• Save
Making Sense of the Numbers (Lean Analytics)
Upcoming SlideShare
Loading in...5
×
 

Like this? Share it with your network

Share

Making Sense of the Numbers (Lean Analytics)

on

  • 1,270 views

This is the keynote presentation on Lean Analytics for the Web Analytics Congress (#wac13) from Amsterdam. It covers the basics of Lean Analytics, along with ways to effectively communicate the value ...

This is the keynote presentation on Lean Analytics for the Web Analytics Congress (#wac13) from Amsterdam. It covers the basics of Lean Analytics, along with ways to effectively communicate the value of analytics to business managers and owners.

Statistics

Views

Total Views
1,270
Views on SlideShare
1,261
Embed Views
9

Actions

Likes
14
Downloads
0
Comments
0

2 Embeds 9

https://twitter.com 7
http://newsbeat.us 2

Accessibility

Categories

Upload Details

Uploaded via as Adobe PDF

Usage Rights

© All Rights Reserved

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Processing…
Post Comment
Edit your comment

Making Sense of the Numbers (Lean Analytics) Presentation Transcript

  • 1. MAKING SENSE OF THE NUMBERS Ben Yoskovitz @byosko #leananalyticsMonday, March 18, 13
  • 2. I’M NOT AN ANALYTICS EXPERTMonday, March 18, 13
  • 3. I’M AN ENTREPRENEUR. INVESTOR. PRODUCT GUY.Monday, March 18, 13
  • 4. I BELIEVE IN GUTS + DATAMonday, March 18, 13
  • 5. I BELIEVE IN BRIDGING THE GAP BETWEEN ANALYTICS AND BUSINESSMonday, March 18, 13
  • 6. Order today! (please) http://leananalyticsbook.com h"p://oreilly.com  (LEAN13)Monday, March 18, 13
  • 7. NOT JUST FOR STARTUPS http://www.flickr.com/photos/98909113@N00/308041146/sizes/z/in/photostream/Monday, March 18, 13
  • 8. More wins than losses GoInstant 1st startup Year One Labs Started blogging Standout Jobs Big pivot 1996 1998 2001 2006 2007 2010 2011 The “I got too comfy” years Failed $0Monday, March 18, 13
  • 9. WHAT DATA LOOKS LIKE TO MOST PEOPLEMonday, March 18, 13
  • 10. WE’RE DETECTIVES + PROBLEM SOLVERS http://www.flickr.com/photos/ollieolarte/3028314931/sizes/z/in/photostream/Monday, March 18, 13
  • 11. BUT WHAT PROBLEMS MATTER AND WHAT SHOULD WE BE LOOKING FOR?Monday, March 18, 13
  • 12. Case study: From friends to moms • Started as Circle of Friends • Grew to 10M users BUT ENGAGEMENT SUCKEDMonday, March 18, 13
  • 13. Case study: Moms are crazy (in a good way!) • Messages to one another were on average 50% longer. • 115% more likely to attach a picture to a post they wrote. • 110% more likely to engage in a threaded (i.e. deep) conversation. • Friends, once invited, were 50% more likely to become engaged users. • 180% more likely to click on Facebook news feed items. • 60% more likely to accept invitations to the app. ENGAGEMENT WAS GREATMonday, March 18, 13
  • 14. WE TURN DATA INTO DOLLARS http://www.flickr.com/photos/jfolsom/5931303869/sizes/l/in/photostream/Monday, March 18, 13
  • 15. HOW TO SIMPLIFY ANALYTICS (so you can explain it to others)Monday, March 18, 13
  • 16. Analytics is the measurement of movement towards your business goals.Monday, March 18, 13
  • 17. Six things you can explain to others about metrics * Simple vs. Complex * Qualitative vs. Quantitative * Ratios vs. Numbers * Exploratory vs. Reporting * Vanity vs. Actionable * Leading vs. LaggingMonday, March 18, 13
  • 18. simplify.http://www.flickr.com/photos/josefeliciano/3849557951/sizes/l/in/photostream/Monday, March 18, 13
  • 19. Qualitative Quantitative Unstructured, Numbers and stats; anecdotal, revealing, hard facts but less hard to aggregate. insight. Warm and fuzzy. Cold and hard.Monday, March 18, 13
  • 20. DISCOVER QUALITATIVELY AND PROVE QUANTITATIVELYMonday, March 18, 13
  • 21. Number Ratio Absolute, difficult Comparative, to analyze or easier to analyze, compare to particularly vs. anything. other cohortsMonday, March 18, 13
  • 22. Exploratory Reporting Speculative, trying to Predictable, keeping find unexpected or you abreast of interesting insights. normal, managerial operations. http://www.flickr.com/photos/50755773@N06/5415295449/ http://www.flickr.com/photos/elwillo/4737933662/Monday, March 18, 13
  • 23. Vanity Actionable Picks a direction. Makes you feel good, but doesn’t change how you’ll act.Monday, March 18, 13
  • 24. VANITY METRICS ARE BAD 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.Monday, March 18, 13
  • 25. http://www.flickr.com/photos/circasassy/7858155676/ If it won’t change how you behave, it’s a bad metric. Monday, March 18, 13
  • 26. LEADING LAGGING Number today Historical metric that shows metric that shows how tomorrow-makes you’re doing- the news reports the news http://www.flickr.com/photos/vegaseddie/3310041214/sizes/l/in/photostream/Monday, March 18, 13
  • 27. What mode of e-commerce are you? How many of your customers Then you are in this Your customers will You are just Focus on buy a second time mode buy from you like in 90 days? Low CAC, 1-15% Acquisition Once 70% high of retailers checkout 15-30% Hybrid 2-2.5 20% Increasing per year of retailers returns Loyalty, >30% Loyalty >2.5 10% inventory per year of retailers expansion (Thanks to Kevin Hillstrom for this.)Monday, March 18, 13
  • 28. The power of leading indicators • A Facebook user reaching 7 friends within 10 days of signing up (Chamath Palihapitiya) • If someone comes back to Zynga a day after signing up for a game, they’ll probably become an engaged, paying user (Nabeel Hyatt) • A Dropbox user who puts at least one file in one folder on one device (ChenLi Wang) • Twitter user following a certain number of people, and a certain percentage of those people following the user back (Josh Elman) • A LinkedIn user getting to X connections in Y days (Elliot Schmukler) (from the 2012 Growth Hacking conference)Monday, March 18, 13
  • 29. ANALYTICS SUPERPOWERS (or what the heck is growth hacking?) http://www.flickr.com/photos/bloke_with_camera/401812833/sizes/o/in/photostream/Monday, March 18, 13
  • 30. 10000 1000 100 10 1 Jan Feb Mar Apr May Jun Jul Aug Sept Oct Nov Dec Ice cream consumption DrowningsMonday, March 18, 13
  • 31. 10000 1000 100 10 1 Jan Feb Mar Apr May Jun Jul Aug Sept Oct Nov Dec Ice cream consumption DrowningsMonday, March 18, 13
  • 32. 10000 1000 100 10 1 Jan Feb Mar Apr May Jun Jul Aug Sept Oct Nov Dec Ice cream consumption DrowningsMonday, March 18, 13
  • 33. Correlated Causal Two variables that An independent change in similar ways, factor that directly perhaps because they impacts a dependent are linked to something one. else. Summer al Ca us us Ca al Correlated Ice cream Drowning consumptionMonday, March 18, 13
  • 34. Causality is a superpower, because it lets you change the future. Correlation lets you Causality lets you predict the future change the future “I will have 420 engaged “If I can make more first- users and 75 paying time visitors stay on for customers next month.” 17 minutes I will increase sales in 90 days.” Optimize the Find correlation Test causality causal factorMonday, March 18, 13
  • 35. LEAN ANALYTICS FRAMEWORKMonday, March 18, 13
  • 36. Basics of Lean Startup Eric Ries http://theleanstartup.comMonday, March 18, 13
  • 37. SO WHAT METRICS SHOULD YOU TRACK?Monday, March 18, 13
  • 38. YOUR BASIC THE STAGE OF BUSINESS YOUR STARTUP MODEL How you make $$ Lifecycle • E-commerce • Empathy • SaaS • Stickiness • Free mobile app • Virality • Media site • Revenue • Collaborative content site • Scale • Two-sided marketplaceMonday, March 18, 13
  • 39. LEAN ANALYTICS “GATE” NEEDED TO STAGES MOVE FORWARD I’ve found a real, poorly-met need EMPATHY that a reachable market faces. I’ve figured out how to solve the problem in a way they will adopt STICKINESS and pay for. I’ve built the right product/features/ GROWTH RATE functionality that keeps users VIRALITY around. The users and features fuel growth REVENUE organically and artificially. I’ve found a sustainable, scalable business with the right margins in a SCALE healthy ecosystem.Monday, March 18, 13
  • 40. Case study: Buffer goes from Stickiness to Scale (through Revenue) • Stage: Scale • Model: SaaS (consumer) • Popular social sharing application. • Focused primarily on customer acquisition • Charged from day oneMonday, March 18, 13
  • 41. Buffer charges early to prove people want the problem solved 20% of visitors create an account (acquisition / Empathy) of sign-ups return in the 1st month 60% (engagement / Stickiness) of sign-ups are active after 6 months 20% (engagement / Stickiness) convert from free to paid 2% (Revenue)Monday, March 18, 13
  • 42. SKIP STEPS AT YOUR OWN RISKMonday, March 18, 13
  • 43. Case study: WineExpress increases revenues • Stage: Revenue • Model: E-commerce • Exclusive wine shop partner of the Wine Enthusiast catalog and website • “Wine of the day” page is highly trafficked, needed optimizationMonday, March 18, 13
  • 44. AMonday, March 18, 13
  • 45. BMonday, March 18, 13
  • 46. Case study: Before and after 41% increase in revenue per visitorMonday, March 18, 13
  • 47. How it all comes together 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 ScaleMonday, March 18, 13
  • 48. How it all comes together 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. ScaleMonday, March 18, 13
  • 49. CHOOSE ONLY ONE METRIC AND DRAW A LINE IN THE SANDMonday, March 18, 13
  • 50. Case study: Timehop aims for virality through content sharing • Stage: Virality • Model: Mobile app • Social network around the past • Focused on virality (but not the viral coefficient)Monday, March 18, 13
  • 51. The One Metric That Matters: Content sharing • Focused on % of daily active users that share content • Aiming for 20-30% of daily active users to share content “All that matters now is virality. Everything else—be it press, publicity stunts or something else—is like pushing a rock up a mountain: it will never scale. But being viral will.” - Jonathan Wegener, co-founderMonday, March 18, 13
  • 52. Case study: SEOmoz reduces the KPIs it tracks • Stage: Scale • Model: SaaS • SEO toolkit (product suite) • Reduced KPIs to focus on Net AddsMonday, March 18, 13
  • 53. Net Adds = “health of the business” indicator If Net Adds: Why & Next Steps:Monday, March 18, 13
  • 54. Net Adds = “health of the business” indicator If Net Adds: Why & Next Steps: • Was a marketing campaign successful? • Was churn lowered? • Were customer complaints lowered? • Was a product upgrade valuable?Monday, March 18, 13
  • 55. Net Adds = “health of the business” indicator If Net Adds: Why & Next Steps: • Was a marketing campaign successful? • Was churn lowered? • Were customer complaints lowered? • Was a product upgrade valuable? • How can we acquire more valuable customers? • Can we increase site conversion? • How can we lower churn? • What product features can increase engagement?Monday, March 18, 13
  • 56. Net Adds = “health of the business” indicator If Net Adds: Why & Next Steps: • Was a marketing campaign successful? • Was churn lowered? • Were customer complaints lowered? • Was a product upgrade valuable? • How can we acquire more valuable customers? • Can we increase site conversion? • How can we lower churn? • What product features can increase engagement? • Are the new customers not the right segment? • Did a marketing campaign fail? • Are too many customers churning? • Did a product upgrade fail to impress or cause issues?Monday, March 18, 13
  • 57. METRICS ARE LIKE SQUEEZE TOYSMonday, March 18, 13
  • 58. YOUR GOAL IS TO MAKE FASTER, MORE INTELLECTUALLY HONEST DECISIONS AND EMPOWER YOUR ORGANIZATION TO DO THE SAMEMonday, March 18, 13
  • 59. TURN DATA INTO DOLLARS http://www.flickr.com/photos/68751915@N05/6551534889/sizes/l/in/photostream/Monday, March 18, 13
  • 60. HOW TO BECOME DATA-DRIVENMonday, March 18, 13
  • 61. The Lean Canvas (leancanvas.com)Monday, March 18, 13
  • 62. AVOID THE PITFALLS @mrogati • Assuming the data is clean • Ignoring seasonality • Data vomit • Metrics that cry wolf • The “not collected here” syndrome • Focusing on noise (look at the big picture) http://www.flickr.com/photos/sheffieldmickey/504565957/Monday, March 18, 13
  • 63. DATA DRIVEN CULTURE • Start small, pick one thing and show value • Make sure goals are clearly understood • Get executive buy-in • Make things simple to digest • Ensure transparency • Don’t eliminate your gut • Get closer to the customer • Ask good questions http://www.flickr.com/photos/jonolave/3619431413/sizes/o/in/photostream/Monday, March 18, 13
  • 64. Once, a leader convinced others in the absence of data.Monday, March 18, 13
  • 65. Now, a leader knows what questions to ask.Monday, March 18, 13
  • 66. Thank you. follow me. email me. @byosko byosko@gmail.com instigatorblog.com subscribe. ORDER! leananalyticsbook.com oreilly.com (LEAN13)Monday, March 18, 13