Targeting the right leads
You’re talking to your customers, but are you talking to the right ones?
Sales and marketing teams today, regardless of the industry, rely on data insights to optimize their efforts and develop targeted campaigns to grow the business.
Life insurance is perhaps the original ‘big data’ industry as insurers have always collected tremendous amounts of data about their customers, however, they struggle to effectively work with their data, to understand it and monetize it. Why?
For starters, because insurance companies work with many different systems that are often incompatible and do not speak the same language, they lack access to much of their data. Moreover, they don’t have the tools to properly understand the implication of the data they process. For example, a mailing address is not just mailing address but also implies certain things when insurers consider neighborhood, income, domestic or business, etc. A change in a policyholder’s address is often treated as an administrative matter, however, it could tell insurers so much more. The same goes for bank account numbers, which beyond just payment, could offer additional value when the bank type is considered. Is the bank account a CitiBank account (a general bank)? USAA (ex-military bank)? TIAA (teachers’ union bank)? You could know a lot more about your policyholders.
With an incomplete understanding of who their policyholders are, sales and marketing teams cannot effectively target the most relevant leads – the ones with high up-sale potential. They simply do not know enough about their customers, and as a result, insurance companies often miss opportunities to target specific types of customers that would be more likely to convert.
You can make X number of calls in a day, but how do you prioritize them? How do you know which calls to make first?
Would you try to sell a tractor to a customer living in New York City? Probably not.
Why market blindly? Why try to sell your customers products they might not need? The ability to know which in-force customers to approach with targeted campaigns puts marketing teams at a huge advantage as they are able to focus efforts in the right place and avoid wasting time on less relevant leads.
By taking into account all existing customer data points and augmenting them with relevant external sources, Atidot is able to build a much more accurate and rich view of the customers so it can provide insurance companies with valuable insights about their policyholders.
For example, consider a life insurance company with a book of business of 100,000 policyholders. The question is: who among this customer base needs to increase their coverage? Legacy technology might group the policyholders by age and gender, whereas big data analytics can provide insights that are much more fine-tuned around parameters like address and employment changes. Once they know which customers are likely in need of increased coverage, marketing teams can develop targeted campaigns to convert these specific leads.
The hidden value in your in-force book of business
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. For example, you likely know what makes a profitable customer and how to market to them, but do you know who is buying and becoming profitable after 3-5 years? Atidot can project and uncover valuable insights like these about policyholders for insurers that they simply did not know they had.
Predictive analytics and big data enable much better lead generation and provide companies with a great trigger point for a conversation with a consumer. With Atidot, marketing teams are able to better understand customer insurance behaviour to detect profitable segments of customers such as: which customers are likely to lapse, which customers are more likely to be over or under-insured, as well as which customers are likely to be more responsive to particular types of campaigns. They can then design campaigns accordingly and approach the right leads at the right time.
Atidot is giving life insurers a whole new layer of understanding on how to approach the customer and how to serve and address their insurance needs properly.
Our client, a mid-size Life Insurance Company with a direct marketing and career agent force wanted to independently cohorts within their existing books of business with high-profit opportunities.
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 cohorts with a high-profit potential to market to.
Policyowners were categorized into separate tiers and grouped according to their behavior, insurance needs, potential conversion rates and risk class enabling the Company to have a high-level view of the opportunities available.
The Company, by shifting their marketing efforts to serve the right segments, increased their premium collection for the same resources by 35%
Do you want to see it in action?