For customer-focused brands, the stakes for getting things right with customers were high well before COVID hit. In 2019, a sobering 81% of customers (B2B and B2C) stated they wouldn’t do business with a brand they didn’t trust. Almost all (89%) of them said they would also abandon a brand that breached their trust.
Now the stakes are even higher. The pandemic spurned a mass customer adoption of digital channels. Yet, despite (or perhaps because of) this mass adoption, 58% of customers surveyed in 2021 said they now expect more from customer service.
This combination of how valued customers expect to feel, the velocity through which complicated issues must be resolved, the sheer variety of digital channels used to engage with contact centers, and visibility challenges related to siloed knowledge is unprecedented for contact centers. As a result, many feel increasingly pinched between their need to provide outstanding customer service and produce substantial operational efficiencies.
This is why artificial intelligence is proving to be such a big deal for customer-focused brands—it empowers contact centers to do both.
Providing a great customer experience used to mean ensuring the customer felt satisfied. But now, great CX means ensuring the customer needs are heard and addressed. And here’s where the benefits of contact center AI begin. AI can be used to provide online chatbots and faster access to information. While this removes the agent, artificial intelligence can also be used to augment agent performance, empowering them to connect with customers more efficiently, in more human ways, and deliver a better overall experience.
Part of the challenge in employing empathy in CX lies in the limitations of quantitative “after-the-fact” methodologies, like customer satisfaction (CSAT) and Net Promoter Scores (NPS). While useful in specific areas, they simply were not designed to capture the emotional sentiment. Empathy involves understanding that, while the language being used might be very similar (e.g., “I can’t believe this is happening…”), a customer might be conveying anger, happiness, disappointment, or appreciation.
Contact center AI can monitor behavior in real-time as conversations unfold between agents and customers. Interaction analytics surfaces patterns and trends within these conversations and provides visualizations and analyses via agent dashboards. Insights gained through this use of AI helps agents quickly craft more effective, empathetic responses.
Contact center AI also helps agents build rapport with customers by eliminating the need for any “hard sell.” Virtual sales coaches can leverage demographics, behavioral profiles, and purchase histories, which are processed against information gained during a call. This process identifies what product a customer is most likely to buy next. And, as the virtual coach brings this to the agent’s attention, it also provides specific, scripted language optimized over time through machine learning to help agents significantly improve their conversion rates.
This constant optimization and empathy enhancement also help to increase an agent’s ability to resolve customer issues during their initial call, in some cases leading to a 2.3x greater annual increase in first resolution rates compared to non-AI users and a 4.5x greater service-level agreement (SLA) attainment rate.
Despite promising advancements in contact center technology, like virtual agents and interactive voice response (IVR), many customers still prefer to resolve their issues by phone with a live agent. This means velocity—the speed at which agents can respond to customer issues while simultaneously providing superior CX—is still paramount. That said, contact centers still have to provide their personal touch efficiently, ensuring average handle times (AHT) don’t go off the rails. Contact center AI helps maintain velocity here in three powerful ways: through automation, human language and sentiment analysis, and most importantly, real-time agent assistance.
Contact center AI refers to tools that can handle numerous, receptive, low-value tasks without the need for human oversight. Customer routing, agent scheduling, and quality assurance are examples of tasks AI can easily handle. Process automation and optical character recognition can help ingest CX documentation, accelerating or completely handling customer processing. AI-powered chatbots can answer low-level questions 80% faster than their human counterparts.
And through automated pre-screening, when a call is escalated to an agent, the basic details for the issue are already available, reducing handle time and burden. But no matter how well you set up agents for success, much of customer support happens in the moment. And a lot is working against agents in the moment.
An average of 14% of time is wasted by agents simply looking for information they need to do their jobs. And much to the detriment of customers on the other end of the line, studies show brain activation for listening drops 53% if an agent is trying to multitask at the same time. What’s more, a study from Harvard Business Review found 24% of repeat calls stemmed from emotional disconnects between agents and customers.
Fortunately, here we have another area where contact center AI is making huge strides. Thanks to recent advancements in natural language processing and machine learning, AI can monitor customer calls and service levels in real-time. Acting as a virtual “co-pilot” for agents as they engage with customers, contact center AI provides guidance and additional relevant information as needed and, over time, can optimize the support it provides through machine learning techniques.
This AI-augmented approach substantially improves agent performance, and even happiness. As reported by LogMeIn, a 2019 study of technologically mature CX firms using AI found that “63% saw an increase in NPS as a result of their current customer engagement strategies and reported an average of 8 points higher than their lesser mature counterparts.” What’s more, “half of these organizations saw an increase in conversation rates, 56% reported an increase in revenue, and 40% saw an increase in order size. Even agent satisfaction increased under the more mature organizations with nearly 50% reporting an increase in overall job happiness.”
And, with real-time analytics available as needed, leadership can adjust strategies as needed instead of waiting months or quarters for reports to be compiled, analyzed, and presented.
Back in 2018, almost 20% of U.S. households contained ten or more connected devices. Again, thanks to COVID, more customers are using a multitude of devices to engage with brands more often. So providing a smooth and efficient customer experience is crucial. This goal of a zero-friction customer experience is a distinct difference between omnichannel and multichannel thinking. And omnichannel, with its emphasis on experience (as opposed to raw information), is where our heads should be.
That said, one of the traps with omnichannel is to assume that every customer’s needs must be met at every touchpoint. Not only is this logistically impossible, chasing these windmills leads to unfocused objectives, slow progress, and wasted budgets. Instead (and much more productively), CX leadership can leverage contact center AI to minimize friction with the customer experience itself.
You can accomplish this through AI’s ability to synthesize today’s omnichannel customer journey consisting of voice, SMS, search, chat, and more into a singular, strategically beneficial, “big picture” journey. This way, the multitude of moving pieces, everything from follow-up surveys to texted coupon codes, come together to provide a consistent, customer-focused experience.
McKinsey details how a leading credit card company found success by doing precisely this, hoping to improve its performance in digital channels:
“It started by gathering interaction, transaction, and customer-profile data with a journey analytics platform to identify drivers of satisfaction for each journey, as well as areas where it could improve. The platform included data on repeat interactions, lead times, and how often customers hopped from one channel to another. It also encompassed more subtle factors, such as whether the company effectively handled negative outcomes and what communications took place at various points in time.”
And, in addition to improving these areas of service excellence, it “ultimately reduced its interaction and operational costs by 10 to 25 percent as a result of the CX and digital transformation.”
Contact centers grappling with omnichannel can experience a siloing of data, information, and insights is its own separate issue. Even worse, this encompasses the experience, perspectives, and best practices possessed by top performers, preventing them from being adopted and put into practice by the team as a whole.
Expertise AI, a subset of contact center AI developed by Cresta, has now evolved to address these silos specifically for real-time chat and voice-based customer service teams. And again, far from replacing contact center agents, Expertise AI is a means to help them do what they do better.
“It’s too hard to measure the ROI of enterprise software today. Companies are looking for outcomes, not more tools,” said Zayd Enam, co-founder, and CEO of Cresta. “We built Cresta to quickly deliver demonstrable value, work at the speed of real-time conversations, and offer the intelligence needed to coach and optimize calls as they take place. “
In Cresta’s case, our AI was developed to gather behaviors and practices by the best-performing agents and apply that across all agents. As they say, a rising tide lifts all boats.
It’s clear now how AI gives contact centers an edge over this troublesome cocktail of value, velocity, variety, and visibility. And that’s a big deal for customer-focused brands. But it’s also clear that contact center AI is evolving into a significant competitive advantage in this post-COVID world. And while overall adoption at the enterprise level is still low, differences in those seeing success with AI become clear when compared to those who are not.
Data from McKinsey shows AI high performers:
Here, again, it’s worth stressing the important role AI can play in helping CX agents upskill through real-time coaching and feedback, modeled on the best practices of a team’s top performers, and provide real-time analytics for supervisors and management.
Because for customer-focused brands, effectively scaling contact center AI to join these high performers means empowering the all-important human factor needs to scale along with it.
Curious what this looks like in action? Learn more about the benefits of turning every agent into your best agent with expertise AI.