HOW TO IMPROVE QUALITY ASSURANCE FOR CALL CENTERS WITH AI?
Any systematic process of determining whether a product or service meets specified requirements is referred to as quality assurance (QA).
QA creates and maintains standards for developing or manufacturing reliable products. A quality assurance system is intended to boost customer confidence and a company's credibility while also improving work processes and efficiency, allowing a company to compete more effectively.
Quality assurance assists a company in developing products and services that meet the needs, expectations, and requirements of its customers. It results in high-quality product offerings that foster customer trust and loyalty. A quality assurance program's standards and procedures help to prevent product defects before they occur.
QUALITY ASSURANCE IN CALL CENTRES
Contact centers are critical for everything from brand loyalty to increasing efficiency and productivity and facilitating growth. According to the 2017 Microsoft State of Customer Service report, 96 percent of consumers worldwide believe that customer service is a defining factor in brand choice and loyalty.
Customer experience must be prioritized. It is what will set your company apart from the competition. That is why your contact center must be regularly monitored, evaluated, and improved.
You can't rest on your laurels and assume that your customer satisfaction standards are being met. You must be able to measure the quality of your service, which is where quality assurance comes in.
What steps can a company take to improve its services and customer experience?
Quality assurance is the answer.
'A Unique Experience for You.'
This is the motto of every service provider and brand manufacturer. The statement is true, particularly in call centers. Around 60,850 contact centers exist today around the world. Every day, agents answer calls from various clients. They listen to a variety of complaints, some new, some old, and some in a different language.
This begs the question of how a business can improve its services and customer experience.
Quality assurance is the answer.
QA FRAMEWORK FOR CALL CENTRES
A call center QA framework is a set of success criteria and metrics used to measure and manage service quality. Consider it a guide to monitoring and analyzing your contact center's performance.
The right framework can assist you in defining your key performance indicators, identifying goals, and determining how to obtain the most strategic long-term value from your contact center. The right QA framework can essentially determine where your contact center is going and how you'll get there.
The key is to create the most effective QA framework for your contact center, which requires a thorough examination of your customer service objectives as well as the processes and tools in place.
Your call center QA framework should provide answers to the following quality assurance questions:
What information will you gather?
How will you evaluate customer interactions (phone calls, emails, live chat, social media, and so on)?
How will you keep track of and assess communication?
What will the results of your analyses look like?
How will you put your quality assurance findings into action?
HOW TO IMPROVE QA WITH AI?
Contact center quality assurance (QA) has been an essential component of improving agent performance, ensuring compliance, and improving the customer experience for more than 40 years. Despite advances in IVR and chatbot technology, phone support remains an important function in contact centers. At the end of the day, customers frequently want to speak with a live person.
In fact, a survey in the United States found that 44% of respondents still favor voice or phone as their main customer service channel. This is 21% higher than any other available channel, with live chat coming in second at 23%.
Every call center, especially contact centers, is designed with the customer journey in mind. The customer journey is the path a customer takes from the first time they hear about your brand until they buy your product or service. It's critical to remember that the customer journey isn't a straight line. The more interaction you have with your customers, the more loyal they will be to your brand.
However, there is one major question: should you use Artificial Intelligence, human voice analytics, or a combination of the two?
The most important thing to remember when designing the customer journey is that the customer is at the center of it. The customer journey is focused on what the customer wants and needs rather than what you want.
However, you must still ensure that your business functions align with the goal of the customer journey: to ensure that customer interactions satisfy everyone and provide a positive experience with your company.
Call centers benefit greatly from artificial intelligence, particularly in terms of speed, accuracy, and repeatability. The ability of AI to recognize words, phrases, and voice tone much faster and more consistently than any human alternative with a higher accuracy rate.
The best part about AI is that it can increase your contact center performance in various ways. For example, all of the interactions you find at the beginning of many calls – “say your name now,” “please tell me your date of birth,” etc., are all features that save time for customer service call center agents. They allow for record retrieval before anyone talks to the agent, decreasing the requirement for drawn-out customer conversation.
AI provides predictive models and advanced analytics of calls based on customer type, allowing for pinpoint actionable insights - all in a fraction of the time it would take human agents to do the same.
Like the algorithms that power predictive dialing, AI call center technology and machine learning often work in the background to expand and enhance outbound capabilities.
Many call center software platforms currently offer AI for lead generation, pre-qualifying leads, quality and compliance monitoring, smart call routing, and more.
Below are some of the best-known (and best-performing) examples of AI and machine learning being adopted by today’s call centers.
INTERACTIVE VOICE RESPONSE (IVR)
Voice response and analytics are used by cloud based intelligent IVR systems to automate call routing. If you need to communicate an automated message, an IVR system can send personalized messages to your leads via multiple channels.
To reduce the automated feel, use a conversational IVR system to guide customers and leads through a series of options. You want a system that can scale up and down as needed.
AI VIRTUAL AGENT
AI in call centers today can sound incredibly realistic. A good AI virtual agent employs machine learning, natural language programming, and advanced speech recognition to conduct natural-sounding conversations. For clients and potential clients, the best IVA solutions provide organic, human-like experiences.
SMS BOTS AND AI FOR LEAD GENERATION
AI implementation can aid in the optimization of your lead generation efforts. While live call centre agents focus on lead conversion, your software can search for more valuable prospects. The intelligent virtual agent determines whether a lead is a good fit for your campaigns based on the answers to prequalifying questions.
Similarly, AI-powered SMS bots can hold entire text conversations with potential customers, opening up a completely new channel of productivity and potential.
AI AND CALL ANALYTICS
One of the most common applications of AI in call centres is to provide detailed analytics on call times, first resolution, and other factors. These technologies can detect trends and have access to customer data, which can reveal whether customers are having a positive or negative experience.
Because AI measures customer sentiment, tone, and personality, it can provide more comprehensive analytics than a human customer support manager.