Lean LaunchPad Workshop: Defining an Analytics Strategy

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Lean LaunchPad Workshop: Defining an Analytics Strategy Ryan Jung Haas MBA 2014 ryan_jung@haas.berkeley.edu

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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

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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.

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History of Analytics 1990s – Web counters 2000s – Click Analytics and SEO 2010s – Behavioral and Predictive Analytics

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Keys to a Great Analytics Strategy Tightly integrated with overall business strategy Iterative process Measurable set of hypotheses, results, and revisions

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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

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Most Important Reports Segmentation (Cohorting) Retention Funnels Revenue Tracking Marketing Campaign Effectiveness Path Analysis Notifications

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Segmentation / Cohorting What segments are getting what value out of your product?

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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?

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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

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Retention Who gets the most value out of your solution?

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How Churn affects LTV Lifetime Value Monthly Churn Source: David Skok Matrix Partners

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Thinking Through Retention Get –> Keep –> Grow = Activation –> Retention –> Engagement Understanding key features Understanding core users and testing their needs Identifying most effective channels

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Retention Reports Mixpanel

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BIG IDEA: LTV drives CAC which drives channel selection Increasing Sales Complexity Log(Acquisition Cost) CAC < LTV

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Funnels How are users interacting with your solution?

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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?

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Funnel Reports Localytics

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Funnel Reports KISSMetrics

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Tying funnels to revenues Revenue = installs x [signups / installs] x [purchases / signups ] x [revenue / purchase] 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:

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Pitfalls to Avoid

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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

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Case Studies

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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/

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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/

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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/

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Cohort analysis Renewal and upsell rates Return on marketing investment

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Note: Excludes inorganic growth. 2011 2010 2009 2008 2007 2006 Highly Loyal Customers

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2006 2008 Cohort 2009 Cohort 2010 Cohort 2011 Cohort 2011 2010 2009 2008 2007 Highly Loyal Customers Note: Excludes inorganic growth. 2007 Cohort Earlier Cohorts