When it comes to selling life insurance, insurance companies still rely on personal interaction and face-to-face interaction to achieve sales results. However, this old, outdated model is no longer sustainable for the insurance industry. With the advent of big data, personalized, and automated marketing, life insurance companies are exploiting artificial intelligence to optimize their sales performance.
AI has completely changed the way that insurance companies engage with their customers—from gathering crucial data about their target customers to formulating new sales strategies—AI-based data analysis allows insurance companies to determine the strengths and weaknesses of their existing sales strategies, enabling them to optimize their marketing campaigns. Further empowering concerned companies, AI-enabled chatbots allow insurance companies to interact directly with their potential customers, eliminating the need to hire and train new staff, reducing administration costs, and gaining valuable insights into customer behavior.
So, if you are in the insurance industry, or aspire to be one that beats the competitors, here are five ways AI can help you with your marketing efforts and strategies to excel in the market.
1. Marketing and Sales:
The life insurance industry is steeped in tradition. It has traditionally been a slow-moving industry, with a long sales cycle, and where the products are similar.
However, those realities are changing. Today, technology is having a significant impact on how life insurers connect with consumers, present their products and improve business operations.
AI and machine learning solutions are helping life insurance companies to better understand their customers and predict their behavior. This includes identifying the best sales leads and predicting how they will respond to marketing campaigns.
One key aspect of AI is its ability to automate repetitive tasks and free up employees to focus on more important aspects of their jobs. Data entry and administrative work are good examples of tasks that can be automated using AI. With this in mind, let’s take a look at how AI can help life insurance companies with their marketing and sales efforts in three main areas:
● Lead generation – Insurance companies have long struggled to generate leads from large amounts of data. While machine learning and AI-powered tools have been able to extract useful insights from unstructured data, they also help to automatically generate potential leads by analyzing both structured and unstructured data across multiple sources.
● Sales automation – Machine learning tools can help insurance companies automate their sales processes. By analyzing large amounts of data on claims history, credit scores, demographics, employment status, and other factors, these tools can predict which customers are more likely to buy life insurance policies and target them for marketing campaigns
● Customer Segmentation – Customer segmentation has always been a core part of marketing, but it’s always been extremely hard to get right, especially in life insurance. Customers all have different needs, so they all require different approaches. How do you identify your ideal customers and then reach out to them with a product they want? AI can help by allowing you to divide your customers into groups based on their needs and preferences. This allows you to tailor your outreach strategy so that it resonates with each group and ensures that they’re getting information about a product that meets their needs.
● Personalized content – What if you could provide each customer with an individualized experience in which content was tailored specifically for them? Artificial intelligence can make this possible. By learning about the demographics and interests of each customer, algorithms are able to generate content that is likely to be appealing for every single user. This is not only more likely to result in conversion but also creates a better experience for potential policyholders.
2. Underwriting:
Underwriting is a tough problem. Insurance companies have hundreds of parameters that they use in determining who qualifies for what type of coverage. To figure out who gets what kind of policy, they look at everything from where you live to your family history to your hobbies and more. The problem is there are thousands of combinations of these different factors that could be used as inputs into an insurance policy’s pricing model. How do companies know what combination is best?
AI can help optimize pricing models by looking at past data — both from their own company and from other companies — and identifying patterns in what types of customers will likely file claims and how much those claims will cost. This helps them set high enough prices to cover potential costs but low enough to attract customers who want affordable policies. This benefits everyone:
- Customers get lower prices.
- Companies make bigger profits.
- Regulators don’t have to worry about the company going out of business because it underpriced insurance.
By using automation, and running a consumers Medical Information Bureau report, Prescription Drug History, Motor Vehicle Report, and Credit Score, underwriting decisions may only take a few minutes. Historically, an underwriting decision would take a month or longer. Using automation can even help people with chronic illnesses qualify for coverage.
Charlie Fletcher with Diabetes 365 shares the following “Certain insurance providers are experimenting offering life insurance for diabetics using automated underwriting. As an example, we’re seeing specific life insurance carriers be able to offer $1.5 million in coverage to a type 2 diabetic in less than 10 mintues.”
3. Personalized Insurance Product Development:
Talk to a modern insurance agent about why someone might want life insurance, and you inevitably hear the word “security.” That’s because, for decades, life insurance has been sold with the promise that it will provide a financial cushion for family members in the event of an unexpected death. It’s the classic security blanket selling point: buy something now that will make it easier for your family to get over a tragic event in the future.
But what if people don’t want security? What if they don’t even believe they need it? And what if they can get all the benefits of life insurance without having to pay any premiums? Those are some of the challenges facing today’s life insurers. That’s why many of them are turning to artificial intelligence (AI) to help them rethink their product development and distribution strategies. By enriching AI with customer data, life insurers can personalize their products for specific segments. They can also use AI to predict sales and trends with greater accuracy—a technique they call predictive analytics to forecast future sales and bring in more business.
4. Conduct Initial Interactions with Potential Clients:
AI can be used to help life insurance companies with their customer engagement. The customer acquisition journey for life insurance companies can be difficult and expensive. While it is important for customers to be well informed about the products available, human interaction can sometimes lead to confusion and distrust. When consumers speak to representatives from insurance companies, they are often met with a hard sell and pressed into making a purchase. To solve this, chatbots can automate the initial interactions for consumers looking for insurance and allow them to pick a product that suits them.
Chatbots are also valuable for handling repetitive tasks, particularly when it comes to booking appointments or rescheduling them. They can also be used to handle large volumes of data such as claims information and assist agents in dealing with individual cases.
Establishing emotional intelligence is another area where AI has been effective in helping insurers connect with their customers. Understanding different personality traits and emotions can help companies provide the best service possible by tailoring the experience to each customer’s needs.
5. Mortality Reserving:
Mortality reserving is one of the most important processes in life insurance. The calculation of the future costs associated with a current policyholder’s death, or mortality reserve, can significantly impact an insurer’s bottom line. If the reserve is too low, the insurer will be under-prepared for future claims; if it’s too high, the insurer won’t be profitable.
The traditional actuarial approach to reserving is based on past mortality experience and can be improved by using a more advanced model that incorporates external factors that impact mortality risks, such as customer health and lifestyle habits.
These models are known as “mortality tables” and are used to calculate premiums for consumers based on their age, gender, and overall health.
Unfortunately, traditional mortality tables are not always accurate because they rely on actuarial assumptions and historical data collected by insurance companies over several years.
This means there may not be enough data available to accurately predict the future with these methods, which makes reserving difficult for insurers who want to remain competitive in today’s market while still being profitable at all times.
One way that AI helps life insurance companies with mortality reserving is by incorporating external factors into their models so they can more accurately predict future costs associated with claims payments based on an individual’s lifestyle and health.