How to improve quality assurance in the call center: Unleashing AI's potential

In the bustling world of call center operations, delivering an exceptional customer experience and upleveling agent performance are the top priorities for most leaders.  Having an efficient quality assurance (QA) process is instrumental to achieving both these goals.

By leveraging AI–in this case, QA automation via machine learning–call centers can save time, eliminate QA scoring bias, offer more targeted coaching, and identify process improvements more efficiently. QA automation software allows you to extract and review insights from 100% of your customer conversations to optimize call center performance, improve key business metrics, and elevate the customer experience.

Figuring out what features to look for in an AI solution for call center QA is half the battle, so we've outlined some tips on what to look for when choosing the right solution for your business. We’re also sharing why call center leaders leverage AI and what best practices can help you be successful with an AI-powered QA solution.

4 ways AI improves quality assurance

1. Time savings

Quality assurance automation reduces the need for manual call evaluations by automatically tracking and reporting on QA criteria. This saves precious time for call center leaders and quality assurance analysts. By automating quality assurance analysis with AI, call center leaders can increase evaluation capacity to 100% of interactions coming through the call center. Through immediate insights and scoring, call center leaders spend less time manually reviewing interactions and more time training agents on where to improve.  This improved efficiency allows for a faster feedback loop between agents and leadership, empowering agents to deliver better customer experiences.

2. Human bias elimination

Oftentimes, people conducting QA can vary their criteria depending on their experience with an agent–even if they don’t realize they’re doing this. AI-powered QA solutions ensure scoring accuracy across 100% of interaction transcripts on the same objective criteria. This makes the feedback and coaching that agents receive more trustworthy and accurately informed. Essentially, with an AI-powered QA platform to evaluate all interactions, a QA manager doesn’t need to pick and choose which calls to review to evaluate an agent’s best performance (or worst), and an agent cannot dismiss feedback from a single interaction. AI-powered QA solutions eliminate human bias by consistently scoring all agents on all their interactions.‍

3. More targeted coaching

AI-powered quality assurance enables call center leaders to customize their QA scorecards to monitor language used by agents across every interaction. Leaders can measure more than they could previously since they’re not limited to manual reviews, and they can see how agents are performing across all calls as a result. Using a conversation intelligence platform that automates QA in the call center provides targeted coaching insights to call center leaders and helps them work with their agents to improve performance quickly, fostering continuous growth. 

4. Identifying process improvements or cost-saving opportunities

AI platforms that automate QA in the call center offer valuable insights into customer interactions at scale. AI helps leaders uncover ways their contact center can reduce costs (e.g., if many agents are having difficulty with a specific topic and stalling on the phone, leaders can provide them with better resources and decrease average handle time). Additionally, AI-powered QA platforms save call center leaders time and money by transcribing and analyzing calls. Even when leaders need to manually review specific calls, they can save time by reading the transcripts instead of listening to recordings. 

By making it possible to holistically evaluate customer interactions, AI empowers call center leaders to make data-driven decisions, improving overall efficiency and reducing costs to achieve greater customer satisfaction and agent performance.

What to look for in an AI-powered QA solution

Choosing the right AI-powered QA solution is crucial for call center leaders looking to improve their QA process. Key features to consider include coaching dashboards, reliable call transcription, and exporting capabilities. Selecting an automated QA solution that checks these boxes and aligns with your specific requirements maximizes the benefits of AI and increases speed to value.

Fast & reliable call transcription

Getting insights and reliable transcripts in your conversation intelligence platform in near real-time helps you keep a pulse on agent performance trends and quickly drill into specific transcripts if you need more information about what happened on any given call. This lets analysts and managers spend less time manually listening to a small sample size of randomly selected calls.

Native integrations and data exporting abilities

An AI-powered QA platform should set call center leaders up for success by having native integrations with commonly used case and interaction technologies like Zendesk, Qualtrics, Salesforce, AWS, and more. Having native integrations with these and other similar platforms will help bridge the data gap between QA processes and existing workflows. 

The AI-powered QA platform's data exporting capabilities shouldn’t limit users to only having access to quality analysis insights inside the platform itself – users should be able to freely export data as needed.

‍Coaching dashboards & ease of insight sharing

The coaching dashboards available with an AI-powered QA solution should serve to help call center leaders identify and quickly see the biggest coaching opportunities for each agent. These dashboard insights should be available both inside and outside the platform for ease of accessibility within the organization. In turn, this will make sharing QA insights and coaching opportunities not only fast, but quite literally as easy as the click of a button. 

Flexibility for complex QA evaluations

An AI-powered solution that helps improve QA should be able to support complex QA evaluations, and be flexible with scorecard inclusions. The AI-powered QA platform should give call center leaders, QA or QM managers the ability to create custom evaluation forms within the platform to track any complex criteria that can’t be easily automated. This will allow leaders or managers to run reports on and gain insights from both an automated QA scorecard and any manual evaluations.

3 best practices for integrating AI into your QA program

1. Build a custom QA score in your automation platform

Tailor your QA score within the automation platform to align with your business objectives and customer expectations. For example, if increasing first call resolution (FCR) is a goal, you’ll want to ensure you customize your QA score to weigh resolution-oriented agent behaviors more heavily. Include metrics that reflect your call center's unique requirements, ensuring that the AI-powered QA solution tracks and evaluates the desired aspects of agent performance.

2. Take the opportunity to reevaluate your QA scorecard items

Integrating AI to improve call center QA provides the opportunity to review and continuously optimize your QA scorecard items as needed. Align scorecard items with the data and insights generated by an AI-powered quality assurance platform to capture all crucial elements of your customer interactions that drive exceptional experiences. Be sure to include positive agent language or statements on your customized scorecard. By reevaluating your QA scorecard, you can ensure that it remains relevant and impactful.

3. Train supervisors to use the platform for coaching

Before rolling out your AI-powered QA platform, make sure your supervisors are comfortable using it. Ensure that your teams are able to navigate the platform to access the insights they need and can properly leverage the platform's insights and features to provide targeted guidance and coaching to agents. 

Summary

By leveraging AI, you can streamline the quality assurance process and increase efficiency. You can also provide more targeted coaching and identify process improvements and cost-saving opportunities in the call center. 

When selecting an AI-powered QA solution, consider features that align with your desired outcomes – native integrations with your call center technology, reliable call transcription and a platform that won’t hold your data hostage are great requirements to start with. Integrate AI into your QA program using best practices like building a custom QA score, reevaluating your scorecard items, and training supervisors for effective coaching. 

Embrace the power of AI to revolutionize your call center's quality assurance and enhance the customer experience. Your QA analysts and QM managers will thank you for saving them time from manually conducting evaluations, and your agents will thank you for giving them helpful coaching that’s accurately informed.