Explore Transcript Keyword Trending using your Granola data

Transcript Keyword Trending analysis reveals how frequently specific terms appear in your Granola meeting transcripts over time, providing crucial insights into conversation patterns and business priorities. For Granola users, this metric transforms raw meeting data into actionable intelligence about team focus, customer concerns, product discussions, and strategic initiatives. By tracking keyword frequency changes, you can identify emerging topics, measure the impact of training programs, and ensure important subjects aren’t being overlooked in customer calls or internal meetings.

Analyzing this data manually creates significant challenges. Spreadsheets become unwieldy when exploring multiple keywords across different time periods, meeting types, and participant segments. The complexity of cross-referencing transcript data with meeting metadata makes formula errors inevitable, while maintaining accurate calculations across hundreds of meetings is extremely time-consuming. Granola’s built-in reporting tools, while useful for basic insights, offer limited segmentation options and can’t address nuanced questions like why certain keywords are trending down or how seasonal patterns affect conversation topics.

Count eliminates these pain points by automatically processing your Granola transcript data and enabling dynamic exploration of keyword trends. You can instantly segment by meeting participants, date ranges, or meeting tags, and drill down into specific conversations to understand context behind trending patterns.

Learn more about Transcript Keyword Trending analysis

“Show me the trending frequency of ‘pricing’ mentions in my Granola meeting transcripts over the last quarter”
This reveals whether pricing discussions are increasing or decreasing in your meetings, helping you understand if this topic is gaining strategic importance or losing focus across your organization.

“Why is transcript keyword trending dropping for ‘customer feedback’ in our sales team meetings?”
Count analyzes patterns in your Granola data to identify potential causes for declining keyword frequency, such as changes in meeting participants, agenda shifts, or seasonal business cycles affecting conversation topics.

“Compare keyword trending for ‘roadmap’ between internal team meetings versus client-facing meetings in Granola”
This segmented analysis shows how strategic discussions vary between internal planning sessions and external stakeholder conversations, revealing communication patterns across different meeting contexts.

“How to improve keyword trending analysis for ‘competitive intelligence’ by meeting type and participant seniority in my Granola transcripts?”
Count examines keyword frequency across Granola’s meeting categorization and participant data to identify optimization opportunities, such as ensuring competitive topics are adequately discussed in leadership meetings or specific team contexts.

“Track correlation between ‘budget constraints’ keyword trending and meeting sentiment scores from Granola data”
This advanced analysis combines keyword frequency with Granola’s sentiment indicators to understand how financial discussions impact overall meeting tone and team morale over time.

Count’s AI agent crafts bespoke SQL and Python analysis specifically for your Granola transcript data — no rigid templates. When you ask “why is transcript keyword trending dropping for ‘customer success’ mentions,” Count writes custom logic to analyze your exact meeting patterns and participant behaviors.

Hundreds of automated queries run in seconds to uncover hidden trends. Count doesn’t just count keyword frequency — it correlates declining mentions with meeting types, participant roles, and seasonal patterns to understand how to improve keyword trending analysis systematically.

Count handles messy Granola data automatically, cleaning inconsistent transcript formatting, normalizing speaker names, and filtering out filler words that could skew your keyword analysis. No manual data preparation required.

Every analysis comes with transparent methodology — Count shows you exactly how it identified trending patterns, which meetings contributed to frequency changes, and what assumptions it made about your transcript data structure.

Results arrive as presentation-ready analysis with trend visualizations, statistical significance testing, and actionable recommendations for improving keyword frequency in future meetings.

Collaborative features let your team explore why certain keywords are trending down together, asking follow-up questions like “Which meeting participants stopped using this term?” or “What topics replaced these keywords?”

Count connects your Granola transcripts with CRM data, support tickets, or sales metrics to reveal whether declining keyword trends correlate with business outcomes — providing complete context for your conversation analysis.

• Full guide to Transcript Keyword Trending
• Conversation Topic Analysis
• Meeting Tag Frequency Analysis
• Meeting Sentiment Analysis
• Knowledge Transfer Effectiveness