This document discusses different machine learning paradigms like supervised learning, unsupervised learning, and reinforcement learning. It provides examples of problems like classification, regression, clustering that can be addressed using these paradigms. It then discusses model development lifecycle and real-life examples. The document focuses on cognitive insight, engagement and automation using AI and discusses use cases and business value like predicting lifetime value, customer segmentation, personalization etc. It emphasizes the importance of data science team, product-AI loop and considerations for product managers in developing AI products.

1. Demystifying AI - Product Strategy and approach
Prasana Murthi
Senior Product Manager

4. Supervised Learning

5. Unsupervised Learning

6. Reinforcement Learning

9. Model Development Lifecycle

11. Problem Classification Learning paradigm Example
Ranking Problem Supervised learning Google and Bing search
Recommendation problem Supervised learning Netflix, Spotify
Classification Problem Supervised learning
Gmail spam/not spam and Facebook photos
(detecting faces)
Regression Problem Supervised learning
Predicting how much a flight will cost in two hours is
an example
Clustering UnSupervised learning
Amazon’s customers-also-bought, Spotify’s playlist
addition recommendations.
Anomaly detection UnSupervised learning
Most “trending” products (Foursquare, Twitter,
Facebook) that surface things that are being
tweeted/visited/talked about more than usual

17. Real life example

19. Product Strategy

21. Cognitive Insight
AI models provides deep visibility into
what has happened in the past, but also into
what is happening now
And
what is likely to happen in the future.

22. Use case Business value
Predicting Lifetime Value
(LTV)
Predicting the characteristics of high LTV customers can help to determine customer
segmentation, identify upsell/cross-sell opportunities and support other marketing initiatives
including campaigns and offers.
Wallet share estimation
Predicting the proportion of a customer's spending in a category that accrues to a firm
allows firm to identify upsell and cross-sell opportunities and thereby increase revenue and
growth.
Churn prediction
Predicting the characteristics of churners allows a firm to make product adjustments,
provide offers and cross-sell product as well as allows the firm to reach out to churners via
personalised channels.
Customer segmentation
Predicting customer segmentation provides wider capability to engage with a group in order
to convert them to high value customers, while, retaining current high value customer.
Reactivation likelihood
Predicting the reactivation likelihood for a given customer and to be able to engage with
them in a personalised way.
Personalised and
targeted offers
Predicting what content, product or service offers that the customer will be interested in and
sending the selection at the most relevant time.
Propensity to pay
Predicting how likely the customer will pay for the product or service will help to determine
best engagement, prioritise engagement, achieve higher revenue growth and also lower
operational cost.
Audience insight analysis
Predicting traits and behaviour of the audience using data from audience insight helps not
only to uncover new audiences and also to convert them. This is mainly applied in the field
of SEO.
Marketing campaign
performance
Predicting how profitable a marketing campaign may be by knowing exactly who is going to
convert and who is not before even launching the campaign

23. Cognitive Engagement
AI agents engage with customers, employees and
suppliers/partners after making predictions.
The end point of prediction is action than prescription

24. Use case Business Value
Omni-channel
personalisation
Based on consumer historical data, AI’s prediction end in personalising the content based on the
consumer’s chosen delivery channel
Content Analytics
Based on prediction, the AI system targets personalised content to the consumer based on past
consumer behaviour
Dynamic Pricing
Based on prediction from user behaviour and demand for the product, an AI models recommends
product/ pricing options, taking into account internal and external factors.
Training
recommendation
Based on employee(s) performance review data and organisational need, an AI model
recommends specific training to the employee or team or division.
Traffic optimisation
Traffic optimisation can be achieved through an understanding of traffic patterns using sensor data,
accidents and roadwork data — where an AI model predicts delays or road obstructions and
recommends a faster route for public buses and consumer and commercial vehicles.

25. Cognitive Automation
AI systems develop deep domain expertise and are able to
automate related tasks that used to be performed by
highly trained people

33. The AI Hierarchy of Needs
Monica Rogati -VP Data @LinkedIn- https://hackernoon.com/the-ai-hierarchy-of-needs-18f111fcc007

34. Product – AI Loop

36. Data Science Team

40. Spotify Model of Data Science Organisation

41. Which horse to pick ?

42. What AI can bring to business ?

43. Dark Data – Innovations ?

44. Product Manager considerations
TRAINING DATA
PERFORMANCE
DEALING WITH ACCURACY ERRORS
PRECISION Vs. RECALL
BIAS Vs. VARIANCE
OVERFITTING Vs. UNDERFITTING
FEEDBACK LOOP
TRANSPARENCY WITH USERS
ALLOW USERS TO MAKE FINAL JUDGEMENT

45. Thank you
https://www.linkedin.com/in/prasanamurthi/
@prasanamurthi