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