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Retention & Conversion Modeling

This seminar covers the importance of retention modeling, how to model it, and its applications in pricing and financial models. It also explores the benefits of renewal and conversion analyses for maximizing profitability and program stability. Attendees will learn about market conditions and customer behavior that impact retention, and how to optimize renewal and conversion rates. Suitable for actuaries and insurance professionals.

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Retention & Conversion Modeling

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  1. Retention & Conversion Modeling 2004 CAS Ratemaking Seminar March 11-12, 2004 Robert J. Walling, FCAS, MAAA

  2. Objectives • Why do it? • What characteristics matter? • How do you model it? • What applications are there?

  3. Why Do Retention Modeling? • More complete picture of your customers and prospective customers • More complete picture of pricing impacts on policy retention, conversion and premium • Better specified pricing and financial models • Allows pricing to focus on program stability and profitable growth

  4. Additional Benefits of Renewal & Conversion Analyses • Can help maximize profitability • Can be used for target marketing & profitable growth • Can enhance program stability • Facilitates consideration of market conditions in realistic customer responses

  5. Rate Impacts: The Current Problem What’s the impact of a +25% rate change? Current Loss Ratio = Loss/Premium Proposed Loss Ratio = Loss/(Premium*1.25) = Loss/Premium*(1/1.25) = Loss/Premium*80% = 80% of Curr. Loss Ratio The only answer is -20% on the Loss Ratio!

  6. The Absurdity (If a little is good…) What’s the impact of a 200% rate increase? Ignoring inflation momentarily. If Current Loss Ratio = Loss/Premium Proposed Loss Ratio = Loss/(Premium*3) = Loss/Premium*(1/3) = Loss/Premium*33.3% = 33% of Curr. Loss Ratio

  7. More Absurdity (What Cycle?) In 1999, PA Med Mal loss costs decreased 13.3% Do you think the market would respond the same way to a 10% decrease today as it did in 1999?

  8. Problem with the Current Pricing Analysis World • No change in response expected from policyholders: • Likelihood of Renewal • Satisfaction of Policyholder • Book Churning/Adverse Selection • Mix of Business Shift • Consideration of Marketing/Underwriting • Satisfaction of Agent • Competition

  9. Why Hasn’t Retention Modeling Been Done? • Sensitive to many factors • Tough parameterization issues • New business penalty poorly understood • More pressing product development and pricing needs • “New Territory” for many actuaries

  10. 100% R = f(P) Demand Curve 0% Price (P) The Flexible Shape of the Retention Demand Curve Renewal Rate (R)

  11. Retention Curve Varies by Many Characteristics

  12. Renewal Behavior Characteristics • Renewal Pricing Change (% or $) • Competitive Position • Customer Rating Characteristics • Market Conditions • Inflation • U/W Cycle • Reinsurance Pricing • Market Capitalization

  13. Renewal Behavior Characteristics • Traditional Rating Factors • Class - Multiple Policy • Territory - Limit • Limit - Account Size • Industry Group • Financial Underwriting Score (Credit, D&B) • Claims/MVR/Underwriting History • Age of Youngest Additional Driver • Satisfaction with Agent/Service • Number of Years Insured • Distribution Channel

  14. Retention Modeling Database

  15. Multivariate Analysis Determines Renewal Probability

  16. Reviewing Renewal Differences

  17. Competitive Position

  18. Changing Market Conditions • Market conditions change over time in the historical data • Historical market conditions are not necessarily predictive of future market dynamics How do you reflect future market conditions in a retention model?

  19. Retention Modeling Database – Market Scenario Testing

  20. Conversion Issues –Premium Quoted • Assumes the potential risk provides accurate information • Assumes only one quote is issued • Assumes the point of sale contact accurately retains all information • Often, records not kept for risks that don’t actually buy a policy

  21. Conversion Issues –Current Premium • Assumes the insured knows actual current premium • Assumes insured knows actual current coverages • May be estimated by rate comparison engine

  22. Conversion Issues –Solutions? • Look to an existing resource for conversion data • At least one Agency Management and Comparison Rating vendor can provide detailed, comprehensive conversion data with rating characteristics and competitive rank and/or competitor premiums along with “hit” statistics

  23. What Applications Are There? • Retention/Conversion by class segment • Improved premium/policy/loss ratio impacts of rate changes • Lifetime Customer Value • Optimal Rate Changes/ Effective Rate Impact

  24. Optimal Pricing Strategy Risk Premium Model Expenses ProposedRates Renewal/ Conversion Model Optimization Algorithm Most Loyal Most Profitable MOST VALUABLE

  25. Parting Thoughts • Where there is no vision, the people perish. • Proverbs 29:18 The data’s ready, The technology’s ready, ARE YOU READY???

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