This document discusses building a product management data strategy. It notes that many new product launches fail and do not meet revenue goals, highlighting the importance of data. Product data can come from various internal and external sources, but capturing a consolidated view across different systems and formats can be challenging. The document recommends using a product analytics platform like Pendo to track user behavior, solicit qualitative feedback, and analyze specific features and user journeys. It provides examples of how product data can help improve feature adoption and provide rapid insights. It emphasizes balancing data with intuition and sharing insights across teams through focused experiments, reviews, and being open to unexpected findings.

1. Building a Product Management Data Strategy
December 16, 2015
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2. Agenda
The product data imperative
Sources of product data
Quantitative & qualitative data in the Pendo platform
Best practices
Q&A
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3. Building great products is hard
46%of new product launches fail
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75%do not meet revenue goals
2Yraverage lifespan of
“successful” products
Sources: Product Development and Management Association, 2004; Harvard Business Review, 2011

4. Are the best product managers truly clairvoyant?
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5. Data can make a critical difference
Users
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Features Journeys
Uncover and understand
user needs to build
meaningful cohorts
Guide roadmap and
feature prioritization based
on real user behavior
Follow and optimize user
funnels through the
product

6. Product data sources - internal objects
Key application stats such as users, licences, etc, that is stored as part of application data
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! Often critical indicators of a product’s
“health”
! Data is consistently collected and is stored /
captured within the application
Challenges
! Can require development resources to
extract and format

7. Product data sources - web analytics
Page-level and user data captured by instrumenting application pages with Google Analytics or other web
analytics software
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! Provides measure of usage volume, and
visitor demographic information
! Track “conversion” events and other specific
actions
Challenges
! Engineering work required to implement /
customize
! Optimized for web visitors - does not provide
user or feature-level detail

8. Product data sources - support cases
Current or archived support requests from help desk (kana, zendesk) or other repository software
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! Users ask for help when they’re stuck -
identify areas or features of the application
where they struggle
! Level of support requests also indicates
feature usage volume
Challenges
! Data is not summarized - requires extensive
reading / digging to uncover insights
?

9. Product data sources - User testing & surveys
Qualitative user feedback from observed UX testing sessions and user surveys
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! Captures direct input and feedback from
users
! User testing allows direct observation of
application use. Can gauge overall feature
utility in addition to UI usability
Challenges
! Data is not detailed, but not necessarily
representative
! Difficult to assemble and get responses from
user groups

10. The cross-referencing challenge
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Product data lives in different
systems in many different
formats
Capturing a consolidated view
requires significant legwork
Product managers have to
become cross-
referencing ninjas
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11. Using a platform for product analytics
Pendo is tailored specifically for rich, complex software products
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Users Features Journeys
Track user / account-
level activity across
the activity
Solicit qualitative user
feedback directly
within the application
Tag specific features for
analysis without
coding
Insights are retroactive to
install date
Define and measure
drop-off across
custom funnels
Follow aggregate, and
individual user paths

12. Pendo analytics: users
Detailed insights into user and account
activity. Create rich segments based on
demographic and behavioral characteristics
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Interactive polling directly within the
application. Capture qualitative feedback,
ratings, and additional user details

13. Pendo analytics: features & journeys
Detailed analysis on specific application
features. In-interface tagging without
additional coding / engineering support
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Create and analyze funnels and paths.
Understand how your users progress
through the application and where they drop

14. Product data improves feature adoption
Challenge
Needed to expand user adoption of new tool
No clear understanding of how features were used,
leading to difficulty prioritizing improvements
A New Approach
Instrumented feature set to measure usage
Tracked users across defined “funnel” to find
breakpoints
Re-designed UI based on observed user activity
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15. Product data provides rapid insight
Challenge
Struggled to capture actionable user data
Metrics and reports needed to be defined prior to
product release - any changes required
development work and application updates
A New Approach
Implemented a product data platform to capture user
events
New feature / user tracking implemented in minutes
without any additional coding
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16. Smarter decision-making balances data and insight
Data is critical, but it isn’t the answer to
everything. A good product data strategy
brings in additional insight without
ignoring the flashes of intuition that can
lead to transformative solutions.
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17. Tenets of a successful product data strategy
1. Use fast, focused experiments: Build insight through multiple, short tests and
prototypes
2. Share your data: Understanding and insights can come from anywhere. The entire
product team should have access to data
3. Formalize product reviews: Don’t over-analyze, or get too close to the development
process. Specific review cycles can help to balance insight and intuition
4. Be open to surprises: Product data isn’t just an answer to a specific question - it’s a
way to openly observe users. Insights are often unexpected.
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18. Questions
Eric Boduch
Founder
Pendo
eric@pendo.io
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Michael Peach
Product Marketing
Pendo
mike@pendo.io
Learn more at www.pendo.io