What Does a Product Analyst Actually Do? (My Kind of Explanation)
What Does a Product Analyst Actually Do? (My Kind of Explanation)
What Does a Product Analyst Actually Do? (My Kind of Explanation)
Ever come across the job title “Product Analyst” and wondered —
“Okay, but what do they really do?”
It sounds like a mix of tech, business, and numbers — and honestly, that’s not far from the truth. As someone building AI-driven products and learning the ropes of product thinking myself, I’ve realized that Product Analysts play a crucial role in making smart, data-backed decisions.
Let’s break it down in a simple, realistic way
But before that, Who is a Product Analyst?
A product analyst is someone who helps product teams understand how users interact with their products -- using data. They don’t just track numbers, they understand them, find trends, and suggest what to improve or build next.
You can say that they are the data-driven voice of reason in a team.
What Does a Product Analyst Actually Do?
1. Asking the right question
Before any analysis, they start with questions like:
· Why are users dropping off after onboarding?
· Is the new feature increasing engagement?
They dig deep into user behavior and product performance.
2. Digging into the data
· SQL to query large datasets
· Excel or Google Sheets for quick analysis
· BI tools like Looker, Tableau, or Power BI for dashboards
They explore patterns and highlight what’s working — and what’s not.
3. Supporting Product discovery
A Product Analyst will look at the numbers — feature usage, engagement, and impact — and provide data-backed answers, not just guesses.
4. Running A/b Tests
Product Analysts often set up and analyse experiments (like testing two versions of a signup flow) to see which performs better. This helps avoid launching features blindly and instead supports evidence-based product changes.
5. Creating reports
They built a visual dashboard to track:
· Daily Active Users
· Retention rates
· Feature usage trends
Tools for Product Analyst:
For data understanding – Tableau, PowerBI, MetaBase
For running experiments – Amplitude, or honestly just use python and basic stats to calculate it yourself.
For reports making – Notion(my favourite), GoogleDocs/Slides, MS Team
Case study:
How Instagram Uses Product Analytics to Drive Growth
Instagram’s rise from a simple photo-sharing app to a billion-user platform is deeply rooted in its smart use of product analytics. Since launching in 2010, Instagram has relied on user data to understand behavior, improve features, and guide product decisions.
Instagram co-founders Kevin Systrom and Mike Krieger insisted on data-driven decision-making. Systrom frequently emphasized how machine learning and analytics—not just instincts—should guide product evolution
The team behind the scene:
Kevin Systrom, Instagram co-founder/CEO (to Sep 2018), emphasized analytics as core to product-building from day one
After founders stepped down (Sep 2018), Adam Mosseri took over product leadership (VP Product since May 2018 and Head of Instagram from Oct 2018), steering continued analytics-led growth across Explore, Stories, Reels, and Shopping initiatives
Senior analytics and data science teams—though not publicly named—have been integral in defining metrics, running A/B tests, and guiding iterations across every major feature
Timeline of feature Releases and Analytics role:
June 2012 – Explore Tab
Instagram launched the Explore tab to help users discover new content. Product analysts looked at likes, comments, and saves to understand what people liked and used that to show more personalized posts.
March/May 2016 – New Feed & Business Insights
Instagram changed its feed from time-based to interest-based. Analysts helped build this by studying what users interacted with the most. Around the same time, they added "Insights" for business accounts to track things like post reach and audience details.
August 2016 – Stories Launched
Instagram added Stories (like Snapchat). Analysts checked how people used them, and based on that, they added cool features like polls, stickers, and Q&As to make Stories more fun and engaging.
September 2018 – Shopping Features
Analysts saw users clicking a lot on posts with products. So, Instagram introduced shopping tags in Explore and let users buy directly from posts.
2019–2020 – Checkout, Shop Tab, and Reels
Instagram added in-app checkout (March 2019), a full Shop tab (May 2020), and Reels (August 2020). All of these came from data showing that users were loving shopping and short videos — and Instagram built features based on those insights.
Product Analysts play a key role in any modern tech team. They make sure decisions are rooted in data, not just intuition. Whether it’s launching a new feature, understanding churn, or measuring the success of an A/B test, they provide the evidence that shapes the product’s future.
If you’re someone who enjoys understanding users, working with data, and influencing product direction without writing code all day, this might just be your path.