How Organisations Are Using Data Mining Techniques To Gain a Competitive Advantage John Spooner SAS UK

How Organisations Are

Using Data Mining

Techniques To Gain a

Competitive Advantage

John Spooner

SAS UK

Agenda

Analytics – why now?

The process around data and text mining

The Value of Information

Why are companies now using Analytics?

Data

•

ERP, POS, Web, etc.

Skills

•

A generation of computer-savvy,

spreadsheet-trained managers

Business need

•

Differentiated capabilities

•

Optimized processes

Why is analytics important?

So organizations can

make more money

save more money

allocate what they have more effectively

by getting better answers faster.

B

u

s

in

e

s

s

V

a

lu

e

Reporting / OLAP

Data Management

Data Access

How Much?

How Many?

What Happened?

Business Intelligence

Simple yet useful questions…

Product

Ti

m

e

L

o

c

a

ti

o

n

Year

Month

Week

S

to

re

S

K

U

B

ra

n

d

Re

gi

on

S

u

b

c

a

te

g

o

ry

A

ll

M

e

rc

h

a

n

d

is

e

Co

un

try

Co

m

pa

ny

Zo

ne

C

a

te

g

o

ry

How much did we sell (by

month, channel, region)?

Which stores sold the most?

What is our best-selling

product?

What is our most profitable

product?

…lead to more compelling questions

How much will we sell next month/quarter/year (for each

product/store location!)?

Why did we sell so much more this month?

How do we optimally replenish inventory?

Which customers are likely to respond to a mailing and of

those who respond, how much are they likely to spend?

How do we attract and retain profitable customers?

How do we optimally allocate marketing dollars to maximize

profits?

Where should we locate new stores to maximize revenue?

Optimization

Predictive Modeling

Forecasting

Reporting / OLAP

Data Management

Data Access

What will happen next?

What’s the best that can happen?

How Much?

How Many?

What Happened?

Beyond Traditional BI to Business Analytics

B

u

s

in

e

s

s

V

a

lu

e

Data Mining Definition

the

process

of selecting, exploring and

modeling

large amounts of data

to uncover

previously unknown information for a

All Impact the Bottom Line

All Impact the Bottom Line

Business Drivers for Data Mining

Customer-focused

•

Life-time value

•

Profiling/segmentation

•

Retention

•

Acquisition/ winback

•

Cross- /up-selling

•

Campaign analysis

•

Channel analysis

•

Channel development

•

Loyalty program analysis

Operations-focused

•

Profitability analysis

•

Pricing

•

Fraud detection

•

Risk assessment

•

Portfolio management

•

Employee turnover

•

Cash management

•

Capacity planning

•

Distribution analysis

Customer Focused Business Questions

Profiling & Segmentation

•

Who are my customers?

•

Why are customers leaving?

•

Who is going to leave next?

•

Which customers are most

profitable?

•

How should my segments be

defined?

Cross Selling Opportunities

•

Which customers are good

candidates for cross or up

selling activities ?

•

Which product combinations and

features do customers want ?

Target Analysis

•

Who should I target next?

•

Which customers are more

likely to respond ?

•

What’s the expected response

rate?

•

Which communication channel

should I use?

Behavioural Modelling

•

What is the customer potential

/ life time value ?

•

Can I customise offerings

based on needs, preferences

and profitability?

The Analytical Intelligence Cycle

Integration of People, Processes, and Technology

Data Manager

– Data Preparation

– Deployment Services

– Report Administration

Business Manager

– Manages Campaigns

– Domain Expert

– Evaluates Processes & ROI

Data Miner

– Exploratory Analysis

– Descriptive Segmentation

– Predictive Modeling

Start

Formulate

Problem

Accumulate

Data

Data

Quality

Analysis

Transform

and Select

Predictive

Modeling

Evaluate

Model

Deploy

Model

Monitor

Results

Data Mining Process – Finding the best model

Sampling

Yes/no

Data

Visualisation

Summary

Statistics

Transformation

Outlier

Elimination

Tree

Based

Regression

Neural

Networks

Other

Stats.

Model

MODIFY

ASSESS

MODEL

EXPLORE

SAMPLE

Data Mining Process: Sample

•

Simple random sampling

•

Stratified random sampling

•

Cluster sampling

•

First N

•

…



Data reduction



Validation

•

Simple analyses (e.g. mean, range for churn vs.

non-churn)

•

Visual exploration

− Histograms

− Scatter plots

− 3d-rotating plots

− Interactive exploration

− Colours and shapes

•

Create new variables

•

Variable grouping

•

Data transformation

•

Outlier elimination

•

Missing values ?!?!

Data Mining Process: Modify

Σ

√

log

e

x

f ()

x -

µ

σ

p

Data Mining Process: Model

Decision Tree:

if TIME REMAINING < 5

and TARIFF=‘FREQUENT’

then churn score= 0.6

else ….

Regression

logit(churn score)= 0.2*TIME

REMAINING + 0.5*AGE +

0.3*(USAGE*GENDER) + …

3

Data Mining Process: Assess

Models should be assessed and compared in terms of:

•

Accuracy of classification

•

Ability to identify small groups of customers with a high proportion of

target behaviour (‘Lift’). Cost savings can be derived from this

10

20 30

40

50

60

70

80

90 100

Percentile

Lift value

Baseline

0

1

Neural

Tree

Percentile

ROI

+ ve

- ve

Text Mining

Text Mining: The process of discovering

and extracting meaningful patterns and

relationships from text collections

=

+

Text

Mining

Data Mining

Natural

Language

Processing

S

S

E

E

M

M

M

M

A

A

Document

analysis

Reading

the text files

Text

Preprocessing

Dimension

Reduction

Singular Value

Decomposition

Term

weighting/rollup

The Analytical Intelligence Cycle

Integration of People, Processes, and Technology

Data Manager

– Data Preparation

– Deployment Services

– Report Administration

Business Manager

– Manages Campaigns

– Domain Expert

– Evaluates Processes & ROI

Data Miner

– Exploratory Analysis

– Descriptive Segmentation

– Predictive Modeling

Start

Formulate

Problem

Accumulate

Data

Data

Quality

Analysis

Transform

and Select

Predictive

Modeling

Evaluate

Model

Deploy

Model

Monitor

Results

Improve Performance

-The Model Management Challenge

Close the gap between model development and

model deployment (ROI!)

Proliferation of Data

& Models

Largely Manual Processes

Integrating with

Operational Systems

Increased Regulation

Automate Model

Deployment

Production

Environment

Integrated with the Model

Development Environment

Model

Development

Environment

SAS Enterprise Miner

SAS Credit Scoring

SAS/STAT

Base SAS

Score

Code

Model Registration

Map to Task

Model Testing

Model Deployment

Model Tracking

Interactive

Batch

Real Time

Development

Environment

Production

Champion Model

Selection

The Analytical Intelligence Cycle

Integration of People, Processes, and Technology

Data Manager

– Data Preparation

– Deployment Services

– Report Administration

Business Manager

– Manages Campaigns

– Domain Expert

– Evaluates Processes & ROI

Data Miner

– Exploratory Analysis

– Descriptive Segmentation

– Predictive Modeling

Start

Formulate

Problem

Accumulate

Data

Data

Quality

Analysis

Transform

and Select

Predictive

Modeling

Evaluate

Model

Deploy

Model

Monitor

Results

Challenge

Solution

Results

In order to meet many global

business challenges & the

operational complexity of the

industry, the airline needed to

change by introducing

analytical excellence;

business modelling, complex

data analysis.

SAS Data Mining have

been applied to measure

customer value, to segment

customer data and predict

customer attrition. Recent

Data Mining projects have

included Executive Club

travel pattern segmentation,

In-flight retail & on-board

customer survey monitoring

Now enable BA to understand

their needs of their customer’s

better to deliver a superior

service resulting in customer’s

staying more loyal to the brand

Challenge

Solution

Results

The company runs the

biggest loyalty programme in

the UK, therefore, it was

essential that they carry out

customer insight, campaign

management, opportunity

identification and

performance measurement

for their sponsors.

The company uses SAS

Data Mining capabilities, in

order to segment

customers. Sponsors can

then select from a wide

range of segments, at a low

cost and target them

accordingly.

Can now provide insight to

sponsors on loyalty card users

increasing response rates rise from

2% to 20%.

Challenge

Solution

Results

Detect and contain warranty

and call center issues before

they become widespread

Automatic monitoring of

free-form text to identify

quality and safety issues

Automatic alert generation

Surface previously unknown vehicle

issues

Fix issues faster

Hundreds of millions of dollars

saved