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CH2019 keynote: Guy Yalif - AI and personalization demystified

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CH2019 keynote: Guy Yalif - AI and personalization demystified

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(video summary at: https://conversionhotel.com/session/keynote-2019-ai-personalization-demystified/)

Guy has a background that is quite impressive. From Princeton to 3 years Boston Consulting Group in the 90’s and then on for his MBA at Stanford and MBA internship at Microsoft. Guy did his time at an online marketplace start-up that got acquired before he moved to Yahoo! to have several product marketing positions for 7 years in a row. He has been the head of global product and industry marketing at Twitter and was member of the executive team at Brightroll. Nowadays he is the founder and CEO of a start-up that uses machine learning to optimize websites.

The number 1 outcome of the #CH2019 attendee question on what the hot topics in our industry are for the coming year, was: personalization. AI and Machine Learnings were also high on this list, the combination of these topics is the sweet-spot where we all know of that the companies we work for are going to invest heavenly in the next coming years. But as true data driven optimizers we also know that many companies won’t have the users to do proper personalization and/or machine learning. We also believe that they should fix the basics first…

Nevertheless we know we are going to have to deal a lot with personalization and machine learning in our jobs. It may sound scary, because you don’t exactly know what it is and how to apply it, but you also know that this is the way to the future (or was McDonald’s buying Dynamic Yield a big fail?). But how to take the first steps in a proper way? When I thought of a potential speaker that could demystify AI and personalization for us – I thought of Guy.

Happy learning,

Ton Wesseling

Founder & host of The Conference formerly known as Conversion Hotel

(video summary at: https://conversionhotel.com/session/keynote-2019-ai-personalization-demystified/)

Guy has a background that is quite impressive. From Princeton to 3 years Boston Consulting Group in the 90’s and then on for his MBA at Stanford and MBA internship at Microsoft. Guy did his time at an online marketplace start-up that got acquired before he moved to Yahoo! to have several product marketing positions for 7 years in a row. He has been the head of global product and industry marketing at Twitter and was member of the executive team at Brightroll. Nowadays he is the founder and CEO of a start-up that uses machine learning to optimize websites.

The number 1 outcome of the #CH2019 attendee question on what the hot topics in our industry are for the coming year, was: personalization. AI and Machine Learnings were also high on this list, the combination of these topics is the sweet-spot where we all know of that the companies we work for are going to invest heavenly in the next coming years. But as true data driven optimizers we also know that many companies won’t have the users to do proper personalization and/or machine learning. We also believe that they should fix the basics first…

Nevertheless we know we are going to have to deal a lot with personalization and machine learning in our jobs. It may sound scary, because you don’t exactly know what it is and how to apply it, but you also know that this is the way to the future (or was McDonald’s buying Dynamic Yield a big fail?). But how to take the first steps in a proper way? When I thought of a potential speaker that could demystify AI and personalization for us – I thought of Guy.

Happy learning,

Ton Wesseling

Founder & host of The Conference formerly known as Conversion Hotel

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CH2019 keynote: Guy Yalif - AI and personalization demystified

  1. 1. Intellimize Confidential AI & Personalization Demystified Friday, November 22, 2019 guy@intellimize.com intellimize.com
  2. 2. Intellimize Confidential 2
  3. 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. 4. Intellimize Confidential Personalization before AI: A/B testing 4
  5. 5. Intellimize ConfidentialIntellimize Confidential If this… Netherlands promoAmsterdam then that… Rules based personalization 5
  6. 6. Intellimize ConfidentialIntellimize Confidential If this… Message Offer Image Audience Page Context Netherlands promoAmsterdam then that… Rules based personalization 6
  7. 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. 8. Intellimize Confidential Data Model training Prediction Machine learning based personalization 8
  9. 9. Intellimize Confidential Myth #1: I can completely turn over website personalization to AI Intellimize Confidential
  10. 10. Intellimize Confidential Myth #2: Everyone talking AI is telling me the truth Intellimize Confidential
  11. 11. Intellimize Confidential Myth #3: AI is too complicated for me to use for personalization Intellimize Confidential
  12. 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. 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. 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. 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. 16. Intellimize Confidential Using ML to learn about prospects 25x faster learning Value messaging Speed messaging Innovation messaging
  17. 17. Intellimize Confidential Supervised / unsupervised / reinforcement machine learning
  18. 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. 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. 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. 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. 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. 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. 24. Intellimize Confidential Getting it right: more than an algorithm 24 Experience Data management Tuning
  25. 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. 26. Intellimize Confidential Questions? guy@intellimize.com

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