The corporate world has always been fond of the adage, "What gets measured gets managed." Historically, businesses that rely on customer support and sales through telephony systems have turned to humans to monitor, score, and provide feedback on a selection of customer calls. However, as the digital transformation accelerates, Artificial Intelligence (AI) is reshaping quality assurance practices. Instead of relying solely on human analysis, many companies are now deploying AI to gauge call quality, ensuring better accuracy and efficiency.
The Traditional Approach: Human Monitored Quality Assurance
Before delving into the world of AI in quality assurance, it's essential to understand the traditional method. Historically, QA teams would randomly select a fraction of calls, listen to them, and evaluate the agent's performance based on various criteria like product knowledge, etiquette, and problem-solving capabilities.
Pros of Human-Based QA in Contact Centers:
Nuanced Understanding: Humans excel at picking up on subtle cues, understanding context, and interpreting the nuances of a conversation, including humor, sarcasm, and complex emotions.
Empathy and Relatability: Humans can empathize with both the agent and the caller, understanding the emotional aspects of an interaction, which can be essential for sensitive topics or complex situations.
Flexibility: Unlike machines, humans can adapt to unforeseen circumstances during a call review, such as unexpected topics, cultural references, or unconventional communication styles.
Customized Feedback: Supervisors or QA specialists can provide personalized feedback, mentoring, and coaching based on the unique strengths and areas of improvement of each agent.
Intuition: Humans often rely on intuition or "gut feelings" when evaluating interactions, which might help identify issues or areas of concern that aren't strictly based on a predefined checklist.
Cons of Human-Based QA in Contact Centers:
Limited Scope: Due to time constraints and workforce limitations, only a small percentage of calls can be reviewed. This means many interactions go unchecked, and insights from those calls are missed.
Subjectivity: Different reviewers might rate the same call differently based on their personal biases, moods, or interpretations. This inconsistency can lead to confusion and frustration among agents.
Time-Consuming: Manual review of calls is time-intensive, diverting human resources from other potentially productive tasks. This also delays feedback to agents.
Potential for Fatigue: Continuously listening to and evaluating calls can be mentally exhausting, leading to reduced attention and effectiveness over time.
Scalability Issues: As the volume of interactions grows, scaling up human-based QA requires significant investment in hiring and training more personnel.
Cost-Intensive: Maintaining a large QA team for manual reviews can be expensive in terms of salaries, benefits, training, and infrastructure.
Lack of Comprehensive Data Analysis: While humans can understand the context and sentiment of individual calls, they might not be as effective as AI in analyzing trends and patterns across thousands of interactions.
In conclusion, while human-based QA brings the unique advantages of empathy, intuition, and nuanced understanding, it also has limitations in terms of scalability, consistency, and comprehensive analysis. In many modern contact centers, a hybrid approach that combines the strengths of both human evaluators and AI-powered tools is being considered the optimal solution for quality assurance.
The AI Revolution in Quality Assurance
With the advent of advanced machine learning models, the scenario is rapidly changing. AI is being integrated into QA processes for call centers and other telephony-based systems.
How does AI-powered QA work?
Voice Recognition and Transcription: The system transcribes calls into text, ensuring every word and phrase is accounted for.
Semantic Analysis: AI models can understand the context and sentiment behind conversations. They can gauge customer satisfaction and agent efficiency by analyzing the content and tone of the conversation.
Pattern Detection: Over time, AI can detect recurring issues or frequent feedback points and provide actionable insights.
Benefits of AI in QA:
Comprehensive Analysis: Traditional methods involve manually reviewing a sample of calls, which means many interactions go unchecked. AI can evaluate all calls, ensuring comprehensive oversight and leaving no interaction unevaluated.
Consistency: Human evaluations are subject to variability, fatigue, and biases. AI ensures a consistent scoring system across all interactions, maintaining uniform standards.
Real-time Feedback: AI can analyze calls in real-time, allowing for instant feedback. Agents can be informed immediately if there are areas of concern, enabling swift corrective actions.
Sentiment Analysis: AI can evaluate the sentiment behind customer interactions, allowing businesses to gauge the mood and satisfaction levels of callers beyond just the content of the conversation.
Trend Detection: AI systems can quickly identify patterns and trends in vast amounts of data. Recognizing recurring issues or frequently mentioned topics can guide training programs and inform business strategies.
Cost-Effective: Over time, AI-driven QA can be more cost-effective. The automation of routine QA tasks reduces the need for large QA teams and minimizes manual hours spent on repetitive tasks.
Data-Driven Decisions: With the vast amount of data AI can analyze, decisions about training, process changes, or other operational tweaks can be based on solid data rather than intuition.
Reduced Human Fatigue: Continuously listening to and evaluating calls can be tiring for human reviewers, which can affect accuracy and consistency. Automating this task ensures evaluations are done without fatigue-induced errors.
Highlight Training Needs: AI can pinpoint specific areas where agents may need additional training, allowing for targeted and effective upskilling sessions.
Enhanced Customer Experience: With improved oversight, real-time feedback, and more data-driven decisions, the overall customer experience is enhanced. Issues are caught and addressed faster, and agents are better equipped to handle interactions.
Scalability: As contact centers grow, the volume of interactions can increase exponentially. AI-driven QA systems can easily scale to handle these increased volumes without a proportional rise in costs.
Integration with Other Systems: AI-driven QA tools can be integrated with other systems like CRM, analytics tools, or training platforms, providing a holistic view of agent performance and customer sentiment.
Potential Drawbacks:
Loss of Nuance: While AI models are improving, they might still miss some nuances or context that a human evaluator would catch.
Dependence on Technology: Over-reliance on AI could lead to reduced human oversight, potentially missing out on the benefits of human intuition and expertise.
The Future of Quality Assurance
The goal isn't to eliminate human intervention entirely but rather to leverage the strengths of both AI and humans. A blended approach, where AI does the heavy lifting by analysing vast numbers of calls, and humans focus on more complex evaluations and providing empathetic feedback, seems to be the ideal future.
To thrive in this evolving landscape, businesses must invest in advanced AI-powered QA systems and upskill their human QA teams to work harmoniously with these tools. By doing so, they can ensure a consistent, high-quality experience for their customers and a productive, supportive environment for their agents.
