The race to delight customers, increase wallet share, and reduce cost to serve for all companies - large and small - is driving a high-stakes gold rush for the artificial intelligence (AI)-driven global contact center market. Valued in the billions, and with players that include iconic technology brands like Microsoft, CISCO, and IBM, the stakes are high for the global call center AI market. A November 2022 Data Bridge Market Research (DBMR) study noted that "the increasing need for better data analytical capabilities to cater to customer inquiries in real-time and increase customer engagement through social media will boost the demand for the call center artificial intelligence in the market."
The same report went on to note that the call center AI market is "poised for explosive growth," expanding from $2.29 bn in 2022 to $7.5 bn by 2030, with AI-powered customer service improvements driving growing profitability.
At the crux of this evolving market is XSELL Technologies, a Chicago-based startup that provides real-time, AI-powered, digital coaching for call center agents via their HIPER agent experience solution. Using patented innovations, XSELL provides real-time coaching grounded in top-performer strategies. XSELL's platform continuously learns from what works and updates daily with the latest changes responsive to business dynamics and automated discoveries from top-performers via causal discovery.
Co-Founder and Chief Technology Officer, Mark Stoehr, noted that the guidance that AI can provide to a live call center agent as they are speaking to a customer "can often be a very small difference in terms of words, but a very large difference in terms of impact, agent performance follows a power law rather than a bell-curve. The very best agents use subtle differences in tactics to achieve double or better conversion or resolution rate in a fraction of the handle time than their peers."
XSELL finds what makes top performers more effective and scales it across all agents. This begins by a novel process of discovery and identification unique to their technology. Machine inference over calls and outcomes is the main workhorse but their algorithms delicately balance that inference with human insight into the fine-grained cause-effect relationships the account for why top performers do better.
"When we approached this problem. We're harvesting the data that tells us what those top performers are doing - and doing well - flagging the words, and strategies that our AI models have identified as causal to a positive outcome," Stoehr notes. "Our causal models are non-parametric and make no numerical assumptions which allow us to drive any outcome or combination of outcomes, whether sales conversion, first contact resolution, handle time reduction, or any other call center metric. Our client is in the driver seat using our novel, causal approach.
A top-performing call center agent makes a series of rapid, successive decisions that steer a conversation in the direction that will best help a customer or resolve a business challenge. "Naïve approaches to identifying 'the why' behind high performance struggle to consistently produce results," Stoehr said, "Since top performers achieve great outcomes everything they do is correlated with good outcomes. The truly causal strategies are a small fraction of what top performers say and often are more about when and how they say certain things." Using their proprietary systems, the tactics used by top-performing agents to cause targeted business outcomes are identified, isolated, and flagged as high-value content. The resulting conversational intelligence is used to generate conversation maps, which enable these insights to be delivered compactly to all agents exactly when they need them in a conversation. Through it all, real people are serving in key roles across the process, ensuring that key elements like nuance, conversational context, and subtlety, are not lost to AI models.
"We use causal models to disentangle what causes successful calls in the conversation rather than what merely happens at the same time as a successful call," Stoehr said. "Our system adaptively plans experimental micro-interventions which perform targeted and controlled small changes to conversations that specific groups of agents are having. Interventional data is the only way to achieve high-precision in top-performer strategies: since it confirms hypotheses and suggests where to look next to derive value."
Effectively - what if XSELL could make every agent your best agent?
By way of the Agent eXp desktop interface, that's exactly what they set out to do. XSELL delivers the resulting real-time, AI support to live agents, including the firm's progress tracking and objection-handling technology. The customer's best agents are given the flexibility to achieve further success, while lower-performing agents are supported as they strive for the next level of excellence.
Added Stoehr, "Different top-performing agents will have different specialties and strengths: our system disentangles what component of a top performer's conversation habits reflects their individual style versus what are substantive strategies that should be shared across many agents."
Yes, XSELL's real-time, AI-powered Agent eXp coaching speaks to the automation that seems to be consuming the world more and more each day. A key differentiator for human and machine intelligence is that human insight is grounded in how real-world situations shape the customer's experiences. Machines detect associations and probable causal insights, but the machine can miss common-sense facts about how real-world business processes operate. ChatGPT, for instance, does not reliably distinguish between truth and fiction which is where humans can provide real-world feedback--closing the loop on separating causation from accidental correlation- a core concept in human-in-the-loop artificial intelligence. XSELL's goal is to merge the two together, balancing the best of both worlds to deliver a service that provides an automated response to a customer, from a human perspective.
Data Bridge isn't the only firm looking at the call center space; market research firm Gartner estimates that conversational AI deployments within contact centers will reduce call center agent labor costs by as much as $80 billion by 2026, according to a recent press release. No matter which view of the data you agree with, the results are the same - with AI-powered coaching tools like XSELL Agent eXp, costs are reduced, revenue is increased, both agent and customer experience benefit.
"Ultimately, we are reducing the cost of doing business, because your agent is performing quicker and more efficiently. Your customer isn't calling back four times. Everyone is positively impacted, the customer receives the answers they need efficiently, the agent has the tools they need and feel empowered, and the company benefits from lower costs and increased revenue due to improved customer satisfaction."
In a May 2022 TED Talk, "Why People and AI Make Good Business Partners," Shervin Khodabandeh, Senior Partner and Managing Director at Boston Consulting Group - and co-leader of BCG's AI business in North America - spoke at length about the mutually beneficial relationship between humans and AI. Citing examples at a Fortune 50 healthcare company - similar to case studies referenced by the XSELL team - Khodabandeh noted that the most powerful opportunity to drive better outcomes for AI deployments exists in the middle ground between humans and AI models, "where humans and AI come together to achieve outcomes that neither one could do alone on their own." And that is music to Stoehr's ears.
If AI-powered customer service improvements are driving profitability improvements as market research data indicates, the XSELL Technologies team is positioning their customers to benefit from more than just an upcoming call center market boom.
Visit www.xselltechnologies.com for more information.
