The Customer LIFE-TIME-VALUE in the INSURANCE-INDUSTRY

Transcription

1 The Customer LIFE-TIME-VALUE in the INSURANCE-INDUSTRY Table of Contents: Introduction Applications of Data Mining Modelling: Life-Time-Value Example: Customer Segmentation Conclusions by Günter Schmölz, Vienna Seugi, June 2000 Günter Schmölz/Portfoliomanagement/ April 2000/Page 1

2 Introduction Needs / Reasons for Data Mining: better software and hardware earnings and profits tough competition CRM, Data Mining, Database Marketing Quantity of data Quality of data downward pressure of prices Günter Schmölz/Portfoliomanagement/ April 2000/Page 2

3 Applications Data Mining is the basis of numerous applications for CRM: Data Mining and direct mailing maximizing reponse rate and customer profitability Data Mining and churn prediction to maximize the retention of profitable customers Data Mining and sales force compensation sales force compensation based on expected customer profitability Data Mining and e-commerce optimizing the designing of Internet homepages maximizing total profitability Data Mining and Call Centre Outbound and Inbound Calling, analysing customer behavior Data Mining and cross selling identifying the liklehood to buy certain products Data Mining and product development a scoring tariff structure leads to positive customer selection Data Mining and reconstruction to identify customers who have been unprofitable in the past and, most likely, unprofitable in the future Maximizing total profitability is only possible with Data Mining! Günter Schmölz/Portfoliomanagement/ April 2000/Page 3

4 Introduction What is DATA MINING? data Methods identification of patterns modelling use of multivariate analyses: regression-analyses, decision tree, neuronal networks tool: SAS-Enterprise Miner optimizing the target value Günter Schmölz/Portfoliomanagement/ April 2000/Page 4

5 Introduction A central question in insurance business: How can customers be valued? Customer A Customer B sex male female age profession civil servant white collor family status married single number of contracts 6 2 total premium amount Ø-discount 12,50% 8,50% customer since loss ratio % 120% attractiveness?? How can I define customer attractiveness? How can it be calculated? Günter Schmölz/Portfoliomanagement/ April 2000/Page 5

6 Modelling Which target value should be selected? Example: sex amount of customers: Ø-premium / customer: loss ratio: 68,6% 62,5% profitability / customer:* prof. / customer incl. costs:** * premium - claims ** 80%*premium - claims man woman???? Günter Schmölz/Portfoliomanagement/ April 2000/Page 6

7 Modelling: Customer Life-Time-Value The Concept of the Customer Life-Time- -Time-Value profitability per year Customer A Customer B cancellation: motor car insurance conclusion: personal accident insurance cancellation of all contracts time all future cash flows of customer A: Zeitachse = all future cash flows of customer B: = Günter Schmölz/Portfoliomanagement/ April 2000/Page 7

8 Modelling: Customer Life-Time-Value The Customer Life-Time- -Time-Value is the most important ratio for customer valuation Cross Selling Potential Expectany of Profitability Customer LIFE - TIME - VALUE Customer Retention Interest Rate Günter Schmölz/Portfoliomanagement/ April 2000/Page 8

9 Modelling: Customer Life-Time-Value The Customer Life-Time- -Time-Value Model is based on a complex mathematical algorithm: accident motor car household response rate, cross selling potential 1: if existing likelihood: if not existing x x Net Present Value of all future cash flows: profitability discounting cancellation rate prof. discounting cancellation rate = CUSTOMER-LIFE-TIME-VALUE Günter Schmölz/Portfoliomanagement/ April 2000/Page 9

10 Modelling: Customer Life-Time-Value Ratios of Customer Behavior Customer: Mr. Stephen Maier, 25 years old, single, Civil Servant, Vienna, 3 contracts Lines of Business Premium Amount 12/99 Expect. of Loss Ratio 2000 Top Profitability 2000 Top Prob. of Cancel. Top Prob. for new contract Top LIFE - TIME - VALUE Top Motor Car % 80% % 12% 90% 6,5% 15% % Accident % 20% % 2% 15% 1% 85% % Household % 3% % 5% 50% 0% 98% % Legal Prot % 10% 4% 60% 5,5% 2% % Total % 25% % 1,0% 75% 9% 15% % This diagram (produced for each customer) is the result of a Customer Behavior Model based on Customer Life-Time-Value. Günter Schmölz/Portfoliomanagement/ April 2000/Page 10

11 Example Example: Customer Segmentation at Allianz Aim : Discount differentiation based on customer s profitability: Top 20% customers shall get better conditions Definition of customer s profit : Target = premiums * 80% - claims + 3% life-premium Procedure: Based on the premise that what customers did yesterday, they are likely to do tomorrow Data from 1997, 1998 Model Scoring Target 1999 Data from 1998, 1999 Result Prognosis 2000 Günter Schmölz/Portfoliomanagement/ April 2000/Page 11

12 Example Modelling - Mining Techniques Univariate Analyses Regression Analyses 100% 95,8% 90% 80% 74,6% 70,5% 70% 65,6% 57,5% 60% 50% 40% 30% 20% -10% -15% -20% -30% >30% Decision Trees Neuronal Networks Günter Schmölz/Portfoliomanagement/ April 2000/Page 12

13 Example Score-Card Card: Expected Customer Profitability 2000 Cust. Loss Ratio 99 + Cust. Loss Ratio 98 + Cust.Loss Freq % % % % % % % % % % % > 100% % % % % > 300% - > 300% Premium + Number Contracts + Customer Period = contract years contracts years contracts years > 5 contracts years 84 > > 15 years - Example: Customer X: LossRatio99= 80%, LossRatio98=0%, LossFreq99=20%, premium= , 5 contracts, since 1995 insured with Allianz Expected Profitability 2000 = Günter Schmölz/Portfoliomanagement/ April 2000/Page 13

14 Example A sample test of the score card shows a significant differentiation of customers Empirical Customer profitability 1999 (validation database) Ranking of the customers by predicted model score top 10%. worst 10% quantil quantil Günter Schmölz/Portfoliomanagement/ April 2000/Page 14

15 Example Allianz identified 20% of it s customers with an Ø-profitability of more than Scenario for 2000: Target Customers get higher discounts Normal Customers get lower discounts assumption: In the end of the year, there will be 2% more Target Customers 12/ 1999 Scenario 12/ 2000 % Cust. Ø-Profitability Ø-discount New % Cust. New Ø-Profitability New Ø-discount Target Customer 20% % 22% % Normal Customer 80% % 78% % Total 100% % 100% % Result: : Ø-Profitabilty per Customer will increase by 6,5%! Günter Schmölz/Portfoliomanagement/ April 2000/Page 15

16 Conclusions Data Mining is the key for a successful future!! Companies have to become customer orientated! Data is key to any analyses! Online calculation of customer evaluation! Campaign management to focus effectively on profitable customers! Data Mining represents a truly competive advantage! Data Mining represents a revolution in the insurance industry Günter Schmölz/Portfoliomanagement/ April 2000/Page 16