From this presentation you will find out more about becoming a Data-Driven Product Manager.
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12. Content
● Data across product lifecycle
● Types of product & data usage
● When to invest in data-driven decisions?
● Pitfalls to watch out for
● Things to remember
"In God we trust, all others must bring
data."
- W. Edwards Deming
13. Data across Product Lifecycle
Product Lifecycle
Stage
Decisions / Questions Potential Data Sources
Product Ideation ● Market Sizing
● Competitor Analysis
● Primary Research - Customer interviews /
Surveys, Sales team feedback, VoC programs
● Secondary Research - Analyst firms’ reports,
Industry Analysts interviews
Product Development ● Problem Statements ● Primary Research - Customer interviews /
Surveys
Product Introduction ● Experiments / MVP ● Customer Feedback
Growth ● Feature gaps
● Demand Analysis &
Segmentation
● Application Instrumentation
● Market Research
Maturity ● Productivity & Efficiency
● Profitability Analysis
● Application Instrumentation
● Market Research
Decline ● Impact of sunsetting on other
products
● Sales data (packaging etc)
14. Types of Products & Data Usage Intensity
Data Usage Typical Product Scope Examples
Data? What
data?
Technology implementations, Raw
Computing Infrastructure
Telecom Products, Software Platforms,
Analytics Platforms
Data is a by-
product
Business Transactional Workflows CRM, ERP Products (SaaS more than
on-prem)
Data drives
Outcomes
Consumer Experience Platforms,
eCommerce Platforms
Personalization Engines,
Recommendation Engines, Search-as-a-
service, Bots
Data is the
Product !
API Products, Knowledge Engines Search Engine, Data Sets / Interfaces
15. Quiz !
You have $100 to spend on data and analysis on the following decisions for your
India-focused startup for personal financial services. How would you spend it?
● How do I increase conversions rate (registrations)?
● What kind of app logging should I build?
● How do I generate personalized recommendations for my repeat users?
● What should be the compliance reporting mechanism?
● How do I attract the next set of new customers?
16. When to invest in data-driven decisions?
https://medium.com/reiterate/how-to-make-data-driven-product-decisions-a88f2b21059a
Invest in data for decisions where data will make a
substantial difference to the outcomes.
Benefits of Metrics-driven Product Building:
● Unambiguous Target to aim for and share with
teams and stakeholders
● Lesser distractions on the product roadmap
● Clear sense of progress towards the long-term
Target
Product Data Usage Likely
Incremental
Value
Data? What data? Low
Data is a by-product Medium
Data drives Outcomes Highest
Data is the Product ! High
17. Pitfalls to watch out for
1. An exclusive focus on one “north star” metric ⇒ missed information
2. Too much focus on data ⇒ analysis paralysis
3. Cognitive bias ⇒ missed insights or incorrect conclusions
4. Inefficient data collection ⇒ decisions that come too late
5. A misinterpreted or over-weighted piece of data ⇒ an inaccurate conclusion
https://www.productplan.com/data-driven-saas-product-management
18. Learnings from personal experience
● Invest in Instrumentation at the right time and the right amount
● Make sure data is sane before using it for decisions - build data quality
checks early
● Iterate based on available data rather than waiting for perfect data sets and
models
● Automate and democratize insights as you climb up the data sophistication
ladder
● Machine Learning might have serious limitations to be applied to your product
context - not (always) a silver bullet
● Benefits from any data set will taper off at some point always
● Strive to be self-sufficient in data analysis - become great at SQL !
21. Agenda
• Product Roadmap
• Ideal roadmap vs Reality
• The death defying blind donkey
• The cow that meandered home
• Product Roadmap vs Portfolio
roadmap
• MVP vs MEP
• The art of story telling
• Q and A
24. The death defying
blind donkey
Product roadmaps suffer from
• Business pressures from external markets for
new features
• Internally agreed upon timelines for committed
features
Shrug off the dirt and get off the pit through data-
25. The cow that
meandered home
• Re-evaluate your roadmap frequently based on
frequent experimentation
• Monitor and modify
• Constantly reassess the route to your north star goal
26. Product Roadmap
vs
Portfolio roadmap
• Decide on what your roadmap is.
• If you manage a portfolio of products, then
build multiple product roadmaps within the
portfolio.
• Build an uber, value – driven portfolio
roadmap
27. MVP vs MEP
• Minimum Viable Product aka MVP is a small sub-set of the full product suite
• Most often MVP is decided based on what we could deliver from a development standpoint.
• Product Managers must focus on MEP - Most Evocative Product.
• MEP is about connecting with the customer’s emotions and feelings rather than providing a watered down feature.
• MVP is internally focused : MEP is externally focused.
• MVP may be a subset of the promise : MEP is the core value proposition!
28. In Summary
1. Begin with the end in Mind
2. Re-evaluate your roadmap constantly
3. Focus on MEP to deliver great customer value
4. Prioritize, Prioritize, Prioritize
29. You are what
your story is
Stories have power in them and a Product Manager should be a good story teller. You should tell
your stories to your internal stakeholders like developers and testers so that they understand that
they are building the temple and not breaking stones. Your story should also resonate with your
peers and upper management for you to get continued support! Your product story is what your
30. A good story
• Sets the context for the audience about the problem idea
• Details the north star vision of the product
• Explains the benefits to the customer
• The road to get there
31. "No animals or humans were harmed in the
making of this presentation”
32. www.productschool.com
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