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AI imbued Contact Centers: The incredible path forward

AI for Contact Centers

If there were a ready-made use case for workplace automation, contact centers would be it. But the industry has a notorious reputation for relying on cheap manpower and overextending the capabilities of its resources while managing only to score ‘lukewarm’ in the customer service scorecard. However, things have changed drastically since the pandemic hit. AI imbued contact centers are no longer an explorative option, but a vital necessity in situations where the availability of human resources are limited or restricted to remote work.

Fortunately, this coincides with the leaps and bounds advancements in natural-language processing that enables companies to build, modify and deploy exceptionally fluid chatbots and voice-based agents more quickly than ever before. The ease of use and affordability of these systems have ensured that many organizations are likely to implement AI in contact centers in the long run. In fact, AI-enabled conversational agents are expected to handle 20% of all customer service requests as early as 2022.

Given the lingering customer memories of dealing with IVR systems that resulted in endless hold times, obtuse service agents, or inescapable handoff loops – customer satisfaction has unsurprisingly become the hill for contact centers to die on. A single bad experience can put a customer off your brand forever. Cost has, by necessity, taken a backseat to customer experience as the driver of contact center performance.

The good news is that AI capabilities powered by high-speed cloud services and feature-rich machine-learning tools has suddenly made it possible for contact centers to truly ‘get into the minds’ of customers and user agents (manual or automated) to better understand the nuances of each conversation, ensure stellar customer experience and do it all at scale.

And, businesses are starting to understand that an investment in AI to ensure great customer service not only benefits the overall customer experience, but also holds immense potential for business intelligence and streamlining processes which had previously rarely (if ever) been explored. According to Markets and Markets, the global conversational AI market size is expected to grow from $4.8 billion USD in 2020 to $13.9 billion by 2025, at a Compound Annual Growth Rate (CAGR) of 21.9% during the forecast period.

Looking for a Contact Center Solution?

Here’s a look at how AI is revamping the call center—and helping redefine customer experience:

Vastly Improved Customer Service Support

Natural Language Understanding (NLU) software leverages multiple language and acoustic models to efficiently ‘translate’ nearly 90% of spoken input from customers. Moreover, the software ‘learns and improves’ on a daily basis as it becomes accustomed to the vernacular of the context it’s being deployed in and becomes capable of ‘understanding’ contexts without manual intervention.

This resolves long-standing issues, such as calls getting routed through multiple queues as customer patience grows thin. The system is now capable of providing cogent and relevant factual results for the most minute of business processes – resulting in shorter calls and happier customers.

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Manage High & Low Call Volumes with Ease

AI imbued contact centers are designed for first-level virtual resolutions and don’t switch to manual agents unless necessary. This reduces wait times and automates a high load of call volumes where people are only looking for ‘surface-level information’ and can reduce the burden on customer agents tremendously.

With organizations forced to run with leaner teams and higher call volume with anxious customers looking for assurance and help in difficult circumstances, organizations will lean more and more on AI systems while reserving their manpower for more difficult secondary and tertiary support to resolve problems that require a complete understanding of the context and emotional intelligence.

Options vs. Rules

The reason why customers (both businesses and individuals alike) found IVR systems so frustrating to deal with was because IVRs were designed on a bunch of simplistic predefined rules that were applied linearly and deterministically. They were built to address average use cases – not individual ones – and were about as useful as a road sign pointing towards a town, but not an actual address within that town.

In contrast, AI built on Natural Language Processing and Machine Learning techniques are designed to ‘read’ contexts and offer up options that users can choose from – like how we use GPS to travel to the exact point we want to go and find the most convenient route to get there. Users are empowered with the necessary information to pick their own journey per their needs.

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Predictive Call Routing

Predictive call routing is built on the ability of AI and advanced analytics to build specific customer personality models and match those callers with an agent most suited to handling those personality types.

Drive Marketing & Sales Efforts

CRMs imbued with AI can empower manual agents with quick access to customer data including their profile, past transactions, purchases, activity history, and more. The system can also point agents toward the most relevant offers, promotions, customized prompts and a host of other solutions and service add-ons based on customer input – all without a single interruption in a fluid human-to-human conversation.

Similar AI applications for sales can help businesses make smarter decisions for customer retention and loyalty. For instance, machine learning tools can help convert dozens of data types, such as the recurrence of negative phrases or expressions containing action statements (“cancel the order”), to develop overall customer risk scores. These scores are then graphed to benchmarks, and the software can alert sales or customer satisfaction teams to take action, like a personalized phone call or an email with recommendations for personalized offers. The entire process is faster than gauging customer sentiments manually and results in both lower churn and higher customer satisfaction. 

Supports Agents to Provide a Better Experience

AI-enabled conversational assistants operate on predictive intelligence about information that customers are most likely to respond to. They don’t just rattle off facts, but deliver intelligent information to human agents to drive better analysis for  faster, more positive outcomes, without customers even being aware of it.

AI can also make use of specialized NLU such as sentiment analysis, wherein the system parses through verbal or written customer inputs to gauge what they are trying to accomplish and offer up a host of options to the agent handling the call to best fit the right solution with additional emotional support. 

Thanks to artificial intelligence (AI), machine learning, and big data, service agents have immediate access to intangible data on customer calls like voice inflection or long pauses which indicate customers’ emotional states like anger or indecisiveness. Moreover, the AI is constantly being modified and improved to meld into different multilingual and cultural contexts, with highly unique linguistic and cultural styles.

Shamrock has Your Back

If you’re considering implementing AI in your contact center infrastructure, Shamrock can help guide you with the most relevant systems to drive both customer satisfaction and business intelligence.

Our expert team has worked with AI in Predictive Analytics and Automation for years and have knowledge and understanding to pinpoint the right solution for your exact business needs.

Ben Ferguson

Ben Ferguson

Ben Ferguson is the Vice President and Senior Network Architect for Shamrock Consulting Group, an industry leader in digital transformation solutions. Since his departure from Biochemical research in 2004, Ben has built core competencies around cloud direct connects and cloud cost reduction, enterprise wide area network architecture, high density data center deployments, cybersecurity and Voice over IP telephony. Ben has designed hundreds of complex networks for some of the largest companies in the world and he’s helped Shamrock become a top partner of the 3 largest public cloud platforms for AWS, Azure and GCP consulting. When he takes the occasional break from designing networks, he enjoys surfing, golf, working out, trying new restaurants and spending time with his wife, Linsey, his son, Weston and his dog, Hamilton.

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