Marketing Festival 2016

2. Why do we attribute
(1838 - 1922)

3. Why do we attribute
Budget Allocation
Media Plan
Channel Performance and Value
Customer Journeys
Data Source: Google Analysis
<Marketing Attribution: Valuing
the Customer Journey>

4. Separate silos
SEARCH AD BUDGET
SEARCH CLICKS &
IMPRESSIONS
SEARCH CONVERSIONS
DISPLAY AD BUDGET
DISPLAY CLICKS &
IMPRESSIONS
DISPLAY CONVERSIONS
PROGRAMMATICPPC
SOCIAL AD BUDGET
SOCIAL
SOCIAL CLICKS &
IMPRESSIONS
SOCIAL CONVERSIONS

5. Uniform data
ADVERTISING BUDGET
CLICKS & IMPRESSIONS
CONVERSIONS
PROGRAMMATICPPC SOCIAL

6. Why own solution?
all impressions
full browsing history
paths which did not make conversion
cross-device
paths in their whole length
(Google cuts them to 4 channels)
sophisticated methods
CRM data

7. Last-click (heuristic) problem
more information in: John Murphy, 2014

8. Last-click (heuristic) problem

9. Last-click (heuristic) problem

10. First-click (heuristic) problem

11. Display campaigns matter

12. Channel dynamics

15. Customer behaviour (consumer funnel) matter
Was the conversion caused by this channel?

16. logistic regression models (Shao & Li 2011; Klapdor 2013)
Data-driven models

17. logistic regression models (Shao & Li 2011; Klapdor 2013)
game theory-based models (Berman, 2015; Dalessandroet al. 2012)
Bayesian models (Li & Kannan 2014; Nottorf 2014)
mutually exciting point process models (Xu, Duan, & Whinston, 2014)
hidden Markov models (Abhishek, Fader, & Hosanagar 2015; Anderl et al. 2014)
Data-driven models

18. logistic regression models (Shao & Li 2011; Klapdor 2013)
game theory-based models (Berman, 2015; Dalessandroet al. 2012)
Bayesian models (Li & Kannan 2014; Nottorf 2014)
mutually exciting point process models (Xu, Duan, & Whinston, 2014)
hidden Markov models (Abhishek, Fader, & Hosanagar 2015; Anderl et al. 2014)
VAR models (Kireyev, Pauwels, & Gupta 2016)
multivariate time-serie models (Anderl et al. 2015)
survival models
Data-driven models

19. Simple Probabilistic Method Shao and Li, 2011
Shapley Value Aspa Lekka, 2014
Hidden Markov Model Anderl et al., 2014
Science behind the models

20. Criteria / Model
Heuristic Simple probabilistic Shapley value Markov
Objectivity and fairness No Yes Yes Yes
Predictive accuracy No Partly - Yes
Carryover and spillover effects No Partly Yes Yes
Data-driven No Yes Yes Yes
Interpretability Yes Yes Partly Partly
Customers’ heterogeneity No Partly Partly Yes
Robustness No Partly - Yes
Algorithm efficiency Yes
Satisfactory for lower
orders
No
Satisfactory for lower
orders
Versatility Yes Yes Yes Yes

21. Criteria / Model
Heuristic Simple probabilistic Shapley value Markov
Objectivity and fairness No Yes Yes Yes
Predictive accuracy No Partly Yes Yes
Carryover and spillover effects No Partly Yes Yes
Data-driven No Yes Yes Yes
Interpretability Yes Yes Partly Partly
Customers’ heterogeneity No Partly Partly Yes
Robustness No Partly Yes Yes
Algorithm efficiency Yes
Satisfactory for lower
orders
No
Satisfactory for lower
orders
Versatility Yes Yes Yes Yes

23. “We have no place to grow; PPC campaigns has used up its potential.”
“Effective revenue share is smaller than was the goal so that we could spend
more money, but it was not where to spend… We put more money to Google in
Slovakia market, and ERS got even cheaper.”
How to get from last-click trap

24. Methodology
Our clients are heterogeneous, but we have to be able to maintain uniform solution.
Data
collection
Data
pre–processing
Run models
Budget
reallocation
Results testing
and validation
Descriptive
analysis
Data
cleaning
Data
selection
Paths
reconstruction

25. Technology:

26. Data Collection
Data collection all raw data including all clicks,
impressions, web entrances
Data granularity channel - campaign - media - placement
Channels free channels are taken into account

27. Data preparation: 80% success
Data cleaning exclude robotic transactions
exclude disabled cookies
exclude not visible impressions
exclude repeated actualisations of
websites
combine impressions in 30-minute interval
Transformation
to journeys
non-conversion taken in account
exclude paths longer than treshold
Data: > 1,5 TB
Rows: > 3,2 billions

28. Consecutive impressions
visualisation: Crossmasters

29. Consecutive impressions
visualisation: Crossmasters

30. Consecutive impressions
visualisation: Crossmasters

31. Modelling
Period analysed monthly basis
Time window 1 month

32. Reporting
CPA
ROAS (%)
channel cost
number of
channel
conversions
channel weight
channel cost
weight
ROAS > 100 % channel is undervalued
channel cost weight =
channel cost
sum of all cost
Proposed
Budget
actual budget * ROAS=
=
=

33. Return of advertising spends (ROAS)
channel weight
channel cost weight
channel cost first-click last-click linear-touch shapley
value
simple
probabilistic
markov
SKLIK 193 000 CZK 162 % 194 % 165 % 147 % 193 % 35 %
RTB 13 000 CZK 110 % 70 % 90 % 70 % 60 % 189 %
RTB 2 10 000 CZK 1057 % 319 % 663 % 561 % 369 % 14 %

34. Proposed Budget = actual budget * ROAS
channel cost first-click last-click linear-touch shapley
value
simple
probabilistic
markov
SKLIK 193 000 CZK 313 000 CZK 375 000 CZK 320 000 CZK 284 000 CZK 373 000 CZK 68 000 CZK
RTB 13 000 CZK 15 000 CZK 9 000 CZK 12 000 CZK 10 000 CZK 8 000 CZK 25 000 CZK
RTB 2 10 000 CZK 108 000 CZK 33 000 CZK 69 000 CZK 57 000 CZK 38 000 CZK 148 000 CZK
... ... ... ... ... ... ... ...
... ... ... ... ... ... ... ...
... ... ... ... ... ... ... ...
SUM 1 300 000 CZK 1 300 000 CZK 1 300 000 CZK 1 300 000 CZK 1 300 000 CZK 1 300 000 CZK 1 300 000 CZK

35. Proposed Budget = actual budget * ROAS
channel cost first-click last-click linear-touch shapley
value
simple
probabilistic
markov
SKLIK 193 000 CZK 313 000 CZK 375 000 CZK 320 000 CZK 284 000 CZK 373 000 CZK 68 000 CZK
RTB 13 000 CZK 15 000 CZK 9 000 CZK 12 000 CZK 10 000 CZK 8 000 CZK 25 000 CZK
RTB 2 10 000 CZK 108 000 CZK 33 000 CZK 69 000 CZK 57 000 CZK 38 000 CZK 148 000 CZK
... ... ... ... ... ... ... ...
... ... ... ... ... ... ... ...
... ... ... ... ... ... ... ...
SUM 1 300 000 CZK 1 300 000 CZK 1 300 000 CZK 1 300 000 CZK 1 300 000 CZK 1 300 000 CZK 1 300 000 CZK

36. Budget optimalization is an iterative process
budget shiftbudget shift
The optimal budget is reached when a channel reaches its maximum conversion.

37. Customer Journeys
RTB212636
RTB2157216
SEARCH
SKL2079081
RTB2125178
DIR
visualisation: Crossmasters

38. RTB and Display drive PPC and Search
conversion rate remained 24 %
CPA remained 0,019 CZK
2x more conversions
2,5x conversion value

39. Conclusion: last-click is a barrier of any growth
Data-driven attribution has sense with channels
which shift customer in consumer funnel
Data-driven attribution gives immediate answers
we couldn’t otherwise measure
High technology costs will return
The results are visible after some time (the need
of enough data!)
Different marketing mix needs different model
scalability
all data at one place
ad-hoc reporting
transparency

40. At the end it’s a human job
“THE ONLY SOURCE OF
KNOWLEDGE IS AN EXPERIENCE.”
ALBERT EINSTEIN
(1879 - 1955)