Cracking the Code on Feedback

Hi, and welcome to The Atomic Builder! 👋

This is where product managers, founders, and non-technical creators learn to build software in the AI era.

Thanks for being here, you could be…refreshing your LinkedIn to see if your last post ‘hit the algorithm,’ (spoiler, mine didn’t) but instead, you chose AI, product strategy, and making things happen. Priorities! 👏

The Feedback Problem We ALL Face

You know it. I know it. Feedback is gold.

Yet it’s scattered everywhere - emails, Slack/Teams, surveys, social media mentions, and even that one Post-it stuck to your monitor from 2021 (did you ever address that…? tut tut).

There’s got to be a better way. I was curious, could AI help cut through the noise and make sense of feedback faster? Instead of applying 50 filters to an excel spreadsheet, I wanted to see if I could build a tool that more readily helps uncover insights without the usual grind.

That’s how Feedback Force came to life. A simple system I created to ingest, cluster and analyze feedback - a better way (?) to explore how AI could transform the feedback process. Whether you're launching an MVP or managing a mature product, this approach might change how you think about collecting and using feedback.

Let’s dive in. 🚀

Managing Feedback is a Nightmare (Let’s Fix That)

After launching a product or feature, if you’re lucky, feedback starts pouring in. Some of it’s useful, some of it’s noise, and much of it gets lost because we don’t have time to analyze everything manually.

Common problems:

• Volume Overload – Too many sources, not enough time.

• Lack of Structure – Feedback isn’t neatly categorized.

• Missed Signals – Critical insights get buried.

• Slow Response Times – By the time you analyze it, the problem has evolved.

I wanted to see if AI could help automate this. So, I built Feedback Force to test the idea: Could AI quickly find patterns in feedback and highlight what actually matters?

Experimenting with AI-Powered Feedback Analysis

AI can do what no human team can: scan, analyze, and categorize thousands of feedback points instantly. Here’s how I set up Feedback Force to test this idea:

1️⃣ Create a synthetic dataset of feedback (mimicking feedback from emails, surveys etc). I then created an upload function in my app to enable data to be analyzed by AI.

2️⃣ Use AI to scan and categorize comments into recurring themes and sentiment (positive, negative, neutral). The Force-Directed graph in the main app helps spot things easily. This was a 100 item feedback dataset - was I pushing the demo app too hard?

3️⃣ Visualize insights in seconds, showing patterns, key frustrations, and emerging trends. I also created a dashboard to chart some of the key data points, I think this helps uncover points the force-directed graph might not.

4️⃣ Get a clear action plan, by visualising feedback, instead of drowning in raw comments, i get a better sense of what’s important - “I need to do xyz pronto!”. The ability to get to that insight was ultimately what I wanted to test, with the creation of this demo.

What I Found Interesting

Recurring issues are obvious…if you have 50 emails on an item, you almost certainly need to deal with it. Where a tool like this might help:

✅ AI could uncover unexpected insights - stuff you might have missed otherwise - underlying themes, common sentiment from a particular group.

This got me thinking - what if more teams used AI in this way? Too many of us are stuck doing things in a rudimentary way - some of that is necessity. Think about how you can adapt to new ways, should the opportunity present itself!

How You Can Experiment with AI Feedback Analysis

Try Feedback Force (or Build Your Own Approach!)

I built Feedback Force as a Research demo to explore what AI can do for feedback analysis and to show you what is possible - why not try it yourself or build your own workflow to analyze feedback?

Here are a few ways to experiment:

🛠 Test the MVP: Upload some real user feedback into Feedback Force and see what insights it surfaces (this is an MVP - so I didn’t build any data persistence in and the data will be local on your computer so I won’t see any of it).

🔍 Compare AI vs. Manual Analysis: Take a batch of feedback, analyze it manually, and then compare it to what Feedback Force highlights. Does it catch things you missed? (Does it miss things you caught?!)

🚀 Build Your Own Version: Notion, Airtable, or a simple AI script can do something similar. Experiment with AI categorization using ChatGPT or Claude.

📊 Share Your Findings: If you uncover something interesting, I’d love to feature it in a future issue!

📌 Real-World Example: Kenko Tea analyzed customer feedback using AI and discovered packaging complaints were causing a 15% drop in repeat purchases. A quick redesign led to a 50% reduction in negative feedback—and higher retention.

Imagine what you (!) could uncover.

Strategy Spotlight: Why This Matters for Product Builders

Product teams and creators that can analyze and act on feedback fast will outpace the competition. AI is a force multiplier - but only if you use it.

✅ From Reactive to Proactive – Instead of waiting for problems to escalate, you can catch patterns early and iterate faster.

✅ From Guesswork to Precision – Gut feelings are great, but data-backed insights are better (mostly). AI helps you focus on what really moves the needle.

✅ From Feedback Overload to Actionable Insights – Instead of drowning in comments, AI surfaces themes that actually impact retention, engagement, and product-market fit.

Now Its Your Turn… 💡

So here’s the challenge - how fast can you turn YOUR (!) feedback into real insights?

Maybe you try Feedback Force and see what themes emerge. Maybe you build your own AI-powered workflow with Notion or ChatGPT. Maybe you just take one piece of feedback you’ve been ignoring and act on it today.

Whatever route you take, AI is making it easier than ever to listen, adapt, and move fast. And in product, speed is everything.

Would love to hear what you uncover - just hit reply.

Final Thought

The Real Competitive Edge? Speed.

The best product teams are the ones who can turn user signals into decisions, fast. If AI can help you cut through the noise, why wouldn’t you use it?

Now, I’m off to find something that I misplaced in 2021, that might be quite important, possibly, probably. It’s not a post-it note….honest… 🙄 

Until next time, keep experimenting, keep building, and as always - stay atomic. 👊

Faisal

This Week’s Build Beats 🎵 

Each issue, we pair the newsletter with a track to keep you inspired while you build.

This week, because cracking the code on feedback is all about decoding the signals hidden in the noise - what better choice than:

🎧 “Decode” – Paramore

Grab the playlist on Spotify - I add to it each week!