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A good QA is not someone who just validates Figma vs. UI.
A good QA is someone who asks questions.
Do we really need this?
Why are we building it this way?
Can we simplify it?
Can we add this as well?
Can we solve this in another way?
What happens in this edge case?
How will this impact the user?
QA should not just find bugs after the feature is built.
A strong QA challenges the requirements, thinks about the user, identifies gaps, and helps the team build a better product, not just a bug-free one.
Testing is not just validation.
Testing is questioning. 👏
#QualityAssurance #SoftwareTesting #QA #SoftwareQuality #Testing
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Understanding Target Customers vs. Target Users in SaaS Product Development
A successful SaaS product starts with understanding who the product is built for — and who will actually use it.
In this lesson from the iiinnovations SaaS Masterclass, I explain two important concepts:
• Why the customer is not always the end user.
• How to identify and define the right target customer segment.
• The difference between buyers, decision-makers, and end users.
• Why customer segmentation matters before defining product requirements and features.
My approach to product development focuses on understanding business needs, user roles, and real-world problems before moving into product design and development.
The key principle: Build for the right people, solve the right problems, and create meaningful product value.
Follow iiinnovations for more practical insights into SaaS Product Development, Product Strategy, UX/UI Design, and Business Analysis.
#SaaSProductDevelopment #ProductStrategy #ProductManagement #CustomerSegmentation #UXDesign
Such an important distinction.
Notal Care – AI-Powered Mental Health Platform
Description:
Modern and clean Elementor Pro conversion from Figma for an AI-driven clinical GGZ (mental health) infrastructure platform. Professional design with strong focus on clarity and credibility.
The pale green feels calming.
Before making a prototype, write down the decision it should help you make.
Will someone understand this workflow?
Can the data support the idea?
Is this useful enough to build further?
That question gives the prototype a purpose and a stopping point. It also makes feedback easier to interpret: you know what you were trying to learn.
Writing the decision down also tells you what to fake. When I prototype with AI, if the question is the workflow I hardcode the data, and if it's the data I skip the UI polish entirely.