Ever hit a dead end with a chatbot and wished you could talk to a human? Conversation intelligence helps contact center leaders turn those dead-end conversations into smoother escalations, ensuring better handoffs when bots reach their knowledge limits and can no longer help the customer. They offer instant responses to customers, deflect repetitive inquiries, and help reduce the overall workload for human agents. But—no chatbot is perfect, and there will inevitably be moments when the bot cannot resolve a customer's issue. This is where a smooth chatbot escalation experience is important. When a chatbot fails to answer a question or address a concern, customers often get frustrated—especially if they struggle to reach a human agent quickly (or sometimes, at all).
As a contact center leader, improving the chatbot escalation experience should be a priority. A seamless transition from chatbot to human support not only minimizes customer frustration during a part of their journey that may already be frustrating but ensures an overall low-effort experience for your customers. One of the most effective ways to improve the escalation process is by using conversation intelligence.
In this post, we'll explore how conversation intelligence can improve the efficiency of your chatbot escalation process and improve the overall customer experience.
What is conversation intelligence?
Conversation intelligence refers to the use of advanced technologies, including artificial intelligence, machine learning, and natural language processing (NLP), to analyze and extract meaningful insights from customer conversations. This technology works across both voice (call recordings) and text-based customer service interactions, enabling businesses to better understand customer intent, pain points, and behaviors.
Conversation intelligence helps contact centers by providing detailed insights into customer-agent interactions, identifying trends, and recommending actions for improving customer experience and operational efficiency. It provides actionable data that helps companies refine their customer support strategies, improve chatbot performance, and equally importantly, improve the chatbot escalation experience.
How to use conversation intelligence for chatbot escalation
Using conversation intelligence to improve chatbot escalations doesn’t have to be complicated. By following a few simple steps, you can create a smoother process for both your agents and customers.
1. Set up a category for fallback responses
If you use a conversation intelligence platform that lets you group and flag specific phrases and events that matter to your business (at Creovai, we call these categories), you can track what's causing chatbot confusion. Start by setting up a category for fallback responses—those moments when the chatbot encounters something it doesn’t quite understand or does not have an answer to. These are also the key flags of when it’s time for a human to step in.
Conversation intelligence helps track when and why the bot uses fallback responses so you can identify common friction points in the customer conversation. By analyzing these interactions, you’ll discover where the chatbot needs to improve and what customers expect. The insights help streamline the escalation process and improve the bot’s responses for the future.
Also, consider setting up a category for variations of trigger phrases that are being missed by the chatbot like “talk to an agent” or “I need help with a representative.” Pinpointing these phrases and variations can help contact center leaders then build those phrases into the chatbot, so it does a better job of recognizing when people want to speak to a person.
2. Spot patterns in customer queries and conversations
Once you’ve identified common reasons for fallback responses, dig into the language and queries that trigger them. Conversation intelligence software makes it easy to spot trends in customer conversations, helping you pinpoint recurring issues the chatbot can’t resolve.
For example, if customers often ask about a product feature that the chatbot doesn’t know about, you can use insights from conversation intelligence to quickly update its knowledge base. These insights can also highlight new escalation triggers, like certain keywords or phrases that would indicate it’s time for a human agent to step in.
By analyzing these patterns over time, you’ll get a better sense of where the chatbot needs improvement, ultimately reducing the need for human intervention and escalations in the future.
3. Review channel-switching data
Another helpful strategy is looking at when customers switch from chat to voice support. Conversation intelligence platforms like Creovai can automatically track these moments when customers express frustration and choose to talk to a human agent instead.
Running reports on these channel-switching interactions lets you spot recurring issues that drive customers away from the chatbot and to a human agent. Are there certain topics that the bot struggles with, causing customers to call the contact center on a regular basis? Or maybe there’s a frustration point in the bot’s workflow that’s leading to the switch. Understanding these causes helps you fine-tune the chatbot’s responses and improve escalation triggers, ensuring a smoother transition when they do happen.
Tracking channel switching also helps refine your overall escalation strategy, ensuring the human agent has all the context needed to resolve the issue quickly and efficiently.
Why improving the chatbot escalation experience matters
Using conversation intelligence to improve the chatbot escalation process offers numerous benefits for both your contact center operations and the customer experience.
Reduced frustration for customers
By ensuring that customers can easily reach a human agent when the chatbot fails, you significantly reduce frustration. This leads to higher customer satisfaction and loyalty.
Faster issue resolution
Conversation intelligence helps you identify and resolve bottlenecks in the customer journey and escalation process, ensuring that human agents can quickly step in when needed and provide effective support.
Improved chatbot performance
By continuously analyzing and learning from escalations with conversation intelligence, you can improve your chatbot’s knowledge base and reduce the frequency of escalations—improving its overall performance.
Data-driven decisions
The insights gained from conversation intelligence allow for more informed decision-making, enabling you to refine your customer support strategies and reduce the reliance on human agents over time. As the bot becomes more capable, escalations become less frequent, streamlining the entire support process.
Reduced AI risks
For Gen-AI-driven chatbots, conversation intelligence also helps mitigate risks associated with incorrect or biased responses. By constantly analyzing chatbot performance and customer feedback, you can give the bot the training data it needs to deliver accurate and reliable information, which reduces the number of escalations triggered by poor bot performance.
Don't let your chatbot be a source of frustration
Delivering a low-effort chatbot escalation experience is key to maintaining a high-quality customer journey and high customer satisfaction. Using conversation intelligence, you can streamline the process, reduce customer frustration, and improve both chatbot and human agent performance.
By implementing strategic tactics like tracking fallback responses and the customer language leading up to them, identifying escalation triggers, and analyzing channel-switching data, your contact center can create a more efficient, low-effort experience that meets the needs of your customers.
By refining the chatbot escalation experience with the help of conversation intelligence, you can ensure that your contact center delivers exceptional support, no matter how complex the issue.
Top takeaways
1. Start by tracking your chatbot's fallback responses: By setting up conversation intelligence categories for fallback responses, you can monitor when and why your chatbot fails to understand customer queries, highlighting areas where you need to improve your chatbot training or escalate to a human agent.
2. Analyze escalation patterns to uncover friction points: Conversation intelligence helps you determine the reasons for escalations, allowing you to pinpoint common issues and refine chatbot responses to reduce customer frustration.
3. Improve chatbot-agent handoffs with contextual insights: Providing agents with detailed context from prior chatbot interactions ensures smoother transitions and more efficient resolutions during escalations.
4. Continuously improve chatbot performance using conversation data: Conversation intelligence allows for ongoing refinement of chatbot behavior, leading to more accurate responses and fewer escalations over time.
5. Elevate your customer experience by minimizing effort during escalations: A seamless handoff from chatbot to human agent, informed by conversation intelligence, reduces customer effort and enhances overall satisfaction.
