Boost Your Auto Insurance Contact Center Efficiency With AI Insights

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!

Auto QA: Automatically Score Every Call

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: 

• Know how each of your agents, groups, or locations is performing at the moment and over time,
• Identify training and coaching opportunities by spotting specific call segments that consistently underperform,
• Determine which of your calls require human follow-up or detailed evaluation, such as calls involving complex issues like disputed claims or escalated complaints,
• Track training effectiveness as you can monitor the improvements (or lack thereof),
• Notify a supervisor when red flags are raised (e.g., an agent evaluation score falls under a certain threshold),
• Monitor script adherence for quality management and compliance, 
• and much more.

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.

Sentiment Analysis: Understand How Your Customers And Agents Feel

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:

1. Customers who call with questions about premiums, payment options, and billing issues get upset because the language in your policy is not clear, causing frequent misunderstandings.
2. Customers are delighted after receiving immediate help after an accident, including towing services and rental car arrangements.

Image: Screenshot of Topical and Sentiment Analysis in the MiaRec Voice Analytics tab.

Topical Analysis: Get Actionable Voice-of-Customer Insights

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:

1. You now have visibility into why customers are calling, and you realize that many of your calls are about customers not having a clear understanding of their coverage details, policy changes, and renewals.
2. You see a drop in calls about claims processing after implementing an online system recently. Customers are now going online to report accidents, check their claim status, and understand the claims process.
3. You experience a sudden spike in quote inquiries after reducing the pricing or offering a new insurance bundle.

AI Call Summary: Cut Down Post-Call Work

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.

AI Insights: Extracting Key Facts In The Format You Need

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:

1. Policy Number: To quickly locate the customer's account and details.
2. Vehicle Information: Make, model, year, and VIN (Vehicle Identification Number).
3. Driver's License Number: To verify the identity and driving history of the policyholder.
4. Accident Details: Date, time, location, and description of the incident.
5. Claim Number: If the customer is inquiring about an ongoing claim.
6. Coverage Details: Specifics about current coverage, deductibles, and limits.
7. Contact Information: Updated phone number, email address, and mailing address.
8. Witness Information: Names and contact details of any witnesses to the accident.
9. Third-Party Involvement: Information about other parties involved in an accident, including their insurance details.
10. Repair Shop Information: Details about the repair shop handling the vehicle's repairs, if applicable.

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.

API Integrations: Push Extracted Key Facts Into Your CRM, Automatically

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. 

AI Coaching: Post-Call Feedback On Every Call

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:

• Observation: You collected the vehicle information but did not confirm the VIN.
• Suggestion: Always repeat the VIN back to the customer to ensure accuracy. This prevents future issues related to incorrect data entry.

Claims Information:

• Observation: The customer inquired about the status of their claim, but you did not provide a specific timeline for follow-up.
• Suggestion: Inform the customer about the expected time frame for updates, e.g., "We expect to have an update on your claim within the next three to five business days."

Cross-Sell Opportunities:

• Observation: When the customer discussed their recent breakdown, there was an opportunity to mention our new roadside assistance package.
• Suggestion: Use prompts like, "Based on your experience, you might find our comprehensive roadside assistance package beneficial. Would you like to hear more about it?"

Customer Empathy:

• Observation: While you provided all the necessary information, a more empathetic tone could benefit the interaction.
• Suggestion: Phrases such as "I understand how frustrating this must be for you" can make customers feel more valued and understood.

Conclusion

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.