analytics in practice sunz 2012
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Analytics in practice Driving your business and saving you money
Todd Nicholson
February 2012
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Three examples of analytics in
practice
Domestic Tourism Segmentation
(Ministry of Tourism)
Serious Injury Model
(ACC)
Claim Duration Model
(ACC)
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Example 1: Domestic Tourism
Segmentation Want to understand targetable groups within the NZ
tourism market
Want to profile each segment so we know:
- who they are
- what they want
- how to reach them
Want to ensure each segment can be targeted
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Key learning: Always keep your
target audience in mind
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Key learning: Always keep your
target audience in mind Ensured all segments were marketable by segmenting on:
- interests
- age
- ideal holiday type
- inhibitors of travel
This gets us away from segmenting on holiday frequency and directs us towards holiday type.
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Key learning: Prettiness matters
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Key learning: Prettiness matters
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Key learning: Prettiness matters
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Example 2: ACC serious Injury
Model ACC has just over 5000 seriously injured clients. For these
clients ACC provides:
- Carers to help with day-to-day living
- Weekly compensation and medical expenses
- Capital items
The outstanding claims liability is currently $9.3 billion.
However, increasing consistency is one of the ways growth in expenses has been successfully dampened.
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Analytical models can give you
transparency and consistency
Nu
mb
er
of
care
ho
urs
per
week
Functional Independence Measure
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Key Learning: Milk your results
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Key Learning: Milk your results
When dealing with people there will
always be variation. So lets measure it!
Nu
mb
er
of
care
ho
urs
pe
r w
eek
Functional Independence Measure
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National serious injury
service - Evidence based needs assessment
- $820 million reduction in outstanding claims liability (due to the hard work of the serious injury team – this is simply a tool they’ve commissioned to help them).
- Minimal bad publicity or media comment involving clients with a disability
- No change in the usual, small number of legal challenges to decisions
- An increase in the number of clients achieving their self directed outcomes
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Example 3: Claim duration
analysis (ACC) • ACC has over 50,000 weekly compensation claims every
year
• They cost almost $1 billion a year
Aim of the analysis: to accurately predict claims duration
based on past experience
Ultimate Goal: to make better decisions about services
required and achieve shorter claim durations
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International tools – Medical
Disability Advisor (MDA)
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Risk Factor - Age
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Key learning: Know the power of
your data!
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Key learning: Know the power of
your data!
• ACC’s data is not perfect. For example, losing your job is a key risk factor but isn’t recorded in ACC data.
• But it is still the best dataset available anywhere in the world for modelling injury recovery
– Longevity
– Universal coverage
– No coming or going
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Know the power of your data!
There is nothing magic in our analysis. Its just
• Survival analysis
• Logistic regression
• Generalized Linear Modelling (GLM)
• Text mining (yes, even free text data can be a
gold mine)
The real difficulty is dealing with such a large
amount of data
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Milk your results (again!)
Two parts of the analysis have been
implemented for frontline day-to-day use
• They allow branches to accurately identify
which claims will require weekly
compensation
• Staff can take a more proactive approach
• Staff can ensure high risk clients receive
the correct services right from the start
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Milk your results
• Predict which claims are likely to last for a
long time and WHY
• Draws case managers’ attention to
potential risks
• Allows accurate allocation of resources –
identifying clients who need no services is
as important as those who need a lot.
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Milk your results
0
0.2
0.4
0.6
0.8
1
-30 20 70 120
Pro
po
rtio
n o
f c
laim
s s
till o
pe
n
Weekly compensation days
High Risk Claim
Low Risk Claim
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So remember
• Always keep your audience at the front of
your mind
• Prettiness is important
• Milk your results
• Models can give you consistency and
transparency
• Don’t underestimate the power of your
data
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