Customer Service Augmentation or Deferral?

In recent years, the use of chatbots in customer service has become increasingly popular. One of the more interesting use cases for Retrieval Augmented Generation (RAG) chatbots is to help in customer service, which can be achieved through two main techniques: augmentation and deferral.

Augmentation in Customer Service

Many companies are actively building chatbot projects to augment customer service interactions. By arming customer support agents with a RAG chatbot containing the complete set of vectorized knowledge relevant to helping customers, agents can be more effective in their roles. This technique is called augmentation.

Augmentation chatbots need to be designed carefully. They should be treated like a Content Management System (CMS) and use techniques like Transactional Knowledge Management to allow senior staff to add, update, or correct information in real-time. These chatbots also need to be low latency and provide clear and concise answers without any additional context, as the agent is just looking for an immediate answer. Latency will be a big driver of user acceptance of these tools.

Deferral in Customer Service

On the other hand, deferral involves having a chatbot handle all the lower difficulty questions to prevent an agent from needing to talk to the customer. Deferral chatbots need to be designed for friendly, longer, detailed answers to questions and can be designed to prompt for additional information to ensure a better understanding of the user’s request.

Deferral chatbots should also escalate to a human agent if the question returns no high-scoring results in the vectorized knowledge base or if it detects unhappy responses from the customer. This ensures that customers receive the help they need when the chatbot cannot provide a satisfactory answer.

The Value of Both Techniques

Both augmentation and deferral techniques have value in modernizing customer service for any organization. Augmentation allows agents to be more effective by providing them with the necessary knowledge, while deferral reduces the workload of agents by handling lower difficulty questions. However, these techniques need to be designed differently to ensure their effectiveness.

Conclusion

In conclusion, RAG chatbots offer a versatile approach to improving customer service interactions. By leveraging augmentation and deferral techniques, organizations can modernize their customer service and improve the overall experience for their customers. As technology continues to advance, we can expect to see more innovative uses of chatbots in various industries, further revolutionizing the way we interact with customer service.

• Human Intervention: None

Facts Used:

• • One of the more interesting use cases for RAG (retrieval augmented generation) chatbots is to help in customer service
• • Many companies are acitvely building chatbot projects to either augment or defer customer service interactions
• • Agumentation is arming customer support agents with a RAG chatbot with the complete set of vectorized knowledge relevant to helping customers, allowing them to be more effective.
• • Deferral is having a chatbot handle all the lower difficulty questions to prevent an agent from needing to talk to the customer.
• • Both techniques have value, but need to be designed very differently.
• • Augmentation chatbots need to be treated like a CMS and use techniques like Transactional Knowledge Management to allow senior staff to be able to add/update/correct information in real time.
• • Agumentation also needs to be low latency and provide clear and consice answers without any additional contex, as the agent is just looking for an immediate answer. Latency will be a big driver of user acceptance of these tools.
• • Deferral chatbots need to be designed for friendly, longer, detailed answers to questions and can be designed to prompt for additional information to ensure a better understanding of the users request.
• • Deferral chatbots should also escalate to a human agent if the question returns no high scoring results in the vectorized knowledge base or if it detects unhappy responses from the customer.
• • Overall both tools are useful in modernizing customer service for any organization.