How I Built the IdeaLens Pain-Point Engine: From Social Noise to Business Insight

I built IdeaLens with one goal: help teams move from scattered community noise to clear product decisions—fast.

This post explains the core design behind the IdeaLens pain-point engine without getting into data acquisition details. If you want to validate demand faster, this will help.

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1. Start with the decision outcome

Many tools provide lots of information but still can’t answer “what should we build next?”

IdeaLens defines the outcome first:

• a clear list of pain points
• each tied to a specific scenario and persona
• sortable by severity and engagement

Everything else exists to make that list trustworthy.

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2. Structure every pain point

Each insight is normalized into a consistent structure to enable filtering and comparison:

• Scenario: where the pain happens
• Persona: who feels it
• Severity (0–10): how painful it is
• Engagement: how many people care
• Keywords: the language users actually use

This makes search and filtering meaningful.

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3. Score for signal, not just emotion

Pain is not just negative sentiment. We combine:

• complaint intensity
• repeated frequency
• presence of workarounds
• engagement strength

The goal is to surface signals that truly shape product direction.

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4. Filters that match product thinking

IdeaLens filters map to real discovery workflows:

• persona
• scenario
• community source
• severity
• time

Filter first, then search, then validate.

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5. Cards as decision units

Each card helps you judge value quickly:

1. scenario + persona at the top
2. a sharp pain statement title
3. a short context paragraph
4. link to the original discussion
5. severity, comments, and time

You can scan quickly without opening every detail.

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6. Favorites build a living insight library

Great insights should not disappear. With Favorites you can:

• save high‑value pain points
• build a long‑term research asset
• reuse signals during roadmap planning

This is IdeaLens’ second value curve.

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7. The 3‑minute workflow

A typical flow looks like this:

1. search a topic
2. filter by persona and scenario
3. sort by most painful
4. open details to verify context
5. favorite and compare

It feels closer to product research than a feed.

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What’s next

We are improving:

• trend detection for early signals
• persona confidence for sharper targeting
• pain‑to‑solution suggestions to speed validation

If you’re building a product and want clearer demand, IdeaLens is built for you.