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NUS-ISS Learning Day 2017 - Artifical Intelligence to Engage Your Customer
- 1. #ISSlearn #ISSlearn ARTIFICIAL INTELLIGENCE TO ENGAGE YOUR CUSTOMER Know thy customer, customer giveth, customer taketh away ... Abhinit Kumar Ambastha © 2017 National University of Singapore. All Rights Reserved
- 2. #ISSlearn#ISSlearn Agenda Introduction ➔ Customer engagement realities ➔ The new picture: AI in the game! ➔ The invisibility cloak ➔ Small introduction to AI and ML AI based Recommendation systems ➔ Who’s into it and why? ➔ Recommendation systems and businesses ➔ Design and develop a recommendation system ➔ Enterprise level recommendation systems AI for pricing ➔ The price is right! ➔ Discount groups ➔ Enterprise use cases 2© 2017 National University of Singapore. All Rights Reserved AI for product ➔ Product insights ➔ Design testing What next! ➔ Jump in!
- 3. #ISSlearn Introduction Let’s get to business 3© 2017 National University of Singapore. All Rights Reserved
- 4. #ISSlearn#ISSlearn Customer engagement realities 4© 2017 National University of Singapore. All Rights Reserved Engage Promote Purchase Receive ❏ Digital services are changing how customers engage ❏ Every employee can and should be empowered ❏ Internet of things ❏ Customer experience will eclipse price and product ❏ Customer engagement is an ongoing commitment
- 5. #ISSlearn#ISSlearn Customer engagement realities ❏ Digital services are changing how customers engage ❏ Every employee can and should be empowered ❏ Internet of things ❏ Customer experience will eclipse price and product ❏ Customer engagement is an ongoing commitment 5© 2017 National University of Singapore. All Rights Reserved 2 billion active social accounts 3 billion internet users 14 billion devices 2.5 million e-commerce websites
- 6. #ISSlearn#ISSlearn Customer engagement realities ❏ Digital transformation ❏ Create more personal computing ❏ Build more intelligent cloud services ❏ Reinvent productivity and business processes 6© 2017 National University of Singapore. All Rights Reserved
- 7. #ISSlearn#ISSlearn Customer engagement realities ❏ Real challenges! ❏ Scale: community vs individual ❏ Pipeline: It’s an important cycle ❏ Data and complexity 7© 2017 National University of Singapore. All Rights Reserved
- 8. #ISSlearn#ISSlearn AI to the rescue! ❏ Recommendation systems ❏ Recommend products ❏ Recommend packages ❏ Individual offers! ❏ Interactive systems ❏ Chat bots ❏ Tunneling customer queries ❏ Offline engagement ❏ Promotions ❏ Social tracking ❏ Ads ❏ Deep sea engagement ❏ Search optimization ❏ Natural engagement ❏ Analytics 8© 2017 National University of Singapore. All Rights Reserved
- 9. #ISSlearn#ISSlearn AI to the rescue! 9© 2017 National University of Singapore. All Rights Reserved
- 10. #ISSlearn#ISSlearn Invisibility cloak ➔ Invisible AI = Ideal AI ◆ Trust builds with time ◆ The blame game! ➔ Simplified business process ➔ Explainable AI 10© 2017 National University of Singapore. All Rights Reserved
- 11. #ISSlearn Basic Introduction to Machine learning and AI “THE living hell” - grad student 11© 2017 National University of Singapore. All Rights Reserved
- 12. #ISSlearn#ISSlearn Machine learning and AI ❏ AI gives computers the ability to learn without explicit programming or teaching ❏ Data is equivalent to experience ❏ Programming is deterministic, machine learning is non-deterministic ❏ Knowledge is stored in connection weights ❏ Teaching is hard! 12
- 13. #ISSlearn#ISSlearn Machine learning stages 13 Collect data Process data Train the system (training dataset) Test (validation and test datasets) ❏ Missing data ❏ Clean data ❏ Less data... ❏ Different formats ❏ Collection methods
- 14. #ISSlearn#ISSlearn Different problems in machine learning 14 ❏ Supervised learning ❏ Classification ❏ Regression ❏ Unsupervised learning ❏ Clustering ❏ Association mining ❏ Reinforcement learning ❏ Policies and rewards ❏ Prediction? ❏ Recommendation?
- 15. #ISSlearn#ISSlearn Methods 15 ❏ Linear regression ❏ Logistic regression ❏ K-Means clustering ❏ Decision trees ❏ Neural networks ❏ Deep learning methods: ❏ CNN ❏ RNN ❏ LTSM
- 16. #ISSlearn#ISSlearn Tools 16 ❏ Python: Scikit learn, Pandas, Matplotlib, Bokeh ❏ Caffe ❏ TensorFlow ❏ Theano
- 17. #ISSlearn AI based recommendation systems Know before they know that they know it 17© 2017 National University of Singapore. All Rights Reserved
- 18. #ISSlearn#ISSlearn ❏ Purpose? ❏ Automation ❏ Scaling ❏ Better performance ❏ Aid and assistance ❏ Google pagerank ❏ Facebook friends and ads ❏ Amazon! ❏ Taobao Who’s doing it? And why? 18
- 19. #ISSlearn#ISSlearn ❏ Scarcity to abundance ❏ There are a few items which are extremely desirable but a majority of items are sold in small quantities ❏ Long tail concept! ❏ Netflix challenge ❏ Amazon recommendations ❏ LinkedIn ❏ Facebook friends ❏ Youtube ads ❏ The idea of discovering an underdog! Recommendation systems and businesses 19
- 20. #ISSlearn#ISSlearn ❏ Large catalog of items ❏ A system which provides you options based on your preferences ❏ It’s an alternate to the search methodology Recommendation systems Items... Search R ecom m end 20
- 21. #ISSlearn#ISSlearn Exempli gratia! ❏ Probably one of the largest recommendation systems! ❏ Data: facebook profiles, Instagram pictures, demographics, preferences, chat conversations, hopes, dreams, your soul ❏ Goal: Recommend possible date(s), disappointing relationships, lower self-esteem ❏ Methods: ❏ Clustering ❏ Semi-supervised ❏ Reinforcement learning 21
- 22. #ISSlearn#ISSlearn Exempli gratia! ❏ Offline computations ❏ Data from direct and indirect sources ❏ Online execution ❏ People are recommended people from the tail! 22
- 23. #ISSlearn#ISSlearn ❏ Rock star recommendations ❏ Bestseller ❏ Top movie of the month, etc. ❏ Curated lists ❏ Top 50 songs worldwide on Spotify ❏ Top 10 movie lists ❏ Mostly hand curated or from user activity ❏ Personalised recommendations ❏ Amazon, Netflix, Facebook, Spotify Generating recommendation 23
- 24. #ISSlearn#ISSlearn ❏ C = Set of customers ❏ P = Set of products ❏ R = Rating of products for the user ❏ u = Utility table which shows the rating for every product for each user Representation P1 P2 P3 P4 C1 R11 ... C2 ... 24
- 25. #ISSlearn#ISSlearn ❏ Simplest method for proposing recommendations ❏ Classify an item as “like” or “dislike” for a given user ❏ Requires a lot of data for each user ❏ Not practical Classification based recommendation 25
- 26. #ISSlearn#ISSlearn ❏ Works based on item to item similarity ❏ Create a product profile for every product based on its attributes: Content-based filtering Actor 1 1 Actor 2 1 Genre 1 0 Action rating 3.5 or [1,1,0,3.5] ❏ User profile = Average of product profile values ❏ We calculate the distance between a user and product ❏ Usually cosine distance 26
- 27. #ISSlearn#ISSlearn ❏ Pros: ❏ No need of data of other users ❏ Able to recommend users with unique taste ❏ Able to recommend products with no previous ratings ❏ Explanations for recommended items! ❏ Cons: ❏ Hand crafted attributes! (images? Music? movies?) ❏ Only recommends items inside user’s content profile Content-based filtering 27
- 28. #ISSlearn#ISSlearn Collaborative filtering ❏ Works based on user to user similarity ❏ Create a user profile based on ratings, user details, etc. ❏ Recommend movies preferred by similar users 28
- 29. #ISSlearn#ISSlearn Collaborative filtering ❏ Works based on user to user similarity ❏ Create a user profile based on ratings, user details, etc. ❏ Recommend movies preferred by similar users 1 2 3 5 2 4 3 5 5 4 ? 29
- 30. #ISSlearn#ISSlearn ❏ Latent semantic analysis ❏ User similarity measure can be derived from external sources ❏ Product interest can be registered to make better associations Collaborative filtering LDA 30
- 31. #ISSlearn#ISSlearn ❏ Pros: ❏ Captures user similarities, which can capture some latent attributes ❏ Can recommend new products outside user’s content profile ❏ Cons: ❏ Requires a user base and user activity base ❏ Doesn’t work well for new users Collaborative filtering 31
- 32. #ISSlearn#ISSlearn ❏ Possible for image based recommendations ❏ Salient associations between items ❏ Items need not be morphologically similar ❏ Products which are mentioned together, can be considered associated Association mining 32
- 33. #ISSlearn#ISSlearn ❏ Siamese networks to derive associations ❏ Automated profiling or feature selection ❏ Latent associations can be realized ❏ Mostly limited to visual associations ❏ Still in academics! Deep learning 33
- 34. #ISSlearn#ISSlearn ❏ Root mean square error metric (RMSE): Absolute distance between the predicted and actual similarity rating ❏ Hidden ratings to create test set ❏ Can only be verified in a supervised manner! ❏ True performance evaluation can be captured only by testing Evaluation of recommendation systems 34
- 35. #ISSlearn Enterprise level recommendation systems Big targets need big guns 35© 2017 National University of Singapore. All Rights Reserved
- 36. #ISSlearn#ISSlearn ❏ Highly sparse dataset ❏ Large amounts of offline calculations ❏ Product profile creation ❏ Lot of stress on engineering section Large databases, bigger problems 36
- 37. #ISSlearn#ISSlearn ❏ Use multiple algorithms: ❏ Singular vector decomposition (SVD) ❏ Deep neural nets ❏ Logistic and linear regression ❏ ... ❏ Offline processing to train models ❏ Nearline to use new data to improve offline models ❏ Online computation to generate recommendations based on user events Netflix recommendation systems 37
- 38. #ISSlearn#ISSlearn ❏ Offline stage uses complex algorithms ❏ Nearline uses comparatively simple algorithms ❏ A/B testing on subset! ❏ Netflix prize: 1 million $ for 10% improvement in their recommendation system ❏ Never used the algorithm! Netflix recommendation systems 38
- 39. #ISSlearn#ISSlearn ❏ Invented Collaborative filtering (CF) in 1998! ❏ Uses a cascade of CF systems ❏ Age old algorithm wrapped in new technologies! Amazon recommendation systems 39
- 40. #ISSlearn#ISSlearn Linkedin recommendation system ❏ Uses both content-based and collaborative filtering 40
- 41. #ISSlearn#ISSlearn Linkedin recommendation system ❏ Has structured user data ❏ Multiple events used to calculate co-interests ❏ Co-view ❏ Skill rating ❏ Connections distance ❏ Bag of models vs. blended models ❏ Engineering driven! 41
- 42. #ISSlearn AI for pricing Customer = money = more customers 42© 2017 National University of Singapore. All Rights Reserved
- 43. #ISSlearn#ISSlearn Pricing strategy using AI 43 ❏ Right pricing = more customers ❏ Uber pricing for routes ❏ Insurance companies like AXA and Aviva ❏ Static pricing vs. variable pricing? ❏ E-commerce bargaining?
- 44. #ISSlearn#ISSlearn Dynamic pricing ❏ Dynamic pricing: Using neural networks to allocate weightage to different factors ❏ Comparing different SKUs (Stock keeping units or categories) ❏ Competitor pricing ❏ Pricing rules for products in a given SKU ❏ Using popularity based pricing (Yield pricing) ❏ Discount groups ❏ Seller oriented ❏ Mostly fake! ❏ Lazada anyone? 44© 2017 National University of Singapore. All Rights Reserved
- 45. #ISSlearn#ISSlearn Pricing strategy using AI 45 ❏ Modelling customer purchasing behaviour ❏ Modelling Product flow behaviour ❏ Price suggestions! Customer behaviors, influencing factors Data science Machine learning Dashboard
- 46. #ISSlearn#ISSlearn Pricing strategy using AI 46 ❏ Dynamic pricing per user ❏ Multiple products testing Customer behaviors, influencing factors Data science Machine learning Dashboard
- 47. #ISSlearn#ISSlearn Dynamic pricing ❏ Discounts: low hanging fruit! ❏ When to give discounts on an e-commerce site? ❏ Abandoned shopping cart ❏ Free user (the browser) ❏ Power user ❏ Never incur a loss! 47© 2017 National University of Singapore. All Rights Reserved
- 48. #ISSlearn AI for products Customers buy what they like not what you like* 48© 2017 National University of Singapore. All Rights Reserved
- 49. #ISSlearn#ISSlearn Product development using AI 49 ❏ Product design for different mediums ❏ Product design for more engagement ❏ Low hanging fruits: ❏ Poster designs ❏ UX / UI designs ❏ Tough cookie: ❏ Product trends ❏ Design principles
- 50. #ISSlearn#ISSlearn Product development using AI 50 ❏ Web design being automated ❏ Automated A/B testing and analysis ❏ UI placements and image content aware positioning ❏ Deep learning and complex data ❏ AI and architecture
- 51. #ISSlearn What next? Developing ALL of these! 51© 2017 National University of Singapore. All Rights Reserved
- 52. #ISSlearn#ISSlearn Think data 52 ❏ Become data oriented, become a collector ❏ Put AI aside, use your own “I” ❏ Visualization of data
- 53. #ISSlearn#ISSlearn Think data 53 ❏ Tableau ❏ Python: Bokeh, graphviz, seaborn ❏ Excel!
- 54. #ISSlearn#ISSlearn Think data 54 ❏ Play with libraries (Python): ❏ Sci-kit learn ❏ Pandas ❏ Numpy ❏ “An average data scientist can hire an excellent data scientist”
- 55. #ISSlearn#ISSlearn E-commerce AI problems 55 ➔ Content tagging ➔ User modelling ➔ Recommendation system ➔ Price management ➔ Targeted pricing ➔ Testing
- 56. #ISSlearn 56 THANK YOU abhinit@comp.nus.edu.sg 56© 2017 National University of Singapore. All Rights Reserved