AI-powered automation in customer support has gained massive popularity over the past few years. Thanks to advancements in AI and ML, companies can now streamline their customer service operations. Plus, they can greatly enhance the customer experience. Tools like chatbots and virtual assistants make an agent’s life easier. They provide partial answers to frequently asked questions or make troubleshooting common problems easier.
However, to make automation efficient and take advantage of customer support capabilities, the team of developers needs to work thoroughly on the database. This is the basis of ML training, which ensures that the bots’ performance will be in line with our expectations.
Let’s take a look at how automation happens and how to take full advantage of its performance.
To answer customer questions, bots or virtual assistants need a database from which they will retrieve answers. Whether you build an assistant based on open space data or only on the documentation of a specific company, you always start development from data collection.
A virtual assistant that recognizes customer intent and suggests possible answers based on historical data from customers draws data from datasets to perform this task. An example of this could be a generative AI chatbot https://cosupport.ai/ It is built on business data. During the training phase, it analyzes historical data of similar inquiries and historical communications and provides similar responses to the ones you have given in the past.
So, what exactly should be in the knowledge base to successfully train a chatbot for further performance?
Important Elements of Training Database
The training database should be broad and diverse. It must be carefully structured and labeled correctly for the bot to have the desired performance going forward. The following are the basic components you need to prepare before building an AI model:
- customer contact data, You should collect all the historical data on previous interactions with the customer.
- Detailed information on product or service, If you are building a bot to answer questions related to a particular company’s products and services, the answers should be based on the company’s specific services.
- Answers to frequently asked questions, Prepare a list of frequently asked questions based on past experience.
- relevant data, This usually includes previous conversations to suggest a personalized response to the customer.
- company policies, All company information helps in giving accurate answers to the customer (for example, delivery dates, return policy, etc.)
The rest will depend on the business case and the specific ML task.
Accuracy is probably the most important criterion based on which you should evaluate your dataset. Since you work with large datasets and large amounts of information, some strategies will help you structure your information and make it as comprehensive as possible.
content standardization
Make sure all the data you collect is in the same format. It should be concise, detailed and classified. Your data used for model training should undergo data labeling. Standardization in understandable language will allow machine learning algorithms to better understand and retrieve content.
Tagging and search functionality
Amazon It also underlines the importance of preparing data before starting ML model training. After collecting and labeling, add tags and keywords for search function optimization. In the case of intent recognition, the list of keywords should be relevant to the search criteria customers typically use to find the information they need. Additionally, metadata will also be effective for effective information retrieval by AI tools.
regular updates
The performance of AI-based tools is enhanced with deep learning techniques. This means that they continue to “learn” by performing and can be continuously improved. It is important to keep your dataset updated with relevant information on products and updated policies. Ultimately, this will affect the performance of the AI ââmodels and the responses customers get from your virtual assistants.
seamless integration
Although virtual assistants typically integrate with CRM tools via API, it is essential to ensure seamless integration of all tools and knowledge bases for optimal performance. The assistant should have uninterrupted access to the company’s knowledge base during interactions with the customer. This will allow immediate and accurate responses to be provided.
A well-structured knowledge base empowers both your AI tools and your human agents to provide exceptional customer service. By prioritizing clear, concise content, robust search functionality, and up-to-date information, you ensure a seamless experience for both customers and AI.
Checking content regularly or maintaining some type of audit ensures it remains fresh and up to date. Thus the tools will be used to address the most relevant customer issues and questions. Incorporating effective tagging navigation models into the knowledge base helps them find the right information quickly. Ultimately, training your AI with quality data is essential to its success. A well-structured training dataset that includes a wide range of customer questions and accurate responses will ensure that your AI system can handle different scenarios effectively.
By following these simple steps, you will increase customer satisfaction and also improve the performance of virtual assistants. In the long run, you will reap the rewards of your operational performance.
