Product Management Event at #ProductCon San Francisco about Decision Making with Data by Senior Product Manager at PayPal, Deboshree Dutta.

1. www.productschool.com
Decision Making with Data by
PayPal Sr Product Manager

2. FREE INVITE
Join 35,000+ Product Managers on

3. COURSES
Product Management
Learn the skills you need to land a product manager job

4. COURSES
Coding for Managers
Build a website and gain the technical knowledge to lead software engineers

5. COURSES
Data Analytics for Managers
Learn the skills to understand web analytics, SQL and machine learning concepts

6. COURSES
Digital Marketing for Managers
Learn how to acquire more users and convert them into clients

7. COURSES
UX Design for Managers
Gain a deeper understanding of your users and deliver an exceptional end-to-end
experience

8. COURSES
Product Leadership for Managers
For experienced Product Managers looking to gain strategic skills needed for top leadership
roles

9. COURSES
Corporate Training
Level up your team’s product management skills

10. Deb Dutta
S P E A K E R

11. DEB DUTTA
SENIOR PRODUCT MANAGER
PAYPAL
Decision Making
with Data

12. 12
About me
What to Product Managers do?
Data in Product Management
PM Cheat Sheet
Agenda

13. 13
About me
Sr Product Manager at PayPal
ML Platform for Personalization, Marketing & Risk
Masters, Computer Science from
Carnegie Mellon University
Fmr Group Product Manager for
Global IoT Smart City solutions at Hitachi Data Systems
Travel & Lifestyle Blogger & Influencer
Instagram - @deesignplay (170K+)
Live with 2 rescued pups + 1 rescued cat

14. 14
EMPOWER your team
so you’re not the only Decision Maker

15. What do Product Managers do?
Sense
- Customer
Discovery
- Market Study
Seize
- Targeting Addressable
Market
- Pricing and Positioning
Build
- Build Minimum Viable Product
- Measurable Goals & Milestones
Measure
- Validate with customer feedback
- Growth and profit
Learn
- Incorporate and iterate
- Grow to profitability & maturity

16. Innovation is a continuous learning process
• Experiment
ation and
Learning
• Customer
Empathy
• Idea
Generation
• Combining &
Refining
ideas
• Insight
Generation
Sensing Seizing
SeizingSensing
Transform
Abstract
Conceptualization
Synthesis
(How?)
Concrete
Experience
Analysis
(Why?)

17. 18
Data in Product
Management
“ ”

18. 19
USER
BEHAVIOR
• What channels are driving best quality of users?
• How are different segments behaving?
RETENTION
• Average lifecycle of a user
• How many users churn out of the system?
USER
ENGAGEMENT
• How many users return daily/weekly/monthly
• How are users engaging with the product?
CONVERSION
• Where are the users getting stuck in the funnel?
• What % of users are completing a task?
FINANCIAL
ROI
• How successful was your marketing campaign?
• How much $$$ did a feature drive?
Metrics that Matter

19. Funnel Analysis
GOAL: The funnel analysis essentially determines stages of churn
Eg: Sign up, Email verification, Checkout success
Simple steps to evaluate Signup Experience:
• Clearly indicates the user drop off at each stage
• Insights on which users are stuck at what stage
• Effective way to compute completion
Begin
Signup
Email
Verification
Add Bank or
Card
Activated
Account

20. Funnel Analysis

21. Funnel Analysis

22. Funnel Analysis

23. Cohort Analysis
GOAL: Analyze a subset of users with common characteristics over time.
Instead of evaluating all the users at once, study behavioral analytics of smaller data sets
and identify related segments for analysis
Why study Cohorts?
• Clearly detect patterns across the user lifecycle
• Allow products and features to be designed with the target cohort or segment in mind

24. Cohort Analysis

25. Segmentation Analysis
GOAL: Further slice a cohort based on common criterion such as age, demographics, device, OS etc
Why study Segments?
• Look for common characteristics such as shared needs, common actions, usage patterns by
demographic
• Clearly identify segments that need attention – high yield or segments that are not converting
• Allow products design, strategy, branding to be designed with the target segment in mind

26. Segment Analysis

27. Avg Page Load Time Analysis

28. Segmentation Analysis

29. Segmentation Analysis

30. Event Based Tracking
GOAL: Monitor each user interaction with screen content through instrumentation
Why study Event Based Tracking?
• Getting into the head of the customer
• The more granular the instrumentation, the more fine grained your tracking will be to study user
interaction from a web page or screen load
• Surface UI and User experience concerns quickly
• Tracking downloads, AJAX calls, image loads etc
• Engineering team to incorporate from start

31. Click Through Analysis
GOAL: Track user’s clicks, taps, links, button clicks for analysis
Why study Click Through Analysis?
• Typically used in Marketing campaigns and Search Engines for calculating Click Through Rate
(CTR) and ROI per click
• Can be used to draw deeper analytics to make Optimizations to product
• Eg: Phone Confirmation experience needs user to fill Verification Code
• Track % of uses click “Get Code”, “Not Now”
• Track % of users that say “Resend code”
• Tie it with Signup – to – Activation analysis

32. A/B Testing
GOAL: Experiment before launch!
Why A/B Testing?
• Its how you can filter the great ideas from the not so great – let the users decide!
• Let users determine what features go into the product
• Save time on developing unwanted features
• Never forget to launch anything without A/B testing for every single thing
including backend products

33. 36
Who is my target Customer?1
What problem are you trying to solve?2
What data do I need to prove my hypothesis:
Define your KPIs
3
How can you get started?

34. 37
PM Cheat Sheet

35. 38
Product Design
Designing a Product:
User: Who’s the primary customer/end user
Problem: What is their biggest problem/goal
Competition & Market Opportunity: What is the market offering today
Gap: How can we make a difference
KPIs: How can we measure our success

36. 39
Product Success
How can you measure the success of your product?
Goal: What is the goal of the product
Depending on the goals, metrics vary in many ways. Some examples:
• User Acquisition: current user base, growth, monthly active users, time spent
on portal, analytics (demographics, geography, age etc), referring others
• Conversion: Free to paid users, referring others
• ROI: Customer acquisition cost, support costs, delivery costs, LTV of a
customer, revenue growth rate

37. 40
Product Improvement
How can you improve a product?
Goal: What is the goal of the product
Metrics: to measure success
Problems:
• Is the user base not growing? Can it be expanded by entering newer markets
or verticals
• Is revenue not growing? Can be done through increasing subscription costs,
charge businesses more for targeted ads?
• Do we need higher user engagement - monthly active users or Time spent
• Increase clickthrough rate or
Competition: What’s the competition doing better?
Solution: can be crazy ideas, depends on risk level, budget, culture, strengths
Roll out: How to implement it & validate solution

38. 41
Test Launch Success
How can you test the success of your product launch?
• Metrics - such as user growth, revenue growth, market growth etc
• Usability & A/B testing - Roll the product and see how many people use it
• Customer feedback - social networks, forums, press coverage etc
• Support metrics - works seamlessly, not much support engagement
• Google analytics - eg: tells you growth in traffic with a product roll out
• Logging - check internal logs for instrumented counters to see how much a
feature was used

39. Thank You
End your presentation with a thank you slide. Leave it up while you answer questions. You want this slide to be plain so the attention is on
you for question period and not the presentation.
The frameworks in this template belong to their respective owners, none were created by this document owner. Frameworks are derived
from various pieces of business literature.

40. www.productschool.com
Part-time Product Management, Coding, Data Analytics, Digital
Marketing, UX Design and Product Leadership courses in San
Francisco, Silicon Valley, New York, Santa Monica, Los Angeles,
Austin, Boston, Boulder, Chicago, Denver, Orange County,
Seattle, Bellevue, Washington DC, Toronto, London and Online