https://www.productmanagementtoday.com/frs/24305865/the-product-corner--maximizing-impact--reducing-hours--and-accelerating-roadmaps-with-data/email In today's hyper-digital landscape, organizations face the challenge of launching successful products while making the most of limited resources. To overcome this challenge, it is crucial to build core product and technology competencies that provide actionable insights through qualitative and quantitative data analysis. By tapping into the wealth of customer and application data, product professionals can identify underutilized features, prioritize improvements, and streamline development efforts in partnership with the development team. In this engaging webinar, we will explore how companies can become more efficient and effective in understanding customer interactions with their products. By leveraging data-driven insights, companies can accelerate time-to-market, enhance product quality, and align offerings with customer needs. This approach helps focus development teams on high-impact areas and fosters agility, continuous improvement, and measurable success, driving long-term growth and gaining a competitive edge. Learning Objectives • Learn how to gather and utilize data to enhance the user experience and optimize development effectiveness • Discover techniques to partner with customers and technology to validate assumptions and uncover new use cases, minimizing the risk of developing products that do not meet user needs • Understand how to build leading and lagging indicator metrics that empower data-driven decision-making to measure product success, identify areas for improvement, and adjust roadmaps accordingly

5. Digital
Transformation

6. 70%
of all transformation projects fail
Source: McKinsey

7. What are the most common reasons for
digital transformation failure?
1. Lack of transformation goals & shared vision
2. No change management strategy
3. Internal resistance to change
4. Poor technology management
5. Misaligned prioritization
6. Lack of understanding customer expectations and needs
7. Inadequate skillsets and training
8. Lack of operation agility
9. Inadequate data and metrics strategy
10. Setting unrealistic expectations
Source: https://whatfix.com/blog/digital-transformation-failures/

8. Product Driven
Transformation

9. Source: https://thenicheguru.com/niche-marketing/product-led-growth-guide/
CUSTOMER

11. Learning Objectives
1. How to use data to improve the user experience and optimize
development effectiveness
2. How to minimizing the risk of developing products that do not meet
user needs
3. How to build leading and lagging indicator metrics that empower
data-driven decision-making

12. Improve User
Experience

15. Heinz spent 9 years and 185,000 hours redesigning the
ketchup bottle cap
It took 45 iterations
Sourcehttps://www.packworld.com/news/sustainability/article/22870868/thepackhub-heinz-nestl-and-better-battery-co-introduce-recycling-innovations#:~:text=It%20reportedly%20took%2045%20iterations,from%20the%20nearly%20emptied%20bottle.

16. User Research
Data Hypothesis Discovery Design Deliver
Web Analytics
360 View of the
Customer
Competitive
Regulations
How Might We?
Opportunity
Solution Tree
Customer Value
Proposition
Jobs-To-Be-Done
Interviews &
Focus Groups
Shadowing
Card Sorting
Survey
A/B Testing
Usability &
Prototype Testing
Cognitive
Walkthrough
User Flow
Analysis
Satisfaction
Survey
Feedback
monitoring

17. Source: https://www.enago.com/academy/qualitative-vs-quantitative-research/

18. Select The Right Type Of Research
Answer “Why?” question
Observation, symbol,
word, etc.
Observe and interpret
Non statistical analysis
Answer “how
many/much?” question
Number Statistical
Results
Measure and test
Statistical analysis
Qualitative Quantitative
Purpose
Data type
Approach
Analysis

19. Qualitative vs. Quantitative Research
Objectives Understand Something Conform or Test Something
Focus Exploring ideas or formulating
hypotheses/theories
Testing hypothesis or theories
Analysis Summarizing, categorizing,
interpreting
Math and Statistical Analysis
Expressed In Words Numbers, graphs, tables, fewer
words
Sample Size Few respondents Many respondents
Questions Open-ended Close-ended or multiple choice
Characterized by Understanding, context,
complexity, subjectivity
Testing, measurement, objectivity,
replicability
Source: https://www.scribbr.com/methodology/qualitative-quantitative-research/

20. Sharing = Influence

21. Source: https://thenicheguru.com/niche-marketing/product-led-growth-guide/
CUSTOMER

22. Optimize
Product
Velocity

23. Backlog + Speed = Agile
+ =
User Stories
Scrum

24. Feature Factory
No connection
from delivery to
value.
Success is binary
feature
deliveries.
Lack of product
strategy,
understanding of
customer
experience.
Large batches,
too big to fail.
Developing
shiny objects to
cover the lack of
value.
Source: https://productized.medium.com/productized-notes-moving-from-a-feature-factory-to-user-value-creation-organization-by-michael-5955b683f85c

25. What the
customer
expected

26. What we
delivered

27. Source: https://www.rocketsource.com/blog/a-product-led-growth-strategy-for-enterprise/
Design

28. Customer
Focus

29. A/B Testing
User Research
Market Research
HIPPO
Revenue & Market
Teams
Sales, Largest
Customer etc.
Siloed Objectives
Balance the focus back to the customer
Using Data
Source: https://www.slideshare.net/Productized/moving-from-a-feature-factory-to-user-value-creation-organization-by-michael-rutledge

30. Direction + Speed = Velocity
+ =

31. Customer Driven Product Metrics
Happiness Engagement Adoption Retention Task Success
Net Promoter Score
(NPS)
Customer
Satisfaction (CSAT)
Customer Support
Response Time
Daily or Monthly
Active Users
Session Duration
Feature Usage
Feature Adoption
Rate
User Onboarding
Completion Rate
New User Sign-up
Rate
User Churn Rate
User Retention Rate
Cohort Analysis
Task Completion
Rate
Error Rates
User Flow Analysis

32. Leading Indicators Lagging Indicators
Assess the current
state of the business
Predicts the future
conditions
Some metrics can be leading or lagging
based on your performance metrics
VS

33. Aligned business objectives to user metrics
Profit
Revenue
Operations
& Sales
Referrals
Speed to
Complete
Task Success
Product
Hypothesis
Cost
Product
Devlopment
Marketing
Growth
Retention
Customer
Satisfaction
Completion
Rate
Complaints
& Feedback
Lagging Indicators
Monthly/Quarterly Impact
Leading Indicators
Daily/Weekly Impact
Source: Page Up

34. Determine your success
metrics at the start!