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How to Optimize Your Product and Business Using Analytics by Dan Olsen

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Talk I gave on analytics at the Traction Conference May 31, 2017

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How to Optimize Your Product and Business Using Analytics by Dan Olsen

  1. 1. How to Optimize Your Product and Business Using Analytics Dan Olsen May 31, 2017
  2. 2. My Background Educa/on n  Engineering background n  Stanford MBA n  UX design and coding Experience n  Product Management leader at Intuit n  CEO & Cofounder, TechCrunch award winner YourVersion n  PM consultant: Box, Facebook, MicrosoK, Medallia n  Founder: Lean Product Silicon Valley TwiMer: @danolsen My slides: hMp://dan-olsen.com Copyright © 2017 @danolsen
  3. 3. The 3 Phases of Your Product Copyright © 2017 @danolsen BEFORE Product-Market Fit AFTER Product-Market Fit BEFORE your product is live Phase 1 AFTER your product is live Phase 2 # of Customers Time 0 lots Phase 3
  4. 4. Quan/ta/ve vs. Qualita/ve Learning Qualita/ve Quan/ta/ve Oprah Spock
  5. 5. The 3 Phases of Your Product BEFORE Product-Market Fit AFTER Product-Market Fit Growth Mainly Quan/ta/ve BEFORE your product is live Phase 1 Test hypotheses, gain confidence before building Mainly Qualita/ve AFTER your product is live Phase 2 Improve product- market fit Qualita/ve & Quan/ta/ve Goal: Methods: Phase 3 Which phase are you in?
  6. 6. n  Book giveaway on TwiMer n  Tweet: include @danolsen n  Hashtags #leanstartup #prodmgmt #growth n  hMp:///ny.cc/LPP That’s Why I Wrote Copyright © 2017 @danolsen
  7. 7. What is Product-Market Fit?
  8. 8. The Product-Market Fit Pyramid
  9. 9. The Product-Market Fit Pyramid
  10. 10. The Product-Market Fit Pyramid
  11. 11. The Product-Market Fit Pyramid
  12. 12. The Lean Product Process
  13. 13. The Lean Product Process
  14. 14. The Lean Product Process
  15. 15. The Lean Product Process
  16. 16. The Lean Product Process
  17. 17. The Lean Product Process
  18. 18. The Lean Product Process
  19. 19. The Lean Product Process
  20. 20. Quan/ta/ve vs. Qualita/ve Learning Qualita/ve Quan/ta/ve Oprah Spock
  21. 21. Qualita/ve Complements Quan/ta/ve Copyright © 2017 Olsen Solu/ons Qual Why? Quant What? How many?
  22. 22. Learning Methods Courtesy of Christian Rohrer, http://xdstrategy.com
  23. 23. HOW TO CREATE PRODUCTS FOR GROWTH
  24. 24. Iden/fy highest ROI idea Design and Implement Analyze How the Metric Changes Brainstorm Ideas to Improve Metric Copyright © 2017 @danolsen Lean Product Analy/cs Process Iden/fy What Your Metrics Are Measure Metrics Baseline Values Evaluate Metrics Upside Poten/al Global Level Metric Level Select Top Metric Learn & Iterate
  25. 25. Valuable to Have a Holis/c Analy/cs Framework: Dave McClure’s AARRR!
  26. 26. Focus on right metric at right /me Iden/fy your “Metric that MaMers Most” (MTMM) Copyright © 2017 @danolsen
  27. 27. What is your “Metric that MaMers Most”?
  28. 28. What’s the best order to op/mize these 5 metrics? (in general)
  29. 29. Copyright © 2017 @danolsen What’s the best order to op/mize? acquisi/on conversion reten/on
  30. 30. Conversion: What % of the water (prospec/ve customers) do you get into the bucket? prospective customers customers
  31. 31. What great Conversion looks like
  32. 32. Acquisi9on: How much water (prospec/ve customers) are you spraying? prospective customers customers
  33. 33. What great Acquisi9on looks like
  34. 34. Reten9on: What % of the water (customers) do you keep in the bucket over /me?
  35. 35. What great Reten9on looks like
  36. 36. If you could only track 1 metric to measure your Product-Market Fit, which metric would it be? Copyright © 2017 @danolsen
  37. 37. Reten/on Rate n  Reten/on rate tracks what % of your customers are s/ll ac/ve over /me ~80% never use app again Curve eventually flattens out
  38. 38. Cohort Analysis Copyright © 2017 @danolsen
  39. 39. Cohort Analysis: Data Copyright © 2017 @danolsen
  40. 40. Improving Reten/on Rate Over Time= Increasing Product-Market Fit
  41. 41. Improving Reten/on Rate Over Time= Increasing Product-Market Fit
  42. 42. Improving Reten/on Rate Over Time= Increasing Product-Market Fit
  43. 43. Optimizing Revenue Copyright © 2017 @danolsen
  44. 44. Profit = Revenue - Cost Unique Visitors x Ad Revenue per Visitor Impressions/Visitor x Effec/ve CPM / 1000 Visits/Visitor x Pageviews/Visit x Impressions/PV New Visitors + Returning Visitors Invited Visitors + Uninvited Visitors # of Users Sending Invites x Invites Sent/User x Invite Conversion Rate Define the Equa/on of your Business “Peeling the Onion” Adver/sing Business Model: Copyright © 2017 @danolsen
  45. 45. Copyright © 2017 @danolsen ( SEO Visitors + SEM Visitors + Viral Visitors ) x Trial Conversion Rate Paying Users x Revenue per Paying User New Paying Users + Repeat Paying Users Previous Paying Users x ( 1 – Cancella/on Rate ) Trial Users x Conv Rate Profit = Revenue - Cost Equa/on of your Business: Subscrip/on Business Model
  46. 46. How to Track Your Metrics n  Track each metric as daily /me series n  Create ra/os from primary metrics: X / Y n  Example: How good is your registra/on page? n  Okay: # of registered users per day n  BeMer: registra/on conversion rate = # registered users / # uniques to reg page Date Unique Visitors Page views Ad Revenue New User Sign-ups … 4/24/08 10,100 29,600 25 490 4/25/08 10,500 27,100 24 480 … Copyright © 2017 @danolsen
  47. 47. Registra/on Page Conversion Rate Daily Signup Page Yield vs. Time New Registered Users divided by Unique Visitors to Signup Page 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 1/31 2/14 2/28 3/14 3/28 4/11 4/25 5/9 5/23 6/6 6/20 7/4 7/18 8/1 8/15 8/29 9/12 9/26 10/1 0 DailySignupPageYield Changed messaging Added questions to signup page Started requiring registration Copyright © 2017 @danolsen Registration Page Conversion Rate vs. Time RegistrationPageConversionRate
  48. 48. View Each Metric as a Gauge Copyright © 2017 @danolsen Minimum Possible Value Maximum Possible Value Current Value
  49. 49. Copyright © 2017 @danolsen Priori/zing Product Ideas by ROI Investment (developer-weeks) Return (Value Created) Idea C Idea B Idea D Idea A Idea F 1 1 2 3 4 2 3 4 ?
  50. 50. Iden/fying the “Cri/cal Few” Metrics n  What is the upside poten/al of each metric? n  How many resources will it take to “move the needle”? n  Developer-days, /me, money n  How much will the needle move? Revenue impact? n  Which metrics have highest ROI opportuni/es? Return Investment Return Investment Return Investment Metric A Good ROI Metric B Bad ROI Metric C Great ROI Copyright © 2017 @danolsen
  51. 51. Case Study Copyright © 2017 @danolsen
  52. 52. Abandonment Rate (7 Day Moving Average) 0% 10% 20% 30% 40% 50% 60% 70% 80% 10/7/02 10/14/02 10/21/02 10/28/02 11/4/02 11/11/02 11/18/02 11/25/02 12/2/02 12/9/02 12/16/02 12/23/02 12/30/02 1/6/03 1/13/03 1/20/03 AbandonmentRate(7DayMovingAverage) Steps 1-2 Copyright © 2017 @danolsen Case Study: Account Signup Process Redesign
  53. 53. Conversion Rate Copyright © 2017 @danolsen Users exposed to ability to take ac/on Users that took ac/on 100% x% You can track many the conversion rates for many different ac/ons
  54. 54. Copyright © 2017 @danolsen Analyzed Drop-Off at Each Major Sec/on 100% 62.3% 58.8% 50.9% 34.4% 32.7% 0% 20% 40% 60% 80% 100% % of Users Sign in / Registra/on Account Type Cash vs. Margin 5 Partner Pages 3 Partner Pages Focus on biggest drop
  55. 55. Copyright © 2017 @danolsen Open Account Sign in Account Selec/on Register 56% 44% Forget Password Registra/on Process 45% drop off (20% of total) 36% overall drop off for this step 70% (32% of Total) 17% drop off (10% of total) 20% drop off (6% of total) 30% (14% of Total) 80% (26% of Total) 55% (24% of Total) 64% of Total Analysis of Sign In/Registra/on Flow Change Password 83% (46% of Total)
  56. 56. Abandonment Rate (7 Day Moving Average) 0% 10% 20% 30% 40% 50% 60% 70% 80% 10/7/02 10/14/02 10/21/02 10/28/02 11/4/02 11/11/02 11/18/02 11/25/02 12/2/02 12/9/02 12/16/02 12/23/02 12/30/02 1/6/03 1/13/03 1/20/03 AbandonmentRate(7DayMovingAverage) Steps 1-2 Copyright © 2017 @danolsen Redesigned User Flow Improved Registra/on Conversion Rate 37% improvement in conversion rate Released New Design
  57. 57. Google Analy/cs Goal Funnel Visualiza/on Copyright © 2017 @danolsen
  58. 58. Case Study Copyright © 2017 @danolsen
  59. 59. •  Which metric has highest ROI opportunity? Case Study: Op/mizing Friendster’s Viral Loop Active Users Prospective Users Invite Click Succeed Invite click-through rate Conversion rate Don’t Click Fail Invites per sender % of users sending invites •  Mul/plied together, these metrics determine your viral ra/o Users % of users who are active = 15% = 2.3 = 85% Registration Process Copyright © 2017 @danolsen
  60. 60. The Upside Poten/al of a Metric 0 100% 0 100% 0 ? Registra/on Process Yield % of users sending invita/ons Avg # of invites sent per sender 2.3 85% 15% Max possible improvement 0.15 / 0.85 = 18% 0.85 / 0.15 = 570% ? / 2.3 = ?% Copyright © 2017 @danolsen
  61. 61. Is anyone feeling a sense of déjà vu right now?
  62. 62. The Upside Poten/al of a Metric 0 100% 0 100% 0 ? Registra/on Process Yield % of users sending invita/ons Avg # of invites sent per sender 2.3 85% 15% Max possible improvement 0.15 / 0.85 = 18% 0.85 / 0.15 = 570% ? / 2.3 = ?% Copyright © 2017 @danolsen Metric B Bad ROI Metric A Good ROI Metric C Great ROI
  63. 63. Okay, so how can we improve the metric? n  How do we increase the average number of invites being sent out per sender? n  For each idea: n  What’s the expected benefit? (how much will it improve the metric?) n  What’s the expected cost? (how many engineer- days will it take?) n  You want to iden/fy highest ROI idea Copyright © 2017 @danolsen
  64. 64. AKer Launching Address Book Importer… Copyright © 2017 @danolsen
  65. 65. AKer Launching Address Book Importer… Copyright © 2017 @danolsen
  66. 66. AKer Launching Address Book Importer… Copyright © 2017 @danolsen
  67. 67. Copyright © 2017 @danolsen A/B Tes/ng
  68. 68. Profitability, anyone?
  69. 69. Profitability, anyone? To be profitable, you want: LTV – CAC > 0 Common SaaS Goal: LTV / CAC > 3 Two key metrics: •  Customer Life/me Value (LTV) •  Customer Acquisi/on Cost (CAC)
  70. 70. Life/me Value (LTV) n  Life/me value of a customer = how much value your average customer will generate n  LTV = ARPU x Avg Customer Life/me x Gross Margin n  ARPU (Avg Revenue / User) = Total Revenue / # of Users n  Average Customer Life/me n  How long your average customer generates revenue n  Equals 1 / churn rate (5% monthly churn = 20 months avg life) n  Gross Margin: the % of revenues leK over aKer subtrac/ng the cost of providing the product/service Copyright © 2017 @danolsen Note: for simplicity, this LTV equa/on ignores the “cost of capital”
  71. 71. Customer Acquisi/on Cost (CAC) n  CAC is the average cost for you to obtain a revenue-genera/ng customer n  Takes into account: n Your cost of acquiring a prospect (CPA) n Your conversion rate for conver/ng prospects to revenue-genera/ng customers n  CPA varies by channel; diminishing returns n  CAC=Cost per Acquisi/on / Conversion Rate Copyright © 2017 @danolsen
  72. 72. What You Want to See Over Time Copyright © 2017 @danolsen n  LTV increasing as you improve your value proposi/on, customer reten/on, & pricing n  CAC decreasing as you op/mize your marke/ng: segments, channels, messaging
  73. 73. Ra/o of LTV to CAC: Real data from HubSpot Copyright © 2017 @danolsen
  74. 74. Iden/fy highest ROI idea Design and Implement Analyze How the Metric Changes Brainstorm Ideas to Improve Metric Copyright © 2017 @danolsen Lean Product Analy/cs Process Iden/fy What Your Metrics Are Measure Metrics Baseline Values Evaluate Metrics Upside Poten/al Metric Level Select Top Metric Learn & Iterate Global Level
  75. 75. Copyright © 2017 @danolsen Op/mal Analy/cs Op/miza/on Process Reten/on Rate Conversion Rate Acquisi/on of Prospects Referral Improve product- market fit Revenue: Customer LTV Convert prospects to customers Increase ARPU Decrease churn Decrease CAC Scale marke/ng Op/mize viral loop Profitability: LTV > CAC
  76. 76. Questions? @danolsen http://dan-olsen.com dan@olsensolutions.com Book: http://tiny.cc/LPP

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