How Customer Intent Prediction Works
Conducting intent analysis involves several steps. To better understand it, we've broken it down into smaller steps:
Data Collection: When a customer interacts with the insurance company, their verbal or written input is collected. For voice interactions, this involves transcribing speech to text using an audio transcription service.
Text Processing: The collected text data is then processed to clean and normalise it, ensuring that the text is in a format suitable for analysis.
Intent Classification: NLP models analyse the processed text to classify the intent. The AI model can be trained to recognise various intents such as claims, policy inquiries, premium payments, etc.
Actionable Insights: Once the intent is identified, the system can route the customer to the appropriate department or provide automated responses, enhancing efficiency and customer satisfaction.
In the diagram below, we provide an example of how AI can be integrated at various touch points into a customer service workflow in an insurance company.
![Monday](/static/media/customer_service_ai.fe6c69b432fcc9299dca.webp)
AI integration diagram
The process starts when the customers initiate contact through either phone calls or chat interfaces. When a call is received, the spoken interactions are transcribed into text by an AI-powered audio transcription service.
Using AI models, for example, Hugging Face's DistilBERT, the transcribed text is analysed to determine the intent behind the customer's query. For instance, phrases like "I had a car accident" are classified under "Car Insurance / Accident," while "Email me my life insurance policy" falls under "Life Insurance / Email Policy."
Based on the classified intent, the system can automatically route the customer to the relevant department or specialist. In the example, the customer who had a car accident is directed to an auto insurance specialist who can address the claims efficiently. The AI models ensure that the responses are tailored to the specific needs of the customer, providing a more personalised and satisfactory experience.