Which customers are we processing?The journey from choosing target groups to selecting target individuals

Håkan Persson

Chef Affärsservice Privat

Länsförsäkringar

Description of the journey...

From product orientation to customer orientation

From basing the process on what we want customers to do, to basing it on what customers is most likely to do

From inside out, to outside in

Make the organisation understand and utilise the benefit

Scored customers and an offering engine

First and foremost, which of our customers has the greatest potential to purchase product X

Followed by, when and how shall we offer product X to the customer with the greatest potential to purchase

Operative – our method?

The offering engine is based on the probability of purchase

The offering engine is presented in our CRM system, through the Yellow Tab

Communicate and work with the Yellow Tab at all customer meetings

Siebel

Scoring

A way to predict an event using statistical methods and historic information at customer level

Used in many areas in society Credit risks (Basel II), quality controls in the industry, etc.

Used in insurance Claims risks, Fraud investigations, Portfolio clearing, Marketing, Premium

setting, etc.

The various methods assess the background information in relation to its usefulness in finding the target data E.g. The most important element for finding future purchasers of X is to have a Y

An example

The Decathlon Principle Although you may not be the individually best in your event, you still have a chance of winning

win since the results from several events are added together

In the same manner, there are several types ofbackground information about customers that may contribute to the probability of purchase

By finding out the sub-totals of “each event”, we will be able to secure more customers

You can view it as getting 3 points for a car,4 points for a bank account and 2 points for home insurance If we set the qualifying limit at 4 points, car and home insurance may be sufficient and we will secure many good customers If we set the qualifying limit on bank account, we will not secure as many, although they all have 4 points

What does a German actuary say...?

What re we looking for...?

The goal here is not to make perfect decisions and find the definite buyer

The goal is to make a better choice than a random one, as often as possible

We can multiply our chancesBut, the process may move from 0.5% to 3% in the group with which we are

working

And radically reduce the costsOne sixthBut the experience for the seller may not be a dramatically better choice

Result of scoring in a DR activity

Well-scored customers purchase more frequently

There is a proven effect of the campaign

Forecast and outcome per decile in the campaign group

0,00%

0,50%

1,00%

1,50%

2,00%

2,50%

3,00%

3,50%

4,00%

4,50%

0,00% 2,00% 4,00% 6,00% 8,00% 10,00%Forecast purchase frequencies 1 year

Act

ual p

urch

ase

freq

uenc

ies

sinc

e ca

mpa

ign

star

t

outcome

New sales of mortgages in practiceProcess of offering engine

Qualify the customer for the offering

Calculate the probability of

purchase

Calculate the anticipated

value

Rank the business potential

Selection limits

[e.g. 20% best]

Option Probability Assessed Comparable Selected

In practice, choose the offering for the customer, not the opposite

MortgageP=0.004SEK=3000

Anticipated value SEK 12

Home ownerP=0.05SEK=300

Anticipated value SEK 15

CarP=0.10SEK=300

Customer already has the product

Home ownerP=0.05SEK=300

Anticipated value SEK 15

Home ownerP=0.05SEK=300

Anticipated value SEK 15

Calculations at customer level to support various business objectives ..... what you want to achieve

•Probability of purchasing more of existing products•Probability of purchasing more of existing products

Additional sales in existing commitment

Higher new sales of a certain product

Increased loyalty and lower number of cancellations

•Probability of purchase •Probability of purchase

•Probability of cancellation •Probability of cancellation

We can work with all three types of offerings in the same prioritisation...

4

Nyförsäljningserbjudande

• ”Just nu har vi ett förmånligt erbjudande för dig som försäkrar din bostad genom Länsförsäkringar”

• Sannolikheten att just den här kunden köper en bostadsförsäkring under det kommande året genom LF är exempelvis 7%

• Täckningsbidraget för en boendeförsäkring antas vara ca 286 kr på ett år

• Här har vi en affärsmöjlighet värd 280 kr * 7% =20 kr

4

Nyförsäljningserbjudande

• ”Just nu har vi ett förmånligt erbjudande för dig som försäkrar din bostad genom Länsförsäkringar”

• Sannolikheten att just den här kunden köper en bostadsförsäkring under det kommande året genom LF är exempelvis 7%

• Täckningsbidraget för en boendeförsäkring antas vara ca 286 kr på ett år

• Här har vi en affärsmöjlighet värd 280 kr * 7% =20 kr

6

Utökningserbjudande

• ”För att fullt ut utnyttja ditt skatteavdrag för pensionssparande kan du öka ditt månadssparande med 300 kr. Gör du det innan årsskiftet så bjuder vi på en julklapp”

• Vi antar ett täckningsbidrag första året om 180 kr på den affären

• Sannolikheten för köp beräknas till 180 kr * 8% = 14,40 kr

6

Utökningserbjudande

• ”För att fullt ut utnyttja ditt skatteavdrag för pensionssparande kan du öka ditt månadssparande med 300 kr. Gör du det innan årsskiftet så bjuder vi på en julklapp”

• Vi antar ett täckningsbidrag första året om 180 kr på den affären

• Sannolikheten för köp beräknas till 180 kr * 8% = 14,40 kr

7

Lojalitetserbjudande

• Under nästa år ger vi dig en självriskcheck påpersonbilsförsäkringen värd X kronor

• Sannolikheten för avhopp är 15%

• Marginalen om kunden stannar kvar är ca 300 kr

• Affärsmöjligheten är värd 15% * 300 kr = 45 kr

7

Lojalitetserbjudande

• Under nästa år ger vi dig en självriskcheck påpersonbilsförsäkringen värd X kronor

• Sannolikheten för avhopp är 15%

• Marginalen om kunden stannar kvar är ca 300 kr

• Affärsmöjligheten är värd 15% * 300 kr = 45 kr

Operative: work with the bonus concept and the offering engine

The offering engine is based on the probability of purchase

The offering engine is presented in our CRM system, through the Yellow Tab

Communicate and work with the Yellow Tab at all customer meetings

Siebel

Organisational progress A few years ago

Categorical customer selection (groups) Long decision-making processes and arbitrary guesses Inside out Product orientation

Today 16 prioritised offerings updated daily The selection includes all customers We choose both customers (individuals) and products, simultaneously Experience-based, assisted by scoring models Customer orientation

Advantages of the scoring models We select individuals, not groups of customers

Every customer has more than one chance of ending up in the target group A more efficient way of using customer information

A reflection of the Länförsäkringar Alliance’s collective expertise for one year

We can simultaneously take into account the possibility of an event and the value of the event An excellent decision-making and prioritisation basis

Customer information can be assessed Customer information can be used systematically and calculated monthly for each

individual

Can be used proactively and reactively

Conclusion Scoring through predictive analyses

Offering engine

Provides an opportunity to develop the offering that is best-suited to our customers every day

This is how we work, our entire customer base is assessed and qualified for our prioritised offerings daily and presented through our customer system - Siebel

Future Predictive models for irrational purchasing decisions

Connect external data to a larger extent

Internal training and further development – a continuous process