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Community Manager
Community Manager

Invoca recently hosted a customer user group exploring how companies can optimize phone interactions and marketing strategies using AI-powered call tracking signals. The session provided insights into real-world use cases and tips for getting more value from signals.


Using Signals to Understand Call Drivers and Intent

The  discussion highlighted how healthcare provider CHRISTUS Health uses signals to understand the drivers behind incoming calls. By tracking campaign IDs, they gained visibility into cross-selling opportunities and were able to identify calls generated from specific marketing campaigns.

CHRISTUS has expanded their signals to include keywords and phrases that indicate a patient’s intent, such as asking for a doctor by name. This helped them customize inbound call routing to get patients scheduled with the right doctor faster.

Attendees discussed the importance of understanding the thematic topics that arise during conversations, particularly in the context of booking appointments. By analyzing the conversations, businesses can identify common themes and topics such as lost cards, passwords, or maintenance fees. This understanding can help improve customer service and address specific issues more effectively.


Driving More Appointments with Optimized Call Routing

By analyzing signals to understand caller intent, CHRISTUS Health optimized their call routing to drive more appointment bookings. They set up customized IVR flows to route calls by campaign source or keywords.

For example, calls mentioning a specific doctor’s name were routed directly to book with that doctor's office. This reduced inbound call handling time and helped ensure patients got scheduled for appointments.


Using Signal Data to Inform Marketing Strategies

The  conversation emphasized how signals can provide insights to optimize marketing campaigns and content. For example, identifying common topics in call transcripts can reveal organic search terms to target through SEO.

Signals can also feed data into CRMs to customize email nurture campaigns based on a prospect’s interaction history. Overall, the session demonstrated the power of signals for gaining customer insights.


AI Signals vs. Keyword Spotting

Using AI Signals for Non-Scripted Conversations

Keith Jellick, Invoca Sales Engineer and Signals expert,  emphasized that AI signals are particularly valuable for non-scripted, organic conversations. Unlike keyword spotting, AI signals provide a more accurate understanding of customer conversations and can be used as leading indicators of conversions or desired interactions. This makes AI signals a powerful tool for building audience and improving marketing strategies.

AI Signals: Differentiating True and False Values

Keith explained that AI signals are created by providing examples of true and false values to the AI system. By analyzing the differences between these examples, the AI creates a custom algorithm based on individual conversations. This allows businesses to have more accurate and personalized interactions with their customers.

AI Upload: Receiving Data from Third-Party Platforms

Keith highlighted the process of AI upload, which involves receiving data from third-party platforms such as Salesforce or back office systems. This data can be transmitted to the AI system through an API endpoint or by sending it as a file. This allows businesses to leverage external data sources to enhance their AI capabilities.


Making Data More Accessible with AI

Keith acknowledged the need to make AI and its insights more accessible to businesses without requiring a team of data scientists. He mentioned the use of word clouds as a visual representation of conversational topics. These word clouds can help distill complex data into actionable insights, allowing businesses to filter and analyze specific signals or marketing data easily.


Key Takeaways

The  user group provided several tips for getting more from Invoca’s AI-powered signals:

  • Use both keyword spotting and AI signals together to identify leading and lagging indicators.
  • Work to minimize campaigns and leverage call drivers for more targeted routing.
  • Consider signal discovery features to analyze call topics and inform content.
  • This can dovetail into Topic Explorer
  • Request trial runs to uncover insights from a sample of calls.

In conclusion, the discussion shed light on the differences between Keyword Spotting and AI Signals, emphasizing the importance of understanding true machine learning versus regular logic. The use of AI signals and AI upload allows businesses to create custom algorithms based on individual conversations and leverage data from third-party platforms. Understanding conversational themes and topics can help improve customer service, while making AI more accessible enables businesses to extract valuable insights without relying on a team of data scientists. Overall, the user group discussion highlighted the potential implications and benefits of AI in various business contexts. 

To learn more about setting up, maintaining and working with Signals, we invite you to attend a future Signal user group. Email to learn about the next event and reserve your spot.