The document discusses data and data analytics for product managers. It defines data as facts and statistics collected for reference or analysis. It identifies common data types like quantitative and qualitative data. It provides examples of potential data sources for a company like customer support, payment processors, analytics tools. It outlines a process for working with data that includes defining goals, gathering data, analyzing results. It discusses considerations for working with data like roles, deliverables, timelines, capacity, and prioritization. It provides real world examples of how companies have used data analytics to address issues like friendly fraud, customer support costs, and payment failures. It also discusses technologies used for working with data like Hadoop, SQL, Python, and data visualization tools.

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8. What is data?
Facts and statistics collected together for reference or analysis

9. Data Types
99
Quantitative
Information about quantities.
Examples: Number of Sales, Refunds, Chargeback Rate, Email Sign-ups, Authorization
Attempts, Site Visits, Conversion
Qualitative
Information about qualities.
Examples: Site Feedback, Social Media Comments, Customer Support Reports, Voice of
Customer, User Testing

10. Customer Support
These could be reported
issues, both quantitative and
qualitative information
Payment Processor
Transactional information
Segment
Events, Tagging
Google
Analytics/Omniture
Web Traffic
Order Management
System
Order status, customer
information
eCommerce Platform
Information in your system
Session Recordings
Inspectlet, Tealeaf record
users on the site
Customer Feedback
Customer Surveys, site
feedback form
10
A Few Data Sources

11. Create A Process
1111
Meeting Second Meeting
Seek Clarity
Gathering Data
(YOU + TEAM)
Share Results
with
Stakeholders
More Research
(You + TEAM)
Close out
analysis &
Retrospective
Define the
Goal (YOU +
Stakeholder )

12. Ways of Working
1212
• Who does what?
• Defining the problem phase
Roles & Responsibilities
• What will be the results of the effort?
Clear Deliverables
• This must be completed by Friday…
• Create a timeline
Setting Expectations
• Is the data analysis you want Ad Hoc
or is this reporting that needs to be
scheduled?
• Is this need monthly or quarterly?
Planning Ahead
• How much work can the data team
handle?
Capacity
• What is the priority of the ask?
• How does this data request rank
against competing requests?
Prioritization

13. Real World Examples
1313
• Analyzed millions of transactions
• Segmentation analysis gave true
customer LTV by card type
• Identified high fraud, risk bank cards
as foreign
• Reduced chargeback risk
Friendly Fraud
High chargeback rates are not
good
• Poured over two years of CS data
• Discovered the cost of each phone
call, email, chat
• Created customer FAQ, changed call
center hours, made chat more
prominent and personalized
• Saved company over $1.25 M in
operating expenses annually
Customer Support
Understanding the cost of doing
business can inform product
decisions.
• Payment reporting showed decrease
in sales
• Analytics Tools pointed towards
mobile issue
• Investigation showed deficit in QA
capabilities and oversight by Product
• Conversion restored to site
Payment Failures
Low conversion reported for
South American website

14. 14
Cluster of machines
that collectively crunch
data very quickly
Hadoop
Great introductory
language, easy to learn
SQL Language
Statistical analysis
R Programming
In memory processing,
very fast, expensive
HANA
Programming
language used by
some data analysts
Python
The ecosystem for data is growing quickly. This is a small portion of the many players in the space. How data is stored, how it is
processed, the languages used to analyze data and the reporting that is built on top of all the data are changing quickly.

15. 15
Sample Hadoop
Diagram

16. 16
There are a wide variety of visualization tools on the market that will help you visualize your data. Google Analytics is a good first step (and FREE!).
However
depending on your needs, funding, resources there are other tools like Looker, Tableau, MicroStrategy that can also give you in-depth reporting.

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