1.
Importance of Data Analytics

2.
Importance of Data
Analytics
…for Product Management

3.
About Me
Twitter: @etugopm
LinkedIn: /etugonwokah
Etugo Nwokah
VP Product,

4.
Data analytics should be a first-class citizen
Importance of Data Analytics
Data analytics team should
be a key stakeholder
Everyone should ‘own’ the data

5.
3 Use Cases

6.
The good…
Use Case 1

7.
Use Case 2
…the bad

8.
Use Case 3
And the really, really ugly.

9.
Introduction to Data Analytics

10.
My first exposure to Data Analytics
for Product Management
2004 2017

11.
Data analytics refers to qualitative and
quantitative techniques and processes
used to enhance productivity and
business gain.

12.
Data Analytics is not:
Big Data
Artificial Intelligence (“AI”)
or Machine Learning
Data Science

13.
Data Science is a concept that unifies
statistics, data analysis and their related
methods in order to understand and
analyze phenomena with data.
It employs techniques and theories drawn
from many fields within the broad areas of
mathematics, statistics, information
science, and computer science.

14.
Big Data is defined as high-volume, high-
velocity and high-variety information
assets that demand innovative forms of
information processing for enhanced
insight and decision making.
1012

15.
Data Science has several sub-domains
MachineLearning
Visualization
DataMining
ClusterAnalysis
Classification

16.
Research and extract information from
various sources to describe the main features
of a data set in support of business operations
and decision making.
Data Analyst/Journalist
Data Scientist
Cleanse data, apply various techniques to
large data sets and create visualizations to
understand to answer more abstract business
questions, provide more predictive insights
and anticipatory behaviors.

17.
Data analytics should
be a first-class citizen
.. especially in software
companies

18.
23
6
19

19.
What is NOT a data-driven organization

20.
5 characteristics of
data-driven organization

21.
Leadership
Liquidity
Usage
Access
Protection

22.
Leadership
Top-down
commitment
“Day 1 Company:
High Quality and High Velocity
Decision Making”
Metrics define your culture

23.
Liquidity
Breaking down
silos of data
Structural - applications are
optimized for their main function,
not to encourage data sharing
Political - Knowledge is power
Growth - Company evolution
results in different
approaches to data
Vendor - Incentivized to store
data exclusively

24.
Usage
How data
is used to make
decisions
Company Scorecard
Avoidance of Proxies

25.
Access
Who has access
and how are they
using it
Meetings
and Cadence
Administrative
Management

26.
Protection
How data is stored
and shared in a
secure manner
Investment
in data security
Training
Data across
various platforms
Regulatory

27.
Data analytics team
should be a key
stakeholder
… not just on-demand

28.
Make friends with your “Data Team”

29.
Organizational Structure - Example
+
Dir of
Product
CPO
Sr Director of
UI/UX
Dir of
Analytics
CEO
Leadership
Team

30.
Ensure someone is representing data
on your agile/scrum team
PMPM
UI/U
X
UI/U
X
EngEng
DataData QAQA
Build metrics into user stories to measure success
Build tags into features
Identify R&D opportunties

31.
Data
Analytics
Data
Analytics
Data
Science
Data
Science
Data
Engineeri
ng
Data
Engineeri
ng
Product Manager
Ensuring data needs are supported is an
important responsibility
Limit user
overhead to
use data
R&D and
strategic input
to PM questions
PM input into
the metrics to
care about

32.
Everyone should
‘own’ the data
.. especially product
managers

33.
The
Modern
Day
Product
Manager

34.
What is this the
problem?
How big is the
problem?
How do we know if
we’ve solved it?Julie Zhou, VP Product Design
Approach to building new
features
Example use of analytics

35.
//
Referral
Acquisition
Activation
Retention
# of app downloads
#of registrations
% of users registered
# of physician reviews
# of searches performed
app reviews
Net Promoter Score
Referral Emails
# of unique return users per day
# of sign-ins per month per cohort
% of conversion to desired behavior
Example
Metrics
The funnel is a critically important framework

36.
Some data analytics tools

37.
How we used it: Across web and apps, understand
effectiveness of customer campaigns and cursory user
patterns.
Description: Powerful analytics tool describes what users are
doing and how they got to your site/app, but not why they are
behaving a certain way.
//
Acquisition

38.
How we used it: Determined primary user focus on homepage
Description: Heat map, scroll maps and click tracking to
understand user focus and what draws their attention
//
Acquisition

39.
How we used it: Across web and apps developed deeper
understanding of how users engaging per page and through the
conversion funnel
Description: Behavioral analytics and engagement tool
//
Activation
Retentio
n

40.
How we used it: Built a set of custom metrics in our admin tool that
looked similar to the kiss metrics UI interface but more dynamic
Description: set of APIs that allow you to stream, compute,
and visualize events
//
Activation
Retentio
n

41.
//
Activation
Retentio
n
Acquisition
How we used it: Funnel analysis, cohort analysis, click behavior
Description: A click-stream technology that auto-tags all of the
activity on your product to enable analytics and insights

42.
How we used it: To produce ROI reports and custom reports for our
large employer customers
Description: Data visualization tool focused on business
analytics

43.
Wrap-up

44.
Help your organization demonstrate the
5 data-driven characteristics
Learn the latest data analytics tools and
methods in the industry
Find the data analytics team
in your organization and make friends
If you don’t have a data team,
be the Lorax for the data
Etugo’s tips:

45.
Appendix

46.
Part-time Product Management courses in New
York