When it comes to customer service, speed and efficiency matter most.
Research shows customers are more than twice as likely to continue supporting companies when they solve problems quickly. As such, businesses everywhere are investing heavily in artificial intelligence (AI) solutions to streamline call center operations. In an age where the customer experience is a key differentiator, smart businesses are doing everything they can to delight customers every time.
In light of this, a performance gap is growing between AI-savvy call centers and those still relying on manual call center QA, or quality assurance, processes. Without AI, QA is much more complex and less effective in the contact center, and it’s exceedingly difficult for contact center leaders to analyze agent performance and identify trends and inconsistencies. Lacking deep visibility into contact center performance, it’s impossible to ensure exemplary customer experiences — which can lead to an uptick in churn.
Keep reading to learn about some common QA challenges contact center leaders face — and how you can overcome them with AI.
Top call center QA challenges
Contact centers today rely on QA to monitor and evaluate interactions between customers and agents and create customer experiences that align with company goals and objectives. However, companies face several hurdles that make it difficult to maintain QA. With that in mind, let’s take a look at a few common challenges QA managers face.
Many contact centers use manual QA strategies, like sample-based quality monitoring, which involves evaluating only a small subset of interactions.
As a result, companies typically lack deep visibility into agent performance, regulatory compliance, and customer experience. Believe it or not, most contact centers only review 1%–5% of customer engagements. Unfortunately, such an approach is ineffective because it’s impossible to know everything that’s happening in the call center.
On top of this, the traditional method of scoring customer interactions is prone to errors and human biases, which often results in poor customer service, inefficiencies, and job dissatisfaction.
Unscalable QA programs
Another downside to traditional QA programs is that they are notoriously difficult and expensive to scale. To achieve greater visibility and awareness, companies have to hire additional QA auditors, which leads to higher costs and requires additional training and administrative work.
What’s more, scaling a traditional QA program is a highly manual and labor-intensive process. QA teams often waste countless hours sitting through silence and non-coachable parts of past interactions. Oftentimes, they fail to produce accurate or relevant results.
Limited options for auto-QA
Many companies use automated QA (auto QA) services to monitor customer interactions. However, this is a relatively small market with limited solutions, and most auto-QA tools fail to deliver actionable results.
For example, when generating QA scores, most auto QA tools only scan for sentiment, keywords, or other more general dimensions. In addition, maintaining a text and speech analytics system with relevant keywords and phrases is a tedious and unrewarding task for QA teams.
Since the market is relatively nascent, auto QA systems are not capable of auto-scoring conversations against the wide variety of dimensions that contact centers want to measure. In fact, even the best services cover roughly 30% of scorecards. What’s more, most emerging generative AI solutions use an Open AI API that’s slow and lacks the critical contact center data training needed to effectively train models.
How to solve call center QA challenges using AI
The good news is that it’s possible to improve call center visibility and deliver personalized interactions at scale with the help of AI. However, not all AI call center tools offer the same level of service and support. As such, companies need to be highly selective when procuring AI for their contact centers and invest in systems that provide reliable, business-specific automation.
For example, most platforms now deliver core capabilities like real-time listening, screen recording, automated call recording (ACR), automated speech recognition (ASR), advanced speech analytics, natural language processing (NLP), and omnichannel support. But if you truly want to take your QA program to the next level, you need a platform with the following features.
A proprietary AI model
It’s essential to use a proprietary AI model that’s purpose-built for contact center applications. Deploying a custom AI model will deliver far greater efficiency and accuracy during agent-customer evaluations than a generic alternative.
For the best results, look for a platform built with natural language understanding (NLU), the model where AI can understand the intent behind the words — not just keywords and phrase matching. Whereas NLP is built for keyword and phrase matching, NLU takes it a step further and understands the true meaning of what’s being said.
The right platform should understand entire conversations — not just keywords. With complete QA automation, you can quickly score every interaction with human-like accuracy. At the same time, you can automatically surface critical conversations that QA managers need to review further or discuss with agents — including both high- and low-scored conversations for both recognition and coaching purposes.
Clear evidence for every score
To trust a QA score, you need supporting details. As such, it’s critical to have a platform that provides evidence for each score along with reasoning and quotes from the conversation. This, in turn, produces unbiased scoring results, feedback transparency, and more effective learning opportunities for agents, among other benefits.
When it comes to compliance, there’s no room for error. Agents must always protect customer data and adhere to regulatory standards — a massive undertaking without the right tools in place. Luckily, ensuring compliance during every interaction is possible using a proprietary AI model that understands business-specific needs and regulatory requirements.
For the best results, look for a solution that has built-in compliance functionality, such as audio and video redaction tools that automatically protect sensitive customer information. Of course, you’ll also need to ensure that the platform has required industry-standard security features and certifications, including GDPR, HIPAA, AICPA SOC, PCI, and ISO 27001.
Elevate your QA program with Level AI
Recent advancements in call center AI technology make it possible to overcome QA challenges and ensure optimal customer experiences. With the right AI technology in place, companies can generate fast, accurate reporting that aligns with their specific needs and goals.
At the same time, the right AI QA solution can also augment the productivity of QA managers and auditors, helping them cover more ground faster and giving them complete visibility into all interactions. This, in turn, improves efficiency and accelerates workflows while setting up contact centers to scale.
Level AI offers QA-GPT, a powerful QA auditor you can tailor to your exact business. This groundbreaking generative AI QA technology learns and improves over time and evaluates every conversation against a custom scorecard with near-human accuracy.
To learn more about how AI can help overcome common QA challenges in the contact center, download Level AI’s 2023 Platform Buyer’s Guide. To see what QA-GPT looks like with your own eyes, request a demo today.
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