CH2019 keynote: Guy Yalif - AI and personalization demystified

1. Intellimize Conﬁdential AI & Personalization Demystified Friday, November 22, 2019 guy@intellimize.com intellimize.com

2. Intellimize Conﬁdential 2

3. Intellimize Conﬁdential Awareness Email Persado Advertising AdWords Facebook Consideration Lead scoring (b2b) InferMadKudu Purchase Chat IntercomDrift Where can we use AI today? 3 AI based website personalization Intellimize Intellimize Conﬁdential

4. Intellimize Conﬁdential Personalization before AI: A/B testing 4

5. Intellimize ConﬁdentialIntellimize Conﬁdential If this… Netherlands promoAmsterdam then that… Rules based personalization 5

6. Intellimize ConfidentialIntellimize Confidential If this… Message Offer Image Audience Page Context Netherlands promoAmsterdam then that… Rules based personalization 6

7. Intellimize ConfidentialIntellimize Confidential If this… Message Offer Image Audience Page Context Netherlands promoAmsterdam then that… Message Offer Image Audience Page Context Message Offer Image Audience Page Context Message Offer Image Audience Page Context Message Offer Image Audience Page Context Message Offer Image Audience Page Context Message Offer Image Audience Page Context Message Offer Image Audience Page Context Message Offer Image Audience Page Context Message Offer Image Audience Page Context Message Offer Image Audience Page Context Message Offer Image Audience Page Context Message Offer Image Audience Page Context Rules based personalization 7

8. Intellimize Conﬁdential Data Model training Prediction Machine learning based personalization 8

9. Intellimize Conﬁdential Myth #1: I can completely turn over website personalization to AI Intellimize Conﬁdential

10. Intellimize Conﬁdential Myth #2: Everyone talking AI is telling me the truth Intellimize Conﬁdential

11. Intellimize Conﬁdential Myth #3: AI is too complicated for me to use for personalization Intellimize Conﬁdential

12. Intellimize Confidential Belief #1: Manage both ABM and anonymous traffic ABM for named accounts using rules + ML Welcome visitor by company name Mention account executive by name Iterate on calls to action and pieces of content Rest of your anonymous traffic using ML Show an industry specific case study Iterate headlines, calls to action, and pieces of content Tailor content based on previous behavior on the site Note: This is an example. These are not things Okta is doing Note: ABM = Account Based Marketing. ML = machine learning

13. Intellimize Conﬁdential Belief #2: Testing and personalization go together Optimize to deliver more revenue / customers / leads A/B testing Find a better way to engage everyone Personalization Find a better way to engage each segment or unique visitor

14. Intellimize Conﬁdential Belief #2: Testing and personalization go together Optimize to deliver more revenue / customers / leads A/B testing Find a better way to engage everyone Rules based personalization Find a better way to engage each segment Faster Find a winner for everyone Slower Test to ﬁnd a winner for each segment

15. Intellimize Conﬁdential Belief #2: Testing and personalization go together Optimize to deliver more revenue / customers / leads A/B testing Find a better way to engage everyone Machine learning personalization Find a better way to engage each segment or unique visitor Slower Find a winner for everyone Faster Find a winner for each unique visitor

16. Intellimize Conﬁdential Using ML to learn about prospects 25x faster learning Value messaging Speed messaging Innovation messaging

17. Intellimize Conﬁdential Supervised / unsupervised / reinforcement machine learning

18. Intellimize Conﬁdential Machine learning branches: supervised learning We teach the computer how to predict something based on input ● Most common machine learning problem ● Trained on historical data where the “right answer” is known ○ These are called ‘labeled’ training examples ● Maps inputs (what you know now) to outputs (success or failure) using historical and accurate data ● Uses this mapping to guess the value of future events that the model does not already know about, based on what the model has already learned

19. Intellimize Conﬁdential Machine learning branches: unsupervised learning The computer teaches itself how to predict something. You don’t yet know what that something is ● Trained on historical examples where the outcome not known (ie you don’t know what success looks like) ○ These are called ‘unlabeled’ training examples ● The objective is to discover structure in the data (eg through a cluster analysis) ○ This objective is not to map inputs to outputs

20. Intellimize Confidential Machine learning branches: reinforcement learning You train the computer to achieve some eventual goal ● Only know the final outcome (aka unlabeled intermediate steps) ● Only get to learn based on the choices model makes ● Explore-exploit tradeoff to balance

21. Intellimize ConfidentialIntellimize Confidential Type of ML problem What is machine learning good at in marketing? ● Lead scoring ● Ideal price ● Ideal promotion amount Regression (predicting a continuous value or number) ● Will this person click on this ad (yes or no) ● High / medium / low lead score ● Email is spam or not spam Classification (predicting among discrete options) ● Which product to show ● Which content to show Recommendation ● Understand speech (ie NLP) ● Recognize the content of an image ● Write an email subject line Speech / image recognition ● Customer segmentation ● Find business insights from data Clustering ● Fraud detection ● Outlier detection Anomaly detection ● Sequence of emails to send ● CRO Reinforcement learning Problem Unsupervised learningRL Supervised learning 21

22. Intellimize ConfidentialIntellimize Confidential Type of ML problem Theory vs realistic application Regression (predicting a continuous value or number) Classification (predicting among discrete options) Recommendation Speech / image recognition Clustering Anomaly detection Reinforcement learning 22 Realistic implementation: content reco Classification: offensive / non-offensive Classification: popular / not popular Collaborative filtering: ranked list Rule: no more than 2 items from each category

23. Intellimize Conﬁdential Type of ML problem Theory vs realistic application Regression (predicting a continuous value or number) Classiﬁcation (predicting among discrete options) Recommendation Speech / image recognition Clustering Anomaly detection Reinforcement learning 23 Realistic implementation: ensemble learning Regression #1 Regression #2 Regression #3 Regression #4 Regression #5 Score

24. Intellimize Conﬁdential Getting it right: more than an algorithm 24 Experience Data management Tuning

25. Intellimize Conﬁdential 1. Most AI you see in market is “if this… then that…” rules based logic 2. Testing and personalization should be used together for revenue/customers/leads 3. The best outcomes happen when we combine optimizers and AI 4. AI can accelerate your testing and personalization materially 5. b2b marketers can tailor prospects’ journeys using both ABM and AI together 6. Practically applying AI is much more than picking the right algorithm 7. You can and should meet your prospects where they are in their journey with you Top 7 takeaways

26. Intellimize Conﬁdential Questions? guy@intellimize.com

CH2019 keynote: Guy Yalif - AI and personalization demystified

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