ashish ahluwalia - finity consulting€¦ · ashish ahluwalia principal tel: +61 2 8252 3373 email:...
TRANSCRIPT
Ashish Ahluwalia
A practical guide to customer analytics
Scope
3
Propensity modelling Customer
journey modelling Optimisation
Offer take-ups
Next action
Churn
Choice sets
Behaviours / lead indicators
Profit
Revenue
Spend
Market share
Other…
Today’s presentation
4
Data issues
and considerations
Setting an
approach with
clear goals
Technical
considerations
2 3 1
Case studies
4
Getting to the starting block
Building a picture of your customer
6
External
data sources
Customer
attributes
Other inputs Prior action
/ behaviours
Customer
Demographic
/Census
Geographic
Economic
Credit
Competitor
information
Survey
information
Header file
Transactional
History summary
Social
Product usage
Building up a view
of your customer is
a multidimensional,
multistage process
Common data issues to overcome
7
Unifying data sources
Missing fields / biases in missing information
Volume of data / processing
Framing the data for modelling
1
2
3
4
Fragmented data landscape and reach
8
What you know about
prospective, past and
current customers is
usually different and can
influence modelling and
action potential.
Population
Mailing lists
Subscribers
Regular and active customers
Historic
customers
Reach
an
d a
ccu
racy
Ex
tern
al d
ata
req
uire
d
Applications vary for each group
Population
Subscribers
and mailing lists
Historic customers
Regular and
active customers
Marketing optimisation
Propensity modelling
Marketing optimisation
Propensity modelling
Offer choice modelling
Marketing optimisation
Price optimisation
Churn management
Propensity modelling
Journey
mapping
Loyalty
Focus is on finding
best path to prospects
Target direct activation
offers based on whatever
is known/inferred about
the customer
Focus reactivation efforts
Design offers
Identify best media paths
Retention activities
Service delivery
Max revenue/customer value/etc.
Cross-sell and up-sell
Lifecycle management
Analytic priorities
Need to think carefully about approach
Modelling
approach Accuracy Transparency
Ease of model
construction
Ease of model
maintenance
Scoring
effort
Scenario
testing effort
Machine learning High High
GLM predictors Medium Medium
Segmented
customer base with
simple linear models Low Low
10
Modelling choice needs to give regard to the important
and desirable features for model output
Some technical
considerations
Model validation is critical
12
In the model validation process,
important to separate the data into:
Training datasets
Validation datasets
Testing datasets
Out of time testing data (e.g. last
three months of data) used
to validate if the models will
produce reasonable estimates
for future periods.
Models can be made transparent
13
Variable InfluenceCompetitor Index 1 21.2Competitor Index 2 19.6Time 11.5Price change 7.6Premium Rate 4.0Marital status 2.5Occupation 2.2Sum insured 1.7Age 1.5
Predicted Density occupation group Sum insured ($000's) Age
ROC curve (AUC = 84%) Competitor Index 1 Competitor Index 2 Time
Decile Chart Price change Premium Rate Marital Status
0%
100%
200%
300%
400%
500%
600%
700%
800%
50 60 70 80 90 100 110 120 130 140
0%
20%
40%
60%
80%
100%
120%
140%
27% 48% 69% 90% 111%132%153%0%
2%
4%
6%
8%
10%
12%
14%
16%
18%
20%
1 2 3 4 5 6 7 8 9 10
Actual
Predicted
Average
0%
20%
40%
60%
80%
100%
120%
1 2 3 4 5 6 7 8 9 101112131415160.0% 5.0% 10.0% 15.0%
0%
50%
100%
150%
200%
250%
300%
350%
400%
50 60 70 80 90 100 110 120 130 1400%
50%
100%
150%
200%
250%
300%
350%
400%
450%
500%
1 7 14 20 27 33 40 46 53 59
0%
20%
40%
60%
80%
100%
120%
140%
160%
180%
200%
0 2 3 5 7 8 10 12 13
0%
20%
40%
60%
80%
100%
120%
140%
160%
Mar
ried
De
Fact
o
Wid
ow
ed
Oth
er
Sin
gle
Div
orc
ed
Sep
arat
ed
0%
20%
40%
60%
80%
100%
120%
140%
Pro
fess
iona
lEl
emen
tary
Cle
rica
l,…A
sso
ciat
e Pr
ofes
sio
nals
Inte
rme
diat
e C
leri
cal,…
Ad
van
ced
Cle
rica
l &…
Oth
er
Inte
rme
diat
e…M
anag
ers
&…
Trad
esp
erso
ns &
…R
etir
ed/P
ensi
on
Cle
rica
lU
nsk
illed
Lab
our
Trad
eLa
bo
ure
rs &
Re
late
d…
0%
20%
40%
60%
80%
100%
120%
140%
160%
19 25 31 37 43 49 55 61 67 73
0%
20%
40%
60%
80%
100%
0% 20% 40% 60% 80% 100%
Model fit
measures
Impact
on target
Case studies
Population
Mailing lists
Subscribers
Regular and active customers
Historic
customers
Propensity modelling to maximise ticket sales
15
Objective
Help a sporting organisation maximise
event ticket sales to its contact list.
Analytic brief
Build propensity to purchase models
Rank customers/prospects
Profile high propensity customers
Modelling framework
16
Repeat New
Models Trained using observed
outcomes
Customer type
Model Score Use information to date.
Overlay forward looking
assumptions
“Repeat” being those that are existing people who have previously actioned; “New” being those that were people who actioned for the first time last year
Repeat customers
Scored
existing customers
Scored
population space
Existing customers
New customers
Population customers
The data
17
Customer Master File
Age, Gender (*)
Location (*)
Indicators on different channels
Defin’d
Socio-demographic
Psychographic
Behaviours/attitudes
Ticketing file
Summarised: Ticket
purchasing history by
venue, season, match type
Derived: supported team
Other channel data
Online accounts
Merchandise
Other sporting affinities
Online viewing subscriptions
Retention customers Activation customers
Results
18
1. Profiled different customer
segments: Propensity
levels (high vs low, repeat,
new customers, value based
etc.)
2. Targeted offers based
on propensity levels and
associated profiles
3. Identify look-alike
characteristics to target
in broader population
Modelled
top quintile
accounted
for ~90% of
sales
Population
Mailing lists
Subscribers
Regular and active customers
Historic
customers
Portfolio pricing optimisation
19
Objective
Optimise pricing of a portfolio of
leased products.
Analytic brief
Construct choice and price elasticity
models
Customer choice forecasts
Impact tool to support pricing
decisions
MATERIAL WITHELD
20
In summary
21
There is no one “customer analytics” problem and no generic approaches
Start with the use case but consider data in parallel
Think about infrastructure up-front
Consider what will win over the end user
Questions?
23
Contact
Ashish Ahluwalia
Principal
Tel: +61 2 8252 3373
Email: [email protected] www.finity.com.au
Distribution & use
This presentation has been prepared for the Finity
Consulting Pricing & Analytics Seminar, held on 18
October 2016. It is not intended, nor necessarily
suitable, for any other purpose.
Third parties should recognise that the furnishing of this
presentation is not a substitute for their own due
diligence and should place no reliance on this
presentation or the data contained herein which would
result in the creation of any duty or liability by Finity to
the third party.
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Finity wishes it to be understood that the information
presented at the Seminar is of a general nature and
does not constitute actuarial advice or investment
advice. While Finity has taken reasonable care in
compiling the information presented, Finity does not
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needs.
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