Competitive positioning study

‘Reverse engineering’ motor insurance

Contents

An introduction to reverse engineering

Example: MTPL competitive positioning of company A

Reverse-engineering process

Determine the price determinant variables for each of the competitors 1

Understand how each price determinant variable individually affects the final price2

Understand the interactions between variables3

Create a pricing calculator that emulates the tariff for each competitor4

Test and refine the pricing calculators to ensure a high degree of accuracy5

Steps to reverse-engineer a tariffNo.

The pricing calculators can be used to analyse the (price) competitiveness of the company in anysegment of the market through a simulation of the insurance market…

The methodology consists of generating a large number of profiles and simulating the prices and discounts of all competitors on each profile

Profile generation Statistical comparisonPrice simulation Statistical analysis

Generation of 13.000 profiles distributed according to the characteristics of the market, whilst maintaining appropriate constraints (eg. on age and driving experience)

Simulation of the premiums charged by each competitor to each profile, using the reverse-engineered models of the tariffs of competitors, as well that of the company, and applying appropriate discounts

Statistical comparison of the overall distribution of premiums, segment analysis by use of different variables and scenario analysis

The simulation uses representative profiles thus providing a precise view of the market, as well as full transparency of the price competitiveness of each market segment tested

Contents

An introduction to reverse engineering

Example MTPL competitive positioning company A

|

Comparing base prices for company A

Average MTPL priceEUR

MedianEUR

Spread 2

EUR

706

721-2%

B

A

743

721+3%

C

A

+22%

D 883

A 721

1134

1176

1134

862

1134

1325

655

626

655

699

655

821

% of profiles where A price is… Percent

12 12 18 18 15 25

40 27 16 9 4 4

39 23 17 10 6 5

cheaper expensiveA

9.549

6.552

10.346

> 20%>10 - 20%-10% – 0 – 10%>10 – 20%> -20%

1 Calculated MTPL prices on a simulated portfolio of 13.000 profiles; no discounts applied2 Distance between 2,5%-97,5% percentile to reduce outlier bias

Sample size used

|

Average MTPL priceEUR

MedianEUR

494

613-19%

B

A

D 662

A 613+8%

964

824

964

689

964

994

557

438

557

559

557

616

% of profiles where Allianz price is… Percent

4 4 7 11 15 60

27 27 21 11 7 6

18 19 21 16 11 14

cheaper expensiveA

Sample size used

> 20%>10 - 20%-10% – 0 – 10%>10 – 20%> -20%

30% B15% A 25% D20% C

1 Calculated MTPL prices on a simulated portfolio of 13.000 profiles; standard discounts applied2 Distance between 2,5%-97,5% percentile to reduce outlier bias

However, when standard discounts are applied, pricing strength of company A against all competitors is significantly weakened 1

Spread 2

EUR

9.549

6.552

10.346

595

613-3%

C

A

Discount applied:

|

Competitive positioning of company A is improved when we increase the discountrate against that of competitors, particularly against C and D 1

Average MTPL priceEUR

MedianEUR

494

576-14%

B

A

595

576+3%

C

A

+15%

D 662

A 576

907

824

907

689

907

994

438

524

559

524

616

524

% of profiles where Allianz price is… Percent

6 6 10 14 16 48

40 27 16 9 4 4

27 22 20 14 8 9

cheaper expensiveA

Sample size used

> 20%>10 - 20%-10% – 0 – 10%>10 – 20%> -20%

30% B20% A 25% D20% C

Spread 2

EUR

9.549

6.552

10.346

1 Calculated MTPL prices on a simulated portfolio of 13.000 profiles; extra A discount and standard competitor discounts2 Distance between 2,5%-97,5% percentile to reduce outlier bias

Discount applied:

|

Scenario analysis shows changes in cheapest offer applying (1) no discount for all competitors, (2) A15% discount, (3) A 20% discount

D

9%C10%

B

47%

A34%

D

12%C

7%

B

68%

A

13%

D

10%C7%

B65%

A

19%

30% B20% C 15% A 25% D 30% B20% C 20% A 25% D

1 2 3

1.212824

7.884

2.2851.437877

8.335

1.5561.0931.244

5.765

4.103

-451

+729

+2.570

-2.547

CBADCBA C

-225

BAD D

# number of profiles with cheapest price

No discount scenario A average discount level scenario Allianz 20% level discount scenario

0% B0% C 0% A 0% D

|

A “Staggered discount model” allows A to optimize its spending on discounts and still have the cheapest offer for the same customer profiles

# of profiles where A has the cheapest price

472

340

530

729

959

30%

3.244

1.333

3.244

25%

2.285

4.577

20%

2.285

1.556

15%

1.556

1.026

10%

1.026

686

5%

686

472214

0%

472

Discount level of A

Results of discount sensitivity analysis

Step 1: With a flat discount of 0% A still offers the

cheapest price for 472 profiles

Step 2: Increasing the flat discount to 5% A offers 214

profiles more as the cheapest price. Conclusion: 214

profiles needs to have a discount between 0% and 5%

Step 3: Perform Step 2 increasing the flat discount level

to 30%.

ConclusionApplying staggered discount model allows A to save

money without losing customers1

“Staggered” discount model:

0%: 472 profiles 0% - 5%: 214 profiles

5-10%: 340 profiles 10-15%: 530 profiles

20-25%: 729 profiles 25-30%: 959 profiles

30%: 1.333 profiles

10.564 Total number of profiles with A offer

1 Assumption is that A value proposition allows convergence of prices close to competitors

“Generation Y” driver31 years old, Fiat Punto, 51kW

“Middle-aged” driver40 years old, Golf VII, 81kW

“Old timer” driver51 years old, BMW 5, 115kW

A’s tariff is cheapest byNumber of postal codes

A’s tariff is cheapest byNumber of postal codes

A’s tariff is cheapest byNumber of postal codes

475401224

> 10 - 20%> 10% > 20%

13

136174

> 10 - 20% > 20%> 10%

211

62

> 10 - 20%> 10% > 20%

A B C D

A’s price competitiveness is varied across different profiles; the average price for the “Generation Y” driver can be raised whilst still retaining the customer

On the whole, A could lower its postal code coefficients in the North and increasethem in the South to increase competitiveness

Postal coefficients versus CPostal coefficients versus B

In the North, A coefficients could be raised. Whilst in the South, A should lower postal zonecoefficients to become more competitive against C and D

Postal coefficients versus D

> 20%>10 - 20%-10% – 0 – 10%>10 – 20%> -20%

cheaper expensiveA

Current A‘s postal code zoning for TPL tariff 1