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Innovation accounting and key metrics for startups

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The traditional accounting in start-ups is usually incredibly simple - revenues, margins, free cash flow and other traditional accounting metrics are zero or very close to zero.

It is also impossible to do financial forecasts for start-ups (P&L, balance sheet,...) since accurate forecasting requires a long and stable operating history. Therefore a start-up must focus on the key metrics that show real progress in the search mode before becoming a stable business and use innovation accounting instead of traditional accounting as a framework for measuring performance.

The presentation covers the basics of being a data-driven organization, the difference between vanity, actionable and other types of metrics, why you should focus on one metrics that matter in different stages of a start-up, what are the common pitfalls when analyzing the data and how to use AARRR as the best framework for analytics, especially for web start-ups.

Published in: Business

Innovation accounting and key metrics for startups

  1. 1. MASTER OF STARTUPS SERIES INNOVATION ACCOUNTING AND KEY METIRCS FOR STARTUPS www.geekhouse.si/en
  2. 2. BLAZ KOS • 10+ years working with start-ups • Management of university incubator, technology park and business angel network • Management of two start-up accelerators and co-working space • PODIM Conference – one of the biggest conferences in Alps-Adriatic region • 600+ lectures in CEE • Mentored over 300 start-ups • Two handbooks about startups, 1500+ pages on two blogs I am on a life mission to make the world a more innovative, organized and transparent place to be by helping individuals, organizations and communities achieve their peak potential and an entirely new level of performance.
  3. 3. www.AgileLeanLife.com An entirely new level of personal performance.
  4. 4. HUMANS DO INSPIRATION MACHINES DO VALIDATION
  5. 5. The accounting in start-ups is incredibly simple Revenue, margins, free cash flow…
  6. 6. Accurate forecasting requires a long and stable operating history. The longer and more stable, the more accurate.
  7. 7. The purpose of analytics is to find your way to the right product and market before the money runs out.
  8. 8. Before PRODUCT – MARKET FIT apathy
  9. 9. A match between product and market segment that results in high growth or high demand. PRODUCT MARKET FIT
  10. 10. “In a great market - a market with lots of real potential customers – the market pulls product out of the start-up” “Conversely, in a terrible market, you can have the best product in the world and an absolutely killer team, and it doesn't matter - you're going to fail.“ Marc Andreesen You don‘t have to ask the question
  11. 11. NAIL IT SCALE IT
  12. 12. AFTER PRODUCT – MARKET FIT • Building a robust, feature-rich product • Crossing the chasm • Designing for virality & scalability • Scaling a sales force • Challenges with corporate partnerships • Building a brand • Scaling the exec team • Metrics, analytics, funnels
  13. 13.  Metrics reduce arguments based on opinion  Measure first, then manage/change  Answers about what really works  What you allow, you encourage  Metrics drive behaviour INSTINCTS ARE EXPERIMENTS. DATA IS PROOF. Source: Lean Analytics
  14. 14. You‘re forced to confront inconvenient truths. Avoid the feeling of fake progress.
  15. 15. • The only metrics that entrepreneurs should invest energy in collecting are those that help them make decisions.” — Eric Ries, The Lean Startup VANITY METRICS ACTIONABLE METRICS
  16. 16. If all metric does it stroke your ego, it won‘t help. WHAT WILL I DO DIFFERENTLY BASED ON THIS INFORMATION? If you can‘t answer the question you probably should not worry about the metric too much.
  17. 17. If you don‘t know which metrics would change your organization‘s behavior, you aren‘t data-driven.
  18. 18. Examples of Vanity Metrics • Number of visits • 1 person, 100 times or 100 people, 1 time • Number of unique visitors • What they did? • Number of likes • Time on site, number of pages • What if it is complants page? • Emails collected • Will they do what you tell them? • Downloads • Activation, account creation,… Source: Lean Analytics
  19. 19. Vanity (Feel Good).::. Actionable (Behavior) Qualitative (Insight) .::. Quantitive (Numbers) Exploratory (Speculative) .::. Reporting (Managerial) Leading (Predictive) .::. Lagging (Past) Correlated .::. Casual (One affects other) Different types of metric Source: Lean Analytics
  20. 20. Good metrics • Comparative • Understandable • Ratio (1:5) or rate (per sth) • Changes your behavoiur • Otherwise you waste your time • Fool yourself in false progress Source: Lean Analytics
  21. 21. Actionable metrics • Success at your core business • Directly relates to revenue • Track individual customers • Illustrate cause and effect • Lead you to what to do next Source: Lean Analytics
  22. 22. Actionable metrics aren‘t magic. They won‘t tell you what to do. The point is that you‘re doing something based on the data you collect.
  23. 23. An organization drowning in data is little better off than one without data. Capture everything, but focus on what‘s important.
  24. 24. On what to focus? • Riskiest areas of your business • Clear goals • Focuses entire company • Inspires a culture of experimentation
  25. 25. Using data to optimize one part of your business, without stepping back and looking at the big pictue, can be dangerous or even fatal. • Public image • Long term goals
  26. 26. OMTM (Changes over time) • It answers the most important question you have • It forces you to draw a line in the sand • It focuses the entire company • It inspires the culture of innovation Simple (number) Immediate (generate it every night) Actionable (change behaviour) Comparable (track it over time)
  27. 27. 10 COMMON PITFALLS • Assuming the data is clean (check the data) • Not normalizing (comparing apples to apples) • Excluding/Including outliers (qualitative perspective) • Ignoring seasonality • Ignoring size when reporting growth (beginnings) • Data vomit (no focus) • Metrics that cry wolf (to sensitive trash holds) • The „Not Collected Here“ syndrome (mashing up) • Focusing on noise (bigger picture) Source: Lean Analytics
  28. 28. David Cancel‘s Funnel
  29. 29.  Best – preforming (%)  Largest – volume (#)  Lowest – cost ($) 1 ACQUISITION CHANNEL
  30. 30. ACTIVATION From signup to their first happy experience of your product • Customers use product for first time • Entry page • First user experience • Product features • ACTIVATION GOALS: Click on something, Sign Up,… • Uptime, Page Load Time, Absence of bugs • Feature Set • Usability & Design • Pages per visit, Time on site, Conversion 2 Source: Dave McClure
  31. 31. ENGAGEMENT – STICKINESS RETENTION • KPI • Customer retention (what % comes back) • Churn rate • Usage frequency • One of the best predictors of success • Tactics to bring them back • Alerts, blogs, E-mail, Social networks,… 3 Source: Dave McClure
  32. 32. CHURN RATE The number of customers who leave in a given time period. 3
  33. 33. RETENTION MANAGEMENT • It takes 6 – 7 customers to replace existing one • If you can stop that one customer per leaving, you have doubled your revenue and cut your cost od customer acquisition in half • Companies who put in a customer retention program are 50% - 95% more profitable • Increasing retention (LTV) directly decreases CAC. Moving from 1% to 2% retention doubles revenue and halves CAC. 3 Source: Lean Analytics
  34. 34. How would you feel if you could no longer use this product or service? If 40%+ of people say they‘d be very disappointed to lose the service, you‘ve found a fit, and it‘s time to scale. 3
  35. 35. REFERRAL - VIRALITY • Viral coefficient • The number of new users that each user brings on • Viral cycle time • Speed with which a user invites another • Type of virality • Inherent: Built into product • Artificial: Forced through reward system • Word-of mouth: Satisfied users 4 Source: Dave McClure, Eric Ries
  36. 36. REFERRAL – VIRALITY What % of users recommend you • Make it easy for them to spread the word • Referral mechanisms • Social Media Sharing • Invite a friend type mechanics • App Reviews • Word of mouth, Contests • Affiliates • E-mails, Widgets, Viral loops 4 Source: Dave McClure
  37. 37. NET PROMOTER SCORE • How likely is that you would recommend our company to a friend or colleague on a scale from 0 – 10? • Promoters (9 – 10) • Passives (7 – 8) • Detractors (0 – 6) • NPS = % Promoters - % Detractors • NPS = 0 is good, NPS + 50 is excellent 4
  38. 38. REVENUE • What % of users become paying customers • Business Model
  39. 39. Metrics depand on your business model In every industry you have typical business models. • E-commerce • SaaS • Advertising • In-app purchases • User generated content (social media) • Two-sided marketplace Source: Lean Analytics
  40. 40. E-commerce • Conversion rate: N of visitors who buy sth • Purchases per year: N of purchases made by each customer per year • Average shooping chart size: Amount of money spent on a purchase • Abandonment: % of people who begin to make a purchase, then don‘t • CAC: Money spent to get soo to buy sth • LTV: Revenue per customer • Top keywords driving traffic to the site • Top Search Terms • Recommendation Engine: Added recommended produst • Virality: Sharing per visitor • Mailing list effetivnes Source: Lean Analytics
  41. 41. Saas • Attention: Attracting visitors • Enrollment: Free trial • Stickiness: Usage of product • Conversion: Paying customers • Revenue per customer: Money from customer in a time period • CAC: Cost of getting a paying user • Virality: Customers spreading the word • Upselling: How often customers increase spending • Uptime and reliability • Churn: How many users leave in a given time period • LTV: Worth of customers from cradle to grave Source: Lean Analytics
  42. 42. Advertising • Audience: How many people visit the site • Unique visitors • Engagement • Churn: How loyal is the audience • Ad Inventory: N of impressions that can be monitized • Number of unique page views • Ad rates (Cost per engagement): How much can a site make • CTR: Impressions turnes into money • Content/Advertising Balance: The balace of ad inventory rates and content that maximizes overall performance. Source: Lean Analytics
  43. 43. Free Mobile Application (gatekeeper hampers experimentations) • Business model: Downloadable content, New features (character appearance, advantages,…), elimination of countdown timers, Upselling to a paid version, in-Game Adds • Dawnloads • CAC • Launch rate: Downloaded, launch it, created account • % of active users: DAU and MAU, daily/monthly active users • % of paying users • Time to first purchase • APRU: Average monthly revenue per user • Ratings click through: % of users who put a ration or a review • Virality • Churn • LTV Source: Lean Analytics
  44. 44. User Generated Content • Number of engaged visitors: Stickiness • Day to week ration: How many today‘s vistors were on the site erlier in the week • Content creation • Engagenemtn funnel changes: How fast are becoming more engaged • Value of created content: Media Clicks • Content sharing and virality • Notification effectivness Source: Lean Analytics
  45. 45. Two Sided Market • Buyer and seller growth • Inventory growth: New listings, completeness of listings • Search effectivness • Conversion funnels and what helps sell items • Ratings and signs of fraud • Pricing metrics for bidding methods Source: Lean Analytics
  46. 46. COHORT ANALYSIS COHORT = Group of users Helps to measure user engagement over time. Growth -> Engagement Users who join you in the first week will have a different experience from those who join later on.
  47. 47. A/B & MULTIVARIATE TESTING • Cross-sectional study (vs. Longitudinal study)
  48. 48. Sustainable Growth Word of Mouth Paid advertising Repeat Use A Side Effect of Using the Product
  49. 49. Sticky Engine Viral Engine Paid Engine
  50. 50. POST LAUNCH GOAL Increase LTV = Improve value to customer • Add features • Improve features • Remove features • Find and foster loyalty behaviours Decrease CAC • Changes to messaging • Changes to marketing channels • Match experience and marketing • Optimize customer acquisition • Optimize funnel • Experience for new users
  51. 51. CAC < LTV (3x+) Months to recover CAC < 12 Months
  52. 52. STEP BY STEP! Empathy (Customer discovery) Stickiness (Retention) Virality (Referral) Revenue (Business Model) Scale (Explosive Growth) Source: Eric Ries
  53. 53. Product-market fit cures many sins of management.
  54. 54. You need to build an institution. The product is not enough. FINAL GOAL: Long term sustainable business Cash is king.
  55. 55. Sources and additional resources • http://blog.startupprofessionals.com/2012/11/10-key-metrics-to-take-startup-to-next.html • http://www.bothsidesofthetable.com/2011/04/04/how-startups-can-use-metrics-to-drive-success/ • http://www.slideshare.net/dmc500hats/startup-metrics-for-pirates-nov-2012 • http://blog.kissmetrics.com/startup-analytics/ • http://www.youtube.com/watch?v=gU3MZXW3uXw • http://vimeo.com/53186912
  56. 56. (sources and references) Learn More Steve Blank @sgblank http://steveblank.com/ Eric Ries @EricRies http://startuplessonslearned.com/ Ash Maurya @ashmaurya http://ashmaurya.com/ Dave McClure @davemcclure http://davemcclure.com/ Alex Osterwalder @AlexOsterwalder http://alexosterwalder.com Brad Feld @bfeld http://feld.com/

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