README.md

metadata

title: Insight Synthesizer
emoji: 💡
colorFrom: indigo
colorTo: purple
sdk: gradio
app_file: app.py
pinned: false

insight-synthesizer

An AI-powered tool for Product Managers to quickly synthesize raw user feedback into actionable insights

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1. Product Framing

Before any code was written, this project was framed around four key questions to ensure clarity and focus.

1.1 The User Problem

• What pain point are we solving, and for whom?

As a Product Manager, I'm often overwhelmed by the volume of raw qualitative data (reviews, support tickets, user interview transcripts). Manual synthesis is extremely time-consuming, costly, and prone to cognitive bias. This process delays our ability to identify key opportunities and make evidence-based product decisions.

1.2 Target User

• Who is the primary user for this tool?

The primary persona is the Product Manager, but it also includes anyone in a product-focused role (e.g., User Researcher, Product Designer) who needs to quickly transform qualitative data into actionable insights to inform their strategy and roadmap.

1.3 Value Proposition

• How does this solution uniquely solve the problem? What's the core promise?

Transform a high volume of raw customer feedback into a structured, actionable analysis in under 30 seconds.

Whether it's App Store reviews, Zendesk support tickets, or user interview transcripts, this tool delivers a concise summary, key positive and negative themes, and suggested actions to radically accelerate the product discovery phase.

1.4 Success Metrics

• How will we measure success for this MVP?

We are tracking two primary metrics to validate the product's value.

• Quality Metric (Qualitative): User Satisfaction (CSAT) After each analysis is generated, the user is asked: "Was this summary helpful?" with a 5-point rating scale. This serves as our North Star Metric to validate that the AI-generated output provides real value. The initial goal is to achieve an average score greater than 4/5. Once we validate that the solution is viable, we can move on to measuring its business impact.

• Efficiency Metric (Quantitative): Estimated Time Saved To make the value proposition tangible, this metric communicates the business benefit of automation. Based on the input's word count, we estimate the manual reading and synthesis time saved. The calculation uses the average adult reading speed (~200 wpm) plus a fixed time allowance for identifying themes (e.g., "Analysis of 5,000 words: approx. 45 minutes of work saved").