Three Ways Conversation Analytics Improves First Call Resolution
Modern conversation analytics, a subset of Artificial Intelligence technology, uses Natural Language Understanding (NLU) and Natural Language Processing (NLP) to deliver continuous, consistent, and comprehensive actionable business insights across the enterprise.
As AI technology evolves, the ability to measure and decipher interactions is multiplying.
The reasons for adopting conversation analytics vary, but businesses need accurate data to help make critical business decisions. A human, not a robot, helps determine the value of the data and can implement the correct solutions.
The human stays in the loop!
Contact center leadership cares about customer success and running efficient operations. As a result, the ability to evaluate KPIs like internal process quality, employee satisfaction, agent performance, productivity, and financial performance has long been measurement staples for contact centers.
Imagine having a holistic view of all your conversation data to benchmark performance, employee satisfaction, and customer success. Consider what could change by having the ability to understand emotional cues, like frustration or happiness.
The ability to measure agent performance and automate workflows can accelerate sales and improve retention. Conversation analytics allows contact center managers to measure operational efficiency to drive financial performance effectively.
Improving First Call Resolution Matters
Converting large volumes of conversation data into searchable text provides the business intelligence you need to run your contact center optimally.
Sorting data by emotion, sentiment, and compliance rather than small samples tracks agent compliance, performance and measures customer satisfaction.
Improving First Call Resolution (FCR) is a crucial goal for any contact center. FCR measures the percentage of calls resolved on the first call or contact.
High FCR rates indicate that the contact center is efficient, productive, and customer oriented.
To improve FCR, first, identify the problem’s root cause. By analyzing, for example, the Voice of Customer (VoC) data, a contact center can gain insights into the issues that customers face during their interactions. These insights provide the necessary data to take corrective action.
One such insight may reveal that calls initially route to the wrong automated queues, indicating an issue with the voice automation system. When an automation system mishandles a call, it may result in long wait times, frequent transfers, and a lower FCR rate.
Frustrated customers are rarely a good sign.
How Conversation Analytics Improves FCR
AI can help improve First Call Resolution in contact centers in several ways.
Firstly, conversation analytics evaluates conversations between agents and customers to identify the root causes of customer issues. Using NLP and machine learning algorithms, conversation AI can identify patterns in customer queries and highlight areas where agents may require additional training or support.
This analysis can identify common customer issues, allowing the contact center to address these issues and improve FCR rates proactively.
Secondly, conversation AI can help agents provide personalized support to customers. By analyzing customer data and call history, conversation AI can give management real-time insights into a customer’s needs and preferences.
Voice data insights can help train agents to tailor their responses to individual customers, improving the chances of resolving the issue during the first call.
Thirdly, conversation AI can assist agents in resolving complex customer queries by providing real-time guidance and support. For example, if an agent needs help to resolve a specific issue, conversation AI can offer suggestions and recommendations based on previous successful resolutions.
Meaningful Data Makes The Difference
Meaningful data, structured to help agents resolve the issue during the first call, will improve FCR rates and customer satisfaction.
Data insights can train agents to ask the right questions at the beginning of conversations. These questions can help determine if the customer’s journey to the agent was smooth or faced any issues.
For example, an agent may ask, “Was it easy for you to reach me today?” or “Did you have to navigate multiple menus before speaking to me?” The responses can then be analyzed using AI-powered conversation analysis tools, which can provide valuable insights into the customer experience.
By combining meaningful insights from advanced analytics and conversation analysis, contact centers can take corrective action to improve FCR rates.
Finally, conversation AI can help contact centers automate routine queries and tasks, allowing agents to focus on more complex issues.
In conclusion, by leveraging advanced analytics and AI-powered conversation analysis, contact centers can gain valuable insights into the root causes of low FCR rates.
Armed with this information, they can take corrective action to improve FCR rates and provide a seamless customer experience. The result will be a more efficient, productive, and customer-oriented contact center that can build long-term customer relationships.
Call Journey helps you hear customers more clearly
The value Al can bring to contact centers in 2023 and beyond is understood— 85% of CX professionals surveyed believe it’s vital to leverage Al and automation now.
Dubbed the best speech analytics for contact centers, VoiceAI from Call Journey is a conversation analytics platform that equips you with real-time tools to unlock every conversation and learn what your customers and employees are saying.
Mine customer data for insights without having to review conversations manually. Measure empathetic customer connections. See opportunities for agent training and retention.
Use AI to expand coverage to 100% of every interaction, every time. Learn how to convert conversations into revenue opportunities.
