By Abhinav Singh, Vice President Business Transformation & Digital Solutions, Teleperformance, USA Customer experience is becoming more and more technologically sophisticated. That same technology can also make it more authentically human.
As anyone knows who purchases goods on an ecommerce site, uses social media platforms or conducts any kind of personal business online, companies have been leveraging machine learning and AI since long before ChatGPT hit the scene in late 2022. From targeted ads to “you might also like” product recommendations to determining the frequency of restocking a shelf and countless other functions, these technologies are ubiquitous in our lives as customers.
With the advent of generative AI, however, technology-driven and supported customer experiences are set to reach unprecedented levels of sophistication. AI-powered mechanisms promise to take the merely personalized to hyper-personalized, understanding preferences, anticipating needs and preempting issues to an astonishingly specific degree that will enable companies to deliver minutely tailored customer experiences at a scaleonce unimaginable.
Increasingly, customers are accepting of tech-driven experiences as well, but not at the cost of quality. A recent survey by Bain found that customers were more likely to be satisfied with brands that may take longer to connect with a live agent but provide high quality resolution, than swiftly getting to a technology/human-driven experience that does not meet their need.1 Automated solutions are preferred by majorities of customers for interactions that are simpler in nature including information requests, FAQs, and basic transactions such as signing up for an account and booking an appointment.
The reason customers have warmed to more technology in their brand interactions isn’t actually because of the tech itself. It’s because the humans working with the technology behind the interaction have made those experiences feel more connected: more attuned to people as individuals; more emotionally sensitive; more understanding and empathetic; more equipped to solve complex problems and provide help with less effort. When an interaction is fast, easy, pleasant or simply less stressful than we’d anticipated, we (consciously or not) feel known, understood and cared for—all deeply human responses.
1Jason Barro, Rahul Sethi et al., “Quality Over Quantity: The Key to Call Center Agents in Banking,” Bain & Company, February 16, 2023
For companies, the business benefits of doubling down on these technologies are clear. Goldman Sachs anticipates generative AI driving a 7 percent (about $7 trillion) lift in global GDP over the next 10 years.2 And McKinsey estimates generative AI will have a potential $4.4 trillion impact across industries, through improvements to self-service, first interaction resolution, reduced response time and increased sales.3
As enterprises race to integrate generative AI and other emerging tech into the customer experience, they must continue prioritizing and aiming for the somewhat paradoxical goal of elevating human-centricity through non-human means. Here are five key areas to focus on.
Strengthen the AI/agent connection
As comfortable as customers are with automated solutions, they overwhelmingly want to be able to talk to a real, live person when they need to. If a customer is experiencing that need, chances are it’s because they’ve already run through the robot gauntlet without success and are at a boiling point of frustration and anxiety.
One of the most promising areas of AI application in the CX space is assistive support: enabling employees with AI-derived historical and predictive insights to inform their understanding of a particular customer’s needs, issues, preferences, journey and more. That understanding empowers the agent to provide true help, not just lip service, to someone in a state of distress: “Hi Mrs. Jones, I can see here you’re trying to resolve a $150 dollar charge you do not believe is accurate, and that you’ve already spent quite a bit of time in our automated system without any luck. I can also see you’re a terrific longtime customer—thank you for that. I imagine you’re pretty frustrated right now. Let me help get this sorted out for you right away so you can get back to your day.”
Equipping employees with AI tools that enable this level of customer support—and, critically, the training they need to fully leverage them—sets everyone up for success and satisfaction, which will improve outcomes on both sides of the interaction. Research shows customer service is one of the biggest contributors to customer loyalty.4
2“Generative AI could raise global GDP by 7 percent,” GoldmanSachs.com, April 5, 2023. 3“The economic potential of generative AI: The next productivity frontier,” McKinsey Digital, June 5, 2023. 4Christina McAllister and Jeremy Vale, “Generative AI: What It Means For Customer Service,” Forrester, July 7, 2023.
Don’t discount soft skills!
Active listening, emotional intelligence and empathy are critical when communicating with a customer and getting them to a satisfactory resolution, especially in a fraught scenario like the one described above. And they’re all skills that can be taught, strengthened and mastered.
Here again, generative AI advancements can be leveraged to assist customer care agents in enhancing the human touch. AI-based coaching tools can provide direct, specific, judgement-free feedback and recommendations to agents in real time, so they can quickly and smoothly recover a conversation that’s faltering. And just as generative AI can model customer journey scenarios, it can also model customer interactions, enabling employees to test and hone their people skills in a variety of highly realistic and dynamic yet risk- and pressure-free virtual scenarios that blow out of the water the two-dimensional online training programs many of us are familiar with.
Reduce effort everywhere
A landmark 2017 HBR study5 found that nearly 75 percent of customers interact with brands via multiple channels—a number that has almost certainly increased in the years since. Those customers don’t know or care about the complexities and challenges of your IT infrastructure when they start a transaction on your mobile app and finish it on your website with a phone call in between. They simply want to complete an action or resolve an issue as quickly and easily as possible and move on with their day. The incentive for companies to get it right at every point is high: 86 percent of customers say they will leave a brand after just two bad experiences, while 60 percent are willing to pay more if they know they’ll get a good experience.6
Generative AI models such as Generative Adversarial Networks (GANs) or Variational Autoencoders (VAEs) can be hugely valuable in optimizing the omnichannel experience by simulating cross-channel customer journeys, surfacing pain points and bottlenecks to help identify improvement opportunities in a risk-free environment.
5Emma Sopadjieva, Utpal M. Dholakia and Beth Benjamin, “A Study of 46,000 Shoppers Shows That Omnichannel Retailing Works,” HBR.org, January 3, 2017. 6“Experience is everything: Here’s how to get it right,” PwC, 2018.
An online retailer, for example, might discover through modeling that customers often initially evaluate products on the website and add them to cart, then jump to a social media platform to read reviews or watch tutorials, and from there move to the mobile app to complete the purchase—a disjointed flow with a lot of cracks for someone to fall through. Armed with an understanding of these journey patterns and the drivers behind them, the retailer can update each touchpoint to give customers access to the information they’re seeking without having to change platforms. Customers get an effortless, delightful experience that will have them coming back for more, and the business reduces abandoned carts and lost revenue. It’s a true win-win.
7Brooke Auxier, Lee Rainie et al., “Americans and Privacy: Concerned, Confused and Feeling Lack of Control Over Their Personal Information,” Pew Research Center, November 15, 2019.
Cultivate a culture of learning, adaptation and communication
The bar of customers’ expectations is constantly being raised, both by all the other brand interactions they have, and simply by their general awareness of broad technological advancement. Businesses must be positioned and conditioned to adapt quickly and decisively.
Generative AI can help you nurture a culture of continuous learningand improvement. Gathering, analyzing and disseminating AI-derived feedback and insights in a data loop that encompasses all parts of the enterprise, from front-line employees back through to product managers, and can help ensure that feedback finds its way into the customer experience. When that happens, customers will notice—and they’ll reward you for it.
Interaction analytics has been providing such insights from the frontline interactions, the customer sentiment, and their interests, frustrations and wants. A hotel chain was able to improve their reservation sales simply by analyzing the key words in a conversation with the customer and recommending a property that fit their requirements.
Relentlessly prioritize trustworthiness at all levels
Customer data is the lifeblood of AI-driven technology. Customers know this, and they don’t feel great about it. According to Pew Research Center, 79 percent of Americans report being concerned about the way their data is being used by companies.7 Layer onto that today’s climate of misinformation and the fakery, falsehoods and bias AI can generate, and it’s easy to see why trust is low... and why brands who do earn their customers’ trust will have a significant competitive advantage.
Generative AI models that, among other things, detect anomalies in behaviorand patterns can be one powerful weapon in the fight to protect customer information and safeguard the customer experience from bad actors and evolving threats. These models will have to be more efficient and faster than the synthetic content that is being created by bad actors.
However, these models are only as effective as the recency and quality of the data they’re trained on and require human oversight. They need to be aided by robust data protection measures and stringent regulatory adherence.
Developing regulations that would model the right usage of GenerativeAI is critical to creating a safer ecosystem for all.
Internally, it is critical to adopt clear ethical frameworks and guidelines for the development and deployment of AI systems to ensure fairness, transparency, inclusivity and accountability and to avoid bias. When onboarding new technologies, grill vendors about their ethical andsecuring standards to ensure theyalign with yours.
Externally, transparency breeds trust: be upfront with customers about how you use their data, the measures you’ve implemented to safeguard it, and the AI usage policies you’ve put in place.Increasingly, customer trust is also driven by knowing what the brands they interact with stand for. If your organization supports specific causes or has a strong point of view on a relevant topic, make it a part of your brand story.
Humanity will drive loyalty in the age of AI
Just as it always has, technology will continue to play an essential, evolving and increasing role in the CX equation. What will never change in that equation is the customer: a real, live person who brings a complex, nuanced set of experiences, feelings and perspectives to every brand interaction, be it over the ether or across a counter.
Integrating advanced AI and other emerging technologies in ways that recognize and accommodate customers’ and employees’ humanness first and foremost—not as an afterthought—is the key to meeting changing expectations, fostering deep loyalty, and driving sustainable growth in the digital age.
Embracing Progress: Excited about AI-driven hyper-personalization and the potential for highly tailored customer experiences.
Tech with a Human Touch: Prefer AI solutions that enhance human interactions, focusing on empathy and problem-solving.
Mixed Feelings: Appreciate AI for certain interactions (e.g., FAQs) but wary of potential privacy concerns and trust issues.
Traditionalist: Prefer a more human-centric approach without heavy reliance on AI in customer interactions.
