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Data is lifeblood for Insurance Providers

Any actuary will tell you that data is a non-negotiable asset in the insurance industry. The right data — combined with advanced techniques for applying it — can make the difference between major profits…or an event with major costs. But data isn’t only essential for delivering coverage, it’s needed for every aspect of a successful insurance company’s success. Here’s the lowdown on the power of data and predictive analytics in insurance and the distinctions to make when targeting clients vs. prospects.

Insurance Marketers are Data Super-Users

Data is highly critical for helping insurance providers better serve their clientele, prospective customers, and even internal stakeholders. This includes every step of the customer journey, from discovery to an ongoing relationship. 

Data has been shown to improve marketing team efforts, and in many cases, has become table stakes in a highly competitive industry for a wide variety of use cases:

  • Broaden prospect universe
  • Manage media buying, bidding, and data costs effectively
  • Personalize content to generate better performance
  • Acquire highly profitable new leads
  • Accelerate the customer digital buying experience
  • Effectively identify new leads prior to under-writing
  • Optimize offers based on prospect intelligence
  • Develop new products or partnerships based on current customers
  • Predict consumer behaviors in new categories or markets
  • Provide value to current customers in communications

In a KPI-driven industry like insurance, the expectations are sky-high. ROI reports need to be amazing, and costs must be accounted for. Data and predictive analytics, like other tools in a marketer’s toolbox, are no exception.

Data’s role in optimizing lead acquisition and customer retention

Lead acquisition efforts include everything from identifying the right target to customizing the communication to drive the desired outcome. But it doesn’t stop there. Fighting churn requires ongoing efforts to deliver value to current clients every day.

Identifying your target. 

In most cases, identifying the best targets begins by analyzing your prior leads that are the most profitable. This is obvious. Insurance marketers do not want leads that will only click. They want ones that will actually apply. 

By looking back at your most successful leads in the past, providers can identify more prospects that look like them. This is where the potential of predictive analytics in insurance really kicks in.

If you are releasing a new product or feature, potentially expanding your target market, using data becomes even more essential.

Your product can now do more and serve more people, so your prospect universe should also grow and change. The use of predictive data to learn more about a new segment of prospects is key.

Optimizing spend across channels

Channel selection

Better media plans begin with better data. That is a challenge when consumer media consumption behavior continues to evolve. Channel preference varies widely not only across life stages and income levels but even from user to user. Correctly identifying if someone prefers email versus direct mail, or CTV versus linear TV, can help with high-level brand spending to maximize reach against high-value targets.

Bidding Optimization

On the flip side, using data for bidding optimization can help you identify areas to save on media spend. Using data, especially lead scores, to inform bidding strategies has slowly become more and more common. Essentially data informs how “hot” a lead may be, enabling the bid to match. 

Example: If the prospect has a lower lead score, potentially because they may be unlikely to apply, the bidding engine can bid a lower value on the media. If another user with a high lead score becomes available, the bidding engine could bid much higher.

The impact is a more efficient bidding strategy overall. You don’t overpay for lower-value leads, but you don’t miss out on bids for high-value prospects either.

Personalizing Creative Messaging (Prospects and Customers)

Personalizing Prospect Content

Once you identify the right target and have purchased media, all efforts will fail if your message is wrong. 

Access additional data points to personalize the message based on their interests, their motivators, and even their household makeup. Consumer datasets help paint a robust picture of prospects so that messaging feels relevant and practically “kismet”.

Personalizing Customer Content

Have you heard the expression “When someone tells you who they are, believe them”? 

This is absolutely true for marketers as they create personalized communications. Especially for current customers, they become frustrated when companies treat them as if they are anonymous numbers

Structuring your first-party data in a way that is both meaningful for communications and accessible for tactical use is the first step. 

The second is appending or enhancing these records to fill the gaps. By bringing more data and predictive analytics into the equation, insurance providers can anticipate the needs of their best customers. 

Example: Sally Brown just got married. She updated her information with her car insurance provider (Company A), but she is still getting communication with her maiden name listed. Unfortunately, their systems have a long cycle to update across data silos. 

Her home insurance provider (Company B) reached out, using her new name, to offer a great bundle deal with home insurance and TWO vehicles included. The message feels relevant and timely as Sally is now shopping for herself and her spouse. This leads Sally to cancel with company A and switch coverage to Company B.

Staying current by bringing multiple data types together

Stagnant data isn’t helpful. It’s safe to say that the customer you sign today may not be the same customer tomorrow, next month, or even next year.  With a current and complete view, you can strike when the iron is hot and proactively reveal opportunities that add value to your current customers as their lifestyle changes.

Cross-channel linkage data can empower better matches to additional marketing data points. This allows you to fill in the gaps where necessary, linking all the data points you need to identify new and relevant opportunities for your clients. 

Treating B2B differently than B2C

Marketing to consumers who are making decisions for their families is obviously a completely different animal than connecting with business owners or leaders. But these two tactics are alike in one way. They are still both targeting people making decisions. 

B2B playbooks from yesteryear indicate that businesses are cold entities that make only logic-based decisions without any human preferences or emotions. Basically…robots. 

But anyone working in B2B marketing can tell you that is simply not the case. Different business leaders have their own communication preferences, motivators, and pain points. People make these decisions, not stock symbols or DBAs.

Although both datasets should be at a personal level, B2B and B2C data are quite different. What someone values when bundling their home and car insurance may be completely different than how they select business insurance for their fleet. 

Good is getting data on both B2B and B2C audiences. Great is having unique datasets, but bringing them together for a cohesive view of your prospect universe. How many of your B2C customers own their own businesses? How many of your current business customers are homeowners? Taking a full view opens up cross-sell opportunities and gives you a better view of top prospects.

Lead Identification (Applicable for Business Applications)

Small businesses make up the largest volume of insurance applicants for business insurance. Unfortunately, they also come with the biggest struggles. After successfully driving them to application, your marketing efforts may hit a wall if your products are unable to effectively identify them immediately. 

Example: Bob Jones fills out an application for coverage for his company “BJs Appliance Repair”. Instead of receiving an immediate quote for coverage, he is told that his application requires additional review and that he will hear back within 48 hours.

The company is unable to connect his DBA (“doing business as”) “BJs Appliance Repair” to the legal name for his business, “Robert Jones LLC”. By the time they connect the dots, he has already applied with another provider and secured the coverage he needs.

A robust dataset, which includes the standard firmographics, but also includes DBAs, and personal leadership data is useful to be able to immediately overcome issues with business identification. This is especially critical for small to micro-businesses which are more likely to use the same address for both their business and their home.

Product Development and Feature Expansion

Top innovation and product leaders understand that a quality product roadmap is built on the foundation of solid data. Expanding features, or new options for coverage will be more successful if you can expand in a way that brings your current customers along. 

Analysis of first-party data, appended with third-party data may reveal unique bundle opportunities, additional vehicle types, unique business capabilities which require specialized coverage, and more. 

Example: Insurance Provider C conducted a data analysis and discovered that many of their current customers were highly unlikely to take risks, and were highly interested in domestic travel. They used these insights to develop a new coverage product that rewarded customers with travel rewards for displaying safe driving behaviors.

Data needs to evolve with your customers

It’s key for insurance providers to evolve alongside their clients in real-time. When assessing the prospect or client, providers should consider the following questions: 

  • Which of your customers or prospects are interested in products or competitive offerings?
  • What attitudes and beliefs do those people hold? 
  • Which audiences would be the early adopters of your new product?


The answers to these questions may change as time goes on and obstacles can arise when you’re trying to meet your customer where they are. Predictive data analytics can bridge the gap and help insurance providers stay ahead to better serve their customers as their lifestyles shift.  

Predictive data is table stakes for innovation. Providers that have the necessary data will be poised to acquire new customers and continue to find new opportunities for their clients. 

Data and predictive analytics in insurance at scale

For insurance providers, the goal is to maximize profitability and minimize waste. This is true no matter the type of coverage you offer and whether you are marketing to prospects or customers.

AnalyticsIQ provides the data insurance teams depend on for marketing and innovation use cases. The proprietary predictive data elements are derived from psychologically based research which makes them a unique asset in a highly competitive industry. This technology empowers insurance companies to tap into a wealth of data that is reflective of today’s consumer beliefs, actions, and sentiments. 

If you have a complex use case and need high-quality data to make it happen, reach out! We love collaborating with insurance-focused teams to innovate new possibilities.