Fractal analytics elasticity based pricing

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  • Fractal analytics elasticity based pricing

    1. 1. ® Squeezing Price Elasticity into the Pricing Matrix By Deepak Ramanathan & Ed Combs Fractal Analytics Inc. Presented at Auto Insurance Report National Conference 2011Confidential | Copyright © Fractal 2011 1
    2. 2. ® Key points to be covered in the next 25 mins1. European insurers have realized substantial benefits by using price elasticity in their pricing models2. In the US, though European approach is prohibited, ‘intuition led’ changes motivated by price elasticity occur3. By being a bit more scientific while incorporating elasticity we can improve performance – Without changing the rating structure – Without introducing new variables in the ROC – While maintaining ‘loss cost’ as the most important component of pricing4. Using price elasticity, we can optimize prices while staying within the allowable band of loss cost indicated relativities Confidential | Copyright © Fractal 2011 2
    3. 3. ® Elasticity based pricing & optimization is a well known concept in the insurance industry It is about reaching the efficiency frontier More volume / same profit Volume (GWP) More profit / same volume Loss cost based pricing Profit (1 – loss ratio)Confidential | Copyright © Fractal 2011 3
    4. 4. ® Why is Elasticity Based Pricing important? 10% 7.5% Premium Lost Opportunity 5.3% Mth 1 Mth 6 Proposed Realized Elasticity Based Pricing has huge potential to improve both top-line and bottom-lineConfidential | Copyright © Fractal 2011 4
    5. 5. ® European insurers leverage elasticity in pricing Selective Discounts Frequent Rate Changes Discounts are offered at point of sale Prices are changed rapidly, some times purely based on elasticity multiple times in a day Price Testing Insurers experiment with prices in the marketplace to create data for elasticity UK market Regulations make it easy for insurers to leverage price elasticityConfidential | Copyright © Fractal 2011 5
    6. 6. ® In the US, elasticity had not been widely used in the past because of… Regulatory constraints Data hurdlesTwo households with the same ratingcharacteristics cannot be charged Lack of good data, inability to price testdifferent rates The wait is over We can capture part of the gain within the regulatory framework People like us are already doing thisConfidential | Copyright © Fractal 2011 6
    7. 7. ® …But ‘intuition led’ Elasticity Based Pricing is common Aren’t factors frequently revised after meeting the sales team? Isn’t actuarially justified discount such as persistency discount overridden? Isn’t rate capping used frequently to avoid disruption? What is the rationale for such "intuition led", "common sense led” decisions? What if we could make these decisions more data driven? We often override pure loss cost in favor of more revenueConfidential | Copyright © Fractal 2011 7
    8. 8. ® Scientific Elasticity Based Pricing requires three essential components Loss Cost Modeling Price Elasticity Cost of doing business Measures customer risk PRICING  Measures customers’ Helps in segmenting reaction to price changes customers based on risk  Helps realize different profit attributes margin depending on price sensitivity Future revenue potential / Lifetime value (LTV) Index for customer loyalty and cross-sell potential This helps insurers go beyond ‘cost plus’ pricing model and incorporate key customer characteristics Confidential | Copyright © Fractal 2011 8
    9. 9. ® …And requires a lead time of up to a year Components of traditional pricing Additional components needed workbench• Policy & Quote data • Policy level elasticity data• Factors used in pricing  Estimated renewal / conversion• Factors Relativities  Estimated renewal premium  Current • Policy level LTV data  Proposed  Estimated survival  Indicated  Estimated future cross-sell• Pricing engine • Elasticity & LTV measurement at various• Competitor data price changes Key Insights • It takes 4 to 6 months to build elasticity & LTV capabilities • It takes an additional 6 months to run a pilot and validate resultsConfidential | Copyright © Fractal 2011 9
    10. 10. ® We can optimize prices by varying a limited number of rating factors using elasticity & LTV Age Limits Territory 35 – 40 years 30/50 Jacksonville Upper Confidence Upper Confidence Upper Confidence Level Level Level Selected Relativity Model Relativity Model Relativity Selected Relativity= Model Relativity Selected Relativity Lower Confidence Lower Confidence Lower Confidence Level Level Level Elasticity & LTV provide new insights about the customer . This can help us select “better” relativitiesConfidential | Copyright © Fractal 2011 10
    11. 11. ® Tests show that this approach works Case Study: Simulation results from a large P & C insurer in the US US Regulatory UK Market ScenarioParameter Scenario (-10% to +10%)Average premium 0% (by design) 0% (by design)changeNumber of policies +0.9% +3.7%Written premium +3.5% +9.7%Loss ratio -1.0% -2.3% Better retention & better top line growth, while remaining risk neutralConfidential | Copyright © Fractal 2011 11
    12. 12. ® The wait is over…Don’t get left behind We can capture part of the gain within the regulatory constraints Our estimate suggests companies that account for ~ 35% of the market share:  Either have this capability  Or in the advanced stages of developing it We expect this number to grow in the next year Other benefits include better forecasting and objective decision makingConfidential | Copyright © Fractal 2011 12
    13. 13. ® Though challenges exist, this is no rocket science Defining price elasticity No price testing The standard definition has to be Due to regulations, price testing cannot modified to include rate avoidance, etc. be used to estimate elasticity Non-linear nature Balancing elasticity and LTV with loss cost Elasticity LTV Elasticity Based Pricing is highly non- Elasticity and LTV can be first computed linear with multiple local optima at a segment level & then at a policy levelConfidential | Copyright © Fractal 2011 13
    14. 14. ® In Summary…• Optimization techniques have evolved to incorporate elasticity• Due to limited regulations and potential upside, European companies have been early adopters• US insurers are realizing the value of elasticity led optimization• While the accrued benefits may not be as high as in the European scenario, there is money to be made within regulatory constraints• It takes a year to build this capability and go to market with itConfidential | Copyright © Fractal 2011 14
    15. 15. ® Thank You Deepak Ramanathan Ed Combs Insurance Director – Fractal Analytics Insurance Advisor – Fractal Analytics deepakr@fractalanalytics.com ed@fractalanalytics.com 323-719-4165 ed@combsconsults.com 818 706-3467 Fractal Analytics Inc. www. fractalanalytics.comConfidential | Copyright © Fractal 2011 15
    16. 16. ® Nothing hereConfidential | Copyright © Fractal 2011 16

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