Quality assurance (QA) in contact centers is essential for upholding high-quality standards and ensuring customer satisfaction. With the advent of technology, there is a new debate in the market regarding the efficiency of manual versus automated quality audits; which approach do you believe will win?
To answer this question, it is essential to closely read the article that judges the benefits and drawbacks of each process and, therefore, will help you to choose the right approach that will work wonders in the context of contact center quality assurance.
Understanding Quality Assurance in Contact Centers
Every contact center has its specific standards and key performance indicators (KPIs) that ensure quality productivity and customer satisfaction. According to a recent survey, 65% of contact centers puts QA in their top three priorities. What then exactly is Quality Assurance, and how can a contact center enable an objective and efficient evaluation of the agents’ performances?
Quality Assurance in contact centers is the monitoring and evaluation of customer calls systematically so that the performance standards of agents are achieved. Effective QA helps detect performance gaps, improve agent training, and boost customer satisfaction scores (CSAT).
Some key quality metrics tracked are:
First Call Resolution (FCR)
Average Handling Time (AHT)
Customer Satisfaction Score (CSAT)
These metrics serve as the foundation for individual agent performance levels, and for overall contact center effectiveness and efficiency. To compare these metrics, two basic quality audit techniques are used by contact centers: Manual and Automated AI-driven audits). Let’s take a closer look at the two techniques.
How Automated QA can help you improve agent retention?
Manual Quality Audit
Contact centers require human reviewers who are referred to as QA specialists. There are different standards specified, and these analysts carry out detailed step-by-step analyses of recorded calls by reviewing chat transcripts or examining email exchanges between agents and customers. The auditor assesses each interaction with a predefined set of standards that follow, adherence to scripts, tone of voice, accuracy of information, and solved problems.
Manual auditing has better sensitivity and flexibility because human auditors understand the context, detect subtle emotional cues, and interpret complex situations. Though this approach allows for personalized coaching and instant feedback, it also has the following limitations:
Limited Scope: The sampling-based nature of manual audits may sometimes miss out on the entire complete scope of agent performance.
Subjectivity: There could be personal perceptions that affect judgments and therefore different ratings.
Time-Consuming: The procedure is labor-intensive and involves much time allocation from the supervisors to review calls as well as prepare reports.
However, manual audits are still beneficial for enhancing the interpretation of individual agent performance; they also provide customized coaching opportunities.
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Automated Quality Inspection using Artificial Intelligence
New technologies, such as machine learning, natural language processing, or sentiment analysis, may be incorporated into AI-based audits, ensuring that the entire quality management process is automated and the scope of the audit is both efficient and comprehensive. Automated quality assurance can be run through audio recordings as well as large-scale text transcripts to identify patterns in agent performance, customer sentiments, and many other quality metrics.
AI-powered QA can process and assess thousands of interactions in minutes, offering consistency, speed, and scalability that manual QA cannot match. Additionally, tools like Mihup.ai can capture context and emotional nuances and adapt to new phrases, accents, or changing customer behaviors at scale. This method provides various advantages:
Full Coverage: All calls can be analyzed by automatic systems, thus gaining coverage for all the agents.
Consistency and Objectivity: AI-driven evaluation negates human biasing, and hence every interaction is dealt with equally.
Real-Time Insights: Advanced tools can monitor sentiment, compliance, and many qualitative metrics in real time, thus enabling timely correction in training or processes.
AI integration into the contact centers’ quality assurance has significantly enhanced operational efficiency while reducing the workload on human supervisors.
Who is the winner?
It is visible that automated QA overshadows manual auditing at a glimpse, but there are some more points to be considered.
| Feature | Manual QA | Automated QA |
| Reviewing Calls | Only a limited number is possible | 100% interactions across all the channels |
| Compliance | Compliance can go unflagged since a limited number of calls are reviewed | Compliance violence can’t go unflagged |
| Agent Coaching | Time-consuming feedback loop | Immediate feedback after each conversation |
Benefits of AI in Contact Centers
The benefits of AI application in contact centers extend quite far beyond then quality assurance:
Better Customer Experience: Artificial intelligence identifies trends and improves areas quickly, thereby allowing agents to offer the best service.
Cost Efficiency: Automating QA processes decreases labor for the cost involved in manual monitoring.
Scalability: As contact centers expand, artificial intelligence solutions are capable of scaling effortlessly to manage increased call volumes while maintaining quality.
These benefits make a compelling case for businesses to consider a shift from manual to automated QA processes.
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Choose the appropriate Technique
The decision to use manual or automated quality audits in contact centers would ultimately depend on various factors such as organizational goals, financial constraints, and existing infrastructure. Even though manual audits provide substantial learnings through customized feedback, the scalability and consistency that are vital aspects of the modern fast-paced business environment can be found in automated systems.
For many organizations, a hybrid approach may be the most effective solution—leveraging the strengths of both methods to enhance overall quality assurance processes. By integrating AI technologies like MIA into their QA frameworks, contact centers can improve operational efficiency and elevate the customer experience to new heights.
Therefore, understanding the differences between a manual and an automated quality audit will help enterprises reach excellence in customer service through decisions that align with their strategic objectives.
