Predictive analytics can be used to disrupt product development in two key ways: 1. By analyzing past user behavior and orders, predictive models like neural networks and recurrent neural networks can predict future user behavior and needs and adapt products accordingly. This was demonstrated through a case study of order data from Instacart. 2. By analyzing attributes of users like driving behavior from a driver app and friends' networks, unsupervised neural networks can cluster users and infer new features for different groups, like incentives or gamification for improving driver safety. This was shown through a road trip tracking app case study. 3. The future of predictive analytics includes using self-organizing maps to predict bugs based on code dependencies and regions

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

6. Predictive Analytics

7. What is missing?

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

28. Time is important!

29. Recurrent Neural Networks

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

39. Using friends network

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/