How Decision Science in insurance distribution could generate a 10% increase in VNB

How Decision Science in insurance distribution could generate a 10% increase in VNB

Life insurers around the world aspire to develop a distribution strategy which allows them to win new business and outperform competitors. According to LIMRA, 89 percent of US life insurance is sold through agents and brokers. These sales are generating an enormous amount of value, but the investment in managing agent and broker performance is rarely grounded in data and evidence. What if we told you investing in Decision Science to improve distribution performance could generate a 10 percent increase in VNB. Think this sounds too good to be true? We’re here to tell you it’s not.

Engaging growth levers and making the right decisions in distribution

There are generally three key ways life insurers can adjust and improve distribution strategy: product features, pricing, and agent commissions. While many insurers lean heavily on agent-based distribution, this area is typically under analyzed  due to its personal and relationship-based nature.

These relationships are an important foundation for agent distribution, as well as the broader distribution strategy - but this approach can leave value on the table. Leveraging a Decision Science approach to inform and improve agent distribution complements these traditional relationships by helping insurers understand and manage agent performance based on market data. Developments in AI, machine learning and advanced analytics enable insurers to uncover insights about distribution that were previously hidden. The question for insurers is, do they want to uncover these insights or not? 

By grounding distribution strategy and decision making in data, insurers eliminate guesswork and improve both the value they gain from distribution and new business growth.

Decision Science in distribution

There is an enormous amount of market data life and health insurers can access to shed light on how to improve distribution strategy - but many insurers haven’t unlocked these insights. Data-based understanding of agent-based sales and distribution helps answer vitally important questions, like:

  • What is the best mix of product features to enable an insurer’s product to win in the market?
  • What impact does pricing have on sales, and what is the optimal price point to meet sales and profitability objectives?
  • What is the best commission rate to optimize agent performance?

Gaining the insights to answer these questions informs the key decisions insurers must make to sharpen distribution strategy and improve agent-based sales.

What specific value can a Decision Science approach to distribution deliver that a traditional approach cannot?

It goes back to those three key levers life and health insurers have to influence distribution: product features, pricing, and agent commissions. Decision Science allows insurers to find the optimal combination of these three levers across the different distribution channels in order to achieve specific goals. 

The following case study demonstrates how a Decision Science approach can create fundamental positive change in distribution strategy:


New business sales are down in certain highly competitive segments, and the insurer needs to understand both why and how to remedy this. Brokers tell the insurer that its prices are too high in those segments, but there isn’t sufficient data supporting this claim. Additionally, competitive insurers are offering brokers higher commission rates, but the insurer can’t identify the specific impact this has on new business sales in its targeted segments.

Decision Science Approach

Montoux's Decision Science approach combines internal and external data sources to analyze new business sales and determine the root causes for sales decline in targeted segments. It provides granular analysis on impact and sensitivity of pricing, advisor commission rates, and product features on these sales. These insights enable the insurer to cut through 'gut-feel' instinct and support distribution decision making with data and evidence.

Specific Example:

Looking specifically at distribution channels; what is the optimal approach to each that will allow you to address the decline in sales? Perhaps direct channels require more basic, commodity products, sold at a lower price, while advice channels may benefit from more feature rich products, sold at a higher price. 

If this is the case, what balance of both do you need in order to reach your targets? Maybe, in one channel, being a leader in price is the optimal approach, while in another it would be best to be a leader in commission or product features.

Decision Science enables insurers to calculate these complex trade-offs with confidence, analyzing all of these variables and incorporating rich market data, as well as specific constraints, to identify the optimal approach to distribution strategy. Gaining this insight ensures the strategic decisions insurers make regarding distribution are fully informed by all the information available to them, ultimately resulting in a winning distribution strategy.

Want to chat about your distribution strategy and how Decision Science can improve the value of your new business up to 10 percent? So do we, reach out here.

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