algorithmic attribution modelling - data innovation summit · 2019. 4. 16. · 7 15 9 19 7....
TRANSCRIPT
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Algorithmic attribution modellingUsing game theory to improve the
measurability of omni-channel marketing
Ola OttossonSebastian Ånerud
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01 Attribution modelling intro
02 Standard approaches
03 Game theory: Shapley values
04 Modelling approach
05 Questions
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What is attribution modelling?
Channels and touchpoints Success factor
An attribution model measures the impact of different touchpoints on the success factor
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Shapley values
Attribution modelling
Markov chains
Predictive modelling
Linear
First clickTime decay
Position based
Last click
The attribution modelling landscape
Rule-based Algorithmic approach
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What are Shapley values?
To each cooperative game it assigns a unique distribution (among the players) of a total surplus generated by the coalition of all players.
Or in plain text...
Give fair credit to each player.
A B
C
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What are Shapley values?
A B
CC
A B A B
C
A B
C
A B
C
A B
C
A B
C
A B
C
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A B
CC
A B A B
C
A B
C
A B
C
A B
C
A B
C
A B
C
All possible combination of players
0 4 6
7 15 9 19
7
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Marginal value with consideration to entry order
A BC A B C AB C
AB C A BC ABC
7 8 4 7 0 12 4 5 10
4 3 12 6 9 4 6 3 10
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(7+7+10+3+9+10) / 6 = 7.67
A
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(7+7+10+3+9+10) / 6 = 7.67
B
C
A
3,17
8,17
19
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Shapley Additive Explanations (SHAP) applied to
Machine Learning modelling
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Why SHAP on ML models?
Complex attribution problem● The number of channels are large● You also want to model the order of the touchpoints● You want to take continuous variables into account
→● Computationally heavy, exact shapley values cannot be
computed and needs to be approximated in some way
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Why SHAP on ML models?
Marketing mix model● You already have, or are building, a ML model for
simulating effects of marketing investments (marketing mix model)
● You want to understand the importance and effect of features for each individual observation
● You want a more fair feature attribution than standard implementations
● →● SHAP package in python: github.com/slundberg/shap
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Marketing mix modelling
Problem● Understand how redirecting spend in different
channels will affect conversion ratesSolution● Given historical leads, their point of contacts and if
they converted or not, try to model the relationship between touchpoints and conversion.
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Standard feature importance approach
Features● Offered price● Contact with an affiliate● Contact with the different online/offline channels● Contact with the different sales agents
Target label● Converted or not
Model● XGBoost (Gradient boosting)● 100 Trees● 0.1 learning rate
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SHAP approach
Features● Offered price● Contact with an affiliate● Contact with the different online/offline channels● Contact with the different sales agents
Target label● Converted or not
Model● XGBoost (Gradient boosting)● 100 Trees● 0.1 learning rate
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SHAP approach (“backwards compatible”)
Features● Offered price● Contact with an affiliate● Contact with the different online/offline channels● Contact with the different sales agents
Target label● Converted or not
Model● XGBoost (Gradient boosting)● 100 Trees● 0.1 learning rate
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Feature values● online_u = 1● offline_g = 1● price = 45● sls_agent_w = 1● ….
SHAP● For each lead, how much did each feature moved
the prediction up or down?
SHAP on an observation level
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SHAP interactions
Feature interactions● price● sls_agent_c
Interpretation● Sales agent c is not as good at
selling at a low price as on higher prices
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Feature interactions● price● sls_agent_q
Interpretation● Sales agent q is not as good at
selling at a high price as on lower prices
SHAP interactions