1. 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
2. 22
Agenda
1. Context for Billabong
2. What we did to get to the solution
3. What we have found – so far
4. 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
5. 55
To unlock the strategic potential of the
business we refocused; an integrated approach
6. 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
7. 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
Global Roll-out of BI
Epicor Roll-Out
eComm
Phase I
eComm Phase 2 eComm Phase 3
BI Phase 1 BI Phase 2
Global Roll-out of BI
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. 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. 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. 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
12. 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
13. 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
14. 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
15. 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
16. 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
17. 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
18. 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
19. 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
21. 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. 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%