The choice of the right attribution model is a challenge for most advertisers. We all know the limitation of Last Click attribution model. We will illustrate the limitations of other, more comprehensive pre-defined attribution models such as Time Decay and Position Based or Data Driven. We will share a solution to implement a Markov Chains based approach and will introduce an approach to test a new attribution model. 3 Key Learnings Overview of attribution models and their limitations How to implement a Markov Chains How to test the impact of an attribution model

1. Why Your Attribution Model Sucks
How to step beyond Data-Driven Models with a Markov Model Approach
Gianluca Binelli | @ktzstyle
Most Innovative Presentation
HeroConf 2019

2. Session Outcomes
● Overview of attribution models and their limitations
● How to implement a Markov Chains based model
● How to test the impact of an attribution model

3. What do we do?

4. What do we do?
Scientific Performance
Marketing Agency

5. Who am I?
● 14y experience in online advertising
● 6.5y at Google between Dublin, NYC & London
● Managed Quarterly $XXM in advertising for
Google as part of the SEM in-house team that
promotes Google’s products (in FB, Bing, Google,
Linkedin etc)
● Advisor for Google’s own equity fund Capital G

6. What got Kermit drunk?

7. It all started with an appletini

8. It all started with an appletini

9. It all started with an appletini

10. It all started with an appletini

11. It all started with an appletini

12. It all started with an appletini

13. Was it the champagne?

14. The smoking gun

15. Path Length

16. Conversion path

17. Multi channel conversion visualiser

18. Google Ads: Search Attribution

19. Facebook Attribution

20. Heuristic Attribution

21. Like work life balance
● No one size fits all

22. Let’s assume tracking is working
● Tags/Pixels
● UTMs
● Single source of truth
○ Google Marketing Platform (AKA DoubleClick)
○ Adobe Analytics
○ Facebook Attribution
○ Google Analytics

23. A few options

24. Data Driven

25. Data Driven
Shapely Value

26. Data Driven
● Google Ads
● Google Analytics

27. Data Driven
● Google Ads
○ 15,000 clicks in 30 days
○ 600 conversions in 30 days
○ Cuts out under 400 conversions per month
● Google Analytics

28. Data Driven
● Google Ads
○ 15,000 clicks in 30 days
○ 600 conversions in 30 days
○ Cuts out under 400 conversions per month
● Google Analytics 360

29. Markov Chains
The Maths

30. What’s a chain?
● Let’s assume we have 2 keywords
● These means we have 4 states for each chains:
○ START
○ "OUR BRAND"
○ Advertise Online
○ CONVERSION

31. What’s a chain?
PATH CONVERSIONS
START > "OUR BRAND" > Advertise Online > Advertise
Online > "OUR BRAND" > "OUR BRAND" > Advertise
Online > "OUR BRAND" > CONVERSION
1
START > "OUR BRAND" > Advertise Online > Advertise
Online > "OUR BRAND" > CONVERSION
1
START > "OUR BRAND" > "OUR BRAND" >
CONVERSION
1
total 3

32. Once we have all the link
counts, we can compute
the transition
probability ( = the
chance to go to a certain
state starting from a
certain point )
edge Link count
Transition
probability
START > "OUR BRAND" 3 3/3
START > Advertise Online 0 0
TOT START 3
"OUR BRAND" > "OUR BRAND" 2 2/8
"OUR BRAND" > Advertise Online 3 3/8
"OUR BRAND" > CONVERSION 3 3/8
TOT A 8
Advertise Online > "OUR BRAND" 3 3/5
Advertise Online > Advertise Online 2 2/5
Advertise Online > CONVERSION 0 0
TOT B 5
What’s a chain?

33. Let’s draw something!
It is worth noticing that once
we are in A and in B we
could end up in A and B
again.
START
"OUR
BRAND"
Advertise
Online
CONVERSION
100%
25%
60%
37.5%
40%
37.5%

34. You never realize what you have until it’s gone

35. You never realize what you have until it’s gone

36. To assess the impact of a keyword we assume it is
gone
● Importance of keyword "OUR BRAND" = the change in conversion rate if
keyword "OUR BRAND" is dropped from the Graph
● or in other terms if keyword "OUR BRAND" becomes a NULL state. A NULL
state is an absorbing state so if one reaches this STATE can’t move on.

37. START NULL
Advertise
Online
CONVERSION
100%
25%
60%
37.5%
40%
37.5%
On a graph it would look like this

38. START NULL
100%
Which means

39. START NULL
100%
Which means
Thus importance of kw
"OUR BRAND" (defined
as the change in
conversion rate) is 1.

40. START
"OUR
BRAND"
NULL
CONVERSION
100%
25%
37.5%
37.5%
Let’s do the same for ‘Advertise Online’

41. START
"OUR
BRAND"
NULL
CONVERSION
100%
50%
50%
Which means

42. START
"OUR
BRAND"
NULL
CONVERSION
100%
50%
50%
Which means
As we can see, once we
removed kw Advertise
Online, the chances to
convert fell to .5.
Therefore the importance of
kw Advertise Online is the
difference between previous
conversion rate (1) and the
chances to convert if we
remove Advertise Online
(.5), i.e. 0.5.

43. ● Once we have all the importance weights for all the channels/keywords we
can finally compute the number of conversions weighted by the
importance of our channels/ keywords.
● We take the previous number of conversions (3 in our example) and we
weight them according to the importance weight:
○ 1 / (1+.5) for kw "OUR BRAND" → 3*(⅔) → 2 conversions
○ .5 / (1+.5) for kw Advertise Online → 3*(⅓) → 1 conversion
To summarize

44. Markov Chains
The Ingredients

45. Google Analytics Conversion Paths

46. Google Analytics Sheets Plugin

47. Create a blank report

48. Enter Metrics & Dimensions from Multi Channel
Funnel API
Full list here

49. Hidden secret to making MCF work

50. Expand hidden rows and enter “mcf” in type

51. We can run for source, source / medium keywords,
campaign etc

52. The Code

53. https://gist.github.com/boosterbox

54. Applications of a better allocation?
● Better Bidding
● More accurate Budget allocation

55. Testing

56. Science
● Control & Treatment
● Dependent Variable
● Independant Variable

57. Science
● Control & Treatment
○ Split target location in smaller locations
○ Split 50/50
● Dependent Variable
○ Existing attribution model in one half of locations
○ Markov in other half of locations
● Independent Variable
○ Conversions/CPA

58. Control and Treatment
Identify sub-regions
DMAs in US

59. Control and Treatment
Split so volume of conversions is roughly even

60. Dependant Variable
Campaign Targeted Locations Attribution model
Awesome Campaign 1 Random locations group 1 Last Click
Awesome Campaign 1 Test Random locations group 2 Markov
Duplicate every campaign

61. Independent Variable
Measure Results