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Data-Driven Attribution Under the Hood - Simon Poulton

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Data-Driven Attribution Under the Hood

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Data-Driven Attribution Under the Hood - Simon Poulton

  1. 1. D A T A - D R I V E N A T T R I B U T I O N U N D E R T H E H O O D PRESENTED BY: SIMON PO ULTON SE N IOR D IR E CTOR OF DIGITAL IN TELLIGENCE, WPROMOTE
  2. 2. A T T R I B U T I O N : N O T A 4 - L E T T E R W O R D 2 THE NEW ZEALAND ALL BLACKS
  3. 3. A T T R I B U T I O N : N O T A 4 - L E T T E R W O R D 3 THE NEW ZEALAND ALL BLACKS
  4. 4. A T T R I B U T I O N : N O T A 4 - L E T T E R W O R D 4 DAN CARTER 112 Matches Played 88% of Games Won 1,598 Points Scored
  5. 5. A T T R I B U T I O N : N O T A 4 - L E T T E R W O R D 5 OWEN FRANKS 100 Matches Played 88% of Games Won 0 Points Scored
  6. 6. A T T R I B U T I O N : N O T A 4 - L E T T E R W O R D 6 OWEN FRANKS
  7. 7. A T T R I B U T I O N : N O T A 4 - L E T T E R W O R D 7 OWEN FRANKS Why continue having Owen Franks in the starting line up when he is not productive?
  8. 8. A T T R I B U T I O N : N O T A 4 - L E T T E R W O R D 8 JUST HURRY UP & EVOLVE ALREADY We’re judging a fish based on its ability to climb a tree.
  9. 9. A T T R I B U T I O N : N O T A 4 - L E T T E R W O R D 9 WORLD’S STRONGEST SCRUM PACK
  10. 10. A T T R I B U T I O N : N O T A 4 - L E T T E R W O R D 10 RUGBY AT-TRY-BUTION + = Given their different skill sets, it’s easy to justify the need to have both players on the team!
  11. 11. A T T R I B U T I O N : N O T A 4 - L E T T E R W O R D 11 RUGBY AT-TRY-BUTION Facebook View-Through Conversion Affiliate Last-Click Conversion + =
  12. 12. T H E C H A L L E N G E O F A T T R I B U T I O N
  13. 13. T H E C H A L L E N G E O F A T T R I B U T I O N 13 FUNDAMENTAL COMPONENTS CUSTOMER JOURNEY MAPPING APPLIED ATTRIBUTION MODEL
  14. 14. T H E C H A L L E N G E O F A T T R I B U T I O N 14 L A S T C L I C K F I R S T C L I C K L I N E A R P O S I T I O N - B A S E D T I M E D E C AY ATTRIBUTION EVOLUTION
  15. 15. T H E C H A L L E N G E O F A T T R I B U T I O N 15 "Rules-based attribution is inherently biased and drives poor decision making.” Me - Right Now ATTRIBUTION EVOLUTION
  16. 16. T H E C H A L L E N G E O F A T T R I B U T I O N 16 D ATA - D R I V E N ATTRIBUTION EVOLUTION
  17. 17. T H E C H A L L E N G E O F A T T R I B U T I O N 17 INCOMPLETE STORIES
  18. 18. T H E C H A L L E N G E O F A T T R I B U T I O N 18 THE GREAT DATA SILOS
  19. 19. T H E C H A L L E N G E O F A T T R I B U T I O N 19 5 0 0 C O N V E R S I O N S G O O G L E 7 0 0 C O N V E R S I O N S F A C E B O O K + = 1 0 0 0 C O N V E R S I O N S T O TA L FUNNY MEASUREMENT MATH
  20. 20. T H E C H A L L E N G E O F A T T R I B U T I O N 20 FROM COOKIES TO PEOPLE 0% 29% 71% Desktop Mobile All Devices 45% 7% 48% C O O K I E - B A S E D M E A S U R E M E N T P E O P L E - B A S E D M E A S U R E M E N T
  21. 21. T H E C H A L L E N G E O F A T T R I B U T I O N 21 INTELLIGENT TRACKING PREVENTION Cookies can be used in a 3rd-party context Cookies can’t be used in a 3rd-party context Cookies purged 0 Days 1 Day 30 Days Days after the most recent interaction with the website
  22. 22. T H E C H A L L E N G E O F A T T R I B U T I O N 22 CLICKS VS. VIEWS CLICK-THROUGH CONVERSION VIEW-THROUGH CONVERSION CLICKS VISIT SITE CONVERT SCROLL PAST ??? CONVERT
  23. 23. T H E C H A L L E N G E O F A T T R I B U T I O N 23 CLICK-TO-SALE "There is no significant correlation between clickthrough rate (CTR) and sales. Correlation is less than 1%.” Source: Facebook Nielsen Study, 2017 AD RECALL BRAND AWARENESS PURCHASE INTENT
  24. 24. D A T A - D R I V E N A T T R I B U T I O N
  25. 25. D A T A - D R I V E N A T T R I B U T I O N 25 SHAPLEY VALUES - EXAMPLE EXAMPLE • 3 “Players” • Player 1 Receives A Left-Hand Glove • Players 2 & 3 Receive A Right-Hand Glove TASK • Form A Pair • Credit Assigned To Each Player After Forming A Pair PLAYER 1 PLAYER 2 PLAYER 3 PLAYER 1 & PLAYER 2 PLAYER 1 & PLAYER 3
  26. 26. D A T A - D R I V E N A T T R I B U T I O N 26 SHAPLEY VALUES - GOOGLE ADS EXAMPLE B R A N D S H O P P I N G N O N - B R A N D 3 GOOGLE ADS CAMPAIGNS MINIMUM 15,000 CLICKS & 600 CONVERSION ACTIONS IN PAST 30 DAYS
  27. 27. D A T A - D R I V E N A T T R I B U T I O N 27 PROBLEM 3 Google Ads Campaigns had 4 sales of $1. How can we distribute the total credit of $4 to the individuals? $4 B R A N D S H O P P I N G N O N - B R A N D
  28. 28. D A T A - D R I V E N A T T R I B U T I O N 28 STEP 1 Compute Normalizing Factors (NF) For Different Sizes Of Sub-Teams S H O P P I N G N O N - B R A N D Number of Campaigns NF Formula NF Team Permutations 1 NF: (o!*2!)/3!=2/6=1/3 33% 2 NF: (1!*1!)/3!=1/6=1/6 16% 3 NF: (2!*o!)/3!=2/6=1/3 33% B R A N D
  29. 29. D A T A - D R I V E N A T T R I B U T I O N 29 STEP 2 Performance Data Points For Individuals B R A N D S H O P P I N G N O N - B R A N D S A L E S $ 2 S A L E S $ 1 S A L E S $ 0
  30. 30. D A T A - D R I V E N A T T R I B U T I O N 30 STEP 3 Brand’s counterfactual gain, i.e. what Brand brings as a value add, is therefore the total sales, minus what Shopping would have achieved on its own. Performance Data Points For Campaigns As Part Of Teams Brand’s counterfactual gain, in a group with Shopping, is $4 - $1 = $3. Similarly Shopping’s counterfactual is the total sales, minus what Brand would have had on its own. Shopping’s counterfactual gain, in a team with Brand, is $4 - $2 = $2. + $4 B R A N D S H O P P I N G COUNTERFACTUAL GAIN $ 2 $ 1
  31. 31. D A T A - D R I V E N A T T R I B U T I O N 31 STEP 3 Brand Shopping Non-Brand Shopping + Non-Brand Non-Brand + Brand Shopping + Brand Brand + Shopping + Non- Brand Sales $2 $1 $0 $2 $1 $4 $4 Brand $2 - - - $1 $3 $3 Shopping - $1 - $2 - $2 $2 Non-Brand - - $0 $0 $0 - $0 Performance Data Points For Individuals As Part Of Teams CounterfactualGain
  32. 32. D A T A - D R I V E N A T T R I B U T I O N 32 STEP 4 Group of 1 Group of 2 Group of 3 Attributed Payout NF 33% 16% 33% 100% Brand $2 $2+$3=$5 $3 33%*$2 + 16%*$5 + 33%*$3 = $2.5 Shopping $1 $1+$2=$3 $2 33%*$1 + 16%*$3 + 33%*$2 = $1.5 Non-Brand $0 $0 $0 $0 Computing Payoff For Individuals From Counterfactual Gains Using Normalizing Factors (NFs)
  33. 33. D A T A - D R I V E N A T T R I B U T I O N 33 DATA-DRIVEN ATTRIBUTION Adjusts to the changing weights and journeys of users over time. Informs on performance associated with top- of-the-funnel initiatives. Allows for a gap analysis with regards to underinvested areas of the funnel.
  34. 34. D A T A - D R I V E N A T T R I B U T I O N 34 CONVERSION SHIFTS TO NON-BRAND WHAT HAPPENED? Attribution weight shifted from remarketing and brand to non-brand upper funnel terms, allowing for a focus on non-brand to drive growth. NON-BRAND Conversions Cost/Conversion Click Conv. Rate 244 $190 0.7% +64% +12% -13% BRAND Conversions Cost/Conversion Click Conv. Rate 69 $111 0.7% -63% +58% -24% *Date Range: 30 days Pre & Post Attribution Model Change. CLIENT TYPE: Auto-Parts Client with a complex path to purchase.
  35. 35. D A T A - D R I V E N A T T R I B U T I O N 35 CONVERSION SHIFTS TO BRAND 1,246 $31 2% -7.1% -12.1% +13.4% 1,450 $1 12% +8.4% +0.2% +4.8% *Date Range: 30 days Pre & Post Attribution Model Change. CLIENT TYPE: Wedding Personalization Company with a strong focus on brand search. WHAT HAPPENED? Brand campaign has started to see more credit. May be an indicator of over- reliance on lower funnel activity. NON-BRAND Conversions Cost/Conversion Click Conv. Rate BRAND Conversions Cost/Conversion Click Conv. Rate
  36. 36. D A T A - D R I V E N A T T R I B U T I O N 36 CONVERSION SHIFTS TO MOBILE 551 $86 1% +10.4% -32.4% +52.2% 522 $60 3% -13.4% -6.1% +6.7% *Date Range: 30 days Pre & Post Attribution Model Change. CLIENT TYPE: Furniture Store with a long consumer research phase pre-purchase. WHAT HAPPENED? Heavier weighting of earlier touch points (on mobile devices) drove a number of mobile bid optimizations and increases. Conversions Cost/Conversion Click Conv. Rate Conversions Cost/Conversion Click Conv. Rate MOBILE Desktop/Tablet
  37. 37. W H A T C A N W E D O T O D A Y ?
  38. 38. W H A T C A N W E D O T O D A Y ? 38 GOOGLE LEADS THE WAY Google Analytics Premium (360): August 2013 DoubleClick (GMP): February 2016
 AdWords (GoogleAds): May 2016
 Google Attribution: ~Q1 2019
  39. 39. W H A T C A N W E D O T O D A Y ? 39 THE CHALLENGE PERSISTS 5 0 0 C O N V E R S I O N S G O O G L E 7 0 0 C O N V E R S I O N S F A C E B O O K + = 1 0 0 0 C O N V E R S I O N S T O TA L
  40. 40. W H A T C A N W E D O T O D A Y ? 40 MATCHED CONTROL EXPERIMENTAL DESIGN
  41. 41. W H A T C A N W E D O T O D A Y ? 41 THE GOD PIXEL
  42. 42. W H A T C A N W E D O T O D A Y ? 42 WPROMOTE ATTRIBUTION DBM - WpromoteDBM - Wpromote
  43. 43. W H A T C A N W E D O T O D A Y ? 43 WHAT ABOUT AMAZON? Sign Up: https://www.amazon.com/amazonattribution
  44. 44. W H A T C A N W E D O T O D A Y ? 44 KEY TAKEAWAYS Rules-based models are still very useful, but inherently contain bias and limit actionable insights. VISION MODELS DDA Uses Shapley Values and counterfactual gains to constantly adjust based on new information available. Attribution modeling is about looking forward to determine how to grow, not about looking back. EDUCATE Real attribution change can only occur when everyone is educated on the “why” and “how.”
  45. 45. W H A T C A N W E D O T O D A Y ? 45 THE BEST TIME TO PLANT A TREE 1 Y E A R 5 Y E A R S 1 0 Y E A R S 1 5 Y E A R S 2 0 Y E A R S
  46. 46. W H A T C A N W E D O T O D A Y ? 46 IT’S ONLY GOING TO GET MORE COMPLEX
  47. 47. W H A T C A N W E D O T O D A Y ? 47 IT’S THE WILD WEST OF DATA OUT THERE
  48. 48. W H A T C A N W E D O T O D A Y ? 48 ABOUT ME Simon Poulton Senior Director of Digital Intelligence at Wpromote Website: www.spoulton.com Twitter: @SPoulton Email: simon@wpromote.com

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