AI in customer service quality assurance: A complete guide

There’s a razor-thin margin between an exceptional customer experience (CX) and a bad one. With customer expectations continuing to rise, speed may not be enough. Customers want quality support consisting of fast, personalized service on a convenient channel. Meeting this demand can set you apart from the competition.

According to the Zendesk Customer Experience Trends Report 2024, 79 percent of CX leaders plan to increase their budget for more strategic agent management tools, such as AI. To avoid falling behind, use AI in customer service quality assurance to help your support team deliver the best CX with every interaction.

More in this guide:

• The role of artificial intelligence in customer service quality assurance
• Challenges of using manual QA instead of AI
• Benefits of using AI in customer service QA
• The future of AI in customer service quality assurance
• Frequently asked questions
• Enhance your QA today

The role of artificial intelligence in customer service quality assurance

AI in customer service quality assurance refers to using artificial intelligence technologies to evaluate and analyze interactions, helping businesses enhance their customer service quality. This includes reviewing customer interactions across multiple channels—like phone calls, emails, chatbots, live chat, and social media—to evaluate whether the support provided meets internal quality benchmarks.

AI in customer service quality assurance uses machine learning (ML) algorithms and natural language processing (NLP) techniques to automatically analyze customer messages, while pre-trained, multilingual models perform tasks like sentiment analysis.

Intelligent automation can provide a quick overview of trends and insights from extensive volumes of customer interactions that human reviewers may otherwise miss. This ensures that customer support aligns with your internal quality criteria, resulting in improved customer experiences, heightened operational efficiency, and ongoing advancements in service provision.

Challenges of using manual QA instead of AI

Traditional, manual approaches to quality assurance in customer service face several limitations that can hinder a business’s understanding of its customer experience. Here are a few manual QA challenges and why AI-powered QA offers a more robust and scalable solution.

Less efficient and more time-consuming

According to Zendesk benchmark data, manual QA processes typically allow human reviewers to cover only 2 to 5 percent of customer interactions. This makes it difficult to identify trends or recurring issues within the broader customer experience.

Businesses use AI in the workplace to automate the QA process, increasing coverage for a more holistic view of CX quality. For example, Zendesk QA enables you to achieve 100 percent coverage of customer service interactions. This AI-powered tool can detect issues in service quality, identify knowledge gaps, and uncover coaching opportunities that can help you improve agent performance and drive customer retention.

Zendesk QA can also help discover the most representative interactions from the larger dataset for deeper human review, ensuring a well-rounded analysis.

Increased risk of human errors and bias

To err is human, as the saying goes. Manual evaluations by QA teams can be prone to human errors, bias, and inconsistencies. Human reviews can experience fatigue, have personal communication preferences, or have subjective interpretations that can cause scoring variations.

AI can mitigate these risks by analyzing interactions based on predefined, objective criteria. This ensures consistent and unbiased scoring across all conversations, leading to a more reliable and accurate assessment of customer service quality.

Higher operational and tech stack costs

Traditional QA may require significant investment in human resources. Recruiting, agent training, and retaining qualified reviewers can be a challenge—particularly given the high employee turnover rates often experienced in customer service teams. Plus, managing a team of manual reviewers can require additional software or hardware, increasing the complexity of your tech stack and driving up operational costs.

AI-powered QA provides a bigger picture of support quality, offering direction for which interactions should be checked by human reviewers. AI eliminates the guesswork and saves time, allowing you to use your resources more efficiently.

Limited scalability

As a business grows, the volume of customer interactions inevitably increases. Manual QA struggles to keep pace with this kind of growth, requiring additional reviewers or compromising the quality of analysis.

AI, however, is infinitely scalable. It can effortlessly handle massive datasets, ensuring a comprehensive understanding of the customer experience regardless of the size of your customer base. This allows businesses to scale their QA efforts seamlessly alongside their growth.

Benefits of using AI in customer service QA

With AI shaping customer support, it’s no wonder that AI in QA is another beneficial tool. AI gives you a deeper understanding of customer needs and fosters continuous improvement of your service delivery processes.

This is where AI in quality assurance steps in, offering a powerful tool to elevate your customer experience and empower your service team. By analyzing vast amounts of data from customer interactions, AI unlocks valuable insights that can significantly benefit your business in the following ways.

Improve customer loyalty and satisfaction

AI in QA goes beyond simply evaluating agent performance. Businesses can use quality assurance for personalized interactions and pinpoint areas for improvement within the customer journey itself. Imagine identifying recurring customer frustrations, such as confusing explanations during interactions, unresolved customer inquiries, or missed opportunities for personalization.

By leveraging these insights, you can address customer pain points and ensure customers feel valued and understood, ultimately leading to stronger customer loyalty and satisfaction.

Reduce customer churn with AI-powered insights

A high customer churn rate is a costly problem for any business. Artificial intelligence in quality assurance can analyze customer interactions to identify trends that might predict potential churn.

For example, AI can identify inefficiencies such as long wait times causing customer abandonment or repeat customer complaints about products. With this knowledge, you can address these concerns before they escalate, increasing customer retention and fostering customer loyalty.

Provide growth and development opportunities for agents

A happy and engaged service team often translates to better customer experiences. AI-powered QA tools can identify knowledge gaps and recurring issues to discover training needs. It can also identify and close customer service gaps across teams regarding products or services.

AI helps management teams find problem areas and provide targeted coaching. This helps agents grow and develop their customer service skills and confidence, leading to a more empowered workforce and reducing employee turnover.

Identify roadblocks in your customer experience

AI in quality assurance can constantly monitor the touchpoints throughout the customer journey. It does this by analyzing interactions to identify roadblocks that might negatively affect customer satisfaction. Roadblocks could include anything from confusing processes to knowledge gaps among your team. These insights can pinpoint areas where training programs need an update or processes require streamlining.

AI can also act as an intelligent Voice of the Customer (VoC) program, uncovering valuable feedback that customers might not have explicitly voiced during interactions. By identifying and addressing these roadblocks, you can continuously improve your customer experience, ensuring a smoother journey for everyone.

The future of AI in customer service quality assurance

Using AI in customer service quality assurance can help businesses increase revenue, improve customer satisfaction scores, decrease customer turnover, and lower the costs associated with customer experience operations. Here are a few trends for AI in QA:

• Provide consistently better answers: AI-driven quality reviews and customer feedback tools provide comprehensive quality control, ensuring complete coverage and expediting the QA process. Combining AI tools with workforce management strategies, you can intelligently monitor customer sentiment, automatically identify problematic cases, and assign reviews.
• Foster agent development: AI can provide continuous feedback and identify areas where agents can benefit from coaching. This approach helps highlight knowledge gaps, recurring issues, and training needs so support teams can maintain the highest standard of customer care.
• Track and highlight performance trends and insights for data-driven decision-making: AI-powered data analysis and insights can help identify areas of the customer experience that need improvement at departmental, team, and individual levels. AI in QA can monitor real-time call center metrics and key performance indicators (KPIs), like average handle time (AHT), to recognize high and low performers while also tracking Net Promoter ScoresⓇ (NPS) and customer satisfaction (CSAT) scores across support representatives and teams.

Frequently asked questions

Enhance your QA today

Implementing AI in quality assurance for customer service can streamline workflows and minimize errors, ensuring a consistent CX with every interaction. Translation: AI technology can help businesses meet and exceed customer expectations. With Zendesk QA, businesses no longer need to choose between quantity and quality. Zendesk uses AI in quality assurance, delivering the best of both worlds.

Net Promoter, Net Promoter Score, and NPS are registered trademarks of Satmetrix Systems, Inc., Bain & Company, Inc., and Fred Reichheld.