The 2,600 P&C insurers in the United States wrote net premiums totaling $456 billion in 2012 alone, [CSM1]  according to the Insurance Information Institute, a figure that represents millions of quotes collectively. For most organizations quote data is, at best, siloed, and, at worst, discarded – resulting in a lost opportunity for valuable business insight. And, as P&C insurers seek a competitive advantage in a challenging market, analytics have played an increasingly vital role in carriers’ ability to compete profitably. While insurers have worked hard to analyze customer, claims, and profitability data to effectively manage their enterprises – quote data largely has been ignored.

From gigabytes to terabytes, providers collect troves of data, often times without the knowledge of how to maximize the value of that data. Quote data often is disregarded due to the sheer volume of data being collected and the complexity of building an environment to analyze it. However, for insurers to remain competitive and increase profits and productivity, they must not overlook the benefits quote data can offer.

Insurers that make the most of quote data can realize several important benefits:

  • Product/Pricing Strategy. Careful analysis of quote data can yield important visibility into the effectiveness of a P&C insurers’ product and pricing strategies. For example, an insurer might discover that its agents are writing quotes for entry-level policies in regions with large populations of recent immigrants, but are not binding the policies. Careful analysis of this trend might reveal that pricing is too high or the products offered do not meet local market needs.
  • Expense Control. Quote data analysis can support various expense control initiatives. For example, by proactively managing attrition after endorsement quotes and renewal quotes, insurers can increase policy renewals and reduce underwriting costs.
  • Cross-Selling and Up-Selling. Using quote analytics, an insurer can readily identify instances in which a policyholder requests a quote for adding a new driver to a policy, but then never follows through with a policy amendment. With this important insight, the insurer (or agent) could follow up with the customer in a timely manner to resolve the issue and ensure adequate coverage.
  • Fraud Identification. Analyzing quote data can enable insurers to quickly determine whether multiple individuals are requesting quotes on the same asset, which could point to fraud. In addition, analyzing quote data can help to identify agents involved in rate evasion.
  • Agent Management. Quote data can reveal important insight into the performance of an insurer’s agent base, such as the number of quotes generated and quote-to-bind ratio for specific agents, offices, and even regions. Equipped with this information, an insurer can address the issue and potentially adjust the agent’s contract if a binding rate threshold is not met.

So how can insurers take the next step to achieve these powerful benefits? As previously mentioned, often times insurers feel the volume of data collected is too large a hurdle to overcome. It can be difficult for providers to determine what quote information is valuable and what they can discard. These challenges are surmountable with a modern analytics environment that enables a holistic view of enterprise performance and provides insurers with accurate data in a timely and cost-efficient manner.
To best analyze the massive amount of quote data insurers collect, they must ask themselves the following questions regarding their analytics environments:

  • Can our environment support an enterprise approach to analytics? Enterprise information, such as quote, claims, profitability, attrition and risk data cannot be analyzed in isolation, as there are complex relationships between these factors. To expand business insight, insurers require the ability to explore these complex relationships across the enterprise and the flexibility to analyze them at various levels.
  • Do we have a comprehensive, industry-specific, unified data model? The data model is one of the most critical parts of any successful analytics initiative. Firms can save considerable time and costs with a commercially available data model. However, it is essential that insurers seek a model that is purpose-built for the industry and incorporates the vendor’s vast experience in the sector.
  • Is our analytics environment easy for business managers to use, and does it deliver the information they need? Analytics solutions must allow line-of-business managers to rapidly create their own queries and reports – without relying on the IT team for support
  • Can our analytics environment handle many different types of information? The environment must be able to handle unprecedented data volumes and integrate data – structured and unstructured – from various enterprise systems such as quotes, policy administration, claims, customer services, and financials.
  • Does our analytics solution provide pre-built statistical models that can be managed centrally and reused across the enterprise? Pre-built models save time and reduce costs, and firms can have confidence that they are industry proven. Similarly, the ability to build a statistical model once and reuse it many times allows firms to realize important efficiency gains and mitigate risk.

Today’s P&C insurers are eager to embrace change and think creatively as they seek new ways to achieve a competitive advantage in a challenging business climate. No stone can go unturned, and this includes cultivating untapped sources of business insight, including quote data. And, as technology evolves and companies – including insurance providers, begin to embrace big data, the powerful benefits of quote data and an enterprise-wide environment that can accommodate this data is irrefutable.
This blog has been reprinted with permission.

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