Conversational AI for customer service: Redefine how you support customers

The first step in any strategy is to determine what you want to achieve. Is it to reduce response times, improve customer satisfaction, or increase efficiency? Maybe it’s all of the above. Regardless of what your specific goals are, it’s important to understand your customers’ expectations and pain points so you can tailor your AI solution accordingly.

Most brands already have the data they need to fuel their AI. A good place to start is to review historical customer service interactions to identify frequently asked questions and common issues.Then, group them into categories, such as account inquiries, technical support, and billing questions. From there, you can rank your use cases based on factors like frequency, complexity, and impact on customer experience.

We already mentioned the key features and capabilities you should look for in your conversational AI platform. Being clear on what you want to implement, whether it’s a chatbot or voice assistant, is a good first step when looking for which platform fits your business needs. Other factors to consider include its ability to integrate with existing systems and the extent to which you can customize its content and functionalities to fit your brand.Having a responsive support team can also ensure you use the platform to its utmost advantage.

Developing customer segments and profiles will help your conversational AI personalize interactions from the start. It’s also helpful to consider multiple user inputs and potential responses for each use case, and plan for scenarios where your AI cannot resolve an issue, so they escalate to a human agent.

Now it’s time to collect historical data, FAQs, and relevant content to train the AI model. A well-structured knowledge base ensures that the AI provides consistent and accurate responses. As the volume of customer interactions grows, a knowledge base allows the AI to scale effectively and handle more queries without compromising quality.To ensure the AI can understand and process natural language inputs effectively, it’s best to test it in controlled environments, gather feedback, and refine its responses.

Before launching your AI assistant to your global audience, first test it with a small audience to monitor performance and gather insights. Use this customer feedback and analytics to make improvements. It’s also important to train your customer service agents on how to collaborate with your AI tools to handle escalations.Once your customer service AI assistant is up and running, it’s time to promote it through digital marketing campaigns. Try pop-up messages or banners within your app or website or implement interactive guides that walk users through using the chatbot.

After your customer service AI assistant has been active for some time, it’s time to monitor its interactions and performance. Continuous optimization enables the AI to learn individual customer behaviors and preferences – leading to more personalized interactions.Interaction data can also reveal gaps in the knowledge base or areas where the AI struggles, which allows you to update the knowledge base or refine the AI’s capabilities to handle specific queries more effectively.