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Rise of the machine (learning algorithms)

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Description of some of the algorithms for optimizing the (initial steps in the) Customer Journey.

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Rise of the machine (learning algorithms)

  1. 1. RiseoftheMachineLearningAlgorithms Rise of the Machine (learning algorithms) data driven website optimization Frank van Lankvelt Senior Big Data Engineer / Architect
  2. 2. RiseoftheMachineLearningAlgorithms The B2B Customer Journey discover compare consider - business consider - technical buy
  3. 3. RiseoftheMachineLearningAlgorithms Inbound / Advertising discover ● SEA ● Display ● Affiliate ● Social ● Native
  4. 4. RiseoftheMachineLearningAlgorithms Machine based Optimization
  5. 5. RiseoftheMachineLearningAlgorithms SEA, Display
  6. 6. RiseoftheMachineLearningAlgorithms Multi-armed Bandits balancing Exploitation with Exploration
  7. 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. 8. RiseoftheMachineLearningAlgorithms Conversion rate Distribution hit: 0 miss: 0 hit: 10 miss: 40 hit: 1 miss: 4 hit: 100 miss: 400
  9. 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. 10. RiseoftheMachineLearningAlgorithms Beyond the banner discover compare ● A/B testing ● content experiments
  11. 11. RiseoftheMachineLearningAlgorithms Rinse & Repeat?
  12. 12. RiseoftheMachineLearningAlgorithms Content Experiments - Setup A B
  13. 13. RiseoftheMachineLearningAlgorithms Content Experiments - Reporting Experiment goal / conversion conversion rates historical servings Multi-armed / Contextual Bandit
  14. 14. RiseoftheMachineLearningAlgorithms Train Model Visits Bandit Model Content Experiments - Data flow Site Sessionize Requestlog
  15. 15. RiseoftheMachineLearningAlgorithms Personalizing compare ● targeting ● personalization ● recommendation ● email marketing consider - business consider - technical
  16. 16. RiseoftheMachineLearningAlgorithms Getting personal?
  17. 17. RiseoftheMachineLearningAlgorithms Targeting & Personalization Default Amsterdam
  18. 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. 19. RiseoftheMachineLearningAlgorithms Audience Experiment does audience configuration improve conversion?
  20. 20. RiseoftheMachineLearningAlgorithms Contextual bandit Conversion rate depends on context: x the context w the weights 𝚽 cdf of normal dist.
  21. 21. RiseoftheMachineLearningAlgorithms CTR prediction - under the hood
  22. 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. 23. RiseoftheMachineLearningAlgorithms Persuasion Principles Persuasion Principles ● Social Proof ● Scarcity ● Authority ● Reciprocity ● Commitment ● Liking
  24. 24. RiseoftheMachineLearningAlgorithms Persuasion API > In pictures
  25. 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. 26. RiseoftheMachineLearningAlgorithms The B2B Customer Journey is this real? or just fantasy?
  27. 27. RiseoftheMachineLearningAlgorithms Work in Progress
  28. 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. 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. 30. RiseoftheMachineLearningAlgorithms onehippo.com pages (inverse) distance LLR color Journey phase (pink: no phase) Computer says NO Exploratory Analysis - Do man & machine agree?
  31. 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. 32. RiseoftheMachineLearningAlgorithms Questions?

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