The Data-Driven Approach to Product Prioritisation

Beyond Gut Feel

Early in my career, prioritisation often came down to who spoke loudest. The biggest client got their feature. The most senior stakeholder won debates. This approach doesn't scale.

What changed my thinking:

A Simple Framework

For every feature in the backlog, I try to answer:

1. How many users/clients does this affect?

2. What's the frequency of the problem it solves?

3. What's the business impact if we don't address it?

Features that score high on all three move up. Those that don't get honest conversations with stakeholders about why.

Balancing Data with Judgment

Data doesn't make decisions; people do. Numbers can tell you what's happening but not always why. That's where user interviews, client conversations, and domain expertise come in.

The goal isn't to remove judgment from the process. It's to make judgment better informed.

Continuous Learning

I'm currently deepening my understanding of AI and machine learning through IIT Madras. The more I learn about how data can reveal patterns, the more I see opportunities to bring these approaches into everyday product decisions.

Data-driven doesn't mean data-only. It means data-informed.