with it?)
Social Media
(what were they
saying?)
126. Complete Web Monitoring
Web Analytics
(what did they
do on the site?)
Competition
(what are they up
to?)
Performance
(could they do
what they wanted
to?)
VoC
(what were their
motivations?)
Usability
(how did they
interact with it?)
Social Media
(what were they
saying?)
“Soft” data
“Hard” data
130. Voice of Customer PerformanceUsabilityWeb Analytics Social Media
Google Analytics Hotjar Usabilla Webpagetest Obi4Wan
Mouseflow
Optimizely
Qualaroo
Hotjar
Soasta mPulseMixpanel Sprout
Visual Web Optimizer
Google Experiments
New RelicLocalytics
Intercom
134. Pick one metric you want grow (yes, only one)
What things today drive growth (correlation)?
Experiment, rinse, repeat to find causation
2
3
4
1 Which stage is my business in?
136. 1
2
3
4
5
6
7
8
9
10
Build your customer life cycle
Identify your points of leverage
Clearly state your measurable
goals
Pick your one metric that
matters
Acquire the right analytics tools
Run your experiments
Test, optimize, measure, rinse
& repeat
Find your own hacks
Pick a new one metric that
matters
Embrace the process and
mindset
Lean Analytics checklist
137. Go buy the book:
http://shop.oreilly.com/product/0636920026334.do