Are you struggling with inefficiencies that lead to long call times, missed information, and frustrated customers? Are your agents overwhelmed with the sheer volume of calls and the complexity of data they need to capture accurately? If so, you are not alone.
At MiaRec, we have helped hundreds of contact centers, including many at large insurance companies, leverage AI to enhance efficiency and improve customer satisfaction. We have seen firsthand how AI can transform operations, ensuring that agents capture all necessary information, follow compliance regulations, and deliver exceptional service.
In this article, you will discover ten powerful use cases demonstrating how AI can streamline your agents' workflows, reduce errors, and elevate the overall customer experience. Let's go!
Traditionally, supervisors could only manually score a tiny fraction (usually 2-5%) of calls. Manual scoring creates tons of tedious, boring, and time-consuming work for you as a supervisor, yet it only yields a fragmented picture of what happens in your contact center. With AI-powered automatic call scoring, you can now score every call automatically. This allows you to:
Image: Screenshot of MiaRec's Auto QA capabilities.
Simply translate your current scorecard into an Auto QA form, tell the AI which calls you want to score (e.g., all inbound calls lasting longer than two minutes), and enjoy the actionable insights you get immediately.
In other words, automatic call scoring will give you visibility into 100% of your calls while eliminating the need to do the initial evaluation manually. This frees up your supervisors to focus not only on those calls that require follow-up, but also on coaching and training their agents.
Generative AI can analyze call transcripts for sentiment, allowing you to understand how your customers and agents feel and how those feelings change over time.
On the customer side, you can use sentiment analysis to correlate calls that are consistently scored negatively (unhappy customers or angry agents) with call reasons to identify what upsets customers or where agents might need more training, coaching, and support because they are struggling. For example, you might find that:
Image: Screenshot of Topical and Sentiment Analysis in the MiaRec Voice Analytics tab.
The AI-powered topical analysis gives you a much deeper understanding of why your customers are calling. This can help you create better training material and scripts. However, it is also incredibly helpful to other parts of your business as you can discover improvement opportunities, track the impact of marketing initiatives or pricing changes, and much more. For example:
Auto insurance contact centers can cut two to four minutes of post-call administrative tasks using AI Call Summary to summarize the conversation automatically. Rather than trying to remember everything and make sense of notes hastily taken during the call, the agent can now review a clean summary and tweak it as needed. Cutting down on after-call work time will lead to much lower AHT, call wait times, and call abandonment rates, resulting in better CX and agent experience.
With Contact Center AI solutions that offer an AI Prompt Designer, like MiaRec, you can ask the AI to create summaries that are as structured or unstructured as you need them to be. In addition, because agents don't have to multitask and take notes, they can pay more attention to the conversation, improving customer service.
Image: Screenshot of MiaRec's Auto Call Summary, Call Categorization, and AI Insights capabilities.
Another highly impactful use for AI in auto insurance contact centers stems from its ability to extract key facts from conversations. Although transcribing conversations makes them searchable, being able to extract key facts, such as accident details, policy numbers, and customer preferences, from every conversation ensures that nothing is missed.
For example, you can ask AI to extract the following:
By doing so, AI reduces the burden on agents to record every detail manually, minimizing errors and ensuring consistent data accuracy. AI can categorize and summarize these facts, providing agents with concise and relevant information. This streamlines the workflow and enables agents to provide faster, more informed responses, ultimately improving customer satisfaction and operational efficiency.
For more detailed information on how to extract key facts and push them into your CRM, check out this article.
Image: Screenshot showing how AI can be prompted to extract answers from the call recording transcript on an example in the auto insurance industry.
Once the key facts are pulled out of the call recording transcript and recorded in the required format (e.g., a VIN is always 17 characters, including digits and capital letters), you can have your Contact Center AI solution automatically push the right information into your CRM. This streamlines another crucial step in your agent's workflow. For example, with MiaRec, these key facts can be stored as custom properties that are pushed into your CRM via API integration.
Right after a call is completed, AI-powered post-call coaching suggestions can be used to point out some areas for improvement. For example, a coaching suggestion could look like this:
Verify and Confirm Details:
Claims Information:
Cross-Sell Opportunities:
Customer Empathy:
In the article above, I shared some of the most common and impactful ways to use AI in your auto insurance contact center. However, this is just the beginning. AI will drastically reshape how we interact with our customers in the coming months and years. I encourage you to identify some of the use cases in your organization and start experimenting with AI.
The adoption of AI in contact centers is accelerating at such a pace that these capabilities will soon be expected table stakes. You cannot afford to be left behind. For more information on how to begin and progress in your AI maturity journey, check out our Contact Center AI maturity model and implement a trial to test drive some of the capabilities in your contact center.