Reducing Costs Without Compromising Customer Satisfaction Using Intelligent Chatbots

60-70% of calls in loan contact centers are related to inquiries about interest calculations, loan payment date or date changes, and terms-conditions-related questions. There are <20% of calls related to new product inquiries, which will convert to revenue.

For loan contact centers maintaining a lower cost becomes extremely important. It has also been observed that ~60% of customers leave the call if they have to wait more than 2 minutes.

To maintain customer satisfaction with lower wait time and reduce cost, FAQ chatbots are an effective solution. This solution can be used to assess the situation first and then redirect conversations – For example: inquiries-related calls can be answered without delay and calls with revenue possibility can be redirected to the contact center.

This helps in balancing cost and customer satisfaction and can be tuned to organizational needs.

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.