Predictive Analytics disrupting Product development
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How you can use predictive analytics to develop new products and add features that customers want.
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Predictive Analytics disrupting Product development
- 1. Predictive Analytics disrupting Product Development Rudradeb Mitra | https://www.linkedin.com/in/mitrar/
- 2. My life journey • Born in a lower middle class family in India. • Published first research paper on AI in 2002. • Went on to work in Germany, Belgium and Scotland as AI researcher. Published 10 papers on AI. • Masters from Univ. of Cambridge. • Lived in 9 countries. Built 4 startups - 2 in US, one in UK and one in Belgium
- 3. Since September 2016 • Mentor - FI, Google Launchpad. • Write (Startups, Sales, AI,…) • Speak (In 2017 spoke at 13 events in 5 countries) • Consult various companies around the world on technology and sales What I do now?
- 4. Why here? • Love meeting people from all over the world. • Share what I know. • Learn from you all.
- 5. Today’s talk • What is Predictive Analytics? • Case studies on using Predictive Analytics for Product development • What’s store in future? Goal: Show something that you can start working on from tomorrow
- 10. How does it fit into product development?
- 11. Imagine.. • If you can predict user behavior and future needs of users • If you can predict future bugs • If you can predict value of your product
- 12. Two Case studies
- 13. Case study I Predicting user behaviour and adapting the product
- 15. Analyzing 3M Instacart Orders • Prior: ~3.2m orders
- 16. Instacart Data product_id user_i d eval_set order_number order_dow order_hour_of_day days_since_prior_order 2539329 1 prior 1 2 8 2398795 1 prior 2 5 7 15 473747 1 prior 3 7 12 20 22544786 1 prior 4 1 7 21 4215438 1 prior 5 3 15 28 2295261 1 prior 6 2 7 19 2295261 1 prior 7 6 20 20 2550362 1 prior 8 5 14 14 1187899 1 prior 9 2 16 0 2168274 1 prior 10 2 8 30 1501582 1 train 11 1 11 10
- 17. Predict what customer will order
- 18. One approach to Prediction - Supervised Neural Networks Training wij wjk
- 19. An example Hidden layer Output layerInput layer 0.5 0.5 0.5 0.5 0.5 0.5 Training Input: 0 1 0 Output: 0.75
- 20. An example Hidden layer Output layerInput layer 0 1 0 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.25 + 0.25 0.5 Desired output 0.75 Error: 0.25 Training Input: 0 1 0 Output: 0.75
- 21. An example Hidden layer Output layerInput layer 0 1 0 0.5 0.5 0.5 0.5 0.5 1.0 0.5 + 0.25 0.75 Back propagation 0.5 0.5 Training Input: 0 1 0 Output: 0.75
- 22. One approach to Prediction - Neural Networks
- 23. One approach to Prediction - Neural Networks
- 24. word2vec- Predicting next word word2vec Hey, you are What doing Input words Predicted next word
- 25. Current approach to prediction for instacart 2539329 2398785 473747 2550368 1187899 word2vec (Product ID) (Predicted product ID)
- 26. What is missing?
- 27. Instacart Data product_id user_i d eval_set order_number order_dow order_hour_of_day days_since_prior_order 2539329 1 prior 1 2 8 2398795 1 prior 2 5 7 15 473747 1 prior 3 7 12 20 22544786 1 prior 4 1 7 21 4215438 1 prior 5 3 15 28 2295261 1 prior 6 2 7 19 2295261 1 prior 7 6 20 20 2550362 1 prior 8 5 14 14 1187899 1 prior 9 2 16 0 2168274 1 prior 10 2 8 30 1501582 1 train 11 1 11 10
- 30. Long Short Term Memory
- 31. Long Short Term Memory
- 32. A new approach to Prediction Day 1 Product x Product y Product z Day 2 Product a Product b Product c Day 3 Product e Product x Product z Day 4 ? ? ? Day 5 ? ? ? Input Prediction
- 33. New Product Day 4 Day 5 Day 6
- 34. Future Products
- 35. Case study II Predicting future needs and adding features
- 37. ROAD VIKINGS Driver’s app Record a trip Trip feedback
- 40. Unsupervised NN - Clustering Hidden layer Output layer Acceleration score Speed score Mobile usage score Friend’s median score Bad driver Good driver
- 41. Unsupervised learning Clustering (SOM)
- 42. New Product Bad drivers Good drivers • High Incentive + penalty • Insurance • Gamification to improve behaviour • Gamification for fun and engagement
- 43. ROAD VIKINGS Driver’s app Record a trip Trip feedback Goals & challenges Rewards
- 44. Results
- 45. What is in future?
- 46. Bug fixing - SOM • Like crime, bugs are also around similar regions of code. • Dependencies of code. • New code likelihood for bugs.
- 47. What is in for you all? • Can you incorporate some of the aspects in your product? • Build a product to do predictive analytics - Billion dollar market.
- 50. Go and build something awesome! Rudradeb Mitra | https://www.linkedin.com/in/mitrar/