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

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tary

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sso

ciat

e Pr

ofes

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nals

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cal,…

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er

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anag

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ns &

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etir

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ensi

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nsk

illed

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our

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rs &

Re

late

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0%

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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: ashish.ahluwalia@finity.com.au 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.

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presentation is not a substitute for their own due

diligence and should place no reliance on this

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