Digital customer service is increasingly crucial in the business landscape, offering enriched touchpoints on web and mobile. It provides a range of communication options, including messaging, video chat, and personalized experiences. But why is digital customer service failing when 70% of customers are using self-service, but only 9% of customers can fully resolve their issues via self-service?
What is Digital Customer Service?
Digital Customer Service covers a broad spectrum of digital customer support options, from chat and messaging to co-browsing and video. Using a combination of chatbots, voicebots and human agents. It is designed to align with the digital platform preferences of today’s consumers. Who increasingly rely on internet-connected devices like smartphones and tablets for interactions. See Building a Robust Digital Customer Service Strategy: A Comprehensive Guide for more info.
Why Digital Customer Service?
Digital customer service appeals to consumers’ desire for choice and speed in interactions with businesses, often improving customer satisfaction and NPS scores. Additionally, digital customer service helps agents resolve customer support inquiries more efficiently, lowering average handle times. Resolution in digital customer service is a win-win for customers and for companies.
However, research from Gartner finds that 56% of service leader are adding new channels, but customers are still calling for customer support. Time-consuming live interactions, even when only one step in the resolution journey, mean that resolution will cost 80 to 100 times more than a fully self-service fix, while 40% of the live volume could be resolved through digital customer service in self-service channels.
Key Components of Digital Customer Service
Digital customer service integrates multiple channels like live chat, social media, and mobile applications, employing AI for automated responses and analytics. It aims to manage customer interactions seamlessly across platforms, offering both self-service options and higher-touch agent-assisted channels. Businesses are increasingly adding AI to their digital support channels to enhance efficiency and customer satisfaction.
By understanding what customers need to accomplish and how they expect to accomplish it, business should:
- Guide customers to the correct channel or resolution
- Optimize each channel to the most appropriate resolution
How GenAI Transforms Digital Customer Service
Generative AI (GenAI) is catalyzing a monumental shift in customer service operations, with its potential to enhance productivity and revolutionize customer interactions. And LLMs (Large Language Models) are already in use in digital customer service.
Examples of Successful Digital Customer Service with LLMs:
Octopus Energy incorporates GenAI to draft emails, resulting in an 18% hike in customer happiness scores.
JetBlue augments its chat channels with GenAI, saving an average of 280 seconds per chat, thereby freeing up agents to focus on more complex issues.
The maturity curve for GenAI in customer support is growing rapidly, and many businesses are already strategizing how a fully AI-enabled customer care center will work as in this graph by BCG:
Improving Digital Customer Service with LLMs
Large Language Models (LLMs) can improve digital customer service support by personalized, scalable and cost-effective customer service. One example being Teneo Adaptive Answers Any business must make sure to counter hallucinations, lack of accuracy, misinformation, make sure that the customers data is protected and that the LLM chatbot does not have access to APIs where it can delete customer information.
- Integrating prompt chaining, can help your LLM avoid propagating misinformation
- Different approaches for output filtering and personal data detection should be used on any LLM-generated text before presenting it to the user
Risks with LLMs in Digital Customer Service
The integration of Large Language Models (LLMs) in digital customer service has brought forward several challenges that businesses must navigate:
- LLM Hallucinations: LLMs may generate outputs that are seemingly coherent but are factually incorrect or nonsensical, leading to potential customer confusion and dissatisfaction.
- Complex Language Patterns: LLMs might struggle with complex or ambiguous language, making it difficult to provide accurate assistance without sophisticated understanding capabilities.
- Misleading Content Risk: The personalization abilities of LLMs are crucial in mitigating risks, but there’s a chance of producing misleading content if the system fails to tailor responses effectively to each user’s context.
- Cost Implications: Repeatedly prompting LLMs for common questions can be costly, and inaccuracies can lead to additional expenses in customer service operations.
- Credibility and Trust Concerns: Ensuring the credibility and trustworthiness of LLM applications is essential, as the spread of false information can damage a company’s reputation and the reliability of its customer service.
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How Teneo Tackles LLM Challenges in Digital Customer Service
Teneo addresses the challenges of employing LLMs in digital customer service by enhancing accuracy and optimizing costs, among other solutions:
- Control Over AI Outputs: Using collected data, Teneo allows the creation of flows for similar use cases, giving companies greater control over the consistency and quality of AI-generated responses.
- Enhanced Accuracy and Reliability: Teneo’s sophisticated tools and analytics help to improve the accuracy of LLMs. Ensuring that customers receive reliable and correct information, thus maintaining the integrity of customer service operations. On a recent test conducted on the Cyara IVR testing platform. Teneo managed to score 99% accuracy and outperform all its competitors.
- Cost Optimization Strategies: Teneo’s platform is designed to optimize operational costs by minimizing the need for human intervention in routine queries. Allowing human agents to focus on higher-value interactions that require a personal touch.
- Precision in Responses: Teneo’s linguistic capabilities ensure that LLM hallucinations are accurately identified and processed. Safeguarding against the provision of incorrect information to customers. Read Teneo LLM Orchestrator for a detailed overview.
- Customized Customer Interactions: Leveraging detailed user data, Teneo personalizes interactions, significantly reducing the chances of generating misleading content and enhancing the relevance and accuracy of responses.
- Cost-Effective Q&A Management: By automating the creation of Q&A flows. Teneo helps businesses save on costs associated with repetitive LLM prompting and ensures more controlled, accurate customer service interactions.
- Continuous Performance Review: Teneo Inquire offers the ability to review and refine how LLMs handle customer sessions. Improving the system’s overall accuracy and trustworthiness by identifying and correcting low-performing queries.
- Comprehensive AI Management: Teneo provides a comprehensive suite of tools for managing the challenges of LLM hallucinations. Balancing the need for advanced AI capabilities with the practical requirements of customer service reliability and cost efficiency.
Digital Customer Service Trends in 2024
In 2024, trends like the growing adoption of AI, omnichannel service, and customer experience analytics are shaping the future of digital customer service. These trends enable brands to tailor experiences across channels and gain deeper insights into their audience’s needs.
- Digital AI Support is a Focus Area: There is a notable gap in satisfaction with digital customer support compared to human channels. However, there is also significant potential for improvement. Enhancing digital support can lead to increased customer satisfaction and could be a crucial differentiator for businesses. Advanced AI chatbots are set to revolutionize customer service with dynamic, context-aware conversations. Businesses using these GenAI chatbots expect a significant increase in efficiency.
- Customer Service Over Price: Research indicates that great customer service is more important than low prices for customer loyalty. Organizations known for excellent customer experiences are more likely to win customers, even in a challenging economy. This suggests that businesses should invest in quality customer support as a key driver for purchases. Ranking above even competitive pricing.
- Proactive Customer Service with GenAI: Businesses are already using LLMs for proactive customer service to demonstrate commitment to customer success. The best approach is building a customer-facing application, either in a framework such as Langchain. Adding a platform that optimizes the information and the constraints provided to LLMs to improve the accuracy of the answers. Defining company-specific keywords within the prompt itself, for example.
A platform allows companies to achieve better levels of control, moderation, and personalization. The rapid maturity in generative AI is already transforming the ways in which companies manage their critical customer service functions. Now, its essential to anticipate how the capabilities of LLMs can disrupt business models. As generative AI will learn more about your products, operations, and customers, they can predict customer behavior. To do outreach to customers in anticipation of their needs and desires.