Enabling Sentiment Analysis With Power Apps and Snowflake

By continuing to use our website, you consent to the use of cookies. Please refer our cookie policy for more details.

Industry

Information Technology (IT)

Based In

United States

Introduction

The customer is a global leader in workforce development, empowering organizations with an AI-driven workforce agility platform to close skill gaps, engage talent, and build future-ready teams. To enhance decision-making, they sought to integrate Gong API, Snowflake, SharePoint, Power Automate, and Power Apps for sentiment analysis from call recordings. Grazitti’s experts seamlessly unified these tools to automate sentiment analysis, identify churn risks, and generate actionable insights. The solution also incorporated role-based access controls and automated notifications, ensuring secure data management and smooth adoption.

The Customer

The customer is a global leader in workforce development, powering the potential of organizations and their people to thrive in the rapidly changing world. Through their AI-powered workforce agility platform, they help organizations identify skill gaps, engage top talent, and provide diverse learning experiences to meet the needs of the modern workforce. With over 7,000 organizations and 140 million users across 186 countries, they enable businesses to build high-performing, future-ready teams and organizations.

The Context

They relied on tools like Gong API & Snowflake to manage and analyze customer interactions. Additionally, they had licenses for SharePoint, Power Automate, and Power Apps to support operational workflows. However, they faced challenges in efficiently analyzing customer sentiment from call recordings. They needed a solution that could seamlessly integrate these technologies, extract meaningful insights from conversations, and present sentiment analysis in a user-friendly interface.

The Objective

The customer wanted to optimize their workflow by storing Gong API-retrieved call recordings securely in SharePoint and utilizing a Power Apps interface for real-time sentiment analysis. This involved analyzing call transcripts to detect positive or negative sentiment, identify potential churn risks, and generate actionable insights. To enhance usability, they sought to implement user permissions, automate sentiment-based notifications, and ensure enterprise-grade data security throughout the solution.

The Solution

Testing & Deployment

Finally, we conducted rigorous testing before deployment, achieving 95% user adoption within the first month through training and continuous support.

Role-Based Access & Notifications

Next, our experts set up user-specific permissions based on roles, regions & hierarchies, and automated email alerts via Power Automate, sending 200 notifications per week for negative sentiment cases.

Sentiment Analysis Interface

Then, our experts built a Power Apps interface connected to Snowflake, enabling detailed sentiment scoring and insights on churn risks & sales opportunities. Currently, 150 sales reps use it.

Call Recording Integration

Firstly, we integrated the Gong API to automate call retrieval and securely store recordings in SharePoint, processing 5,000 calls per month.

The Highlights

Hours Saved Weekly With Automated Sentiment Analysis

15%

More Accurate Risk Detection

10%

Higher Lead Conversion With Actionable Insights

20%

Increase in Customer Satisfaction With Proactive Issue Resolution

200

Weekly Alerts With Automated Notifications

The Conclusion

By integrating Microsoft Power Platform and Snowflake, our experts revolutionized the client’s call analysis process, eliminating manual reviews, providing real-time insights, and enhancing customer engagement. This AI-driven sentiment analysis solution empowered sales teams with data-backed decision-making, improved churn prediction, and streamlined customer interactions.

The impact was immediate with 40 hours saved weekly, a 15% increase in risk detection accuracy, and a 10% rise in lead conversions. With secure, role-based data access and automated alerts, stakeholders could proactively resolve issues, leading to a 20% boost in customer satisfaction.

Furthermore, completed in 12 weeks, the solution delivered a 25% ROI within the first quarter and established a scalable framework for continuous process optimization.