With generative AI changing the face of customer experience everywhere, how can businesses master the art of personalization leveraging AI-powered support?
Over the last few decades, customer service operations have undergone substantial evolution. Innovations like interactive voice response (IVR) and chatbots have notably enhanced the efficiency of customer service agents. Looking forward, large language models (LLMs) possess the capability to substantially broaden the scope of automation and execute essential customer service responsibilities.
Freshworks’s Global AI Workplace Report reveals that around 73% of APAC workers surveyed benefit from using AI at work.
Generative AI has been shown to boost customer service productivity. The million-dollar question is: how should businesses deploy it to derive the greatest value and create personalized omnichannel customer journeys at scale?
In this Q&A, we find out from Simon Ma, Managing Director, Asia, Freshworks, the key considerations and approaches to hyper-personalization with AI-powered support for customer experience.
What are the most important considerations when implementing AI in customer service?
Ma: Hyper-personalization, efficiency and effectiveness are key to ensuring a positive customer experience (CX). There are some essential considerations when implementing AI in customer service: chatbots and virtual assistants. Chatbots can handle straightforward or repetitive tasks by guiding customers through processes and answering basic queries. In contrast, virtual assistants manage more complex interactions by understanding the context and providing more relevant responses.
AI in customer service can indirectly save time and manpower resources through various capabilities like predictive analytics and personalization, enabling analysis of historical data to optimize current service for each customer. Performance data analysis with AI significantly boosts CX by offering actionable insights to support metrics and agent performance. AI-driven recommendations help optimize processes, shorten response times, and personalize interactions, resulting in more efficient and tailored customer service.
At Freshworks, we embody these principles with AI-powered recommendations and automated workflows. Our approach not only empowers customers to manage routine issues independently but also allows customer service agents to focus on critical tasks with Freddy AI (Freshworks’ integrated AI engine) and enhances cross-functional collaboration through advanced tools. Leveraging AI also allows customers to get a personalized and tailored experience.
What are the common challenges you see businesses face when integrating AI into existing customer service workflows?
Ma: Although there are excellent tools available for improving customer experience, a common challenge for some businesses is applying AI as a one-size-fits-all solution. To avoid this, it’s important to first assess whether an issue is simple or complex before using these tools.
Another common challenge involves skill development. Organizations must not only implement the right chatbots, Generative AI (GenAI) tools, and other solutions for their teams and customers but also ensure they have human agents proficient in AI and capable of handling complex issues that machines cannot.
This necessitates recruiting new agents and retraining existing ones with skills and qualifications that differ significantly from those of traditional service representatives.
How can AI be used together with omnichannel engagement to enhance personalized customer interactions without compromising privacy?
Ma: In today’s digital-first landscape, customers expect instant recognition and understanding of their needs from service agents. This is where omnichannel CX enters the conversation.
Omnichannel CX is a strategy that integrates all customer interactions across different channels into a seamless experience. It ensures that a customer who begins a conversation via live chat and then continues through email or in person, receives consistent service quality and context throughout. This cohesive experience is elevated by technologies of AI and machine learning, which provide predictive insights and personalized suggestions using customer data.
For instance, if an agent has interacted with a customer, they can access a comprehensive view of all interactions from the past four months, including communications via WhatsApp, social media, and email. They can also see the types of cases raised, such as refunds or sizing issues. Furthermore, the agent can consider contextual details, such as previous purchases and the customer’s interests, to offer relevant insights and suggest products that might appeal to the customer.
Effective compliance is of utmost importance for omnichannel strategy as it addresses data privacy and security requirements no matter where the business is located. Regulations such as GDPR ensure fairness, transparency, accuracy, security and respect for customers’ data rights, which further foster consumer trust and engagement.
Regulatory compliance supports innovation by providing a unified regulatory framework, which complements omnichannel strategies to power customer experiences that connect personally, build loyalty, and deliver value in an increasingly interconnected world.
How do you see AI transforming customer support and the role of customer service agents in the next five years?
Ma: As we look to the future, AI will transform customer support, bringing a new dimension to the role of service agents. Personalization will reach new heights, moving beyond current practices where users are grouped by company or type. AI will enable support tailored to individuals within a company.
For example, when a customer submits a query, AI can recall their previous interactions and either connect them with the same agent or predict their next question based on similar cases, resulting in a more enriching customer experience.
Efficiency will also see a significant boost. GenAI can suggest the next best action for customer service agents. AI helps customer service agents focus on complex issues by streamlining workflows, reducing resolution times, and facilitating proactive customer service through anticipation of potential issues.
Additionally, GenAI will advance multilingual support, offering solutions and content in various languages, which is vital for a diverse customer base. This will support the democratization of knowledge by consolidating data from sources like customer forums, communities, and support interactions.
The future of AI-driven customer support will hinge on the seamless collaboration between AI and human agents, constantly refining their partnership to deliver exceptional customer experiences. This synergy will lead to more effective resolutions, heightened efficiency, and deeply personalized support, with AI proactively identifying and addressing potential issues before they arise.
Furthermore, AI will foster continuous improvement through feedback loops, gradually enhancing its problem-solving capabilities. This will create a dynamic, ever-evolving support landscape. A well-integrated AI approach will also underpin the development of more precise and timely content for knowledge bases, ensuring that support processes remain agile and responsive.
