Empowering Customer Service Reps with AI Tools  

AI has become one of the most relevant advances in recent years when it comes to automating processes, analyzing large amounts of data and interacting with customers. Its use has spread to all sectors, as it helps people achieve better results and be more efficient. The call center is no exception and artificial intelligence has allowed improvements to occur to offer a higher quality service to the customer. 

The potential of AI in customer service is very high, since there are certain operations that can be carried out more efficiently thanks to it. This is because contact centers store a large amount of complex data that can benefit from the information provided by analysis software powered by AI. What impact does this have on a practical level? Better customer service, increased productivity, and efficiency of agents. 

AI is used to make more accurate forecasts, help leaders proactively identify and manage issues, as well as offer customers different self-service solutions, among others. 

AI Tools that Drive Great Performance and Customer Service 

Let’s take a look at some of the AI advancements that help customer service representatives perform better at their job: 

1. Intelligent Agent Assistance 

Real-time interaction guidance in a call center leverages AI to listen and analyze each call as it happens. That is, it provides real-time feedback to agents about their social skills. AI-enabled real-time coaching can help redirect tense interactions, provide immediate feedback, and can help correct suboptimal behavior before it has a chance to perpetuate itself. Some practical cases are the following: 

  • If the agent repeatedly interrupts a customer, the system could tell the agent to use active listening skills. 
  • If the interaction guidance tool determines that a caller is stressed, it could remind the agent to show empathy. 
  • The AI assistant could hear that a caller has a question about the software’s functionality and automatically search for the knowledge base article relevant to the agent. 

The positive impact that AI can have on agent performance is not limited to real-time interaction guidance. AI-powered training and coaching platforms can shift the way agents are trained and guided in their interactions. The ability to practice voice interactions means that AI-powered solutions can also help agents solve problems. Intelligent assistants therefore help improve FCR rates, increase accuracy and reduce handling times, thereby improving customer service. 

2. Self-Service 

Self-service has established itself as a channel through which satisfactory experiences can be provided. Customer service-focused organizations design and optimize self-service so that customers can successfully resolve their own issues or transfer them to an agent for additional assistance. 

A clear example that self-service works is that according to a Gartner report, 70% of customers use self-service channels to resolve their issues quickly. To do this, a tool like IVR is used. This has been greatly improved thanks to a form of natural language processing, which allows the system to understand what customers are saying and respond to them to answer their question, guide them through self-service or connect them with an agent. Additionally, a call center can integrate AI-powered virtual agents into its IVR to create smarter experiences. Thanks to this, the mythical “press 1 if you want…” will no longer be necessary, which makes the service much more humanized. 

3. Forecast and Programming 

As we have discussed previously, AI is of great help in analyzing large amounts of data to identify patterns and make predictions. In this case, it determines which is the best algorithm to apply in each contact center and make the best predictions of customer behavior based on historical data. 

Call center scheduling software that leverages artificial intelligence meets business and customer needs by ensuring the right number of trained agents are scheduled at the right times while accommodating agent preferences. 

4. Chatbots and Virtual Agents 

Virtual agents use artificial intelligence, natural language processing, machine learning, and related technologies to understand human speech and intent. This makes virtual agents capable of handling more complex interactions than a rules-based chatbot. 

On the other hand, chatbots can interact with a person by presenting two or three response options. The data reveals that 63% of users surveyed were satisfied with their experience when interacting with a chatbot. However, human assistance is still essential when serving customers, as this percentage stated that they would like to be served by a real employee to help them if necessary. 

5. Supervisor Efficiency and Productivity 

Quality management analytics can make the quality monitoring process more efficient and accurate for supervisors evaluating interaction samples. This software analyzes and classifies interactions, making it easy to identify the right ones to evaluate. 

In addition, it provides efficient problem resolution by allowing supervisors to focus on the analysis of specific types of interaction. AI-infused forecasting and scheduling capabilities will also reduce time-consuming monitoring tasks. 

Gartner predicts that customer service organizations that adopt AI will gain 25% efficiency, which shows how important this tool has become to provide the best possible support.

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