Data-Driven Attribution Under the Hood

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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. D A T A - D R I V E N
A T T R I B U T I O N

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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. W H A T C A N W E D O
T O D A Y ?

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. 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. W H A T C A N W E D O T O D A Y ? 40
MATCHED CONTROL EXPERIMENTAL DESIGN

41. W H A T C A N W E D O T O D A Y ? 41
THE GOD PIXEL

42. W H A T C A N W E D O T O D A Y ? 42
WPROMOTE ATTRIBUTION
DBM - WpromoteDBM - Wpromote

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. 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. 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. 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. 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. 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