The journey towards data driven decisions – how can we benefit from data (even if we don’t own them)?

Karl Ove Aarbu, Head of Customer & Claim Analytics, Tryg

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•Second largest Nordic non-life player

•DKK 18bn of premiums (EUR 2.41bn or GBP

2.16bn)

•Largest player in Denmark, 4th in Norway and

5th in Sweden

•Approx market cap of DKK 60bn (EUR 8.1bn)

NorwayMarket position: #4

Market share: 13.3%CR in Q2 18: 82.8%

SwedenMarket position: #5Market share: 3.1%CR in Q2 18: 98.3%

DenmarkMarket position: #1Market share: 22%

CR in Q2 18: 77.2%

Business split 2017

Gross premium split by products 2017

Percentage

Tryg at a glance

3 1. september 2019

4 1. september 2019

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…data must be refined ….build CustomerTimeLineData…act when the customer is responsive

1. september 2019

Call centresystem

Claimsystem

WebdataProduction

systemSales

systemetc M

CustomerTimeLine Data

Right time

Right person

Right channel

Right message

Time

Churn prob.

«LoveYouCall»

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…and our actions must focus on maximizing CLV…

1. september 2019

𝑴𝒂𝒙𝒊𝒎𝒊𝒛𝒆 𝑪𝑳𝑽 =

𝒊=𝟏

𝑴

{𝑷𝒓𝒆𝒎𝒊𝒖𝒎− 𝑬𝒙𝒑𝒆𝒄𝒕𝒆𝒅(𝑪𝒍𝒂𝒊𝒎𝒄𝒐𝒔𝒕𝒔) − 𝑨𝒅𝒎_𝒄𝒐𝒔𝒕} ∗ 𝑬𝒙𝒑𝒆𝒄𝒕𝒆𝒅(𝑳𝒊𝒇𝒆𝒕𝒊𝒎𝒆)

𝑨𝒅𝒎_𝒄𝒐𝒔𝒕 (decrease adm. costs)STP (Straight Through

Processing)

Elements of CLV Action areas

Expected net saving with manual handling

Manual or automatic

Actions (treatments)AI Models (examples)

M (increase #customers) Find profitable segments in portfolio

Customer value models

Online banners

𝑳𝒊𝒇𝒆𝒕𝒊𝒎𝒆 (Increase lifetime

existing customers)Churn/Survival

Short term churn model

“love you” call

“love you sms/email”

“pleasant surprise”

Facebook “twins”Linkedin “twins”

Target discounts/”pleasant

surprise

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Mindset/Culture Technology and data Analytics/ML/AI methods -

predictions

Behavioral understanding

and policy optimization

KPIs

Maximize

CustomerLifeTimeValue

System- and data silos.

Hard coded rules.

Customer TimeLine and

Analytics as a Service (cloud)

GLM Models –

static features –

long term

Always full blown

implementation

Experimentation and

Reinforcement

Short term Prescriptive

ML-models

..so to become data driven…we have to climb some stairs…

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…but how do we know that the «action» works?

1. Predictions are not

Prescriptions

2. AB testing in order

check effectiveness of

actions…

3. ..always relevant to

not act at all…

1. september 2019

A B

x

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Business problem: High churn..

1. september 2019

Random split (A_B)

Profitable customers

Bring business problem to data=Model

Profitable «disloyal» customers

Control group Love you calls Email/SMS

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..and it works….

1. september 2019

• Significant differences in

churn rate between

control and treatment

group

• Most effective among

youngest and oldest

customers..

Age category

Churn

rate

90 d

ays

aft

er

treatm

ent

Group

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..event data are extremely important!

1. Some data are more important than

others

1. Static or determenistic: Age,

gender, adress…..

2. Semi-static: Number of products,

premium…..

3. Events: Webvisit,

outbound/inbound call, claim

event……..

2. CTL should include all types of data –

especially events

1. september 2019

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Webvisit Not webvisit

Churn rate 30 days after treatment

Treatment Control

2.4 times more churn in web group

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..and what about using data you do not own?….

1. Seed audience – Facebook / Linkedin

1. september 2019

Profitable customers

Secure login«MyPage»

«Twins»

Banner

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..and…..we consider to stop this “treatment/campaign”… should

we?

…how large should the control group be?….

..…how should we design the experiment?...

..signs of success – when we naturally asks….

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…summary….the road to data driven decisions requires….Summary…..

1. Getting your data to sing is no walk in the

park….

1. Mindset and culture change

2. New technologies – analytics as a service!

3. Short term prescription models that aims

towards maximizing CLV

4. Experimentation, Experimentation,

Experimentation

2. Everlasting process – business, technology,

datawarehouse and analytics hand in hand..?

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aarkar@tryg.no