This template replaces scattered files by organizing your fictional buyer profiles and real user interviews into one connected system. It gives cross-functional teams a single source of truth for understanding your target audience.
You can seamlessly link specific interview logs to the correct persona, and map those profiles directly to your core products. Rollup fields automatically track how much feedback you have gathered for each user type.
Built-in AI automatically extracts key takeaways from long interview transcripts, scores user sentiment, and generates concise persona summaries without manual effort.
Managing qualitative user research across flat spreadsheet rows quickly becomes impossible.
Spreadsheets force you to copy-paste feedback across tabs or lose context as your text heavy cells become unreadable. There is no clean way to maintain complex relationships between products, personas, and dozens of interview transcripts.
A relational structure lets you link a recorded interview directly to a persona and product without duplicating data. This ensures your research scales smoothly without breaking fragile VLOOKUPs.
This is exactly what Softr Databases are designed for, keeping your qualitative data structured and connected as your business grows.
Tie qualitative research to real business outcomes by mapping interview transcripts to specific product categories automatically. Following the rule of one table per object ensures your user feedback stays highly organized.
Turn long, messy user feedback into actionable insights instantaneously. Built-in Database AI agents summarize raw transcripts into three top bullet points and score sentiment without manual tagging.
Manage software offerings and track related user personas and categories
Define user profiles with AI summaries based on specific goals and pains
Log user sessions using AI to extract takeaways and detect sentiment
This system is built for teams that rely on deep customer understanding to build better products and campaigns.
Easily customize this template to fit your specific research methodology. Add new product categories or update the AI prompts to extract specific competitor mentions from your transcripts.
Bring your past research to life instantly. You can bulk import existing interview logs via CSV or API to populate your personas immediately.
When your team needs to share insights broader, you can build an app on top of this data. A well-structured database makes it incredibly easy to spin up front-end apps for your stakeholders.
This lets you create a centralized, secure research portal. Set up users and permissions to control who can edit user profiles versus who can only view interview takeaways.
It is a structured system that centralizes fictional buyer profiles, real user interviews, and product mapping. It tracks demographics, pain points, and live feedback to ensure teams maintain a unified understanding of their target audience.
A spreadsheet quickly turns into a messy wall of text when tracking long interview transcripts or complex team relationships. A no-code database enforces structure, cleanly linking user feedback to specific buyer profiles and products without fragile formulas.
Database AI agents can automatically analyze long interview transcripts to extract key takeaways and concisely detect user sentiment. They can also instantly summarize a persona's overarching goals and pain points natively, speeding up analysis dramatically.
Yes, you can use our native interface builder to turn this system into a collaborative research portal. You can safely share insights with marketing, sales, or external stakeholders while restricting access to sensitive interview recordings.
Yes, this template is completely free to copy and start using. Databases are included in Softr's free plan, with higher tiers offering expanded record limits for scaling comprehensive research programs.
Because it relies on relational tables instead of flat rows, every interview record can be linked dynamically to a corresponding persona profile. A rollup field then automatically counts the number of interviews associated with each specific user type.