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From Vanity to Results What’s the story with metrics? Aleksi Rossi @ Lean Startup Circle Wien 2013-07-26

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The  Open  Ministry   Open ministry Approved by Communication officials Equal marriage Dismantle alcohol monopoly Fur-farming ban Copyright law reform Government debt ceiling Energy drink prohibition for under 15 years

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is a vanity metric? is a good metric? kind of successes have you had with metrics? would you like to do better in your startup? What

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Lean Startup Build Product Measure Data Learn Ideas Minimum Viable Product Continuous deployment Scientific experiment Customer development Eliminate waste Split testing 5 Whys

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Methodology Lean Startup

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Methodology of maximizing probability of start-up success Lean Startup

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Methodology of maximizing probability of start-up success by eliminating waste Lean Startup

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Methodology of maximizing probability of start-up success by eliminating waste by repeatedly finding the right idea by talking to customers Lean Startup

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Methodology of maximizing probability of start-up success by eliminating waste by repeatedly finding the right idea by talking to customers and honing it into a vision. Lean Startup

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Methodology of maximizing probability of start-up success by eliminating waste by repeatedly finding the right idea by talking to customers and honing it into a vision. Avoiding assumption it’s the actually the idea that wins. Lean Startup

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Methodology of maximizing probability of start-up success by eliminating waste by repeatedly finding the right idea by talking to customers and honing it into a vision. Avoiding assumption it’s the actually the idea that wins. Building a minimal product (/service) iteratively Lean Startup

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Methodology of maximizing probability of start-up success by eliminating waste by repeatedly finding the right idea by talking to customers and honing it into a vision. Avoiding assumption it’s the actually the idea that wins. Building a minimal product (/service) iteratively by defining riskiest assumption Lean Startup

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Lean Startup Methodology of maximizing probability of start-up success by eliminating waste by repeatedly finding the right idea by talking to customers and honing it into a vision. Avoiding assumption it’s the actually the idea that wins. Building a minimal product (/service) iteratively by defining riskiest assumption, and validating it by devising a ”scientific experiment”.

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Lean Startup Methodology of maximizing probability of start-up success by eliminating waste by repeatedly finding the right idea by talking to customers and honing it into a vision. Avoiding assumption it’s the actually the idea that wins. Building a minimal product (/service) iteratively by defining riskiest assumption, and validating it by devising a ”scientific experiment”. Eventually, if you’re lucky

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Lean Startup Methodology of maximizing probability of start-up success by eliminating waste by repeatedly finding the right idea by talking to customers and honing it into a vision. Avoiding assumption it’s the actually the idea that wins. Building a minimal product (/service) iteratively by defining riskiest assumption, and validating it by devising a ”scientific experiment”. Eventually, if you’re lucky, you’ll have ”product/market fit”

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Lean Startup Methodology of maximizing probability of start-up success by eliminating waste by repeatedly finding the right idea by talking to customers and honing it into a vision. Avoiding assumption it’s the actually the idea that wins. Building a minimal product (/service) iteratively by defining riskiest assumption, and validating it by devising a ”scientific experiment”. Eventually, if you’re lucky, you’ll have ”product/market fit” and can concentrate on optimizing and scaling.

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Good metric gives answers! No, that’s vanity!

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How to help with the dirty laundry? Build Product Measure Data Learn Ideas

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Week Users 1 1111 2 2666 3 3554 Vanity metric 0   500   1000   1500   2000   2500   3000   3500   4000   1   2   3  

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Less Vanity metric Week Users Signed in Weekly active users (WAU) 1 1111 1111 100% 2 2666 1888 71% 3 3554 1466 41% 0%   25%   50%   75%   100%   1   2   3   Weekly  ac)ve  users  

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Story so far: A new feature X was added and announced How do you know the new feature is any good?

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Week Users Signed up Signed in Used feature X Usefulness 1 1111 1111 1111 666 60% 2 2666 1555 1888 444 24% 3 3554 888 1466 333 23% Is the new feature useful? 0%   25%   50%   75%   100%   1   2   3   Usefulness   No! It should be removed!

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However, cohorts might tell a different story! Features are useful only if they deliver value if they are used!

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Cohort is a similar group of people. In this example, a group of people who started using at the same week. Week : 1 2 3 Cohort from week Signed up Signed in Used feature X Useful- ness Signed in Used feature X Useful- ness Signed in Used feature X Useful- ness 1 1111 1111 666 60% 333 167 50% 111 72 65% 2 1555 1555 277 18% 467 70 15% 3 888 888 191 21% Sums: 3554 1111 666 1888 444 1466 333

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Let’s simplify! Week : 1 2 3 Cohort from week Usefulness Usefulness Usefulness 1 60% 50% 65% 2 18% 15% 3 21%

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Week : 1 2 3 Cohort from week Usefulness Usefulness Usefulness 1 60% 50% 65% 2 18% 15% 3 21% 0%   25%   50%   75%   100%   1   2   3   Usefulness   All  users   Cohort  week  1   Is the new feature useful? Yes! First users use it!

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Week : 1 2 3 Cohort from week Usefulness Usefulness Usefulness 1 60% 50% 65% 2 18% 15% 3 21% Is the new feature useful? No! New users don’t find it! 0%   25%   50%   75%   100%   1   2   3   Usefulness   All  users   Cohort  week  1   Cohort  week  2   Cohort  Week  3  

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What could help? Announcing was effective Teach them Listen! New users don’t find it!

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We just learned a thing! Build Product Measure Data Learn Ideas

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Increase skillset and toolset by introducing a Data Scientist! How to come up with the data in Lean startup?

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Common pitfalls of metrics Counts Counts of unimportant things Measuring wrong things No baseline, no comparison Too many metrics Measured for fun, not to remove the pain

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Clinic  A  is  killing  45%  more   Simpson's   paradox   Treatment  successes   PorBons   Clinic  A   Hospital  B   Clinic  A   Hospital  B   Dead   26   9   Dead   2.6%   1.8%   Survived   974   492   Survived   97.4%   98.2%   Total   1000   501   Total   100.0%   100.0%  

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Cancer  –  Stage  A   Treatment  successes   PorBons   Clinic  A   Hospital  B   Clinic  A   Hospital  B   Dead   1   1   Dead   0.2%   0.3%   Survived   499   399   Survived   99.8%   99.8%   Total   500   400   Total   100.0%   100.0%   Cancer  –  Stage  B   Clinic  A   Klinikka  B   Clinic  A   Hospital  B   Dead   25   8   Dead   5.0%   7.9%   Survived   475   93   Survived   95.0%   92.1%   Total   500   101   Total   100.0%   100.0%   Hospital  B  is  killing     50%  more  on  Stage  A  and   Over  50%  more  on  Stage  B    

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Simpson's   paradox   Treatment  successes   PorBons   Clinic  A   Hospital  B   Clinic  A   Hospital  B   Dead   26   9   Dead   2.6%   1.8%   Survived   974   492   Survived   97.4%   98.2%   Total   1000   501   Total   100.0%   100.0%   Cancer  –  Stage  A   Treatment  successes   PorBons   Clinic  A   Hospital  B   Clinic  A   Hospital  B   Dead   1   1   Dead   0.2%   0.3%   Survived   499   399   Survived   99.8%   99.8%   Total   500   400   Total   100.0%   100.0%   Cancer  –  Stage  B   Clinic  A   Klinikka  B   Clinic  A   Hospital  B   Dead   25   8   Dead   5.0%   7.9%   Survived   475   93   Survived   95.0%   92.1%   Total   500   101   Total   100.0%   100.0%   Hospital  B  is   killing     50%  or  more   Clinic  A  is   killing     50%  more  

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Test case: How do you know you improved registration process? Possible metrics: 1)  Registrations are up 2)  More activity on the main functions 3)  Less page loads 4)  Less loss of people between ”Register” and registered 5)  Less %-loss between ”Register” and registered 6)  Less average time between Register and registered 7)  People report happy onboarding / Less compaints

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Time for Questions!

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Aleksi.Rossi@iki.fi @AlekRossi http://aleksirossi.com https://speakerdeck.com/rossi/LSC-Vienna-Metrics Thank you!

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http:// www.flickr.com/ photos/ visualpanic/ 2823427263/ sizes/l/in/ photostream/ http:// www.flickr.com/ photos/kikasz/ 6186223885/ sizes/l/in/ photostream/ http:// www.flickr.com/ photos/baboon/ 115446241/ sizes/l/in/ photostream/ http:// www.flickr.com/ photos/ ricksflicks/ 5348698725/ sizes/l/in/ photostream/ http:// www.flickr.com/ photos/ jornidzerda/ 4935388653/ sizes/l/in/ photostream/ http:// www.flickr.com/ photos/ visualpanic/ 3236219002/ sizes/l/in/ photostream/ http:// www.flickr.com/ photos/ le_plochingen/ 6595415697/ sizes/l/in/ photostream/ http:// www.flickr.com/ photos/ miuenski/ 5394654161/ http:// www.flickr.com/ photos/myxi/ 4327438430/ sizes/l/in/ photostream/ http:// www.flickr.com/ photos/ 29393867@N07/ 4286828753/ sizes/l/in/ photostream/