rise of the machine (learning algorithms)
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Rise of the Machine(learning algorithms)
data driven website optimization
Frank van LankveltSenior Big Data Engineer / Architect
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The B2B Customer Journey
discover compare
consider - business
consider - technical
buy
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Inbound / Advertising
discover
● SEA● Display● Affiliate● Social● Native
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Machine based Optimization
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SEA, Display
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Multi-armed Bandits
balancingExploitation
withExploration
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Bayes’ Theorem
Proposition A and evidence B,P(A), the priorP(A|B), the posteriorthe quotient P(B|A)/P(B) represents the support B provides
for A.
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Conversion rate Distribution
hit: 0miss: 0
hit: 10miss: 40
hit: 1miss: 4
hit: 100miss: 400
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With multiple options
Multiple distributionsA - the incumbentB - the challenger
How often should B be shown?
Thompson Sampling:sample distributionsshow variant with highest conversion rate Red: old configuration
Blue: new configuration
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Beyond the banner
discover compare
● A/B testing● content experiments
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Rinse & Repeat?
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Content Experiments - Setup
A B
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Content Experiments - Reporting
Experimentgoal / conversionconversion rateshistorical servings
Multi-armed / Contextual Bandit
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Train Model Visits
BanditModel
Content Experiments - Data flow
Site
Sessionize
Requestlog
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Personalizing
compare● targeting● personalization● recommendation● email marketing
consider - business
consider - technical
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Getting personal?
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Targeting & Personalization
Default
Amsterdam
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Behavioral Targeting
Use metadata / context on documents or visitor to personalize
Visitor looks mostly at clothing?show more clothing
Visitor looks mostly at shoes?show more shoes
I.e. use meta-data provided by the editor (a human) to augment the experience.
Based on rules, metadata => still much control for humans
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Audience Experiment
does audience configuration
improveconversion?
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Contextual bandit
Conversion rate depends on context:
x the contextw the weights𝚽 cdf of normal
dist.
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CTR prediction - under the hood
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CTR Context / Features
Ad featuresbid phrasesad titlead textlanding page URLlanding page itselfa hierarchy of advertiser, account,
campaign, ad group and ad
Query featuressearch keywordspossible algorithmic query expansioncleaning and stemming
Context featuresdisplay locationgeographic locationtimeuser datasearch history
Cardinalities varies● gender (2 values)● userID (billions)
x, w very large (sparse) vectors
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Persuasion Principles
Persuasion Principles
● Social Proof● Scarcity● Authority● Reciprocity● Commitment● Liking
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Persuasion API > In pictures
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Modeling the Visitor
Multi-armed bandit per visitor
● each principle gets an arm● model persuasion principle susceptibility
raise conversion by
10%
Authority
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The B2B Customer Journey
is this real?
or just fantasy?
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Work in Progress
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Mapping the Customer Journey
Man: behavioral targeting
annotate pages withpersona andphase
Machine: derived from data
cluster visitors - persona
cluster pages in visit - phase
do these agree?
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Log Likelihood Ratio - relatedness of pages
A not A
B x 20 - x 20
not B 40 - x 140 + x 180
40 160 200
LLR A, B
total # visitors
visitors of B
visitors of A
visitors of A & B
LLR as “weight” between vertices
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sonehippo.com pages
(inverse) distanceLLR
colorJourney phase(pink: no phase)Computer
says NO
Exploratory Analysis - Do man & machine agree?
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Summarizing
buy?
Algorithms to use in anger
● contextual / multi-armed banditparticularly in (realtime) reinforcement learning
● Log Likelihood Ratio
The Customer Journey is complex
● should it be left to man?
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Questions?