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Lean Analytics and Local Government - Alistair Croll - Code for America

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Slides from a webinar on applying Lean Startup and Analytics to local government initiatives held April 22, 2013 by Code For America.

Slides from a webinar on applying Lean Startup and Analytics to local government initiatives held April 22, 2013 by Code For America.

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  • 1. April 22, 2013 LEAN ANALYTICS & LOCAL GOV ALISTAIR CROLLTuesday, 23 April, 13
  • 2. Alistair Croll Co-Author, Lean AnalyticsTuesday, 23 April, 13
  • 3. Lean Analytics Use data to build a better business faster. www.leananalyticsbook.com @byosko | @acroll @leananalyticsTuesday, 23 April, 13
  • 4. Analytics is the measurement of movement towards your business goals. http://www.flickr.com/photos/itsgreg/446061432/Tuesday, 23 April, 13
  • 5. Most startups don’t know what they’ll be when they grow up.Tuesday, 23 April, 13
  • 6. Most startups don’t know what they’ll be when they grow up. Paypal first built for PalmpilotsTuesday, 23 April, 13
  • 7. Most startups don’t know what they’ll be when they grow up. Freshbooks Paypal was invoicing first built for for a web Palmpilots design firmTuesday, 23 April, 13
  • 8. Most startups don’t know what they’ll be when they grow up. Freshbooks was invoicing Wikipedia Paypal for a web was to be first built for design firm written by Palmpilots experts onlyTuesday, 23 April, 13
  • 9. Most startups don’t know what they’ll be when they grow up. Freshbooks Mitel was invoicing Wikipedia was a Paypal lawnmower for a web was to be first built for company design firm written by Palmpilots experts onlyTuesday, 23 April, 13
  • 10. Most startups don’t know what they’ll be when they grow up. Freshbooks Mitel was invoicing Wikipedia was a Paypal lawnmower for a web was to be first built for company design firm written by Palmpilots experts only Hotmail was a database companyTuesday, 23 April, 13
  • 11. Most startups don’t know what they’ll be when they grow up. Freshbooks Mitel was invoicing Wikipedia was a Paypal lawnmower for a web was to be first built for company design firm written by Palmpilots experts only Flickr Hotmail was going to was a be an MMO database companyTuesday, 23 April, 13
  • 12. Most startups don’t know what they’ll be when they grow up. Freshbooks Mitel was invoicing Wikipedia was a Paypal lawnmower for a web was to be first built for company design firm written by Palmpilots experts only Flickr Hotmail Twitter was going to was a was a be an MMO database podcasting company companyTuesday, 23 April, 13
  • 13. Most startups don’t know what they’ll be when they grow up. Freshbooks Mitel was invoicing Wikipedia was a Paypal lawnmower for a web was to be first built for company design firm written by Palmpilots experts only Flickr Hotmail Twitter Autodesk was going to was a was a made desktop be an MMO database podcasting automation company companyTuesday, 23 April, 13
  • 14. Kevin Costner is a lousy entrepreneur. Don’t sell what you can make. Make what you can sell.Tuesday, 23 April, 13
  • 15. Tuesday, 23 April, 13
  • 16. The basic Lean message is learn and adapt, fast.Tuesday, 23 April, 13
  • 17. “What information consumes is rather obvious: it consumes the attention of its recipients. Hence a wealth of information creates a poverty of attention, and a need to allocate that attention efficiently among the overabundance of information sources that might consume it.” (Computers, Communications and the Public Interest, pages 40-41, Martin Greenberger, ed., The Johns Hopkins Press, 1971.)Tuesday, 23 April, 13
  • 18. http://www.flickr.com/photos/art_es_anna/288880795/Tuesday, 23 April, 13
  • 19. Tuesday, 23 April, 13
  • 20. Tuesday, 23 April, 13
  • 21. Tuesday, 23 April, 13
  • 22. Lean Analytics lesson 1: Most government projects have an attention or a connectivity problem. This is where you will spend most of your time innovating.Tuesday, 23 April, 13
  • 23. Empathy stage: Localmind hacks Twitter • Stage: Empathy • Model: UGC/mobile • Real-time question and answer platform tied to locations. • Needed to find out if a core behavior—answering questions about a place— happened enough to make the business realTuesday, 23 April, 13
  • 24. Localmind hacks Twitter • Before writing a line of code, Localmind was concerned that people would never answer questions. • This was their biggest risk: if questions went unanswered users would have a terrible experience and stop using Localmind. • Ran an experiment on Twitter • Tracked geolocated tweets in Times Square • Sent @ messages to people who had just tweeted, asking questions about the area: how busy is it; is the subway running on time; is something open; etc. • The response rate to their tweeted questions was very high. • Good enough proxy to de-risk the solution, and convince the team and investors that it was worth building Localmind.Tuesday, 23 April, 13
  • 25. Tuesday, 23 April, 13
  • 26. A metric from the early, foolish days of the Web. Hits Count people instead.Tuesday, 23 April, 13
  • 27. A metric from the early, foolish days of the Web. Hits Count people instead. Marginally better than hits. Unless you’re displaying Page views ad inventory, count people.Tuesday, 23 April, 13
  • 28. A metric from the early, foolish days of the Web. Hits Count people instead. Marginally better than hits. Unless you’re displaying Page views ad inventory, count people. Is this one person visiting a hundred times, or are a Visits hundred people visiting once? Fail.Tuesday, 23 April, 13
  • 29. A metric from the early, foolish days of the Web. Hits Count people instead. Marginally better than hits. Unless you’re displaying Page views ad inventory, count people. Is this one person visiting a hundred times, or are a Visits hundred people visiting once? Fail. This tells you nothing about what they did, why they Unique visitors stuck around, or if they left.Tuesday, 23 April, 13
  • 30. A metric from the early, foolish days of the Web. Hits Count people instead. Marginally better than hits. Unless you’re displaying Page views ad inventory, count people. Is this one person visiting a hundred times, or are a Visits hundred people visiting once? Fail. This tells you nothing about what they did, why they Unique visitors stuck around, or if they left. Followers/ Count actions instead. Find out how many followers friends/likes will do your bidding.Tuesday, 23 April, 13
  • 31. A metric from the early, foolish days of the Web. Hits Count people instead. Marginally better than hits. Unless you’re displaying Page views ad inventory, count people. Is this one person visiting a hundred times, or are a Visits hundred people visiting once? Fail. This tells you nothing about what they did, why they Unique visitors stuck around, or if they left. Followers/ Count actions instead. Find out how many followers friends/likes will do your bidding. Time on site, or Poor version of engagement. Lots of time spent on pages/visit support pages is actually a bad sign.Tuesday, 23 April, 13
  • 32. A metric from the early, foolish days of the Web. Hits Count people instead. Marginally better than hits. Unless you’re displaying Page views ad inventory, count people. Is this one person visiting a hundred times, or are a Visits hundred people visiting once? Fail. This tells you nothing about what they did, why they Unique visitors stuck around, or if they left. Followers/ Count actions instead. Find out how many followers friends/likes will do your bidding. Time on site, or Poor version of engagement. Lots of time spent on pages/visit support pages is actually a bad sign. How many recipients will act on what’s in them? Emails collectedTuesday, 23 April, 13
  • 33. A metric from the early, foolish days of the Web. Hits Count people instead. Marginally better than hits. Unless you’re displaying Page views ad inventory, count people. Is this one person visiting a hundred times, or are a Visits hundred people visiting once? Fail. This tells you nothing about what they did, why they Unique visitors stuck around, or if they left. Followers/ Count actions instead. Find out how many followers friends/likes will do your bidding. Time on site, or Poor version of engagement. Lots of time spent on pages/visit support pages is actually a bad sign. How many recipients will act on what’s in them? Emails collected Number of Outside app stores, downloads alone don’t lead to downloads lifetime value. Measure activations/active accounts.Tuesday, 23 April, 13
  • 34. 2-sided market model: AirBnB and photography • Stage: Revenue • Model: 2-sided marketplace • Rental-by-owner marketplace that allows property owners to list and market their houses. Offers a variety of related services as well.Tuesday, 23 April, 13
  • 35. AirBnB tests a hypothesis • The hypothesis: “Hosts with professional photography will get more business. And hosts will sign up for professional photography as a service.” • Built a concierge MVP • Found that professionally photographed listings got 2-3x more bookings than the market average. • In mid-to-late 2011, AirBnB had 20 photographers in the field taking pictures for hosts.Tuesday, 23 April, 13
  • 36. NIGHTS BOOKED 10 million 8 million 6 million 20 photographers 4 million 2 million 2008 2009 2010 2011 2012Tuesday, 23 April, 13Friday, November 9, 12
  • 37. Pick the right experiments http://www.flickr.com/photos/bootbearwdc/1243690099/Tuesday, 23 April, 13
  • 38. http://www.flickr.com/photos/circasassy/7858155676/ If it won’t change how you behave, it’s a bad metric. Tuesday, 23 April, 13
  • 39. The five Stages of Lean AnalyticsTuesday, 23 April, 13
  • 40. The five Stages of Lean Analytics The stage you’re atTuesday, 23 April, 13
  • 41. The five Stages of Lean Analytics Empathy The stage you’re atTuesday, 23 April, 13
  • 42. The five Stages of Lean Analytics Empathy The stage you’re at StickinessTuesday, 23 April, 13
  • 43. The five Stages of Lean Analytics Empathy The stage you’re at Stickiness ViralityTuesday, 23 April, 13
  • 44. The five Stages of Lean Analytics Empathy The stage you’re at Stickiness Virality RevenueTuesday, 23 April, 13
  • 45. The five Stages of Lean Analytics Empathy The stage you’re at Stickiness Virality Revenue ScaleTuesday, 23 April, 13
  • 46. The five Stages of Lean Analytics The business you’re in E- 2-sided Mobile User-gen SaaS Media commerce market app content Empathy The stage you’re at Stickiness Virality Revenue ScaleTuesday, 23 April, 13
  • 47. The five Stages of Lean Analytics The business you’re in E- 2-sided Mobile User-gen SaaS Media commerce market app content Empathy The stage you’re at One Metric Stickiness Virality Revenue That Matters. ScaleTuesday, 23 April, 13
  • 48. Lean Analytics lesson 2: Choose one metric around which to rally support, and reject vanity metrics ruthlessly.Tuesday, 23 April, 13
  • 49. Choose only one metric.Tuesday, 23 April, 13
  • 50. Metrics are like squeeze toys. http://www.flickr.com/photos/connortarter/4791605202/Tuesday, 23 April, 13
  • 51. Metrics in practice: The Lean Analytics CycleTuesday, 23 April, 13
  • 52. Metrics in practice: The Lean Analytics Cycle Pick OMTMTuesday, 23 April, 13
  • 53. Metrics in practice: The Lean Analytics Cycle Pick OMTM Draw a line in the sandTuesday, 23 April, 13
  • 54. Metrics in practice: The Lean Analytics Cycle Pick OMTM Draw a line in the sand Find a potential improvementTuesday, 23 April, 13
  • 55. Metrics in practice: The Lean Analytics Cycle Pick OMTM Draw a line in the sand Find a potential improvement Without data: make a good guessTuesday, 23 April, 13
  • 56. Metrics in practice: The Lean Analytics Cycle Pick OMTM Draw a line in the sand Find a potential improvement Without With data: data: make a find a good guess commonalityTuesday, 23 April, 13
  • 57. Metrics in practice: The Lean Analytics Cycle Pick OMTM Draw a line in the sand Find a potential improvement Without With data: data: make a find a good guess commonality HypothesisTuesday, 23 April, 13
  • 58. Metrics in practice: The Lean Analytics Cycle Pick OMTM Draw a line in the sand Find a potential improvement Without With data: data: make a find a good guess commonality Hypothesis Make changes in productionTuesday, 23 April, 13
  • 59. Metrics in practice: The Lean Analytics Cycle Pick OMTM Draw a line in the sand Find a potential improvement Without With data: data: make a find a good guess commonality Design a test Hypothesis Make changes in productionTuesday, 23 April, 13
  • 60. Metrics in practice: The Lean Analytics Cycle Pick OMTM Draw a line in the sand Find a potential improvement Without With data: data: make a find a good guess commonality Design a test Measure the results Hypothesis Make changes in productionTuesday, 23 April, 13
  • 61. Metrics in practice: The Lean Analytics Cycle Pick OMTM Draw a line in the sand Find a potential improvement Did we move the needle? Without With data: data: make a find a good guess commonality Design a test Measure the results Hypothesis Make changes in productionTuesday, 23 April, 13
  • 62. Metrics in practice: The Lean Analytics Cycle Success! Pick OMTM Draw a line in the sand Find a potential improvement Did we move the needle? Without With data: data: make a find a good guess commonality Design a test Measure the results Hypothesis Make changes in productionTuesday, 23 April, 13
  • 63. Metrics in practice: The Lean Analytics Cycle Success! Pick OMTM Draw a line in the sand Pivot or give up Find a potential improvement Did we move the needle? Without With data: data: make a find a good guess commonality Design a test Measure the results Hypothesis Make changes in productionTuesday, 23 April, 13
  • 64. Metrics in practice: The Lean Analytics Cycle Success! Pick OMTM Draw a line in the sand Pivot or give up Draw a new line Find a potential improvement Did we move the needle? Without With data: data: make a find a good guess commonality Design a test Measure the results Hypothesis Make changes in productionTuesday, 23 April, 13
  • 65. Metrics in practice: The Lean Analytics Cycle Success! Pick OMTM Draw a line in the sand Pivot or give up Draw a new line Find a potential Try again improvement Did we move the needle? Without With data: data: make a find a good guess commonality Design a test Measure the results Hypothesis Make changes in productionTuesday, 23 April, 13
  • 66. Lean Analytics lesson 3: There’s no “finished.” Just more iterations.Tuesday, 23 April, 13
  • 67. The B2B stereotype • Domain expert knows industry and the problem domain. Has a Rolodex; proxy for customers. http://www.techdigest.tv/2007/02/im_a_pc_im_a_ma.html • Disruption expert knows tech that will produce a change Sees beyond the current model. Domain Disruption expert expert OperationsTuesday, 23 April, 13
  • 68. The B2B stereotype • Domain expert knows industry and the problem domain. Has a Rolodex; proxy for customers. http://www.techdigest.tv/2007/02/im_a_pc_im_a_ma.html • Disruption expert knows tech that will produce a change Sees beyond the current model. Domain Disruption expert expert OperationsTuesday, 23 April, 13
  • 69. Three typical approaches Create a popular consumer Dropbox Enterprise pivot product then pivot to tackle the enterprise Take an existing consumer or Yammer, Copy and rebuild open source idea and make it MapR enterprise-ready Convince the enterprise to Taleo, Disrupt a problem discard the old way because of Google overwhelming advantages. AppsTuesday, 23 April, 13
  • 70. Lean Analytics lifecycle for an enterprise-focused startup Stage Do this Fear this Consulting to test ideas and Lock-in, IP Empathy bootstrap the business control, overfittingTuesday, 23 April, 13
  • 71. Lean Analytics lifecycle for an enterprise-focused startup Stage Do this Fear this Consulting to test ideas and Lock-in, IP Empathy bootstrap the business control, overfitting Standardization and integration; Ability to Stickiness shift from custom to generic integrate; supportTuesday, 23 April, 13
  • 72. Lean Analytics lifecycle for an enterprise-focused startup Stage Do this Fear this Consulting to test ideas and Lock-in, IP Empathy bootstrap the business control, overfitting Standardization and integration; Ability to Stickiness shift from custom to generic integrate; support Word of mouth, references, case Bad vibes; Virality studies exclusivityTuesday, 23 April, 13
  • 73. Lean Analytics lifecycle for an enterprise-focused startup Stage Do this Fear this Consulting to test ideas and Lock-in, IP Empathy bootstrap the business control, overfitting Standardization and integration; Ability to Stickiness shift from custom to generic integrate; support Word of mouth, references, case Bad vibes; Virality studies exclusivity Growing direct sales, professional Pipeline, revenue Revenue services, support recognition, compTuesday, 23 April, 13
  • 74. Lean Analytics lifecycle for an enterprise-focused startup Stage Do this Fear this Consulting to test ideas and Lock-in, IP Empathy bootstrap the business control, overfitting Standardization and integration; Ability to Stickiness shift from custom to generic integrate; support Word of mouth, references, case Bad vibes; Virality studies exclusivity Growing direct sales, professional Pipeline, revenue Revenue services, support recognition, comp Channels, analysts, ecosystems, Crossing the Scale APIs, vertically targeted products chasm; GorillasTuesday, 23 April, 13
  • 75. The Zero Overhead principle A central theme to this new wave of innovation is the application of core product tenets from the consumer space to the enterprise. In particular, a universal lesson that I keep sharing with all entrepreneurs building for the enterprise is the Zero Overhead Principle: no feature may add training costs to the user. DJ PatilTuesday, 23 April, 13
  • 76. Lean Analytics lesson 4: Government can learn from enterprise-focused startups: Disrupt a known problem with new technology.Tuesday, 23 April, 13
  • 77. Skunk Works for intrapreneurs • The Lockheed Martin Skunk WorksTuesday, 23 April, 13
  • 78. Span of control and the railroads • Daniel C. McCallumTuesday, 23 April, 13
  • 79. The BCG matrix • How businesses think about products or Question marks! increase
 Pivot to 
 Stars! companies (low market share, market
 (high growth rate, share
 high growth rate) through
 high market share) May be the next big thing. virality,
 What everyone wants. As • Lean is about moving Consumes investment, but attention market invariably stops will require money to growing, should become up and to the right Growth rate increase market share. cash cows. Milk with
 Pivot to
 Pivot to
 revenue
 redefine problem/
 increase growth
 optimization as
 solution through
 rate through
 growth slows empathy disruption Dogs! Cash cows! (low market share, (high market share, low growth rate) low growth rate) Barely breaks even, may Boring sources of cash, to be a distraction from better be milked but not worth opportunities. Sell off or additional investment. shut down. Market shareTuesday, 23 April, 13
  • 80. BCG and policy Widely popular Public support Widely ridiculed Tiny Impact on society HugeTuesday, 23 April, 13
  • 81. BCG and policy Widely popular Public support Widely Pork ridiculed Tiny Impact on society HugeTuesday, 23 April, 13
  • 82. BCG and policy Widely popular Public support Banning leaded gasoline Widely Pork ridiculed Tiny Impact on society HugeTuesday, 23 April, 13
  • 83. BCG and policy Widely popular “I Declare today Jebbediah Springfield Public support day” Banning leaded gasoline Widely Pork ridiculed Tiny Impact on society HugeTuesday, 23 April, 13
  • 84. BCG and policy Widely popular “I Declare today Jebbediah Springfield Public support day” Banning leaded gasoline No more big soda Widely Pork ridiculed Tiny Impact on society HugeTuesday, 23 April, 13
  • 85. The Lean Analytics lifecycle for an Intrapreneur Stage Do this Fear this Get buy-in Political fallout Beforehand Find problems; don’t test demand. Entitled, aggrieved Empathy Skip the business case, do analytics customersTuesday, 23 April, 13
  • 86. The Lean Analytics lifecycle for an Intrapreneur Stage Do this Fear this Get buy-in Political fallout Beforehand Find problems; don’t test demand. Entitled, aggrieved Empathy Skip the business case, do analytics customers Know your real minimum based on Hidden “must haves”, Stickiness expectations, regulations feature creepTuesday, 23 April, 13
  • 87. The Lean Analytics lifecycle for an Intrapreneur Stage Do this Fear this Get buy-in Political fallout Beforehand Find problems; don’t test demand. Entitled, aggrieved Empathy Skip the business case, do analytics customers Know your real minimum based on Hidden “must haves”, Stickiness expectations, regulations feature creep Build inherent virality in from the Luddites who don’t Virality start; attention is the new currency understand sharingTuesday, 23 April, 13
  • 88. The Lean Analytics lifecycle for an Intrapreneur Stage Do this Fear this Get buy-in Political fallout Beforehand Find problems; don’t test demand. Entitled, aggrieved Empathy Skip the business case, do analytics customers Know your real minimum based on Hidden “must haves”, Stickiness expectations, regulations feature creep Build inherent virality in from the Luddites who don’t Virality start; attention is the new currency understand sharing Consider the ecosystem, channels, Channel conflict, Revenue and established agreements resistance, contractsTuesday, 23 April, 13
  • 89. The Lean Analytics lifecycle for an Intrapreneur Stage Do this Fear this Get buy-in Political fallout Beforehand Find problems; don’t test demand. Entitled, aggrieved Empathy Skip the business case, do analytics customers Know your real minimum based on Hidden “must haves”, Stickiness expectations, regulations feature creep Build inherent virality in from the Luddites who don’t Virality start; attention is the new currency understand sharing Consider the ecosystem, channels, Channel conflict, Revenue and established agreements resistance, contracts Hand the baton to others gracefully Hating what happens Scale to your babyTuesday, 23 April, 13
  • 90. Lean Analytics lesson 5: When working from within, the difference between a special operative and a rogue agent is a mandate.Tuesday, 23 April, 13
  • 91. E-commerce enterprise • Stage: Scale • Model: e-commerce • EMI was a big music company trying to understand how its customers bought contentTuesday, 23 April, 13
  • 92. David Boyle tackles a small problem • David Boyle, SVP Insight, EMI Music, ran the insight group • Had billions of rows of data, but nobody wanted to analyze it • Instead started a survey project, got a million responses, used this data to sell the idea of “data-driven” business • Then got support for the broader data initiative.Tuesday, 23 April, 13
  • 93. David Boyle tackles a small problem • Talked to 1M people in 3y • At any point, surveying 12 people in the world • Boiled this down to a few fundamental profilesTuesday, 23 April, 13
  • 94. Lean Analytics lesson 6: Start with a less important project (preferably one that involves intelligence gathering.) Then use that success for bigger undertakings.Tuesday, 23 April, 13
  • 95. Where your lean lives: It’s learning: Places with lots of data It’s rapidly iterated: Apps or software It’s popularizing: Moving up a box It’s impact-increasing: Moving right a box It has high uncertainty: De-risk with an MVP It’s boundable: Lean hates molassesTuesday, 23 April, 13
  • 96. Pic by Twodolla on Flickr. http://www.flickr.com/photos/twodolla/3168857844Tuesday, 23 April, 13
  • 97. ARCHIMEDES HAD TAKEN BATHS BEFORE.Tuesday, 23 April, 13
  • 98. Once, a leader convinced others in the absence of data.Tuesday, 23 April, 13
  • 99. Now, a leader knows what questions to ask.Tuesday, 23 April, 13
  • 100. Ben Yoskovitz byosko@gmail.com @byosko Alistair Croll acroll@gmail.com @acrollTuesday, 23 April, 13
  • 101. Thank you! Further discussion: muni-innovation@googlegroups.com Contact staff: pn-staff@codeforamerica.orgTuesday, 23 April, 13

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