How Decision Science delivers granular insights to optimize new business profitability

How Decision Science delivers granular insights to optimize new business profitability

New business sales and the impacts it has on profitability is critically important information for life insurance executives and sales teams. This is particularly true as insurers increase their focus on profitability of new business, rather than pure volume. 

Existing approaches to understanding new business sales create frustrating limitations on the usability and relevance of these insights in strategic decision making. Many of these limitations come from lack of granularity and understanding around how specific pieces of the value chain are performing, and impacting end profitability. 

A combination of rich new data sources and sophisticated technologies enable life insurers to understand both the individual profitability and performance of different distribution channels, as well as their relationship to market trends. Making this shift starts with accepting the fixed limitations of life insurers’ existing new business monitoring practices.

The devil’s in the new business details you’re not getting

Currently, the process of analyzing new business sales is frustrating on a number of levels, both in terms of insights and workflow:

  • For executives, a lack of granularity and detail in the results limits their understanding of what is and isn’t working and their ability to adjust and react in a timely and strategic way
  • For finance and pricing teams, the analysis itself is a cumbersome and resource intensive process due to outdated tools and the heavily manual nature of the process

Because the analysis is time intensive, it’s usually conducted monthly. The timing and level of detail existing analyses provide doesn’t clearly tie the insights to specific customer cohorts or distributors, making it difficult for executives and sales teams to use them effectively. The manual nature of the process prevents finance and pricing teams from conducting it more frequently, or with more granularity.

The limitations of the financial models used in this process are in part thanks to the fact that they are typically built with a regulatory lens. At best they take a distribution channel view which is too high level for sales or customer facing teams to use in tactical decision making. 

Without addressing the limitations inherent in these existing processes, life insurers cannot expect to improve their ability to leverage new business insights and information in a significant enough way to move the dial.

The disconnect between new business and profit metrics

The single most costly disconnect and limitation in this process is between new business insights and insurer’s sales teams. Sales is usually the fastest moving piece of an insurer’s value chain, and if teams can react quickly and efficiently to new information, including competitor movements and customer/market trends, they can have an immediate impact on same quarter results. In order to do this, however, they need specific, timely, and clear insights and information to act on.

Because new business insights aren’t granular or frequent enough to expose particular customer or distributor behaviors, sales teams can’t use them to effectively understand and target particular customers or advisors. This costly disconnect can make a significant difference to a life insurer’s ability to achieve quarterly sales targets, as well as accurately planning and projecting for the following quarter.

Closing the gap between new business insights and profitability

Life insurers set business objectives each year, and while they can track monthly results against those objectives, they can’t currently understand why new business sales performance is different from expectations in an actionable way. 

Insurers already do the work to optimize new business performance, they just don’t have a process that ties it all together, so their activity is only 70 percent effective. In order to close this gap, insurers must leverage a Decision Science platform to merge business plan expectations, a granular customer and distributor lens, and broader decision making data. The limitation is not in life insurers’ teams, it’s in the tools and technology they leverage in order to perform their analysis. 

For a life insurer, being able to combine broad data sources, like competitor analysis, market share, and quote activity, makes the entire process more strategic. Insurers gain the ability to effectively plan, predict, and react in new business sales processes as well as rich, holistic decision making capabilities as a result.

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