Lean LaunchPad: Analytics Workshop
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Transcript of Lean LaunchPad: Analytics Workshop
Lean LaunchPad Workshop:Defining an Analytics Strategy
Ryan Jung
Haas MBA 2014
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
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.
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 set of hypotheses, results, and revisions
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
Most Important Reports
• Segmentation (Cohorting)
• Retention
• Funnels
• Revenue Tracking
• Marketing Campaign Effectiveness
• Path Analysis
• Notifications
Segmentation / Cohorting
What segments are getting what value out of your product?
Value Proposition / Customer Segment
Who is our customer?
What problem are we reallysolving 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
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
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
Understanding core users and testing their needs
Identifying most effective channels
Retention ReportsIn-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
BIG IDEA:LTV drives CAC which drives channel
selection
Increasing Sales Complexity
Log(
Acq
uis
itio
nC
ost
)
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 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?
Funnel Reports
Localytics
Funnel Reports
KISSMetrics
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:
Pitfalls to AvoidProblem 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.
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
Case Studies
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/
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/
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/
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 Loyal Customers
2007 Cohort
Earlier Cohorts
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