AI-enabled customer service is now the quickest and most effective route for institutions to deliver personalized, proactive experiences that drive customer engagement.
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GLOMcKinsey & Company’s published a report covering the fundumentals of AI-enabled customer engagement.
In the realm of business-to-consumer (B2C) enterprises, a key concern is how to effectively capture and sustain the interest of customers, especially in the face of disruptions caused by digital platforms reshaping traditional business models. This is particularly significant due to the growing impact of these digital platforms, which are redefining the nature of business operations. The focal point now revolves around engaging customers and maintaining that engagement at a crucial level.
Companies are actively exploring methods to ensure that customers stay engaged, as engaged customers tend to display stronger loyalty, interact more frequently with their preferred brands, and contribute more value over their patronage period.
However, the financial sector has encountered difficulties in achieving the same level of deep consumer engagement that is commonly observed in other sectors facilitated by mobile applications. In contrast, visits to banking apps are notably briefer compared to the time spent on online shopping or gaming apps. Consequently, the arena of customer service emerges as a potential avenue to transform financial interactions into meaningful and lasting engagements.
Meeting the increasing demands of customer expectations is proving to be a challenge. For example, a significant portion of millennials now expect instant customer service, while a substantial number of all customers anticipate a consistent experience across various communication channels. Balancing these expectations with the rising cost pressures creates a dilemma – simply hiring more well-trained staff to provide exceptional customer service is not a viable solution.
Thus, businesses are turning their attention to artificial intelligence (AI) as a means to offer proactive and personalized assistance to customers, precisely tailored to their preferences and even predicting their needs in advance. This is especially relevant for companies undergoing a transformation, as AI-driven customer service holds the potential to elevate customer engagement. This, in turn, can result in more opportunities for cross-selling and upselling, all while reducing the expenses associated with providing service. Notably, within the global banking sector, research conducted by McKinsey in 2020 suggests that AI applications could potentially yield up to $1 trillion in added value annually, with a significant portion attributed to revamped customer service.
While certain leading institutions have already started overhauling their customer service using applications and innovative interfaces such as social platforms and streamlined payment systems, a considerable portion of the industry is still striving to catch up. Organizations are acknowledging that effectively harnessing AI tools for transforming customer service isn’t solely about adopting the latest technology. Pioneers in customer service are grappling with challenges that span from pinpointing the most crucial AI use cases to seamlessly integrating this technology with existing systems, as well as sourcing the right talent and establishing effective organizational governance structures.
Nevertheless, when executed proficiently, a customer service transformation driven by AI has the potential to yield substantial advantages for the business. This sets off a positive cycle where enhanced service leads to heightened satisfaction and, consequently, greater customer engagement.
McKinsey’s vie on 3 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.
McKinsey’s view of 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. Exhibit 1 captures the new model for customer service—from communicating with customers before they even reach out with a specific need, through to providing AI-supported solutions and evaluating performance after the fact.
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McKinsey’s take 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.
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Over the course of a year, the bank completely reimagined its approach to engaging customers. It made significant changes to existing communication channels, enhancing the efficiency of self-service options and introducing new dedicated platforms for video and social media interactions. To deliver a personalized customer experience, these service channels are backed by AI-powered decision-making mechanisms, including tools like speech and sentiment analysis that allow for automated recognition and resolution of customer intents. To ensure progress towards customer engagement goals, real-time tracking of performance was improved through enhanced measurement practices, encompassing customer engagement targets and service level agreements. Additionally, new governance models and processes were established to address challenges such as backlogs in service requests.
This transformation is supported by a tech stack driven by APIs (application programming interfaces). The future potential of this stack includes incorporating cutting-edge technologies such as solutions for suggesting the next best action and behavioral analytics. Ultimately, this comprehensive transformation is executed and maintained through an integrated operating model that brings together leaders from service, business, and product divisions, alongside a capability-building academy.
As a result of these changes, the use of self-service channels doubled to tripled, service interactions reduced by 40 to 50 percent, and the cost of service decreased by over 20 percent. Instances requiring assistance on dedicated service channels also decreased by 20 to 30 percent, leading to improvements in both customer and employee experiences.
Seizing the opportunity
To surpass competitors in utilizing customer service for cultivating engagement, financial institutions can begin by addressing several key priorities:
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Envision the future of service: Put customers and their engagement at the forefront, while also defining the strategic value to be achieved. This might involve increasing the share of customer spending, expanding specific services or business lines, or targeting particular demographics.
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Rethink every customer touchpoint: Evaluate every interaction point, whether it’s digital or assisted, to identify opportunities for enhancing the experience and improving efficiency.
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Maximize customer service interactions: Utilize every customer service engagement to foster stronger customer relationships, nurture loyalty, and increase customer value over their entire lifetime.
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Harness AI and a comprehensive technology stack: Leverage AI and a complete technology infrastructure to deliver a more proactive and tailored customer service experience, catering to both self-service and decision-making needs of both customers and employees.
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Embrace agile and collaborative methodologies: Drive transformation through flexible and collaborative approaches that involve subject matter experts from various business and support functions within the organization.
