March 27, 2023 | Article McKinsey editado por Luiz de Campos Salles em 19/6/24 luiz@csalles.com.br
AI-enabled customer service is now the quickest and most effective route for institutions to deliver personalized, proactive experiences that drive customer engagement.
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.
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.
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The perils and promise of AI customer engagement
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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
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.
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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.
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.
Exhibit 1
The future of customer service builds on AI to deliver engaging experiences and generate lasting value.
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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. 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.
Seizing the opportunity
To leapfrog competitors in using customer service to foster engagement, financial institutions can start by focusing on a few imperatives.
Envision the future of service, keeping customers and their engagement at the core while also defining the strategic value to be attained—for example, a larger share of wallet with existing customers? Expansion of particular services, lines of business, or demographics?
Rethink every customer touchpoint, whether digital or assisted, together with opportunities to enhance the experience while also increasing efficiencies.
Maximize every customer service interaction, to deepen customer relationships, build loyalty, and drive greater value over the customer’s lifetime.
Leverage AI and an end-to-end technology stack, to provide a more proactive and personalized customer service experience that supports self-service and decision-making for customers as well as employees.
Adapt agile and collaborative approaches to drive transformation, comprised of SMEs from different business and support functions of the organization.
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.
ABOUT THE AUTHOR(S)
Avinash Chandra Das is an associate partner in McKinsey’s Bengaluru office, where Malcolm Gomes is a partner and Ishwar Lal Patidar is an expert. Greg Phalin is a senior partner in the Charlotte office, Rakshit Sawhney is an associate partner in the Gurugram office, and Renny Thomas is a senior partner in the Mumbai office.
The authors wish to thank Amit Gupta, John Larson, and Thomas Wind for their contributions to this article.
Segue uma conversa minha com o copilot sobre como medir client satifactio com os serviços de call centers bancários 19/6/24
Você
Which are the metrics used to measure client satisfaction in the usage of Bank call centers run by AI directed sotwares?
Copilot
A gerar respostas para si…
Certainly! When it comes to measuring client satisfaction in AI-directed bank call centers, there are several key metrics to consider:
1. Abandonment Rate: This metric reflects the percentage of callers who hang up before reaching an agent. A high abandonment rate may indicate dissatisfaction with the service or long wait times1.
2. Service Level: Service level measures the percentage of calls answered within a specified time frame (e.g., 20 seconds). Higher service levels generally lead to better customer satisfaction1.
3. Net Promoter Score (NPS): NPS gauges customer loyalty by asking how likely customers are to recommend the service to others. It provides insights into overall satisfaction and loyalty1.
4. First Call Resolution (FCR): FCR tracks the percentage of issues resolved during the initial call. Higher FCR indicates efficient service and better customer experience2.
5. Average Handling Time (AHT): AHT measures the time agents spend on each call. Balancing AHT with quality service is crucial for customer satisfaction2.
6. Customer Satisfaction Score (CSAT): CSAT surveys customers after interactions to assess their satisfaction. It’s a direct measure of how well the call center meets customer expectations3.
Remember that these metrics can vary based on the specific context and goals of the bank call center. Implementing AI effectively requires aligning these metrics with the desired customer experience and business outcomes4. Is there anything else I can assist you with? 😊
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Você
How can banks ensure a balance between efficiency and quality in their call centers?
Copilot
A gerar respostas para si…
Certainly! Balancing efficiency and quality in bank call centers is crucial for providing excellent customer service. Here are some strategies:
1. Agent Training and Empowerment: Well-trained agents who understand both the technology and customer needs can efficiently handle inquiries while maintaining quality. Regular training updates keep agents informed about AI systems and best practices.
2. Optimized Call Routing: AI can route calls based on complexity, urgency, and agent availability. Simple queries can be handled by AI, while more complex issues are directed to human agents.
3. Effective Knowledge Base: Maintain an up-to-date knowledge base accessible to both AI and agents. This ensures consistent and accurate responses, reducing call duration.
4. Monitoring and Feedback: Regularly monitor calls to identify areas for improvement. Provide constructive feedback to agents and AI systems to enhance quality.
5. Performance Metrics: Use metrics like First Call Resolution (FCR), Average Handling Time (AHT), and Customer Satisfaction Score (CSAT) to track efficiency and quality. Set targets and align them with business goals.
6. Customer Self-Service Options: Implement self-service options (e.g., IVR systems, chatbots) for routine tasks. This frees up agents to focus on more complex issues.
Remember that striking the right balance requires continuous evaluation and adaptation. 😊 Is there anything else I can assist you with?
2de30respostas
O conteúdo gerado por IA pode estar incorreto
• How can banks improve agent training?
• Tell me more about call routing strategies.
• What are some common challenges in maintaining quality?
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