Rise of the machine (learning algorithms)

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This document discusses how machine learning algorithms can be used to optimize websites through data-driven experimentation and personalization. It describes techniques like multi-armed bandits, Bayesian modeling, and Thompson sampling that can be used to test multiple variants and identify high performing content. Behavioral targeting and personalization approaches are also discussed that use visitor data to tailor website experiences. While machine learning shows promise in analyzing customer journeys, the document notes that fully replacing human judgment may still be premature given the complexity involved.

RiseoftheMachineLearningAlgorithms
Rise of the Machine
(learning algorithms)
data driven website optimization
Frank van Lankvelt
Senior Big Data Engineer / Architect

RiseoftheMachineLearningAlgorithms
The B2B Customer Journey
discover compare
consider - business
consider - technical
buy

RiseoftheMachineLearningAlgorithms
Inbound / Advertising
discover
● SEA
● Display
● Affiliate
● Social
● Native

RiseoftheMachineLearningAlgorithms
Machine based Optimization

RiseoftheMachineLearningAlgorithms
SEA, Display

RiseoftheMachineLearningAlgorithms
Multi-armed Bandits
balancing
Exploitation
with
Exploration

RiseoftheMachineLearningAlgorithms
Bayes’ Theorem
Proposition A and evidence B,
P(A), the prior
P(A|B), the posterior
the quotient P(B|A)/P(B) represents the support B provides for
A.

RiseoftheMachineLearningAlgorithms
Conversion rate Distribution
hit: 0
miss: 0
hit: 10
miss: 40
hit: 1
miss: 4
hit: 100
miss: 400

RiseoftheMachineLearningAlgorithms
With multiple options
Multiple distributions
A - the incumbent
B - the challenger
How often should B be
shown?
Thompson Sampling:
sample distributions
show variant with highest conversion rate Red: old configuration
Blue: new configuration

RiseoftheMachineLearningAlgorithms
Beyond the banner
discover compare
● A/B testing
● content experiments

RiseoftheMachineLearningAlgorithms
Rinse & Repeat?

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Content Experiments - Setup
A B

RiseoftheMachineLearningAlgorithms
Content Experiments - Reporting
Experiment
goal / conversion
conversion rates
historical servings
Multi-armed / Contextual Bandit

RiseoftheMachineLearningAlgorithms
Train Model Visits
Bandit
Model
Content Experiments - Data flow
Site
Sessionize
Requestlog

RiseoftheMachineLearningAlgorithms
Personalizing
compare
● targeting
● personalization
● recommendation
● email marketing
consider - business
consider - technical

RiseoftheMachineLearningAlgorithms
Getting personal?

RiseoftheMachineLearningAlgorithms
Targeting & Personalization
Default
Amsterdam

RiseoftheMachineLearningAlgorithms
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

RiseoftheMachineLearningAlgorithms
Audience Experiment
does audience
configuration
improve
conversion?

RiseoftheMachineLearningAlgorithms
Contextual bandit
Conversion rate depends on context:
x the context
w the weights
𝚽 cdf of normal dist.

RiseoftheMachineLearningAlgorithms
CTR prediction - under the hood

RiseoftheMachineLearningAlgorithms
CTR Context / Features
Ad features
bid phrases
ad title
ad text
landing page URL
landing page itself
a hierarchy of advertiser, account,
campaign, ad group and ad
Query features
search keywords
possible algorithmic query expansion
cleaning and stemming
Context features
display location
geographic location
time
user data
search history
Cardinalities varies
● gender (2 values)
● userID (billions)
x, w very large (sparse) vectors

RiseoftheMachineLearningAlgorithms
Persuasion Principles
Persuasion Principles
● Social Proof
● Scarcity
● Authority
● Reciprocity
● Commitment
● Liking

RiseoftheMachineLearningAlgorithms
Persuasion API > In pictures

RiseoftheMachineLearningAlgorithms
Modeling the Visitor
Multi-armed bandit per visitor
● each principle gets an arm
● model persuasion principle susceptibility
raise conversion by
10%
Authority

RiseoftheMachineLearningAlgorithms
The B2B Customer Journey
is this real?
or just fantasy?

RiseoftheMachineLearningAlgorithms
Work in Progress

RiseoftheMachineLearningAlgorithms
Mapping the Customer Journey
Man: behavioral targeting
annotate pages with
persona and
phase
Machine: derived from data
cluster visitors - persona
cluster pages in visit - phase
do these agree?

RiseoftheMachineLearningAlgorithms
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

RiseoftheMachineLearningAlgorithms
onehippo.com pages
(inverse) distance
LLR
color
Journey phase
(pink: no phase)
Computer
says NO
Exploratory Analysis - Do man & machine agree?

RiseoftheMachineLearningAlgorithms
Summarizing
buy?
Algorithms to use in anger
● contextual / multi-armed bandit
particularly in (realtime) reinforcement learning
● Log Likelihood Ratio
The Customer Journey is complex
● should it be left to man?

RiseoftheMachineLearningAlgorithms
Questions?

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Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-und-domino-lizenzkostenreduzierung-in-der-welt-von-dlau/ DLAU und die Lizenzen nach dem CCB- und CCX-Modell sind für viele in der HCL-Community seit letztem Jahr ein heißes Thema. Als Notes- oder Domino-Kunde haben Sie vielleicht mit unerwartet hohen Benutzerzahlen und Lizenzgebühren zu kämpfen. Sie fragen sich vielleicht, wie diese neue Art der Lizenzierung funktioniert und welchen Nutzen sie Ihnen bringt. Vor allem wollen Sie sicherlich Ihr Budget einhalten und Kosten sparen, wo immer möglich. Das verstehen wir und wir möchten Ihnen dabei helfen! Wir erklären Ihnen, wie Sie häufige Konfigurationsprobleme lösen können, die dazu führen können, dass mehr Benutzer gezählt werden als nötig, und wie Sie überflüssige oder ungenutzte Konten identifizieren und entfernen können, um Geld zu sparen. Es gibt auch einige Ansätze, die zu unnötigen Ausgaben führen können, z. B. wenn ein Personendokument anstelle eines Mail-Ins für geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche Fälle und deren Lösungen. Und natürlich erklären wir Ihnen das neue Lizenzmodell. Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt näherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Überblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten. Diese Themen werden behandelt - Reduzierung der Lizenzkosten durch Auffinden und Beheben von Fehlkonfigurationen und überflüssigen Konten - Wie funktionieren CCB- und CCX-Lizenzen wirklich? - Verstehen des DLAU-Tools und wie man es am besten nutzt - Tipps für häufige Problembereiche, wie z. B. Team-Postfächer, Funktions-/Testbenutzer usw. - Praxisbeispiele und Best Practices zum sofortigen Umsetzen

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In this second installment of our Essentials of Automations webinar series, we’ll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation. We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios. Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!

HCL Notes and Domino License Cost Reduction in the World of DLAU

panagenda

Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-and-domino-license-cost-reduction-in-the-world-of-dlau/ The introduction of DLAU and the CCB & CCX licensing model caused quite a stir in the HCL community. As a Notes and Domino customer, you may have faced challenges with unexpected user counts and license costs. You probably have questions on how this new licensing approach works and how to benefit from it. Most importantly, you likely have budget constraints and want to save money where possible. Don’t worry, we can help with all of this! We’ll show you how to fix common misconfigurations that cause higher-than-expected user counts, and how to identify accounts which you can deactivate to save money. There are also frequent patterns that can cause unnecessary cost, like using a person document instead of a mail-in for shared mailboxes. We’ll provide examples and solutions for those as well. And naturally we’ll explain the new licensing model. Join HCL Ambassador Marc Thomas in this webinar with a special guest appearance from Franz Walder. It will give you the tools and know-how to stay on top of what is going on with Domino licensing. You will be able lower your cost through an optimized configuration and keep it low going forward. These topics will be covered - Reducing license cost by finding and fixing misconfigurations and superfluous accounts - How do CCB and CCX licenses really work? - Understanding the DLAU tool and how to best utilize it - Tips for common problem areas, like team mailboxes, functional/test users, etc - Practical examples and best practices to implement right away

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Rise of the machine (learning algorithms)

1. RiseoftheMachineLearningAlgorithms Rise of the Machine (learning algorithms) data driven website optimization Frank van Lankvelt Senior Big Data Engineer / Architect

2. RiseoftheMachineLearningAlgorithms The B2B Customer Journey discover compare consider - business consider - technical buy

3. RiseoftheMachineLearningAlgorithms Inbound / Advertising discover ● SEA ● Display ● Affiliate ● Social ● Native

4. RiseoftheMachineLearningAlgorithms Machine based Optimization

5. RiseoftheMachineLearningAlgorithms SEA, Display

6. RiseoftheMachineLearningAlgorithms Multi-armed Bandits balancing Exploitation with Exploration

7. RiseoftheMachineLearningAlgorithms Bayes’ Theorem Proposition A and evidence B, P(A), the prior P(A|B), the posterior the quotient P(B|A)/P(B) represents the support B provides for A.

8. RiseoftheMachineLearningAlgorithms Conversion rate Distribution hit: 0 miss: 0 hit: 10 miss: 40 hit: 1 miss: 4 hit: 100 miss: 400

9. RiseoftheMachineLearningAlgorithms With multiple options Multiple distributions A - the incumbent B - the challenger How often should B be shown? Thompson Sampling: sample distributions show variant with highest conversion rate Red: old configuration Blue: new configuration

10. RiseoftheMachineLearningAlgorithms Beyond the banner discover compare ● A/B testing ● content experiments

11. RiseoftheMachineLearningAlgorithms Rinse & Repeat?

12. RiseoftheMachineLearningAlgorithms Content Experiments - Setup A B

13. RiseoftheMachineLearningAlgorithms Content Experiments - Reporting Experiment goal / conversion conversion rates historical servings Multi-armed / Contextual Bandit

14. RiseoftheMachineLearningAlgorithms Train Model Visits Bandit Model Content Experiments - Data flow Site Sessionize Requestlog

15. RiseoftheMachineLearningAlgorithms Personalizing compare ● targeting ● personalization ● recommendation ● email marketing consider - business consider - technical

16. RiseoftheMachineLearningAlgorithms Getting personal?

17. RiseoftheMachineLearningAlgorithms Targeting & Personalization Default Amsterdam

18. RiseoftheMachineLearningAlgorithms 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

19. RiseoftheMachineLearningAlgorithms Audience Experiment does audience configuration improve conversion?

20. RiseoftheMachineLearningAlgorithms Contextual bandit Conversion rate depends on context: x the context w the weights 𝚽 cdf of normal dist.

21. RiseoftheMachineLearningAlgorithms CTR prediction - under the hood

22. RiseoftheMachineLearningAlgorithms CTR Context / Features Ad features bid phrases ad title ad text landing page URL landing page itself a hierarchy of advertiser, account, campaign, ad group and ad Query features search keywords possible algorithmic query expansion cleaning and stemming Context features display location geographic location time user data search history Cardinalities varies ● gender (2 values) ● userID (billions) x, w very large (sparse) vectors

23. RiseoftheMachineLearningAlgorithms Persuasion Principles Persuasion Principles ● Social Proof ● Scarcity ● Authority ● Reciprocity ● Commitment ● Liking

24. RiseoftheMachineLearningAlgorithms Persuasion API > In pictures

25. RiseoftheMachineLearningAlgorithms Modeling the Visitor Multi-armed bandit per visitor ● each principle gets an arm ● model persuasion principle susceptibility raise conversion by 10% Authority

26. RiseoftheMachineLearningAlgorithms The B2B Customer Journey is this real? or just fantasy?

27. RiseoftheMachineLearningAlgorithms Work in Progress

28. RiseoftheMachineLearningAlgorithms Mapping the Customer Journey Man: behavioral targeting annotate pages with persona and phase Machine: derived from data cluster visitors - persona cluster pages in visit - phase do these agree?

29. RiseoftheMachineLearningAlgorithms 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

30. RiseoftheMachineLearningAlgorithms onehippo.com pages (inverse) distance LLR color Journey phase (pink: no phase) Computer says NO Exploratory Analysis - Do man & machine agree?

31. RiseoftheMachineLearningAlgorithms Summarizing buy? Algorithms to use in anger ● contextual / multi-armed bandit particularly in (realtime) reinforcement learning ● Log Likelihood Ratio The Customer Journey is complex ● should it be left to man?

32. RiseoftheMachineLearningAlgorithms Questions?

Rise of the machine (learning algorithms)

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