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How Insurance Companies Can Find Success with Data Analytics

Mark Kosin
7 minute read

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Mark Kosin
7 minute read

Looking ahead to a new decade, insurance companies have much to consider for 2020 and beyond. Many novel challenges lie in wait, especially concerning the use of technology. As more insurance companies adopt tech solutions for how they run their business, they are also in a position to leverage new innovations to get more value from their marketing. In fact, companies that aren’t able to take advantage of tech in their marketing risk falling behind.

Data analytics has become an especially powerful tool for insurers. Many insurance carriers have seen the impact that advanced data analytics has on their underwriting processes. Using predictive analytics, insurance carriers can model for catastrophe events for property insurance or build algorithms for rating auto insurance drivers. Data science is a necessity for insurance companies who need to calculate risk and gain insights on key business decisions.

The same value added that data analytics brought to the carriers’ ability to underwrite and serve customers can also be utilized in marketing. There are myriad ways that data science and predictive analytics can help companies optimize their sales funnel—delivering a better return-on-investment and uncovering new revenue opportunities.

Insurance companies that understand and implement advanced data analytics in their marketing will put themselves in a position for long-term success.

How Is Marketing Data Analytics Used?

Customer Acquisition Cost

Customer Acquisition Cost, or CAC, is an important metric for insurance companies to focus on. As an average of your sales and marketing spend divided by the number of new customers you acquire, your CAC tells you how much you are spending to get your new policyholders.

Data analytics can help insurers get a clear view of their CAC, showing both how much they’re spending for organic lead generation efforts or the costs from third-party vendors. A performance summary can show how different lead generation and acquisition efforts fare, helping inform decisions over where firms should spend their resources. 

Lead Scoring

Data analytics can also play a role in the scoring of leads. With lead scoring, insurers can determine how close they are to underwriting a policy for a new consumer. This process works by ascribing specific values to different data points for a lead—these values add up to create a “score” for the lead.

You can use these lead scores to create marketing qualified leads and sales qualified leads. This informs your marketing and sales teams when a lead is closest to conversion.

Predictive Modeling

One of the most effective insurance machine learning use cases is predictive modeling. By using predictive modeling, insurers can leverage existing data to create models that will offer predictive outcomes of future marketing initiatives.

For example, a carrier can see the cost/benefit analysis for accepting third-party leads over the weekend or during off-peak hours. By utilizing their own first-party data, insurance companies can make determinations over their strategies for lead generation and acquisition. Other revelations from predictive analytics might include highlighting seasonal upticks from consumers or how spending can change across different demographics and locations.

The data analytics benefits of predictive modeling are not just a way for insurance carriers to keep an edge over the competition, they also provide clear pathways for increased profitability and long-term growth.

Improving Insurance Marketing with Data Analytics

Each insurance company must create their own strategy to best leverage their first-party data using data analytics. Lead management software can help businesses with tools for data analytics to face their marketing challenges and optimize their lead acquisition. Here is a roadmap for insurers who want to take advantage of the benefits of data analytics.

1. Data Capture

The first stage for any business looking to extract more value from their data is to ensure that they are capturing as much first-party data as available. Insurance companies would want to look into call tracking software for inbound calls and lead tracking software for web leads.

Tracking your inbound leads will give you a wealth of data that can be utilized for better understanding your customer journeys and be the first building block for an optimized marketing approach.

2. Reporting & Analytics

Once you have set up the technology solutions to capture your data, the next step is management and analysis of that data. Businesses must determine what metrics are going to be most important for insights into target audiences. Once these metrics are identified, you can utilize dynamic real-time reports and data analytics to spot trends and identify opportunities to earn more for your marketing and lead acquisition spend.

3. Optimization

The final stage is putting your results into action. Using predictive modeling, implement changes and see how well the reality matches the prediction. By using the predictive outcomes as a guide, you can lower your risk on new marketing initiatives.

In order to build a strategy for long-term success, insurance companies must continually capture, analyze, and optimize. The changes in consumer behavior or other trends will be noticeable in the data, which is why data remains so vital for insurers. It ensures that you have strategies in place that reflect the current state of consumer activity. By using data analytics to keep your business plugged in, you can continually attract and acquire waves of new policyholders.

Mark Kosin avatar
Mark Kosin
Mark Kosin was a Content Writer at Phonexa, covering topics such as SaaS technology, call tracking, lead generation, and digital marketing.

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