AI-Driven UX Research Strategy

Developed a GDPR-compliant framework for analyzing customer support transcripts using Claude AI to uncover UX patterns and inform product decisions.

Challenge

  • Existing tools (Gong, Microsoft Copilot) lacked capabilities for multi-transcript UX research and thematic analysis.

  • Manual synthesis of qualitative data was slow and inconsistent, involving hours of re-watching calls, pausing and rewinding, creating matrices, and manually spotting patterns.

  • Needed to ensure GDPR compliance while leveraging AI tools.

Approach

  • Authored two strategy documents:

    • Why Use Claude AI: Built a case for adopting Claude AI for UX research initiatives, highlighting benefits over existing manual processes and tools.

    • UX Data Analysis Guide: Created a step-by-step, GDPR-compliant process for transcript cleaning, anonymization, and safe AI analysis.

  • Defined advanced prompt structures tailored for UX research insights (themes, friction points, product improvements).

  • Designed research prompts to cover multiple integration categories (PSA, RMM, Documentation, Licensing, Cloud, Security).

  • Created synthesis prompts to identify cross-integration patterns after reviewing multiple transcripts.

  • Ensured the framework adhered to GDPR principles (data minimization, privacy by design).

Outcome

  • Automated analysis eliminates the need for labor-intensive matrix creation and manual call reviews.

  • Reduced analysis time by an estimated 70%, allowing weeks of manual review to be completed in just hours.

  • Improved accuracy of UX insight discovery by reducing human error and bias.

  • Enables scalable, multi-transcript analysis for product and UX research.

  • Supports cross-team collaboration: Multiple team members can analyze the same project with custom prompts, amplifying insights.

  • Framework designed for adoption across design, PM, and CS teams.