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Lean LaunchPad Workshop:
Defining an Analytics Strategy
Ryan Jung
Haas MBA 2014
ryan_jung@haas.berkeley.edu
Why Are Analytics Important?
• Failure to define an
analytics strategy can be
a fatal error for a startup
in 2015.
• Analy...
Why You Need an Analytics Strategy
• Learn faster by
creating
feedback loops
• More clarity
based on
behavior
• Consensus ...
History of Analytics
• 1990s – Web counters
• 2000s – Click Analytics
and SEO
• 2010s – Behavioral
and Predictive
Analytics
Keys to a Great Analytics Strategy
1. Tightly integrated
with overall
business strategy
2. Iterative process
3. Measurable...
The Modern Data-Driven Lean Startup
Goal is to optimize a set of
business objectives in a logical
progression leveraging q...
Most Important Reports
• Segmentation (Cohorting)
• Retention
• Funnels
• Revenue Tracking
• Marketing Campaign Effectiven...
Segmentation / Cohorting
What segments are getting what
value out of your product?
Value Proposition / Customer Segment
Who is our customer?
What problem are we really
solving for them?
Will they buy from ...
Segmentation Example
• Look at aggregated
events and then
segment by properties
• See who is doing
particular actions and
...
Retention
Who gets the most value out of your
solution?
How Churn affects LTV
Lifetime Value
Monthly
Churn
Source: David Skok Matrix Partners
Thinking Through Retention
Get –> Keep –> Grow = Activation –>
Retention –> Engagement
Understanding key features
Understa...
Retention Reports
In-session retention In-app retention
Key Question(s) Where do users spend their time in
your app? What ...
BIG IDEA:
LTV drives CAC which drives channel
selection
Increasing Sales Complexity
Log(AcquisitionCost)
CAC < LTV
Funnels
How are users interacting with your
solution?
Sales Funnels
Where are we losing
customers?
How do we know if we
are doing well or not
well in sales?
How can we do bette...
Funnel Reports
Localytics
Funnel Reports
KISSMetrics
Tying funnels to revenues
Revenue = installs x [signups / installs] x [purchases / signups ] x [revenue / purchase]
Back-e...
Pitfalls to Avoid
Problem Explanation
Search vs. Execution
Metrics
Are we measuring KPIs or are we testing
hypotheses?
Van...
Summary
• You need to be thinking about analytics
because your competition probably already is
• Analytics is evolving, so...
Case Studies
Airbnb
• Challenge: Initially wanted to optimize booking flow
• Allowed them to identify to distinct classes of users
• Ca...
Khan Academy
• Challenge: increase engagement and the rate at which people
learn
• Used funnels to optimize search and reg...
Jawbone
• Challenge: Assess the viability of Jawbone UP
• Used Segmentation reporting to better understand their users
• H...
Cohort analysis
Renewal and upsell rates
Return on marketing investment
Revenue by Cohort – Each Year Builds on a Stronger Base
Note: Excludes inorganic growth.
201120102009200820072006
Highly L...
Revenue by Cohort – Each Year Builds on a Stronger Base
2006
2008 Cohort
2009 Cohort
2010 Cohort
2011 Cohort
2011201020092...
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Lean LaunchPad: Analytics Workshop

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Workshop we hold for our Lean LaunchPad / I-Corps classes

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Lean LaunchPad: Analytics Workshop

  1. Lean LaunchPad Workshop: Defining an Analytics Strategy Ryan Jung Haas MBA 2014 ryan_jung@haas.berkeley.edu
  2. Why Are Analytics Important? • Failure to define an analytics strategy can be a fatal error for a startup in 2015. • Analytics has changed the landscape • A great analytics strategy is tightly integrated with the overall business strategy
  3. Why You Need an Analytics Strategy • Learn faster by creating feedback loops • More clarity based on behavior • Consensus on future action There exists a host of tools to help you with these objectives.
  4. History of Analytics • 1990s – Web counters • 2000s – Click Analytics and SEO • 2010s – Behavioral and Predictive Analytics
  5. Keys to a Great Analytics Strategy 1. Tightly integrated with overall business strategy 2. Iterative process 3. Measurable set of hypotheses, results, and revisions
  6. The Modern Data-Driven Lean Startup Goal is to optimize a set of business objectives in a logical progression leveraging quantitative and qualitative facts in order to delight customers in a scalable, repeatable fashion
  7. Most Important Reports • Segmentation (Cohorting) • Retention • Funnels • Revenue Tracking • Marketing Campaign Effectiveness • Path Analysis • Notifications
  8. Segmentation / Cohorting What segments are getting what value out of your product?
  9. Value Proposition / Customer Segment Who is our customer? What problem are we really solving for them? Will they buy from us? How do we reach them? • Build customer archetypes • Add properties to define the user • Use segmentation to look at differences in customers • Good for looking at actions, but need to understand causation to be actionable Using Analytics
  10. Segmentation Example • Look at aggregated events and then segment by properties • See who is doing particular actions and identify trends • Want to segment as far as possible • Point you to needs and how your product adds value Google Analytics
  11. Retention Who gets the most value out of your solution?
  12. How Churn affects LTV Lifetime Value Monthly Churn Source: David Skok Matrix Partners
  13. Thinking Through Retention Get –> Keep –> Grow = Activation –> Retention –> Engagement Understanding key features Understanding core users and testing their needs Identifying most effective channels
  14. Retention Reports In-session retention In-app retention Key Question(s) Where do users spend their time in your app? What features are valuable? Are users coming back and using the app repeatedly? Who are users that are more likely to come back? Value Proposition Features that are most valuable Users that get most value out of product Tool Addiction Recurring or Segmented Retention Mixpanel
  15. BIG IDEA: LTV drives CAC which drives channel selection Increasing Sales Complexity Log(AcquisitionCost) CAC < LTV
  16. Funnels How are users interacting with your solution?
  17. Sales Funnels Where are we losing customers? How do we know if we are doing well or not well in sales? How can we do better? Core Idea: Track conversion rates between levels of funnel to see where “leakage” occurs and create strategies to minimize this loss. Is my marketing spend being used efficiently?
  18. Funnel Reports Localytics
  19. Funnel Reports KISSMetrics
  20. Tying funnels to revenues Revenue = installs x [signups / installs] x [purchases / signups ] x [revenue / purchase] Back-end tells you this Analytics tells you this Analytics can tell you this You control this The main point here is that you can break revenue into measureable components • Tie how you earn revenue to what you measure • Then understand where you are doing well and not well • Then use your analytics solution to design tests to figure out how to drive more lifetime value Mathematically:
  21. Pitfalls to Avoid Problem Explanation Search vs. Execution Metrics Are we measuring KPIs or are we testing hypotheses? Vanity metrics If it only goes “up and to the right” and / or if it’s not actionable, it’s a waste of time to measure it. Biased tests Be sure that the hypotheses that you are testing are not set up to confirm your assumptions. Take the approach of trying to disprove your hypothesis. Data overload “Measuring everything and then mining for insights” creates too much noise for most to get any real value from.
  22. Summary • You need to be thinking about analytics because your competition probably already is • Analytics is evolving, so keeping up is imperative • Analytics needs to be tied to your overall business strategy, should be hypothesis- driven, and is an iterative process
  23. Case Studies
  24. Airbnb • Challenge: Initially wanted to optimize booking flow • Allowed them to identify to distinct classes of users • Can better target users and their needs More info: https://mixpanel.com/case-study/airbnb/
  25. Khan Academy • Challenge: increase engagement and the rate at which people learn • Used funnels to optimize search and registration processes • Start with a definition for “user engagement” More info: https://mixpanel.com/case-study/khanacademy/
  26. Jawbone • Challenge: Assess the viability of Jawbone UP • Used Segmentation reporting to better understand their users • Helps to build customer archetypes • Faster iterations and faster time to product-market fit More info: https://mixpanel.com/case-study/jawbone/
  27. Cohort analysis Renewal and upsell rates Return on marketing investment
  28. Revenue by Cohort – Each Year Builds on a Stronger Base Note: Excludes inorganic growth. 201120102009200820072006 Highly Loyal Customers 2007 Cohort Earlier Cohorts
  29. Revenue by Cohort – Each Year Builds on a Stronger Base 2006 2008 Cohort 2009 Cohort 2010 Cohort 2011 Cohort 20112010200920082007 Highly Loyal Customers Note: Excludes inorganic growth. 2007 Cohort Earlier Cohorts

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