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In the current soft market general insurers are finding it increasingly difficult to sustain higher prices. However, reducing prices to retain customers can lead to a significant loss of profitability per policy and not always give the desired result in retention rates.
What is needed is a way to price intelligently, tailoring each quote to the individual circumstances of the sale. This should ideally be done in real time, automatically and dynamically adjusting pricing models to reflect successful quote engagements and changing market conditions. Contemi Solutions offers such a unique dynamic pricing solution, combining advanced statistical methods and leading edge software with our PriceBuilder.Net product.
Contemi's Solution
Contemi’s Pricing Solution builds on the acknowledgement that traditional risk-based pricing is always the foundation for implementations of market- and situation-based methodologies such as price optimisation, target pricing and full-curve scheduling.
Contemi’s methodology does not replace traditional actuarial pricing work, but uses the risk-prices as input in its search for additional profit.
The other key premis is that different sales situations will face insurers with very different elasticity curves and although the risk prices may be the same in all situations, insurers should apply different price models for new sales, renewals, up-sales, cross-sales and win-back situations.
Key Challenges We Overcome
When driving out added bottom-line profit from price optimisation in real insurance markets there are three major variables that obscure the theoretical picture and limit true ‘optimisation’. These are the very elements that the PriceBuilder approach is able to calibrate and embed into the pricing models.
• Variation between segments
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Price elasticity in relation to risk price or lowest available net price varies substantially from segment to segment, meaning that an optimal price adjustment level across a full book of business will not deliver any benefits. PriceBuilder ensures the pricing models are adjusted for customer segment.
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• Variation between sales situations
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The price elasticity facing an insurance supplier is very different in different sales situations, meaning that the optimal price and optimal commission level varies between new sales, renewals, up-sales, cross-sales and win-back situations. Again PriceBuilder methods and tools ensure that this information is captured and factored into the optimisation process.
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• Variation over time
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Changes in the competitive situation occur much more frequently than changes in the underlying risk relativities. The rapid changes to price elasticity (and for a broker, to the lowest available net price) mean that maintaining optimal prices over time cannot be successfully addressed by traditional off-line analysis. Automated model recalibration as found in PriceBuilder is necessary in order to maintain the bottom-line profit level over time.
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Theory Turned Into Reality
The science around PriceBuilder can be summarised as follows:
• A unique over-arching value function is mathematically modeled based on the insurer’s pricing strategy.
• Sub-functions are then created that differentiate between the customer segments and sales situations.
• These combine into a set of algorithms that dynamically optimize each quote as they are produced.
The sub-functions are self-tuning: the latest purchase-results are used to remodel and adjust them automatically. Thus changes in market conditions can be embraced and utilized without the need for lengthy manual analysis.
It may sound like magic however the results are real and proven. PriceBuilder to date has delivered between 2% to 10% of gross written premium to the annual bottom-line.
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