Some of your policyholders are likely to leave you, but do you know which ones?
Probably not. There are hundreds of different reasons why policyholders lapse their life insurance policies, but it is very difficult for insurers to know which policyholders are headed toward lapsation or the reasons for such.
Sure, insurance companies can try to attribute lapse to some of the more obvious reasons such as under-insurance or over-insurance, but the data they have about their policyholders can tell them so much more. It’s just not accessible to them.
That’s right, insurers have tons of information about their customers, but it’s all scattered throughout different systems, siloed, and unusable. The data is there, but insurance companies struggle to leverage it to drive business decisions and are unable to monetize it properly.
Identifying lapse: know who, why and when
Being able to recognize when a policyholder is likely to lapse his or her policy, and knowing the reasons for and timeframe of potential lapse enables insurers to approach customers strategically. For example, the ability to identify cases of lapse is critical for proactive retention strategies. With timely insight about a customer who might be nearing lapse, insurers can target individual customers with personalized products, up-sale or cross-sale, which would likely prevent them from leaving.
Insurance companies today understand that the more they know about their customers, the greater their ability to sell them relevant products that they want and need, which of course translates into greater profits at the end of the day.
Leveraging predictive analytics: understand what is driving customers to lapse
With Atidot, insurers can predict policyholders’ behavior, can proactively identify potential policy lapsation, and can build an appropriate strategy to address this.
Atidot augments customer data with public, open source information, and can predict insurance-specific behavior based on big data analysis and trends. Atidot developed an algorithm that automatically scans all data resources and pinpoints relevant insights to leverage business opportunities. The solution enables insurance companies to know their customers better – and to know precisely who among them will potentially lapse in order to develop plans to increase customer satisfaction and retain profitable customers.
Uncovering the embedded value in your book of business. Let the data tell you why customers are lapsing.
Insurance companies know what’s going on above the surface when it comes to their customers, but not many know what’s going on beneath it. Atidot uncovers valuable insights about policyholders for insurers that the carriers simply did not know they had.
Our client, a mid-size Life Insurance Company, is selling life product to the mid-market. Focused on direct channels and career agents, the Company wanted to project accurately their lapses and to retain profitable policies.
The Company undertook to complete a 90-day program which covered on-boarding of data, defining a business goal, adjusting the model to carrier’s needs, validating the model compared to internal benchmark and historic results, preparing the model for the field test, gaining feedback and adjusting the model accordingly.
The Company’s policy data was augmented with external data (open source, financial and address based data) and Atidot’s predictive model to detect policy owners who were in high lapse potential and their potential reaction to various preservation methods.
Profit assumptions were built by grouping the population according to their policy, the market in their engagement with the policy (cash value, loan amount, premium payment patterns) enabling the Company to have a high-level view of the opportunities available. Leads were then distributed to their relevant channels, and feedback/results were fed back into the platform to allow the model to adapt and become more precise.
The company had doubled the accuracy of their projections by cutting the margin of error in half. Atidot helped the company to understand behaviors with strong correlation to lapse based on purchasing patterns, loans, suspensions, occupation categories and more.
Do you want to see it in action?