Date post: | 17-Jan-2017 |
Category: |
Marketing |
Upload: | marketing-festival |
View: | 624 times |
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Why do we attribute
Budget Allocation
Media Plan
Channel Performance and Value
Customer Journeys
Data Source: Google Analysis <Marketing Attribution: Valuing the Customer Journey>
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
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
Last-click (heuristic) problem
more information in: John Murphy, 2014
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
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
Simple Probabilistic Method Shao and Li, 2011
Shapley Value Aspa Lekka, 2014
Hidden Markov Model Anderl et al., 2014
Science behind the models
Criteria / ModelHeuristic 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 YesSatisfactory for lower
ordersNo
Satisfactory for lower orders
Versatility Yes Yes Yes Yes
Criteria / ModelHeuristic 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 YesSatisfactory for lower
ordersNo
Satisfactory for lower orders
Versatility Yes Yes Yes Yes
“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
Methodology
Our clients are heterogeneous, but we have to be able to maintain uniform solution.
Data collection
Data pre–processing Run models Budget
reallocationResults testing and validation
Descriptive analysis
Data cleaning
Data selection
Pathsreconstruction
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
Data preparation: 80% success
Data cleaning exclude robotic transactions exclude disabled cookiesexclude not visible impressionsexclude repeated actualisations of websitescombine impressions in 30-minute interval
Transformation to journeys
non-conversion taken in accountexclude paths longer than treshold
Data: > 1,5 TB Rows: > 3,2 billions
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=
=
=
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 %
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
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
Budget optimalization is an iterative process
budget shiftbudget shift
The optimal budget is reached when a channel reaches its maximum conversion.
RTB and Display drive PPC and Search
conversion rate remained 24 %
CPA remained 0,019 CZK
2x more conversions
2,5x conversion value
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