Cost Reduction Without Sacrificing Customer Satisfaction Through Intelligent Chatbots

Insurance contact centers face a very different type of call mix compared to other businesses. It often involves the health of family members in dire situations and hence tolerance for delays and servicing lags is very minimal. Customers may often get strong biases in such situations leading to attritions. Customers in such cases like to have minimal hold time and resolve their queries.

Many calls are directly revenue-related as well as critical to the customers like claims settlement, renewals, cancellations, and billing. But a fair percentage of calls are related to claims status, illness coverage, network hospitals, account management, and policy change-related questions which can be repetitive and definitive.

The contact center costs in these cases can be lowered by first automating the FAQs that are standard and the same for all customers. This also helps in lowering hold time for customers. In case of revenue-related questions, customers can still be redirected to the contact centers.

This solution offers a balanced approach toward managing cost and customer satisfaction in servicing.

This solution is a conversational AI bot designed to answer queries from a knowledge base, such as an FAQ document and rule based training. It retains previous interactions and uses the conversation history to generate subsequent messages.

This can become the first interface that customers use to state their problem. For common repetitive problems the chatbot will be able to solve the customer query while it will also have the intelligence to redirect queries to contact centre in case of complexity.

A simple interface that can have long term impact to cost and customer satisfaction.

This solution caters to organizations that:

1. Operate chat services with agents and aim to lower the cost of servicing.
2. Lack chat services but handle a high volume of calls from customers with simple inquiries.

The business benefits include:

1. Cost: This solution reduces the manual time agents spend chatting with customers.
2. Faster & accurate answers: This solution minimizes manual errors in chats and delivers faster, more accurate responses to customers from extensive document sets.
3. Customer Satisfaction : Reducing the wait time for simpler questions results in customer satisfaction, while customers also get to the contact centre in case of complex questions.
4. Data management: It standardizes the data collection process by generating standardized questions and capturing option-based or open-ended answers from customers, thereby boosting data analytics.

This solution requires documents to be in digital format for model training. For non-digital documents, optical character recognition (OCR) solutions can be utilized to convert them to digital format.

This solution is built on top of the Azure AI Studio infrastructure and is powered by Large Language Models (LLMs) using the OpenAI API or locally deployed LLMs.

1. On prem ( on customer systems or on DeepQ-AI Environment)
2. On hosted cloud space ( Customer or DeepQ-AI Environment)
   ( deployment is subject to data availability in the same environment, or feasibility of seamless data transfers within secured environments)

1. Final outputs could include an application that can be hosted on the desired platform.
2. Outputs can be shared through API requests. By sending an API request with inputs, you can receive a response. This can be integrated into an existing application or a separate web app can be created.