Big Data & Technology at Billabong

1
19 September 2013
1
Big Data & Analytics Innovation Summit
Big Data & Analytics at Billabong – A Case
Study for Driving Change
Jason Millett
Group Executive Technology, eCommerce & Transformation
Billabong International Limited

22
Agenda
1.  Context for Billabong
2.  What we did to get to the solution
3.  What we have found – so far

3
19 September 2013
3
Context for Billabong

4
A Diverse and Multi Dimensional Global Business

FROM
TO

Wholesale

Wholesale,
Retail,

e
-‐
Commerce

Surf
Surf/Skate/Snow

Australia
Global

Single
Brand
PorDolio
of
Brands

55
To unlock the strategic potential of the
business we refocused; an integrated approach

66
Six priorities identified within IT Review
Establish a Global Operating model for IT with appropriate
resourcing, accountability and funding to operate.
Establish a Technology Refresh Programme as part of
Transformation to create enablers for success
Source non core activities and functions to best supplier in
market on a global basis
Combine the roll-out of ERP for Australia and North
America
Create a Retail Innovation Centre to support the evolution
and development of leading edge retail technology
eCommerce Asset Consolidation and IT organization set
up.
1.
2.
3.
4.
5.
6.
6

77
Developed an IT Road Map (Directional View)
Americas
Infrastructure
In-Flight
Key
Dependencies
Europe
Australasia
•  Funding of IT Programmes to deliver capability in alignment with Transformation
•  Sufficient IT resources to support programme implementation and maintain BAU support
•  Appropriate Executive sponsorship and Global governance support execution
Resulting
Capabilities
Other Global
Capabilities
•  Global ERP
•  Global BI
•  Global Retail
Platform
•  Global HR /
Payroll
•  Global eComm
Solution with
Fulfilment
•  Global CRM
•  Global Product
Management
•  Global SCM
Solution
•  Global
Infrastructure
FY13 FY14
1 2 3 4 5 6 7 8 9 10 11 12
FY15 FY16
1 2 3 4 5 6 7 8 9 10 11 123 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6
Lawson Phase I
BI Phase I
eComm
Phase I
Lawson
Planning SW
Selections & Roll-Out
SurfStitch Application
Review
IT Sourcing
Roadmap
CRM Solution Requirements, Selection,
Configuration
PLM Global
Roll-out
VPN/Infra
Design and Planning
Lawson Phase 2 – WMS/Fixed
Assets
BI Phase 2
Data Consolidation Standards
BI Phase 3
Global Roll-out of BI
eComm Phase 2 eComm Phase 3
Epicor Roll-Out
eComm
Phase I
eComm Phase I eComm Phase 3
BI Phase 1 BI Phase 2
Epicor Roll-Out
eComm
Phase I
eComm Phase 2 eComm Phase 3
BI Phase 1 BI Phase 2
Maple Lake
CRM
Roll-out 1
CRM
Roll-out 1
CRM
Roll-out 1
Deployment and Upgrade – Integrated Desktop, Email, Intranet, Active Directory,
Office 365, Private Cloud
Sourcing Option

8
Objective is to not only manage an initiative pipeline,
but also inform the strategic rationale
Technology, eCommerce, &
Transformation
Improves Customer
Experience
Improved
information /
analytics
Enabling Technology
Initiative’s Primary Benefit

9
Strategic
Value
ProposiGon

Mature
a
global
Business
Intelligence
capability

Educate
and
train
in
the
use
of

BI
tools
and
capabili9es
to
be:er
support

business
performance
measurement
and
fact-‐based
analysis

Deliver
of
a
managed
core
global
repor9ng
suite
Priori9se

KPI
and
management
repor9ng
across
global
business

func9ons

Treat
corporate
data
and
informa9on
assets
to
comply
with
audit,

informa9on
security
and
external
regulatory
requirements

Develop
processes
and
procedures
to
accurately
reflect
data
as
it
is

collected
and
managed
in
Billabong
Interna9onal
key
business
systems

and
systems
of
record

Establish
a

global
BI
centre
of
excellence

(COE)
including
governance,

processes
and
controls

that
are
leveraged
to
support

a
global
change

programme

Approach includes Traditional BI Elements
Benefits
Benefit
Type

Reduced
lead
and
cycle
9mes
for
standard
repor9ng
Avoided cost
Improved
access
to
shared
corporate
data
and
informa9on
Be:er
access
to
informa9on

Consolidated
repor9ng
methods
and
tools
Bankable
saving

Improved
confidence
in
accuracy
and
completeness
of
reported

data

Avoided cost
Federated
approach
to
mul9ple

informa9on
records

across

Billabong
Interna9onal’s

business
es
and
systems

Avoided cost
Consolidated
views
across

global
wholesale
and
retail

opera9ons

Be:er
access
to
informa9on

Improved

real-‐9me
visibility
into
current
state
of
Billabong

financials,
budget
tracking,
etc.
allowing
global
monitoring
and

informing
central
decision-‐making

Be:er
access
to
informa9on

“Everybody
does
his
or
her
best
to
get
the
informa4on
you
ask
for,
but
it’s
not
necessarily

always
readily
available”

Industry
Leader,
Shop
Eat
Surf,
July
2013

Business Intelligence
9

10
Core
PlaYorm

Rules

Engine

Opportunities to apply Big Data for Business Change
Customer
Servicing
Repor9ng

Conﬁgura9on
Opera9ons

Member
Website

Mobile
App

Behaviour
Tracking

Data
Exchange

500

pts

%
VIP

En9tlements
&
Scoring

Integra9on
could
include
an
in-‐house
App,
POS/eCommerce
solu9ons,
Call
Centre
systems,

Social
Media
tools
or
a
mobile
app
–
The
API
opens
up
the
plaYorm
to
the
needs
and

crea9ve
vision
of
our
businesses.

Extensible

Database

-‐-‐-‐-‐-‐-‐

-‐-‐-‐-‐-‐-‐

-‐-‐-‐-‐-‐-‐

-‐-‐-‐-‐-‐-‐

-‐-‐-‐-‐-‐-‐

-‐-‐-‐-‐-‐-‐

-‐-‐-‐-‐-‐-‐

Single
Customer
View

API
Layer

External
Tools

11
19 September 2013
11
What we did to get to the Solution

1212
Framing the Problem
•  WHAT - Increase in ROI via Analytics
•  HOW - Operational Analytics (Big Data) / Managed Service /
OPEX / ‘as-a-service’
•  WHY - Strategic Analytics – ‘insights’
– Customer profiling
– Sense making
– Looking for drivers of campaign response
– Executive decision support

1313
Business Transformation with Big Data Analytics - Journey
ObjecGve

SeLng

QuesGon

IdenGﬁcaGon

BDA

Maturity

Assessment

Priority

SeLng

AnalyGcs

Methods

Data
Sets

Big
Data
AnalyGcs

Technology

ExecuGon

1414
What was on Offer
•  Market Basket Analysis
•  Fraud Detection
•  Campaign Optimisation
–  Create a predictive model based on the campaign, with targeting optimised to the recipient for
maximum probability of conversion
–  Calculate the lift (and therefore ROI) on any future targeted campaign aimed at the same
population relative to the current scattergun approach - there are benefits to more careful
targeting
–  Determine the drivers of conversion - provide "insights", strategic input/tell a story about what
makes people convert - informs broadcast advertising, branding, pricing, product design
•  Price Elasticity modelling - this is a method for determining optimal pricing given own and
competitor pricing, and detecting product cannibalisation, reinforcement, brand competition
and other effects.
–  More sophisticated and involved than Campaign Optimisation, requires retail scanner data of
volume and price of own and competitor products across a range of stores.
•  Forecasting - sales, supply chain, production

1515
Contexti ™ Big Data Analytics Maturity Model
Scale

Op9mise

Transform

Capture

Organise

Analyse

Ac9on

Intelligence
Func4on

Data
Supply
Chain

Sponsor

Focus
Analy9cs
Business
Technology

Data
as
a

Strategic

Asset
for

Compe99ve

Advantage

Data
as
a

Cost
of

Business

Business

Analy9cs
Informa9on
Technology

Database
Warehouse
Analy9cs
Business
Data
Informa4on
Insights
Decisions

Volume,
Velocity,
Variety
Value

GM
Level
CXO
Level

1616
Questions, Methods, Data Sets
QUESTIONS

Sales
&
Profit

Targets

Product
bundling

Targeted
offers

Product
associa9ons

METHODS

Forecas9ng

Market
Basket

Analysis

Price
elas9city

Campaign

Op9misa9on

Fraud
Detec9on

DATA
SETS

Online
Transac9ons

Offline
Transac9ons

Loyalty
Card
Data

Web
logs

Campaigns

1717
Analytics Methods
Forecas9ng
Trends,
seasonality
and
expected
sales
volume
&
dollars

Market
Basket
Tac9cal
offers,
store
posi9oning
and
bundled
products

Price
Elas9city

Tac9cal
value
of
effec9ve
pricing
strategies,
op9mised
to

boost
revenue,
profit
or
volume

Campaign

Op9misa9on

Predic9ve
modelling
to
more
effec9vely
target
the
most

likely
respondents
and
to
learn
WHY
they
respond
leading

to
be:er
product
design,
marke9ng,
offers,
branding
etc

Fraud
Detec9on

Highligh9ng

sta9s9cal
anomalies
and
suspect

transac9ons

1818
Big Data Analytics Technology
Technology
Data
Science
OperaGons
Data

Big
Data
AnalyGcs

Managed
Services

Architecture

IntegraGon

Monitoring

Support

-‐-‐-‐-‐-‐-‐-‐-‐

Hadoop
NoSQL

Primary

External

-‐-‐-‐-‐-‐-‐-‐-‐

Structured

Unstructured

Batch

Real-‐Gme

Models
&

Algorithms

-‐-‐-‐-‐-‐-‐-‐-‐

Custom

PredicGve

Machine-‐

Learning

Ingest

Process

Publish

-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐

AcGons

Real-‐Gme

Periodic

sFTP

1919
Big Data Analytics Technology – Under the hood
•  A
plaYorm
using
‘Cloud’
running
on

•  Interfacing
via
a
web
browser,
u9lising

•  Which
runs

code
interac9vely,
that
connects
to…

• 

and

using
Hive2
connec9vity
services,
on

• 

for
ETL
and

Machine
Learning
for
Market
Basket

Clustering
Analysis.

•  For
‘small
data’
aggregates,
data
is
fed
into

using

•  Automa9on
of
workﬂow
execu9on
is
taken
care
of
by

•  For
service
wide
security,
all
authen9ca9on
and
authorisa9on
uses

20
19 September 2013
20
What we have found – So far

21
Our Traditional View
73% LFL growth in one
piece styles

80% LFL growth in
overswim

Sell through increased
from 51% to 68%

78% LFL growth in
beach bags

Sell through increased
from 66% to 75%*

44% LFL growth in
bikini sets

20% LFL growth in
swim mix ups

21

22
Our Traditional View
Q37: IN THE LAST 12 MONTHS, WHICH OF THE FOLLOWING STORES HAVE YOU VISITED?
BASE: AWARE BILLABONG N=318. < 4% RATED THEIR EXPERIENCE IN BILLABONG WORSE THAN OTHER STORE. 34% HAD VISITED
NONE OF THESE STORES
30%

29%

24%

22%

17%

14%

13%

7%

6%

4%

2%

City
Beach

A
Billabong

store

A
Rip
Curl
store

Surf
Dive
'n'
Ski

General
Pants

A
Quicksilver

store

Je:y
Surf

Ozmosis

Rush

Hurley

Surfec9on

80%
visited
the

men’s
sec9on

56%
visited
the

women’s
sec9on

34%
visited
the

children’s
sec9on

65%

55%

54%

54%

52%

48%

46%

46%

45%

43%

43%

36%

34%

33%

31%

29%
HAD
VISITED
A
BILLABONG
STORE
IN
THE
LAST
YEAR

DISPLAY

PRODUCTS
AMBIENCE
AND
SERVICE

38%

be:er

vs.
BB

10%

14%

34%

6%

28
21 August 2013
28
Questions?
Thank you

Big Data & Technology at Billabong

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