Predictive metrics can forecast the future.
Some do this using forecasting models, which trawl through reams of historical data to predict likely outcomes.
In contact center workforce management (WFM), these predictive metrics are already commonplace, as planners forecast contact volumes, handling times, and occupancy rates.
Nevertheless, until recently, few service operations had embraced the broader application of predictive metrics beyond forecasting. GenAI is helping to change that.
Using its reasoning capability, GenAI can inhabit a role and then predict the most likely outcome based on an interaction.
For example, consider the expected net promoter score (xNPS) available on evaluagentCX, a widely utilized contact center quality assurance (QA) platform.
xNPS harnesses GenAI to inhabit the shoes of a customer and, based on the service interaction, share the outcome that customer would have likely left on an NPS survey. So now, they don’t have to fill in a survey at all.
Diving deeper, Ben Cave, Product Director at evaluagent, said:
“In more than 85 percent of cases, the solution predicts the same NPS outcome that the customer actually gave the conversation.”
According to Cave, up to 97 percent of customers ignore the post-call NPS survey. As such, CX teams may miss 97 percent of customers open to an upsell or likely to churn.
xNPS may change that, exemplifying how predictive metrics can boost the future contact center.
During his session at the recent Contact Center Performance Summit, Cave explored this further, sharing four ways predictive metrics will shape the contact center of tomorrow.
1. Predictive Metrics Will Support More Personalized & Unique Experiences
Nowadays, many contact centers put automated services in front of the live agent, offering self-service, gathering upfront information, or simply clarifying the customer’s contact reason.
Yet, with “containment” a central objective, some businesses do everything possible to keep customers within the conversational AI interface.
That irritates customers who just want to speak to a live agent. Many contact centers don’t take enough account of this.
One reason why is that it’s tricky to predict which customers have a strong preference for human-to-human interaction. Noting this, Cave said:
“We are working on ways in which you can better forecast how a customer would like to interact with the contact center, so it can route them optimally.”
Cave suggests that evaluagent is getting closer to predicting which resolution will best suit an individual customer and leveraging that information to tailor a human or AI agent’s responses.
“All of these things are possible by predicting more about what a customer wants from what they say, their previous interactions, and broader customer journey,” he added.
“By using more of these predictive metrics in the future, no two people’s experience with a contact center will be quite the same.”
2. Predictive Metrics Will Enable Fairer Evaluations of Agent Performance
Across the industry, live agents must handle more complex customer questions as AI snaps up all the simple, transactional queries they previously used to take a breather.
A focus on hyper-specialized agents, improved routing, and upgraded supervisor support should come with that transition. Yet, contact centers must also refocus their agent performance metrics.
After all, as contacts become more com flecting negatively on agents who may have faced a prolonged sequence of challenging conversations.
That’s not necessarily fair, and evaluagent is fighting back with predictive metrics. Cave noted:
“We have the ability to difficulty-adjust the scores given to agents in the QA platform, so you can begin to take account of conversation complexity.”
In doing so, evaluagent monitors signals such as how much abuse the customer used and how easily they accepted resolutions. Then, it adjusts scores accordingly and builds a fairer view of agent performance.
Additionally, the vendor offers aptitude forecasting, taking the last three months of an agent’s performance data to model forward, determine a longer-term trend, and predict their path.
For instance, is the agent on track to become a team leader? Are they right-sized? Or, are they struggling to manage their current responsibilities? That information may enable impactful preventative interventions to ensure agent retention and engagement.
3. Predictive Metrics Will Turn QA Teams Into Customer Experts
A lot of the work contact center QA teams do is still routine, manual, and cumbersome.
Sorting through call recordings, checking tick boxes, listening out for key phrases… these are all time-wasting activities that contact centers can automate quickly.
As operations do so, QA teams in forward-looking contact centers will have more time to dive deeper into conversational intelligence trends and spotlight new performance insights.
For instance, they may leverage tech to predict customer intent. They can then group interactions, isolate broken processes, share insight across the business, and orchestrate new customer journeys.
“QA analysts are becoming detectives within the contact center,” added Cave. “They’re making the transition from routine form fillers to analysts/investigative journalists.”
As this trend accelerates, QA teams will evolve into customer experts, driving value by better coaching agents and advising their businesses.
4. Predictive Metrics Will Inform AI Agent Implementations
GenAI-powered AI Agents handle customer queries by spinning up relevant knowledge base and website content. As such, the quality of these resources limits their success.
Recognizing this, some conversational AI leaders are boosting that knowledge by tracking how agents handle particular queries and drafting articles based on their approach.
However, they would ideally only model AI Agents on a contact center’s best agents. “We don’t want customers to receive the C+ answer; we want to give them the A* answer,” stated Cave.
evaluagent enables this by using predictive metrics to pinpoint the best customer conversations on a given topic and providing the ideal resolution path for the AI Agent to work from.
Cave continued: “We’re also seeing increased interest in bringing human beings into the training loop for the conversational AI Agents, and our response is: who’s better than the quality team?!”
Indeed, the QA team – or “customer experts” – can guide AI Agents on how to handle queries better, especially now that much of this training is possible through natural language interfaces.
evaluagent: Leading the Way with Predictive Metrics
As the scientist Lord Kelvin once said: “If you can’t measure it, you can’t improve it.” That rings true in the contact center.
Yet, to fully comprehend performance, large contact centers must monitor data from QA, conversational intelligence, chatbot observability, gamification, and other solutions.
These solutions are often disjointed, tricky to integrate, and sometimes require expensive specialists with narrow niches.
evaluagent differentiates by building the tools above into a single platform that simplifies processes for quality and insights teams.
In doing so, it’s compiling insights, tracking new-wave metrics, and considering how to best leverage predictive metrics to empower contact centers with new knowledge.
For more on its mission and software, visit evaluagent.
