Providing top-notch customer service is critical to the success of any business. Therefore, ensuring that customer service representatives (CSRs) provide the best possible service to customers is a crucial challenge call center managers face.
Historically, call center quality assurance (QA) has been done manually, which can be time-consuming and prone to errors. Manual call center quality checking is a process where call center managers listen to recorded calls and evaluate the performance of CSRs based on specific criteria.
Difficulties with Manual Quality Checking
While manual quality checking was once the norm, it comes with a number of difficulties, including:
Time-Consuming
Manual quality checking can be a time-consuming process. Managers have to listen to every single call and evaluate the CSR’s performance, which can be a tedious task that takes up a lot of time.
Prone to Errors
Humans are prone to errors, which means there may be inconsistencies in the evaluation process. For example, different managers may evaluate calls differently, leading to a lack of consistency in the evaluation process. This can result in inaccuracies and inconsistencies in the performance evaluation of the CSR.
Subjective and Inconsistent
It’s difficult for humans to remain objective and consistent when evaluating calls. Factors such as the manager’s mood, personal biases, or understanding of the evaluation criteria can all affect their evaluation of the CSR’s performance. As a result, manual assessment may not always be objective or consistent, leading to inaccurate evaluations.
Limited Scope
Manual evaluations typically only cover a small percentage of calls due to the time-consuming nature of the process. As a result, managers may not fully understand the quality of customer service being provided. This can limit their ability to identify improvement areas or customer satisfaction issues.
Unclear Trends and Patterns
Manual quality checking can make identifying trends and patterns across calls challenging. This can limit the ability of call center managers to identify common issues or areas of improvement that can help to improve overall customer satisfaction.
However, with the advent of technology, automated quality assurance processes using artificial intelligence (AI) are now available, which can help address these issues.
What is Automated Quality Assurance?
Automated quality assurance processes that leverage AI technology involve the automatic evaluation and analysis of customer interactions with CSRs.
Through the use of algorithms and machine learning, the technology can assess various elements of the call, which can otherwise be missed in manual QA. This enables managers to rapidly and accurately collate the data of numerous calls while avoiding potential biases inherent in human evaluation, resulting in more consistent and objective assessments.
How Is Automation Using AI a Solution?
Automated quality assurance processes using AI can help address the difficulties faced with manual quality checking and provide numerous advantages to call center managers.
Benefits of automated quality assurance processes:
Time-Efficient
Automated QA is much quicker than manual evaluations, as AI can analyze large volumes of calls in a short amount of time.
Objective and Consistent
AI eliminates human biases and ensures that evaluations are consistent, objective, and accurate.
Comprehensive Evaluation
AI can analyze customer interactions for specific keywords and phrases, tone of voice, and even emotional sentiment, providing a more comprehensive evaluation of the CSR’s performance.
Ability to Identify Trends and Patterns
AI can identify trends and patterns across calls, helping managers pinpoint areas where CSRs need improvement and tailor training programs to address these needs.
Improved Customer Service
Automated QA can help businesses provide a high level of customer service, ultimately leading to increased customer loyalty and satisfaction.
Ability to Identify Customer Issues
AI-powered QA can help companies identify customer issues and trends they may not be aware of, leading to improvements in products and services.
Cost-Effective
While the initial investment in automation may be higher, the long-term benefits and return on investment are significant, making it a cost-effective solution for call centers.
How to Implement AI for Automated Quality Testing?
An AI-driven automated quality testing process requires the right solutions to be put in place.
- Create a comprehensive set of minimum requirements or standards that must be met for your product.
- Use AI-powered software like Quality Pack that auto-analyzes the preset parameters. This can help you immediately identify issues, if any, enabling you to take corrective action before they cause more significant problems.
- Include the testing of your product performance in the AI-driven automated QA process with tools like automation testing and simulation modeling.
- Create a data-driven system that can detect small changes in the user experience to maximize the potential of AI-driven automated QA.
- Develop meaningful and actionable criteria for evaluation.
- Ensure continuous monitoring and feedback loops to update product features or identify potential issues.
Bottom Line
While manual call center quality checking has been the norm for several years, automated quality assurance processes using AI are the way forward. With the ability to quickly and accurately evaluate large numbers of calls, remain objective and consistent, and identify trends and patterns, AI-powered quality assurance is an invaluable tool for any call center.
The benefits of automation are clear, and any prospective business should consider investing in this technology to improve their customer service and gain a competitive edge in the marketplace.