How Predictive Analytics Drives Results for Insurance Companies

The insurance industry is partly built on the idea of making accurate predictions: An insurance provider sets premiums based on projected costs from insurance claims. It’s only natural that predictive analytics would find a home in the insurance industry.

For many years, actuaries have used mathematical models to forecast the risk of injury, property loss, and various types of damage. But increasingly, insurance companies supplement traditional approaches with high-powered predictive analytics. This modern approach is a proven way to increase profit margins, boost productivity, increase sales, and provide better customer service. In fact, one study found that life insurance companies using predictive analytics saw a 67 percent drop in expenses and a 60 percent boost in sales.

Indeed, the future success of insurance companies depends on their ability to take advantage of new technology and data sets, according to Deloitte’s report on the insurance industry. In a recent survey report, Willis Towers Watson Life revealed four key factors that life insurance companies rated as driving forces behind the adoption of big data analytics for risk and insurance:

  • Competition in pricing and product development
  • Earnings and profitability
  • Technological innovation
  • Customer relationship management

The same report identified three areas where predictive analytics will likely have the greatest positive impact:

  • Decreasing expenses
  • Increasing sales
  • Increasing profitability

Using Predictive Analytics in Insurance Companies

In addition to providing products, large auto insurance companies must also provide good customer service. They must do this while keeping down labor and other operational costs.

Predictive analytics for insurance brokers can allow auto insurance companies to minimize costs related to customer service by accurately anticipating the levels of customer assistance requests for 30-, 60-, and 90-day periods. Additionally, using these predictions based on historical service and operational data, auto insurance companies can optimally allocate resources and staffing. This can significantly reduce overstaffing while still supporting a superior customer experience.

Predictive analytics for insurance companies can also be used to identify and increase the number of cross-selling opportunities. The legacy approach to cross-selling involves sales representatives offering expanded insurance bundles to existing policyholders. However, this approach typically results in low cross-sell conversion rates, an inefficient use of sales team labor, and decreased customer experience. Using historical sales and customer data, predictive analytics can be used to identify customers who are most open to cross-selling.

Predictive models have been shown to significantly improve cross-selling campaigns. Insurers see higher sales, greater return on labor investments, and improved customer satisfaction scores. That said, predictive analytics for insurance can support greater customer satisfaction in ways that go beyond cross-selling.

Proactive Strategies for Insurance with Predictive Analytics


Today’s customers want companies that can not only meet their current needs but also anticipate future needs. Predictive analytics can help insurance companies approach customers with personalized, well-timed offers based on their likely insurance needs. Importantly, this is a more efficient and proactive approach compared to the traditional approach of relying on direct mail and advertising to bring in customers.

There are also several more general business use cases for predictive analytics in the insurance industry. In marketing departments, predictive analytics can be used to uncover campaign insights using data on product types and demographics. The technology can reveal consumer preferences and the likelihood consumers will engage with different types of messaging. To be sure, the result is an increased return on marketing spend while avoiding costly marketing mistakes.

Predictive analytics can also be used to retain customers before they churn, optimizing customer lifetime value. Using customer data, predictive analytics can identify policyholders who are most likely to churn, with accurate predictions made for various types of insurance products. Because gaining new customers is much more expensive than retaining existing customers, this application of predictive analytics can significantly boost profit margins. In addition, reducing customer churn has also been shown to attract new customers. Satisfied customers become brand advocates for the business.

Converting Data to Insights to Action with Predictive Analytics

By making predictions using large amounts of data, predictive analytics can help insurance companies with all kinds of business functions, from upselling and cross-selling to underwriting. Let’s break down the predictive process into five distinct steps, based on using a powerful, user-friendly platform like Pecan.

  • Data connections. Predictive models can easily be set up to integrate with existing data infrastructure. If companies are already using Salesforce, Snowflake, Hubspot, or Microsoft SQL Server as part of their analytics operations, adding predictive analytics into the mix is simple.
  • Template-based AI data set creation. The Pecan platform allows users to apply predefined templates based on impactful business use cases. Templates from our growing library pull together relevant data with the help of ready-made data connectors.
  • Automated model optimization. Users select a predictive model based on their data and use case. Data analysts and business stakeholders then direct the system to generate the desired type of predictions.
  • No-code integration of predictions into business workflows. Predictions generated by the models then flow to business systems — such as CRMs, ERPs, and other databases — where they inform decision-making.
  • Ongoing usage and feedback. After a predictive analytics system is deployed, business objectives and data usage may change. Ongoing monitoring and model refinement will help a predictive analytics system to maintain its significant value.

Optimizing Insurance Company Operations with Pecan AI

Our low-code predictive analytics platform levels up insurance companies’ existing analytics capabilities, allowing them to get even more value from the data they already collect.

Instead of hiring a team of data scientists, an insurance company’s in-house data analysts and business professionals can easily build models. Then, they can quickly deploy them using their SQL skills and analytics knowledge, without additional advanced training. As a result, our insurance clients quickly put predictions to work and rapidly see a return on their investment.

Contact us today if you would like to learn more about how Pecan can quickly boost your insurance company’s KPIs through the power of prediction.

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