Analyzing AI Interview Results & Data

You've successfully deployed your AI interviewer using Agent Interviews, and the conversations are rolling in. Now comes the crucial part: turning those raw conversations into meaningful insights. Unlike survey data, AI interview results offer rich, qualitative context – but how do you effectively analyze it?

Accessing Your Interview Data

First, you need to locate the completed interview records. Within Agent Interviews, you can typically find these:

• Associated with the specific Interviewer instance that conducted the sessions.
• In a central Interviews or Results section within your Project.

Each record represents a single conversation session.

Key Components of Interview Results

When you open an interview record, you'll generally find several key pieces of information:

• Metadata: Details like interviewee information (if collected), start/end times, duration, and session status (e.g., Completed, Abandoned).
• Transcript: This is the core output – a textual record of the entire conversation between the AI and the interviewee. This is where the qualitative gold lies.
• Session Data / Extracted Information: If your Interviewer Template utilized function calling or specific data extraction tools, you might see structured data points the AI pulled out during the conversation. This could include:
  • Sentiment scores
  • Mentioned keywords or topics
  • Specific answers categorized by the AI
  • User preferences or dislikes
• Audio Recording (Optional): Some setups might include audio recordings, offering additional context like tone of voice.

Strategies for Analysis

Analyzing conversational data requires a different approach than analyzing numerical survey results:

1. Read the Transcripts: Start by reading through individual transcripts. Get a feel for the flow of the conversation, the user's language, and their overall sentiment. Look for:

   • Recurring Themes: What topics or issues come up repeatedly across different interviews?
   • Pain Points: Where do users express frustration or difficulty?
   • Unexpected Insights: Did users mention things you didn't anticipate?
   • Direct Quotes: Pull out impactful quotes that illustrate key findings.

2. Leverage Extracted Data (If Available): If your AI extracted structured data, use it to quickly identify trends. For example:

   • Filter interviews by sentiment score.
   • See which keywords were mentioned most frequently.
   • Analyze categorized answers. This structured data can help guide your deeper dive into the transcripts.

3. Look for Patterns Across Interviews: Don't just analyze interviews in isolation. Compare responses from different users. Are there common threads? Are there interesting divergences between user segments (if you have that data)?

4. Tagging/Categorization (Manual or Platform-Assisted): As you review, apply tags or categories to transcripts or specific sections (e.g., Usability Issue, Positive Feedback, Feature Request). This helps organize your findings.

5. Synthesize Findings: Condense your observations into key insights, themes, and actionable recommendations. Use direct quotes and examples from the transcripts to support your conclusions.

The Value of Conversational Data

Analyzing AI interview data provides a depth of understanding often missed by quantitative methods. By carefully reviewing transcripts and leveraging any extracted data, you can gain authentic insights into user needs, motivations, and experiences, leading to more informed decisions.

Explore the documentation on viewing results in Agent Interviews to learn more about accessing your data.