The document discusses the principles of lean analytics and its application in improving business performance, focusing on key concepts such as the lean startup methodology, the importance of selecting actionable metrics, and the cycles of analysis for continuous improvement. It emphasizes the necessity of distinguishing between vanity and actionable metrics, providing case studies to illustrate effective use of analytics in various business models. Additionally, it outlines the steps for creating a lean analytics cycle to facilitate data-driven decision-making.

1. Understanding
Lean Analytics
and how analytics
helps business win
Ben Yoskovitz
@byosko
byosko@gmail.com
#leananalytics
http://leananalyticsbook.com
Monday, March 18, 13

2. More wins than losses
GoInstant
1st startup Year One Labs
Started blogging
Standout Jobs
Big pivot
1996 1998 2001 2006 2007 2010 2011
The “I got too comfy” years Failed $0
Monday, March 18, 13

3. I BELIEVE IN
GUTS + DATA
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4. What we’ll cover today
• Lean Startup
• Key aspects of metrics
• Lean Analytics framework
• The One Metric That Matters
• The Lean Analytics Cycle
• Exploring business models
• Analytics in marketing
• Trends in analytics
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5. LEAN STARTUP
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6. Build Measure Learn
Eric Ries
http://theleanstartup.com
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7. THREE ENGINES OF GROWTH
Sticky
Virality
Paid
http://www.flickr.com/photos/fftang/4941889792/sizes/l/in/photostream/
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8. The Lean Canvas (leancanvas.com)
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9. BASICS OF
ANALYTICS
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10. Analytics is the measurement of
movement towards your
business goals.
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11. Things you can explain to others
about metrics
* Qualitative vs. Quantitative
* Exploratory vs. Reporting
* Vanity vs. Actionable
* Leading vs. Lagging
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12. Qualitative Quantitative
Unstructured, Numbers and stats;
anecdotal, revealing, hard facts but less
hard to aggregate. insight.
Warm and fuzzy. Cold and hard.
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13. DISCOVER QUALITATIVELY
AND
PROVE QUANTITATIVELY
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14. Exploratory Reporting
Speculative, trying to Predictable, keeping
find unexpected or you abreast of
interesting insights. normal, managerial
operations.
http://www.flickr.com/photos/50755773@N06/5415295449/ http://www.flickr.com/photos/elwillo/4737933662/
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15. Case study: From friends to moms
• Started as Circle of Friends
• Grew to 10M users
BUT ENGAGEMENT SUCKED
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16. Case study: Moms are crazy (in a good way!)
• Messages to one another were on average 50% longer.
• 115% more likely to attach a picture to a post they wrote.
• 110% more likely to engage in a threaded (i.e. deep) conversation.
• Friends, once invited, were 50% more likely to become engaged users.
• 180% more likely to click on Facebook news feed items.
• 60% more likely to accept invitations to the app.
ENGAGEMENT WAS GREAT
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17. Vanity Actionable
Picks a
direction.
Makes you feel
good, but doesn’t
change how you’ll
act.
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18. VANITY METRICS ARE BAD
A metric from the early, foolish days of the Web.
Hits
Count people instead.
Marginally better than hits. Unless you’re displaying
Page views
ad inventory, count people.
Is this one person visiting a hundred times, or are a
Visits
hundred people visiting once? Fail.
This tells you nothing about what they did, why they
Unique visitors
stuck around, or if they left.
Followers/ Count actions instead. Find out how many followers
friends/likes will do your bidding.
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19. http://www.flickr.com/photos/circasassy/7858155676/
If it won’t change
how you behave,
it’s a
bad
metric.
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20. LEADING LAGGING
Number today Historical metric
that shows metric that shows how
tomorrow-makes you’re doing-
the news reports the news
http://www.flickr.com/photos/vegaseddie/3310041214/sizes/l/in/photostream/
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21. What mode of e-commerce are you?
How many of
your customers Then you are in this Your customers will You are just
Focus on
buy a second time mode buy from you like
in 90 days?
Low CAC,
1-15% Acquisition Once 70% high
of retailers checkout
15-30% Hybrid 2-2.5 20% Increasing
per year of retailers returns
Loyalty,
>30% Loyalty >2.5 10% inventory
per year of retailers expansion
(Thanks to Kevin Hillstrom for this.)
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22. ANALYTICS SUPERPOWERS
(or what the heck is growth hacking?)
http://www.flickr.com/photos/bloke_with_camera/401812833/sizes/o/in/photostream/
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23. 10000
1000
100
10
1
Jan Feb Mar Apr May Jun Jul Aug Sept Oct Nov Dec
Ice cream consumption Drownings
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24. 10000
1000
100
10
1
Jan Feb Mar Apr May Jun Jul Aug Sept Oct Nov Dec
Ice cream consumption Drownings
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25. 10000
1000
100
10
1
Jan Feb Mar Apr May Jun Jul Aug Sept Oct Nov Dec
Ice cream consumption Drownings
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26. Correlated Causal
Two variables that An independent
change in similar ways, factor that directly
perhaps because they impacts a dependent
are linked to something one.
else.
Summer
al
Ca
us
us
Ca
al
Correlated
Ice cream Drowning
consumption
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27. Causality is a superpower, because it lets you change
the future.
Correlation lets you Causality lets you
predict the future change the future
“I will have 420 engaged “If I can make more first-
users and 75 paying time visitors stay on for
customers next month.” 17 minutes I will increase
sales in 90 days.”
Optimize the
Find correlation Test causality
causal factor
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28. LEAN ANALYTICS
FRAMEWORK
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29. simplify.
http://www.flickr.com/photos/josefeliciano/3849557951/sizes/l/in/photostream/
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30. YOUR BASIC
THE STAGE OF
BUSINESS
YOUR STARTUP
MODEL
How you make $$ Lifecycle
• E-commerce • Empathy
• SaaS • Stickiness
• Free mobile app • Virality
• Media site • Revenue
• Collaborative content site • Scale
• Two-sided marketplace
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31. LEAN ANALYTICS “GATE” NEEDED TO
STAGES MOVE FORWARD
I’ve found a real, poorly-met need
EMPATHY that a reachable market faces.
I’ve figured out how to solve the
problem in a way they will adopt
STICKINESS and pay for.
I’ve built the right product/features/
GROWTH RATE
functionality that keeps users
VIRALITY
around.
The users and features fuel growth
REVENUE organically and artificially.
I’ve found a sustainable, scalable
business with the right margins in a
SCALE
healthy ecosystem.
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32. Case study: Buffer goes from
Stickiness to Scale (through Revenue)
• Stage: Scale
• Model: SaaS (consumer)
• Popular social sharing application.
• Focused primarily on customer
acquisition
• Charged from day one
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33. Buffer charges early to prove people
want the problem solved
20% of visitors create an account
(acquisition / Empathy)
of sign-ups become active
64% (start of Stickiness)
of sign-ups return in the 1st month
60% (engagement / Stickiness)
of sign-ups are active after 6 months
20% (engagement / Stickiness)
convert from free to paid
2% (Revenue)
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34. How it all comes together
The business you’re in
E- 2-sided Mobile User-gen
SaaS Media
commerce market app content
Empathy
The stage you’re at
Stickiness
Virality
Revenue
Scale
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35. How it all comes together
The business you’re in
E- 2-sided Mobile User-gen
SaaS Media
commerce market app content
Empathy
The stage you’re at
One Metric
Stickiness
Virality
Revenue That Matters.
Scale
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36. Case study: SEOmoz reduces the
KPIs it tracks
• Stage: Scale
• Model: SaaS
• SEO toolkit (product suite)
• Reduced KPIs to focus on Net Adds
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37. Net Adds = “health of the business”
indicator
If Net Adds: Why & Next Steps:
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38. Net Adds = “health of the business”
indicator
If Net Adds: Why & Next Steps:
• Was a marketing campaign successful?
• Was churn lowered?
• Were customer complaints lowered?
• Was a product upgrade valuable?
Monday, March 18, 13

39. Net Adds = “health of the business”
indicator
If Net Adds: Why & Next Steps:
• Was a marketing campaign successful?
• Was churn lowered?
• Were customer complaints lowered?
• Was a product upgrade valuable?
• How can we acquire more valuable customers?
• Can we increase site conversion?
• How can we lower churn?
• What product features can increase engagement?
Monday, March 18, 13

40. Net Adds = “health of the business”
indicator
If Net Adds: Why & Next Steps:
• Was a marketing campaign successful?
• Was churn lowered?
• Were customer complaints lowered?
• Was a product upgrade valuable?
• How can we acquire more valuable customers?
• Can we increase site conversion?
• How can we lower churn?
• What product features can increase engagement?
• Are the new customers not the right segment?
• Did a marketing campaign fail?
• Are too many customers churning?
• Did a product upgrade fail to impress or cause issues?
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41. DRAW A LINE IN THE SAND
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42. HighScore House defines an “active user”
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43. What’s your OMTM?
E- 2-sided Mobile User-gen
SaaS Media
commerce market app content
Empathy Interviews; qualitative results; quantitative scoring; surveys
Loyalty, Inventory, Engagement, Downloads, Content, Traffic, visits,
Stickiness conversion listings churn churn, virality spam returns
CAC, shares, Inherent WoM, app Invites, Content
Virality reactivation
SEM, sharing
virality, CAC ratings, CAC sharing virality, SEM
(Money from transactions) (Money from active users) (Money from ad clicks)
Transaction, Transactions, Upselling, CLV, Ads, CPE, affiliate
Revenue CLV commission CAC, CLV ARPDAU donations %, eyeballs
Affiliates, Other API, magic Spinoffs, Analytics, Syndication,
Scale white-label verticals #, mktplace publishers user data licenses
Monday, March 18, 13

44. METRICS
ARE LIKE
SQUEEZE
TOYS
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45. YOUR GOAL IS TO MAKE
FASTER,
MORE INTELLECTUALLY
HONEST DECISIONS
AND
EMPOWER YOUR
ORGANIZATION
TO DO THE SAME
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46. What these have in common:
The Lean Analytics Cycle
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47. What these have in common:
The Lean Analytics Cycle
Pick a KPI
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48. What these have in common:
The Lean Analytics Cycle
Pick a KPI Draw a line
in the sand
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49. What these have in common:
The Lean Analytics Cycle
Pick a KPI Draw a line
in the sand
Find a
potential
improvement
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50. What these have in common:
The Lean Analytics Cycle
Pick a KPI Draw a line
in the sand
Find a
potential
improvement
Without
data: make a
good guess
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51. What these have in common:
The Lean Analytics Cycle
Pick a KPI Draw a line
in the sand
Find a
potential
improvement
Without With data:
data: make a find a
good guess commonality
Monday, March 18, 13

52. What these have in common:
The Lean Analytics Cycle
Pick a KPI Draw a line
in the sand
Find a
potential
improvement
Without With data:
data: make a find a
good guess commonality
Hypothesis
Monday, March 18, 13

53. What these have in common:
The Lean Analytics Cycle
Pick a KPI Draw a line
in the sand
Find a
potential
improvement
Without With data:
data: make a find a
good guess commonality
Hypothesis
Make changes
in production
Monday, March 18, 13

54. What these have in common:
The Lean Analytics Cycle
Pick a KPI Draw a line
in the sand
Find a
potential
improvement
Without With data:
data: make a find a
good guess commonality
Design a test
Hypothesis
Make changes
in production
Monday, March 18, 13

55. What these have in common:
The Lean Analytics Cycle
Pick a KPI Draw a line
in the sand
Find a
potential
improvement
Without With data:
data: make a find a
good guess commonality
Design a test
Measure
the results Hypothesis
Make changes
in production
Monday, March 18, 13

56. What these have in common:
The Lean Analytics Cycle
Pick a KPI Draw a line
in the sand
Find a
potential
improvement
Did we move
the needle? Without With data:
data: make a find a
good guess commonality
Design a test
Measure
the results Hypothesis
Make changes
in production
Monday, March 18, 13

57. What these have in common:
The Lean Analytics Cycle
Success! Pick a KPI Draw a line
in the sand
Find a
potential
improvement
Did we move
the needle? Without With data:
data: make a find a
good guess commonality
Design a test
Measure
the results Hypothesis
Make changes
in production
Monday, March 18, 13

58. What these have in common:
The Lean Analytics Cycle
Success! Pick a KPI Draw a line
in the sand
Pivot or
give up Find a
potential
improvement
Did we move
the needle? Without With data:
data: make a find a
good guess commonality
Design a test
Measure
the results Hypothesis
Make changes
in production
Monday, March 18, 13

59. What these have in common:
The Lean Analytics Cycle
Success! Pick a KPI Draw a line
in the sand
Pivot or
give up Draw a new line Find a
potential
improvement
Did we move
the needle? Without With data:
data: make a find a
good guess commonality
Design a test
Measure
the results Hypothesis
Make changes
in production
Monday, March 18, 13

60. What these have in common:
The Lean Analytics Cycle
Success! Pick a KPI Draw a line
in the sand
Pivot or
give up Draw a new line Find a
potential
Try again improvement
Did we move
the needle? Without With data:
data: make a find a
good guess commonality
Design a test
Measure
the results Hypothesis
Make changes
in production
Monday, March 18, 13

61. EXPLORING
BUSINESS MODELS
Monday, March 18, 13

62. MEDIA COMPANIES
• Audience & churn (how many people visit the site, and how
loyal they are)
• Ad inventory (the number of impressions that can be monetized)
• Ad rates (sometimes measured in cost-per-engagement--
essentially how much a site can make from those impressions based
on the content it covers and the people who visit)
• Click-through rates (how many of the impressions actually turn
into money)
• The content/advertising balance (the balance of ad
inventory ad rates and content that maximizes overall performance
Monday, March 18, 13

63. Monday, March 18, 13

64. E-COMMERCE COMPANIES
• Conversion rate (the # of visitors who buy something)
• Purchases / year (the # of purchases made by each customer per year)
• Average shopping cart size (the amount of money spent / purchase)
• Cost of customer acquisition (the money spent to get someone to buy something)
• Revenue / customer (the lifetime value of each customer)
• Top keywords driving traffic to the site (those terms that people are looking for,
and associate with you--a clue to adjacent products or markets)
• Top search terms (both those that lead to revenue, and those that don’t have any
results)
• Effectiveness of recommendation engines (how likely a visitor is to add a
recommended product to their cart)
• Virality (word of mouth, and sharing per visitor)
• Mailing list effectiveness (click-through rates and ability to make buyers return and
buy)
Monday, March 18, 13

65. Case study: WineExpress increases
revenues
• Stage: Revenue
• Model: E-commerce
• Exclusive wine shop partner of the
Wine Enthusiast catalog and website
• “Wine of the day” page is highly
trafficked, needed optimization
Monday, March 18, 13

66. A
Monday, March 18, 13

67. B
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68. Case study: Before and after
41%
increase in
revenue per
visitor
Monday, March 18, 13

69. Don’t forget the real world
Shipping time, stock availability,
logistics, ratings, and other factors
have a real impact on most e-
commerce companies.
Shipping time, stock availability,
DON’T logistics, ratings, and other factors
FORGET THE have a real impact on most e-
REAL WORLD commerce companies.
Monday, March 18, 13

70. Monday, March 18, 13

71. Outside In: The Power
of Putting Customers
at the Center of Your
Business
http://www.amazon.com/Outside-Putting-Customers-Center-Business/dp/0547913982
Monday, March 18, 13

72. ANALYTICS IN
MARKETING
Monday, March 18, 13

73. HOW IT USUALLY HAPPENS
• Some CxO catches on to the latest thing from talking to someone in
social media, or tries out the latest cool app (Vine, Pinterest)
• That person rushes into the office, exclaiming, “We need to do a
Pinterest campaign now!”
• The marketing team scrambles to come up with a campaign
• They use that campaign to back into a metric
• They run the campaign and report the results (maybe!)
...which of course means no line in the sand, no
discipline, and attempts to move metrics that may not
be core to the business in the first place.
Monday, March 18, 13

74. HOW IT SHOULD BE DONE
• There’s a known business objective / problem to solve
• There’s a key metric for it (OMTM)
• There’s a goal (line in the sand)
• We come up with a campaign or effort to move the needle
(hypothesis)
• We decide the medium that we want to try that on
(campaign details)
• We execute
• We measure and adjust (learn & iterate)
Monday, March 18, 13

75. Monday, March 18, 13

76. TRENDS IN
ANALYTICS
Monday, March 18, 13

77. From: To:
Aggregate Individual
Segment Cohort
Generic Vertical
Silos Aggregation
Daily Realtime
Reports Exceptions
Pages Events
Funnels Influences
Desktop Mobile
Accounting Predictive
Monday, March 18, 13

78. Aggregate to individual
Aggregate to individual
e.g. KISSmetrics
Monday, March 18, 13

79. Segment to cohort
Segment to cohort
e.g. Mixpanel
Monday, March 18, 13

80. Generic to vertical
Generic to vertical
SaaS' Mobile' Publishing'
e.g. Totango, Flurry, Parse.ly
Monday, March 18, 13

81. Silos to aggregation
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82. Daily to realtime
Daily to realtime
Remember: right time is better than real time
Monday, March 18, 13

83. Reports to exceptions
Reports to exceptions
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84. Pages to events
Pages to events
e.g. Chartbeat
Monday, March 18, 13

85. Funnels to influences
Funnels to influences
Monday, March 18, 13

86. Desktop to mobile
e.g. Keen.io
Monday, March 18, 13

87. Reporting to predictive
e.g. Synapsify
Monday, March 18, 13

88. Once, a leader convinced others in
the absence of data.
Monday, March 18, 13

89. Now, a leader knows what
questions to ask.
Monday, March 18, 13

90. Thank you.
follow me. email me.
@byosko
byosko@gmail.com
instigatorblog.com
subscribe.
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Monday, March 18, 13