In today’s business landscape, data is the fuel that drives success. However, while structured data from CRM systems, transactions, and performance metrics are often well-managed, the wealth of unstructured interaction data remains largely untapped. How can organizations turn conversations into powerful insights and actions? By combining Symbl.ai’s contextual AI with Snowflake’s data capabilities, organizations can elevate their analytics, empower search-driven insights, and build agentic workflows that drive impactful business outcomes.
This blog post will dive into three core capabilities enabled by integrating Symbl.ai with Snowflake: Search, Data Enrichment, and Centralized Agentic Workflows.
1. Search: Making Interaction Data Actionable
Interaction data often holds critical information buried in conversations—whether they are customer calls, sales discussions, or internal meetings. Extracting valuable insights from this data requires more than basic transcription. With Symbl.ai’s AI-driven insights, unstructured interaction data becomes searchable, actionable, and a true source of business intelligence.
Using Symbl.ai, businesses can turn conversational data into structured insights such as action items, key questions, and topics discussed. When integrated with Snowflake, these insights become part of the broader data ecosystem, enabling powerful, context-aware search capabilities that make finding the right information quick and efficient.
Cortex Search: Powered by Purpose-Built Models and Custom AI
Cortex Search, utilizing advanced language models, offers semantic search functionality that enables users to look for specific interaction data with precision and context. Businesses have the option to leverage purpose-built models from Symbl.ai that are ready to deliver immediate value, as well as train custom models to fit their unique needs. This flexibility provides the foundation for tailored interaction intelligence, ensuring that businesses can achieve both rapid deployment and customized insights. Imagine being able to search for specific action items discussed in sales calls, identify customer objections across support conversations, or instantly find questions asked in product demos—all of these are made possible by combining Symbl.ai’s insights with Snowflake’s data management capabilities.
2. Enriching Existing Analytics: Supercharging Data with Interaction Insights and Industry-Specific KPIs
Most organizations already have structured data from CRM systems, sales performance metrics, and customer history. However, these data sources only tell half the story. Symbl.ai provides a new layer of contextual insights by extracting information from conversations—such as call sentiment, engagement levels, and action items—and enriching existing analytics in Snowflake. This enrichment adds depth and context, allowing for a much more comprehensive analysis of business performance.
Adding a New Layer of Richness to Business Analytics and Understanding Key Industry KPIs
Consider customer journey analytics, which traditionally relies on CRM and transaction data. Different industries have specific KPIs that can be enriched by interaction data. For example, in financial services, KPIs like customer satisfaction, cross-sell rate, and issue resolution time can be enhanced by analyzing customer conversations. In healthcare, metrics such as patient satisfaction and care quality can be improved through insights derived from patient interactions. Retail organizations often track customer loyalty, conversion rate, and average order value—all of which can be enriched with sentiment and engagement data. Finally, in telecommunications, metrics like customer retention, net promoter score (NPS), and average handling time can benefit significantly from deep insights into customer conversations. By incorporating sentiment trends from customer conversations into these analytics, organizations can better understand the emotional trajectory of their customers’ journey—insight that can help in personalizing the customer experience and addressing pain points more effectively.
Take the example of a sales call. Symbl.ai can provide metrics like call scoring that evaluates how well a sales rep is performing based on criteria such as communication, engagement, and adherence to the sales process. In retail and consumer goods, call scoring can also track product preferences and sentiment trends, helping sales teams to better tailor offers and promotions. When these scores are combined with CRM data in Snowflake, it gives sales managers a complete view of not just sales outcomes but also what drives those outcomes—leading to better training, more effective coaching, and ultimately improved sales performance.
Real-Time Data Enrichment
With Symbl.ai’s programmable APIs, the process of enriching data can be automated to deliver real-time insights to Snowflake. This means that enriched analytics dashboards are always up to date, providing decision-makers with the most current information available. Imagine a dynamic sales dashboard that not only displays the numbers but also reveals the sentiment and effectiveness of each sales interaction—allowing leaders to take immediate action where needed.
3. Centralized Backbone for New Agentic Workflows
Beyond enriching existing analytics, the integration of Symbl.ai with Snowflake lays the foundation for a centralized backbone that supports new agentic workflows. These workflows are proactive, contextually aware, and driven by real-time insights from interaction data.
What Are Agentic Workflows?
Agentic workflows are workflows that respond intelligently to business events, guided by the data at their disposal. By centralizing interaction insights from Symbl.ai in Snowflake, businesses can build workflows that do more than react—they anticipate, adapt, and act based on real-time information.
Filling Knowledge Gaps with Real-Time Insights
Symbl.ai’s real-time streaming APIs allow organizations to process data from live interactions and use it to fill gaps in the knowledge base that powers customer service and internal decision-making. Imagine a scenario where a customer support team is frequently asked questions that are not well documented in the existing knowledge base. With Symbl.ai providing real-time insights, these gaps can be identified and filled immediately, ensuring that the knowledge base evolves dynamically to meet customer needs.
In addition, Symbl.ai’s capabilities can support Retrieval-Augmented Generation (RAG) implementations that provide enhanced context to customer interactions. By continuously feeding new, relevant information into Snowflake, these RAG models can deliver more accurate and context-driven responses, empowering both AI-driven agents and human support teams with the latest information.
By using Snowflake as a centralized repository, these insights can be stored, analyzed, and used to continuously improve the performance of these knowledge-based workflows. The data stored in Snowflake serves as the backbone that makes these workflows more intelligent and effective over time.
Connectors for Seamless Integration
Symbl.ai offers connectors that integrate interaction data across various communication platforms, such as Genesys, Five9, and CPaaS systems. These connectors make it easy to aggregate and analyze interaction data from different sources into a centralized Snowflake repository. This means organizations can maintain a unified view of all customer interactions and use it to drive consistent and informed agentic workflows.
For example, a financial services customer support workflow could be enhanced to automatically identify recurring issues and notify the product team for resolution, thus improving customer satisfaction and reducing churn. In telecommunications, workflows can help identify common network issues from customer interactions and drive faster resolution times. Similarly, marketing workflows in the retail sector can use interaction data to tailor campaigns more effectively based on the topics and sentiments observed during customer conversations, directly impacting campaign ROI and customer engagement. Similarly, marketing workflows can use interaction data to tailor campaigns more effectively based on the topics and sentiments observed during customer conversations.
Bringing It All Together: A Modern Interaction Intelligence Ecosystem
The integration of Symbl.ai with Snowflake enables organizations to build a powerful ecosystem for interaction intelligence, centered on three key capabilities:
- Search: Make unstructured interaction data easily discoverable and actionable through advanced, context-aware search.
- Data Enrichment: Enhance existing business analytics with rich, contextual insights from interaction data, driving deeper understanding and more informed decision-making.
- Centralized Agentic Workflows: Establish a centralized backbone for real-time, proactive workflows that respond intelligently to customer needs, enhancing both operational efficiency and customer experience.
Next Steps: Building Your Future with Symbl.ai and Snowflake
Integrating Symbl.ai and Snowflake provides a scalable, future-proof solution that enables organizations to harness the full potential of their interaction data. From powerful search capabilities and data enrichment to real-time, agentic workflows, this partnership offers the tools needed to transform raw data into actionable business outcomes.
Check out our step by step guide on how to build a sample real-time performance dashboard for the sales team by processing call recordings and transcripts from your existing meeting platforms – augmenting with salesforce data.
