7. Feature Lifecycle
Business Case /
PRD
Prove that the problem is worth solving.
Provide clarity to the team.
Make sure the solution reaches the end
customer in good quality.
Delivery
Find a solution to the problem that
solves the core user problem.
Discovery
Demonstrate the impact the feature
made against the goal.
Outcome
8. Business Case
Prove that the problem is worth solving.
Provide clarity to the team.
● Formulate the problem
● Connect it with a high level objective
● Provide evidence: qual / quant
● High level concept
● Identify risks
● Get buy-in
● Define metrics
Building Blocks
9. Hypothesis Testing
3 essential component to a solid hypothesis:
1. Falsifiable
2. Testable
3. Based on objective data and insights
Source: experimentationhub
⛔ Not Specific enough
“Introducing social logins will make it
easier to sign up”
✅ Much Better
“Introducing social logins will
improve the sign-up rate by 20%.”
11. Discovery
Find a solution to the problem that solves
the core user problem
Building Blocks
● Solutionize the problem
● Validate solution with future users
● Iterate based on feedback
14. Delivery
Make sure the solution reaches the end
customer in good quality.
Building Blocks
● Provide good documentation
● Make tradeoffs
● Ensure good quality
● Enable customer success & sales
15. MoSCow Prioritization
Source: Productplan
Must have: Non-negotiable product needs that are mandatory for the team
M
S Should have: important initiatives that are not vital, but add significant value
C Could have: nice to have initiatives that will have a small impact if left out
W Will not have: Initiatives that are not a priority for this specific time frame
18. Outcome
Demonstrate the impact the feature made
against the goal
Building Blocks
● Ensure KPIs are measurable &
trustworthy
● Build dashboards
● Communicate results
24. Hypothesis Testing
Design like you're right
Based on [quantitative/qualitative insight].
We predict that [product change] will cause [impact].
Test like you're wrong
Assuming the change has no effect (the null hypothesis) and running an experiment for 1 week.
If we can measure a Y% statistically significant change in [metric] then we reject the null
hypothesis and conclude there is an effect.
Source: experimentationhub