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Machine Learning for Marketers by Mike King at The Inbounder New York

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Mike King's presentation at The Inbounder New York, May 22 2017.

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Machine Learning for Marketers by Mike King at The Inbounder New York

  1. 1. MICHAEL KING MACHINE LEARNING FOR MARKETERS @iPullRank
  2. 2. IPULLRANK.COM @ IPULLRANK Agenda Machine Learning Doomsday ML vs DL vs AI? Marketing Use Cases Models & Use Cases Tools For Marketers Wrapping Up Real World Examples
  3. 3. Machine Learning Doomsday What everyone is afraid of
  4. 4. IPULLRANK.COM @ IPULLRANK Smart People are Scared of Artificial Intelligence
  5. 5. IPULLRANK.COM @ IPULLRANK Shockingly, He Now Wants To Sell Us Something
  6. 6. IPULLRANK.COM @ IPULLRANK Really Smart People…
  7. 7. It’s All Olivia Pope’s dad’s fault Either the Robots Enslave Us
  8. 8. Or Kill Us All
  9. 9. Or Evolve to a Point We Can’t Understand
  10. 10. Or we achieve singularity
  11. 11. IPULLRANK.COM @ IPULLRANK Singularity is Considered a Very Real Theory Ray Kurzweil believes that we will achieve singularity by 2045.
  12. 12. IPULLRANK.COM @ IPULLRANK …And Larry & Sergey from Google are All Set Though
  13. 13. IPULLRANK.COM @ IPULLRANK But if We’re Nerding Out, Don’t Forget Isaac Asimov
  14. 14. but in the meantime…
  15. 15. IPULLRANK.COM @ IPULLRANK Apparently Machine Learning Can Write Copy For you There is a sub-field of artificial intelligence called Natural Language Generation that has made the concept of content spinning a lot more viable and has been used for sports recaps and financial reports.
  16. 16. IPULLRANK.COM @ IPULLRANK But It Can Also F*ck It Up
  17. 17. IPULLRANK.COM @ IPULLRANK Improved Google Translate from Scratch in 2 Months A team rebuilt the broken Google Translate using Machine Learning and within 2 months it was already as good as the version that had taken years to build.
  18. 18. IPULLRANK.COM @ IPULLRANK AI Is Gonna Steal Your Job? One of the more common fears of middle America around the idea of artificial intelligence is that robots will replace humans in their jobs.
  19. 19. IPULLRANK.COM @ IPULLRANK Obama Had Some Measured Thoughts On His Way Out
  20. 20. The real fear of machine learning and artificial intelligence should be its ability to reflect and amplify our biases and the lack of diversity of the people creating it.
  21. 21. IPULLRANK.COM @ IPULLRANK
  22. 22. IPULLRANK.COM @ IPULLRANK For now though, it can get you a date (h/t @goutaste)
  23. 23. IPULLRANK.COM @ IPULLRANK And you can go there in a self-driving Uber Lyft
  24. 24. WARNING! This is going to be some heady stuff.
  25. 25. This my daughter Zora. She’ll show up whenever y’all get bored
  26. 26. Machine Learning vs. Deep Learning vs. AI The Core Concepts
  27. 27. IPULLRANK.COM @ IPULLRANK They Are Not the Same Thing
  28. 28. IPULLRANK.COM @ IPULLRANK AI is Comprised of Many Disciplines Deep Learning is a subset of Machine Learning is a subset of Artificial Intelligence. AI many branches of which machine learning is a core branch that we can execute.
  29. 29. Artificial Intelligence as it is represented in sci-fi is “general” artificial intelligence. What we have achieved so far is “narrow” artificial intelligence.
  30. 30. IPULLRANK.COM @ IPULLRANK Types of Artificial Intelligence Explained Using “The Lawnmower Man” Narrow Artificial Intelligence Machines that can do a specific task or series of tasks exceedingly well and very efficiently. General Artificial Intelligence A machine that is as smart as a human in that it can take in new situations and make decisions. Artificial Superintelligence A machine that is potentially orders of magnitude smarter than a human in all categories simultaneously
  31. 31. IPULLRANK.COM @ IPULLRANK Experts Disagree on When General Intelligence Will Happen The primary issue keeping this from happening is computing power.
  32. 32. IPULLRANK.COM @ IPULLRANK Experts Disagree on When General Intelligence Will Happen The primary issue keeping this from happening is computing power.
  33. 33. IPULLRANK.COM @ IPULLRANK Accelerating Moore’s Law Google has been working on quantum computing to accelerate Moore’s Law
  34. 34. IPULLRANK.COM @ IPULLRANK 100mm times faster than a classical computer by using a D-Wave quantum computer NewScientist.com
  35. 35. Ok. So, What Is Machine Learning? “Machine learning is a type of artificial intelligence that provides computers with the ability to learn without being explicitly programmed.”
  36. 36. It’s Really Just Using Math to Guess and Check
  37. 37. IPULLRANK.COM @ IPULLRANK Supervised Learning The machine looks for patterns that match the labeled data that you provide and classifies new data based on that.
  38. 38. IPULLRANK.COM @ IPULLRANK Unsupervised Learning The machine identifies patterns in the data and creates clusters based on what it finds.
  39. 39. IPULLRANK.COM @ IPULLRANK Reinforcement Learning With reinforcement learning, the model is continually trained based on new data thereby improving the classifier’s ability to perform.
  40. 40. And Deep Learning? “Deep Learning is a subfield of machine learning concerned with algorithms inspired by the structure and function of the brain called artificial neural networks.”
  41. 41. That’s not what we’re talking about today.
  42. 42. Machine Learning vs. Statistics Machine Learning learns from data without relying on rules-based programming, statistical modeling identifies relationships in the form of mathematical equations.
  43. 43. IPULLRANK.COM @ IPULLRANK All Values vs. Linear Representation Machine Learning examines all potential values based on probability whereas statistics looks for a linear function to describe the trend.
  44. 44. IPULLRANK.COM @ IPULLRANK Machine Learning is the “Growth Hacking” of the Statistics World However, in some ways machine learning and statistics are so similar that many statisticians just feel as though machine learning is just a rebranding of what they do much like “growth hacking” is just a rebranding of marketing.
  45. 45. IPULLRANK.COM @ IPULLRANK The Machine Learning Process GET & PREPARE YOUR DATA You identify and clean your dataset in preparation for solving the machine learning problem CHOOSE YOUR MODEL TRAIN YOUR CLASSIFIER You chose the algorithm or model that you believe will yield the best results then run it in order to train your classifier. SCORE AND EVALUATE You score the accuracy and precision of the classifier and test it against other algorithms to see what performs best. PREDICT OR IDENTIFY OUTCOMES Once you are happy with the results, you use the classifier moving forward to make conclusions about new data.
  46. 46. IPULLRANK.COM @ IPULLRANK This is an example of how you could predict the demand of cars for a car rental company. It follows the same framework. Car Rental Example
  47. 47. IPULLRANK.COM @ IPULLRANK Training Chatbots Training chatbots is similar to training ML classifiers in that you take a knowledge base and run it through NLP then tune it with regard to conversations.
  48. 48. Marketing Use Cases How can you use it?
  49. 49. IPULLRANK.COM @ IPULLRANK Predictive Analytics
  50. 50. IPULLRANK.COM @ IPULLRANK Marketing Campaign Performance Prediction
  51. 51. IPULLRANK.COM @ IPULLRANK Customer Churn Prediction
  52. 52. IPULLRANK.COM @ IPULLRANK Personalization
  53. 53. IPULLRANK.COM @ IPULLRANK Customer Segmentation
  54. 54. IPULLRANK.COM @ IPULLRANK Natural Language Processing
  55. 55. IPULLRANK.COM @ IPULLRANK Clustering & Classifying Keywords http://ipullrank.com/clustering-vs-classification-speed-keyword-research/
  56. 56. Follow Vicky Qian @vickyqian24
  57. 57. IPULLRANK.COM @ IPULLRANK Sentiment Analysis
  58. 58. IPULLRANK.COM @ IPULLRANK Natural Language Generation
  59. 59. IPULLRANK.COM @ IPULLRANK Computer Vision There are services that leverage machine learning and computer vision to identify objects in pictures.
  60. 60. IPULLRANK.COM @ IPULLRANK Chatbots
  61. 61. IPULLRANK.COM @ IPULLRANK Training Chatbots Training chatbots is similar to training ML classifiers in that you take a knowledge base and run it through NLP then tune it with regard to conversations.
  62. 62. Key Takeaway Machine Learning, like all of digital marketing, is a complicated form of guess and check.
  63. 63. IPULLRANK.COM @ IPULLRANK Shit, Google Doesn’t Even Know How Rankbrain Works Yet, the world’s greatest search engine has deployed it to production.
  64. 64. IPULLRANK.COM @ IPULLRANK Remember this? A team rebuilt the broken Google Translate using Machine Learning and within 2 months it was already as good as the version that had taken years to build.
  65. 65. IPULLRANK.COM @ IPULLRANK Look Closer A team rebuilt the broken Google Translate using Machine Learning and within 2 months it was already as good as the version that had taken years to build.
  66. 66. IPULLRANK.COM @ IPULLRANK Guess and Check
  67. 67. Don’t be afraid of it, just try something. Anything.
  68. 68. Real World Examples Some things we work on
  69. 69. IPULLRANK.COM @ IPULLRANK We Re-Ranked the Inc. 500
  70. 70. IPULLRANK.COM @ IPULLRANK Follow Up Blog Content
  71. 71. IPULLRANK.COM @ IPULLRANK Company-level Report
  72. 72. IPULLRANK.COM @ IPULLRANK Retargeting Ads
  73. 73. IPULLRANK.COM @ IPULLRANK Super-specific Retargeting Ads
  74. 74. IPULLRANK.COM @ IPULLRANK We Built a Simple Marketing Automation System
  75. 75. IPULLRANK.COM @ IPULLRANK Each Contact Has a Unique URL
  76. 76. IPULLRANK.COM @ IPULLRANK Integrates with Reply
  77. 77. IPULLRANK.COM @ IPULLRANK LinkedIn Sales Navigator
  78. 78. IPULLRANK.COM @ IPULLRANK Prospectify for finding Emails Quickly
  79. 79. IPULLRANK.COM @ IPULLRANK Salesperson Writes Mail Merge Templates
  80. 80. IPULLRANK.COM @ IPULLRANK 42% Open Rate!
  81. 81. 732 Leads
  82. 82. IPULLRANK.COM @ IPULLRANK The Methodology is the Machine Learning Part We took all available domain-level link features for the Searchmetrics losers and winners and figured out (5-fold cross validation, random forest and lasso) which ones correlated best with the results and then used that model to re-rank the Inc. 500. (I probably shoulda asked Marcus for more data, but whatever).
  83. 83. IPULLRANK.COM @ IPULLRANK Methodology behind the Vector Report We broke it into two types of machine learning questions. Classification and Logistic Regression to predict the probability of continued visibility in Organic Search. Goal: identify SEO winners and losers and predict a site’s performance in SEO Classification Random Forest Gradient Boosting Machine Support Vector Machine Logistic Regression Regularization
  84. 84. IPULLRANK.COM @ IPULLRANK Have You Met @tomcritchlowbot?
  85. 85. IPULLRANK.COM @ IPULLRANK The Bot wit the Solid Delivery.
  86. 86. IPULLRANK.COM @ IPULLRANK This is Twitter bot built from Markov Chains
  87. 87. IPULLRANK.COM @ IPULLRANK Adwords Scripts http://searchengineland.com/machine-learning-adwords-scripts-google-prediction-api-217936
  88. 88. IPULLRANK.COM @ IPULLRANK Programmatic Display Lookalike moderlign
  89. 89. Models Types of Models when you should Use Them
  90. 90. IPULLRANK.COM @ IPULLRANK There are Tons of Different Models Your best bet is to test and learn.
  91. 91. IPULLRANK.COM @ IPULLRANK Seriously Tonnnnnns
  92. 92. IPULLRANK.COM @ IPULLRANK The Uses of Each Type Are Difficult to Memorize
  93. 93. IPULLRANK.COM @ IPULLRANK Models & Use Cases Random Forest Lead Qualification Logistic Regression Customer Churn Prediction Decision Trees Customer Churn Prediction
  94. 94. IPULLRANK.COM @ IPULLRANK Models & Use Cases (Cont’d) Support Vector Machines Text Categorization Apriori Market Basket Analysis (Amazon) Naïve Bayes Sentiment Analysis Recommendation Systems Spam Classification
  95. 95. IPULLRANK.COM @ IPULLRANK K-Fold Cross Validation Try out a model and validate it using k-fold cross validation.
  96. 96. IPULLRANK.COM @ IPULLRANK K-Fold Cross Validation is a Guess and Check Try out a model and validate it using k-fold cross validation.
  97. 97. IPULLRANK.COM @ IPULLRANK How to Choose a Machine Learning Model https://docs.microsoft.com/en-us/azure/machine-learning/machine-learning-algorithm-choice
  98. 98. Ok, that was a lot. How about a song?
  99. 99. Tools for Marketers Doing ML the easy way
  100. 100. IPULLRANK.COM @ IPULLRANK
  101. 101. IPULLRANK.COM @ IPULLRANK
  102. 102. IPULLRANK.COM @ IPULLRANKMost machine learning is done in R or Python, but those are programming languages.
  103. 103. IPULLRANK.COM @ IPULLRANK yHat Science Ops yHat allows you to deploy machine learning models as REST APIs that can then be integrated with your site like any other API.
  104. 104. IPULLRANK.COM @ IPULLRANK Beeswax Bidder-as-a-Service Beeswax allows you to set up custom models to run your Display RTB campaigns.
  105. 105. Those are tools that allow marketers to take control with a data scientist.
  106. 106. IPULLRANK.COM @ IPULLRANK mTurk - Labeling Data for Supervised Learning Exploratory Data Analysis helps identifying general patterns in the data and serve as initial explorations of correlations.
  107. 107. IPULLRANK.COM @ IPULLRANK API.AI Generating Chatbots http://api.ai
  108. 108. IPULLRANK.COM @ IPULLRANK NanoRep http://www.nanorep.com
  109. 109. IPULLRANK.COM @ IPULLRANK MonkeyLearn & Orange We will primarily talk about MonkeyLearn and Orange as two tools marketers can use to do machine learning right now.
  110. 110. IPULLRANK.COM @ IPULLRANK These Examples Use the Iris Petals Dataset https://archive.ics.uci.edu/ml/datasets/Iris
  111. 111. IPULLRANK.COM @ IPULLRANK Exploratory Data Analysis Exploratory Data Analysis helps identifying general patterns in the data and serve as initial explorations of correlations.
  112. 112. IPULLRANK.COM @ IPULLRANK Exploratory Data Analysis: Scatter Plot Two-dimensional scatter plot shows class density.
  113. 113. IPULLRANK.COM @ IPULLRANK Exploratory Data Analysis: Distributions Compare the distributions of different type of iris.
  114. 114. IPULLRANK.COM @ IPULLRANK Classification Tree Observe the pattern across nodes to discover important variables.
  115. 115. IPULLRANK.COM @ IPULLRANK Predictive Text Classification
  116. 116. IPULLRANK.COM @ IPULLRANK Import Text Mining Add- on Install the free text mining add-on in order to use Orange’s text mining capabilities.
  117. 117. IPULLRANK.COM @ IPULLRANK Load and Preprocess Dataset Preprocess text to find meaningful words only.
  118. 118. IPULLRANK.COM @ IPULLRANK Word Cloud Using the word cloud, we can determine the frequency of keywords in the list.
  119. 119. IPULLRANK.COM @ IPULLRANK Hierarchical Clustering We can use this to determine similarity in the corpus or dataset.
  120. 120. IPULLRANK.COM @ IPULLRANK Hierarchical Clustering Once we understand the hierarchy, we can dig into the documents in the viewer to see how the model has organized them.
  121. 121. IPULLRANK.COM @ IPULLRANK Classification
  122. 122. IPULLRANK.COM @ IPULLRANK Test & Score is your Guess and Check
  123. 123. IPULLRANK.COM @ IPULLRANK SVM: Linear vs. Non- linear Linear SVM often outperforms non-linear in text classification.
  124. 124. IPULLRANK.COM @ IPULLRANK Confusion Matrix: Non-linear SVM Send misclassified samples to corpus viewer.
  125. 125. IPULLRANK.COM @ IPULLRANK Nearest Neighbors
  126. 126. IPULLRANK.COM @ IPULLRANK Logistic Regression: Ridge vs. Lasso Logistic regression with l2 penalty achieve higher accuracy.
  127. 127. IPULLRANK.COM @ IPULLRANK Confusion Matrix: Lasso Send misclassified samples to corpus viewer.
  128. 128. IPULLRANK.COM @ IPULLRANK Naive Bayes
  129. 129. IPULLRANK.COM @ IPULLRANK Compare Models – Again, Your Guess & Check Linear SVM and logistic regression outperform the other two models.
  130. 130. IPULLRANK.COM @ IPULLRANK Prediction Predict with winning classifiers.
  131. 131. IPULLRANK.COM @ IPULLRANK Prediction SVM and logistic regression all hit 100% accuracy.
  132. 132. IPULLRANK.COM @ IPULLRANKMonkey Learn is a text mining cloud platform.
  133. 133. IPULLRANK.COM @ IPULLRANK MonkeyLearn Now Works with Google Sheets Monkey Learn is a text mining cloud platform.
  134. 134. IPULLRANK.COM @ IPULLRANK Monkey Learn: Train Category Tree
  135. 135. IPULLRANK.COM @ IPULLRANK Monkey Learn: Tree Parameters
  136. 136. IPULLRANK.COM @ IPULLRANK Monkey Learn: Classify with Category Tree
  137. 137. IPULLRANK.COM @ IPULLRANK Codementor
  138. 138. IPULLRANK.COM @ IPULLRANK Experfy
  139. 139. IPULLRANK.COM @ IPULLRANK Kaggle
  140. 140. IPULLRANK.COM @ IPULLRANK
  141. 141. Wrapping Up Who am I and where am I from?
  142. 142. IPULLRANK.COM @ IPULLRANK I’M #ZORASDAD First and foremost.
  143. 143. IPULLRANK.COM @ IPULLRANK MY NAME IS MIKE KING Razorfish, Publicis Modem alum Full Stack Developer Full Stack Marketer Moz Associate
  144. 144. IPULLRANK.COM @ IPULLRANK I Run a Better Marketing Agency Called iPullRank
  145. 145. IPULLRANK.COM @ IPULLRANK We Do These Things Machine Learning SEOContent Strategy Paid Media Measurement & Optimization Marketing Automatio n
  146. 146. THAT’S ALL I’VE GOT
  147. 147. IPULLRANK http://ipullrank.com THANK YOU Michael King Managing Director mike@ipullrank.com

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