How to engage customers—and keep them engaged—is a focal question for organizations across the business-to-consumer (B2C) landscape, where disintermediation by digital platforms continues to erode traditional business models. Engaged customers are more loyal, have more touchpoints with their chosen brands, and deliver greater value over their lifetime.
Yet financial institutions have often struggled to secure the deep consumer engagement typical in other mobile app–intermediated services. The average visit to a bank app lasts only half as long as a visit to an online shopping app, and only one-quarter as long as a visit to a gaming app. Hence, customer service offers one of the few opportunities available to transform financial-services interactions into memorable and long-lasting engagements.
Those customers are getting harder to please. Two-thirds of millennials expect real-time customer service, for example, and three-quarters of all customers expect consistent cross-channel service experience. And with cost pressures rising at least as quickly as service expectations, the obvious response—adding more well-trained employees to deliver great customer service—isn’t a viable option.
Companies are therefore turning to AI to deliver the proactive, personalized service customers want, when and how they want it—sometimes even before they know they want it. For transformed organizations, AI-enabled customer service can increase customer engagement, resulting in increased cross-sell and upsell opportunities while reducing cost-to-serve. In global banking alone, research from McKinsey conducted in 2020 estimates that AI technologies could potentially deliver up to $1 trillion of additional value each year, of which revamped customer service accounts for a significant portion.1“AI bank of the future: Can banks meet the AI challenge,” McKinsey, September 19, 2020.
While a few leading institutions are now transforming their customer service through apps, and new interfaces like social and easy payment systems, many across the industry are still playing catch-up. Institutions are finding that making the most of AI tools to transform customer service is not simply a case of deploying the latest technology. Customer service leaders face challenges ranging from selecting the most important use cases for AI to integrating technology with legacy systems and finding the right talent and organizational governance structures.
But done well, an AI-enabled customer service transformation can unlock significant value for the business—creating a virtuous circle of better service, higher satisfaction, and increasing customer engagement.
The perils and promise of AI customer engagement
Multiple converging factors have made the case for AI-based customer service transformation stronger than ever. Among the most important: increased customer acceptance of (and even preference for) machine-led conversational AI interactions. Meanwhile, related technologies such as messaging platforms are becoming more accessible, and customer behaviors are becoming more understandable with the relentless expansion of data pools institutions can collect and analyze.
Three challenges
But challenges also loom. First, complexity. The COVID-19 pandemic acted as a major catalyst for migration to self-service digital channels, and customers continue to show a preference for digital servicing channels as the “first point of contact.” As a result, customers increasingly turn to contact centers and assisted-chat functions for more complicated needs. That raises the second issue: higher expectations. Customer confidence in self-service channels for transactional activities is leading them to expect similar outcomes for more involved requests. Businesses are therefore rapidly adopting conversational AI, proactive nudges, and predictive engines to transform every point of the customer service experience. Yet these moves raise demand for highly sought-after skills, generating the third challenge: squeezed labor markets that leave customer service leaders struggling to fill crucial roles.
How leaders fulfill AI’s customer engagement promise
What AI-driven customer service maturity looks like
A few leading institutions have reached level four on a five-level scale describing the maturity of a company’s AI-driven customer service.
Level 1: Manual and high-touch, based on paper forms and offered largely via assisted channels.
Level 2: Partly automated and basic digital channels, with digitization and automation of servicing in assisted channels.
Level 3: Accessible and speedy service via digital channels, with self-servicing on select channels and a focus on enabling end-to-end resolution.
Level 4: Proactive and efficient engagement deploying AI-enabled tech, with self-servicing enabled by proactive customer interactions and conversational user experience (UX).
Level 5: Personalized, digitally enabled engagement, bringing back the human touch via predictive intent recognition.
Leaders in AI-enabled customer engagement have committed to an ongoing journey of investment, learning, and improvement, through five levels of maturity. At level one, servicing is predominantly manual, paper-based, and high-touch. At level five—the most advanced end of the maturity scale—companies are delivering proactive, service-led engagement, which lets them handle more than 95 percent of their service interactions via AI and digital channels (see sidebar, “What AI-driven customer service maturity looks like”).
The most mature companies tend to operate in digital-native sectors like ecommerce, taxi aggregation, and over-the-top (OTT) media services. In more traditional B2C sectors, such as banking, telecommunications, and insurance, some organizations have reached levels three and four of the maturity scale, with the most advanced players beginning to push towards level five. These businesses are using AI and technology to support proactive and personalized customer engagement through self-serve tools, revamped apps, new interfaces, dynamic interactive voice response (IVR), and chat.
A few leading institutions have reached level four on a five-level scale describing the maturity of a company’s AI-driven customer service.
Myth busters: Unexpected insights on contact centers
Toward engaging, AI-powered customer service
To achieve the promise of AI-enabled customer service, companies can match the reimagined vision for engagement across all customer touchpoints to the appropriate AI-powered tools, core technology, and data.
The human factor in AI-supported service
AI-powered does not mean automation-only. It’s true that chatbots and similar technology can deliver proactive customer outreach, reducing human-assisted volumes and costs while simplifying the client experience. Nevertheless, an estimated 75 percent of customers use multiple channels in their ongoing experience.2“The state of customer care in 2022,” McKinsey, July 8, 2022. A reimagined AI-supported customer service model therefore encompasses all touchpoints—not only digital self-service channels but also agent-supported options in branches or on social-media platforms, where AI can assist employees in real time to deliver high-quality outcomes.
Even before customers get in touch, an AI-supported system can anticipate their likely needs and generate prompts for the agent. For example, the system might flag that the customer’s credit-card bill is higher than usual, while also highlighting minimum-balance requirements and suggesting payment-plan options to offer. If the customer calls, the agent can not only address an immediate question, but also offer support that deepens the relationship and potentially avoids an additional call from the customer later on.
AI service in the field: an Asian bank’s experience
Put together, next-generation customer service aligns AI, technology, and data to reimagine customer service (Exhibit 2). That was the approach a fast-growing bank in Asia took when it found itself facing increasing complaints, slow resolution times, rising cost-to-serve, and low uptake of self-service channels.
Over a 12-month period, the bank reimagined engagement. It revamped existing channels, improving straight-through processing in self-service options while launching new, dedicated video and social-media channels. To drive a personalized experience, servicing channels are supported by AI-powered decision making, including speech and sentiment analytics to enable automated intent recognition and resolution. Enhanced measurement practices provide real-time tracking of performance against customer engagement aspirations, targets, and service level agreements, while new governance models and processes deal with issues such as service request backlogs.
Underpinning the vision is an API-driven tech stack, which in the future may also include edge technologies like next-best-action solutions and behavioral analytics. And finally, the entire transformation is implemented and sustained via an integrated operating model, bringing together service, business, and product leaders, together with a capability-building academy.
The transformation resulted in a doubling to tripling of self-service channel use, a 40 to 50 percent reduction in service interactions, and a more than 20 percent reduction in cost-to-serve. Incidence ratios on assisted channels fell by 20-30 percent, improving both the customer and employee experience.
Seizing the opportunity
To leapfrog competitors in using customer service to foster engagement, financial institutions can start by focusing on a few imperatives.
Holistically transforming customer service into engagement through re-imagined, AI-led capabilities can improve customer experience, reduce costs, and increase sales, helping businesses maximize value over the customer lifetime. For institutions, the time to act is now.
This content was originally published here.