building a product management data strategy
TRANSCRIPT
Agenda
The product data imperativeSources of product dataQuantitative & qualitative data in the Pendo platformBest practicesQ&A
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Building great products is hard
46%of new product launches fail
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75% do not meet revenue goals
2Yr average lifespan of
“successful” products
Sources: Product Development and Management Association, 2004; Harvard Business Review, 2011
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
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
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
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
?
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
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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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
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
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
Product data improves feature adoption
ChallengeNeeded to expand user adoption of new toolNo clear understanding of how features were used,
leading to difficulty prioritizing improvementsA New Approach
Instrumented feature set to measure usageTracked users across defined “funnel” to find
breakpointsRe-designed UI based on observed user activity
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Product data provides rapid insight
ChallengeStruggled to capture actionable user dataMetrics and reports needed to be defined prior to
product release - any changes required development work and application updates
A New ApproachImplemented a product data platform to capture user
eventsNew feature / user tracking implemented in minutes
without any additional coding
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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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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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Questions
Eric [email protected]
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Michael Peach Product Marketing Pendo [email protected]
Learn more at www.pendo.io