5 Ways AI and Automation Can Improve Your QM Processes
You can’t go a day (or maybe even an hour) without stumbling across the term “artificial intelligence” or “AI.” Across all industries and in our personal lives, AI is coming into its own as a tool to increase efficiency, automate tasks, and reduce costs.
But how do AI and automation technologies actually help make your contact center more efficient? Here’s the truth: They’re best used to eliminate time-consuming, repetitive tasks that don’t directly affect customer service. By applying them to this type of work, you can give time and energy back to your managers and agents to spend on activities that directly improve customer experience.
One area where you can ease AI and automation into your contact center is in your quality management (QM) processes. Automated quality management helps quality analysts be more efficient in their work. The results? Improved customer experience and agent performance.
What is Automated Quality Management?
Before we discuss automation and AI for QM processes, it’s important to establish clear definitions in the context of contact centers.
Definition of Quality Management
Today customers are more fickle than ever — making quality management more important than ever. According to a Zendesk survey, 73% of consumers will switch to a competitor after multiple bad experiences — and more than half will abandon a company after just one poor interaction.
Keeping a close eye on quality prevents costly customer churn. Losing even a small percentage of your customers will add up, so learning how to prevent them from turning to competitors is vital.
Quality processes are always seen as a cost initiative. Overall, we have seen that more companies now recognize quality is not a means to ‘police’ but to ensure customer experiences are exceptional and customers remain loyal. That said, these processes are not tied to any direct revenue so they are seen as part of a cost center, but obviously impact revenue through indirect channels.
Let’s start by defining what is considered quality management. Many people mistake this as merely scoring a conversation or checking some boxes, and that could not be further from the truth. But to be fair, this has evolved tremendously over the years.
Quality Assurance, or QA, started in the contact center as a very compliance-driven process. Companies began by creating checklists to ensure agents were meeting all requirements on calls. These could be simply things like using the customer’s name or saying mandatory compliance statements word for word. QA was very black and white at this time with no room for gray.
Then businesses started to realize there is actually a lot of gray when measuring the quality of interactions because customers’ needs are unique. QA evolved into Quality Management or QM, and required a more dynamic way of measuring success and experience. QM also includes more than just scoring interactions and is made up of a variety of processes that are aimed to help improve and engage the agents. QM is more of a comprehensive program that focuses on both performance management & process improvement by using calibration, appeals, motivation, coaching, and gamification.
Quality assurance (QA) tools are great, but by the time an issue is found, the customer experience has already suffered. QA analyzes problems after they happen. QM — the people, processes, and systems a contact center uses to monitor customer interactions to ensure they are being handled by contact center agents in the desired fashion — lets you stop problems before they start.
Definition of Automation
Automation is a software-directed action based on a set of predefined rules created by humans. Automation removes manual tasks that often aren’t a productive use of a human’s time.
Definition of AI
Artificial intelligence (AI) is intelligence demonstrated by machine learning. AI analyzes complex data sets and makes recommendations or takes action based on what it learns. It continually evolves to create increasingly better results. AI solutions remove the complexity of working with large data sets and reveal insights humans can then use to do their work more efficiently.
Let’s Put Them Together: Automated Quality Management
Quality still carries a cost even though through a strong QM process you are bound to improve the experience and retain customers. Finding ways to reduce time and money will always be top of mind for any contact center operations leader. With technology continuing to evolve, customers are looking to invest in solutions that have more AI-driven features and automation as that is the clear next step to creating more efficiency and reducing costs. Customers also need a way to scale when growth demands, and not add more costs to high-cost processes.
That said, the contact center industry has traditionally moved very slowly in the adoption of new technologies. Historically, the root cause has been the incredible expense in moving to a new ACD, hardware, or software solution in an industry that favored on-premises deployments and perpetual licenses. Because of the reliance on interaction recordings, quality was even more shackled than WFM was in the past. Investments in things like Speech Analytics also struggled because of the costs for licenses AND hardware. Indeed, the standard industry default for speech analytics was to only use it on approximately 20% of recordings.
While the use of AI technology might have seemed intrusive to customers in the past, most now embrace companies using it — if it will make their customer interactions better.
As the phrase suggests, automated quality management helps increase efficiency by automating the entire quality management process of a contact center — from assisted scoring to agent coaching.
For example, due to the sheer volume of data they ingest, most contact centers only analyze a small percentage (<2%) of their interactions. Because proper evaluation takes several minutes — or even up to an hour — to complete, there’s simply not enough time in the day to analyze every single interaction.
Now imagine how powerful it would be if an AI-infused quality management system could analyze the sentiment in every single interaction and understand the topics discussed during every single encounter. AI can surface the most pressing issues across a large sample and direct them to your quality analysts for a deeper look. AI can then assist the analyst to score and provide feedback. Better yet, this is something that can be phased into the business to ensure processes are not disrupted.
Taking it a step further, if you automate coaching needs based on a larger sample of quality issues will move the customer satisfaction needle more — and faster.
Powerful stuff for QM analysts and contact center operations teams.
5 Ways AI and Automation Can Help Improve Your QM Processes
Fifty-eight percent (58%) of contact centers plan to invest in an AI solution to monitor agent performance and automate quality processes this year. If you’re one of those organizations looking to improve business processes and make faster data-driven decisions, here are five ways AI and automation can help improve your QM processes.
- Automate workloads for analysts and evaluators – AI can provide valuable insights and make suggestions to help analysts zero in on quality issues in less time and address them faster.
- Surface contact trends – Look at the contact trends by using topics and categorization, and understand why people are reaching out and contacting you. These insights will help you make more informed decisions regularly and guide coaching to ensure your agents are ready to handle the most important inquiries.
- Infuse AI into your scoring process – Leverage AI to assist analysts in scoring interactions to save time and money. AI can also provide feedback to justify the scoring and also help agents improve. All of this can be reviewed by a quality analyst before being released to the agents, which provides fewer disruptions and maintains the integrity of your quality process.
- Prompt the right coaching – Prompt agent feedback is vital, and automation tools such as Playvox QM enable contact center teams to prevent and close employee skill gaps faster with personalized coaching. After gaps are identified, coaches can build coaching sessions using current and historical data, and automatically track progress against goals once a session is complete.
- Understand customer sentiment – AI allows you to quickly detect customer sentiment across all channels, throughout a conversation, and take action faster in order to resolve issues and close service gaps early.
Key Benefits of Automated QM
AI and automation are powerful tools that can improve many areas of your contact center. Automated quality management can be a good place to start, and implementing it yields numerous benefits to analysts, managers, agents, and customers.
- Improved agent performance and employee experience (EX)
- Increased customer satisfaction and positive customer experiences from better service quality
- More efficient workflows and quality monitoring
- Prevent costly CX problems before they grow
- Cost-effective because the technology takes over the tedious, manual QM tasks, allowing analysts, managers, and agents to focus on CX and EX.
Automated Quality Management Doesn’t Have To Be Daunting
Using AI and automation to improve your quality management processes gives you a competitive advantage by helping you find ways to create a better experience for your agents, your analysts — and most importantly, your customers. Playvox customers are finding a way to phase AI technology into their core processes in an intentional way, but also be confident that the AI is providing accurate results. The last thing you want to do is flip a switch and change all your core processes, which are linked to agent preferences, performance reviews, and compensation. Unsure of how to ease into AI and automation in your contact center? Check out our webinar “How to Infuse AI Into QM: What You Need to Know” for expert advice.
