ai for customer support

Customer support has become increasingly important, with 88% of buyers saying the experience a company provides matters as much as its products or services. About 72% of customers demand immediate service and nearly 70% expect anyone they interact with to have full context. However, this level of customer care is expensive, leading business leaders to look into AI for higher cost efficiency and hopefully high service levels.

AI is not a magic pill, and most bot interactions still end up with consumers requesting to connect with a human agent. However, conversational agents are becoming more natural and human-like, while consumers are becoming more open to interactions with AI if it allows them to get quick and high-quality service.

We believe that customer experience is one of the most fruitful areas for the application of artificial intelligence. Through machine intelligence, we can gain a deeper insight into customer needs and deliver consistently amazing experiences at a reduced cost. 

Predictive AI and Generative AI in Customer Support

Predictive AI has been enhancing customer support for some time now, offering advanced analytics, feedback analysis, and resource allocation. The rise of generative AI technologies with next-level conversational AI agents is taking customer support to new heights.

Predictive AI applications aim at reducing costs of customer support and improving customer experience through:

  • Automated ticket routing. Using predictive analytics to automatically route customer tickets to the most appropriate support agent based on past performance and expertise.
  • Resource forecasting. Predicting the demand for support resources at different times, enabling better staff allocation and reduced waiting times for customers.
  • Issue prediction. Predicting common issues or questions customers might have, allowing for proactive measures to resolve them before they escalate.
  • Churn prediction. Identifying customers who are likely to churn, enabling timely intervention to retain them.
  • Lifetime value prediction. Predicting the lifetime value of customers to prioritize support and resources accordingly.
  • Predictive maintenance. For products that require maintenance, predicting when maintenance is due or when a failure is likely to occur, ensuring timely support, and minimizing downtime.

On the other hand, generative AI can improve the effectiveness of your customer agents and reduce their workload by powering:

  • Advanced conversational agents. Creating advanced chatbots and virtual assistants capable of engaging customers in natural, meaningful interactions to resolve inquiries or provide information.
  • Knowledge base generation. Generating and continually updating knowledge base articles or FAQs based on common inquiries and evolving customer needs.
  • Internal search tools. Boosting internal search tools with generative AI to provide more accurate and contextually relevant results when support agents or customers search for information within a knowledge base or support portal.
  • Automated summary generation. Summarizing lengthy customer interactions or feedback for easier analysis and follow-up by support agents.
  • Predictive typing. Assisting support agents with predictive typing, making the process of responding to customers faster and more efficient.
  • Response drafting. Assisting support agents by drafting initial responses to customer emails, saving time, and ensuring consistency in communication.
  • Automated response generation. Generating responses to customer inquiries based on historical interactions and the contextual understanding of the issue at hand.
  • Response personalization. Creating personalized content and responses based on customer data to improve engagement and satisfaction.
  • Script and training material generation. Creating scripts and training materials for support agents based on common scenarios and evolving customer service protocols.

Predictive AI excels at boosting productivity through task automation and advanced analytics, while generative AI enhances customer support by empowering human agents to provide quick, relevant, and personalized assistance to customers.

Now, let’s delve deeper into conversational agents and AI-powered contact centers as the most prominent examples of AI application in customer support.

Conversational Agents

Chatbots for customer support have been around for a while, but they could only handle the most basic service requests until recently. The latest advances in large language models (LLMs) capabilities have revolutionized customer support applications, as LLM-powered bots can now handle much more complex conversations than their predecessors. However, we should not expect generative AI to fully replace customer support agents in the near future. The technology is not yet reliable enough and may produce factual errors, which we cannot afford in direct communications with customers.

Generative AI methods will likely be combined with predictive AI and other software methods to deliver complete solutions for basic requests and assist human agents with more complex requests. For example, conversational agents can directly answer FAQs, authenticate customers by asking a series of security questions, and detect customer intent to route inquiries to the right human agent. Additionally, they can help customer support agents provide faster and better service by summarizing long customer requests, drafting responses with the consideration of past interactions with a customer, and translating requests and responses into various languages to provide multilingual support.

Conversational AI agents can be implemented in a variety of ways, from building custom LLM-powered agents from scratch to using ChatGPT-like service as-is. Most businesses seek a balanced solution that offers good performance, sufficient control and transparency, and meets their budget. Two common approaches are:

  • Selecting a pre-trained language model, proprietary or open-source, and fine-tuning or augmenting it with an internal knowledge base for better and more reliable performance.
  • Partnering with AI companies that specialize in developing and deploying conversational AI agents and can provide businesses with access to the latest technologies and expertise. Some examples of these solutions include Amazon LexIBM watsonx Assistant, and LivePerson.

The best approach for a particular business will depend on its specific needs and resources.

Contact Centers

When we talk about AI powering customer support, it goes far beyond chatbots. The latest advances in text-to-speech and speech-to-text AI models have enabled a wider range of AI applications in contact centers, where AI is now deployed to handle not only written requests but also customer calls.

Solutions like Amazon ConnectContact Center AI by Google, Cresta, and Poly AI claim to significantly increase customer satisfaction scores and decrease average handle times by offering 24/7 assistance via multiple channels. For example, Poly AI claims that its assistants can handle up to 50% of incoming calls. They can authenticate callers, let customers make payments over the phone, handle bookings and reservations, answer FAQs, help customers track orders and reschedule deliveries, and guide callers through troubleshooting and tech support processes – all through natural conversations and in multiple languages.

For cases when a call cannot be handled by a voice bot, AI offers multiple solutions to boost the productivity of human agents by optimizing call routing, eliminating after-call work through automatic note-taking and summarization, and rapidly surfacing the internal knowledge base to suggest solutions even for the most complex cases.

AI is already having a significant impact on customer interactions, and as it continues to develop, we can expect to see even more innovative and effective ways to deploy AI for customer support.