big data & technology at billabong
DESCRIPTION
Jason Millett, Head of Technology at BillabongTRANSCRIPT
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19 September 2013
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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
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Agenda
1. Context for Billabong
2. What we did to get to the solution
3. What we have found – so far
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19 September 2013
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Context for Billabong
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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
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To unlock the strategic potential of the business we refocused; an integrated approach
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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.
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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 12 3 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
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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
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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
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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
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“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
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Core PlaYorm
Rules Engine
Opportunities to apply Big Data for Business Change
Customer Servicing Repor9ng
Configura9on Opera9ons
Member Website
Mobile App
Behaviou
r 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
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19 September 2013
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What we did to get to the Solution
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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
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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
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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
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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
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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
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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
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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
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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
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What we have found – So far
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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
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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%
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Outputs & Insights
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Outputs & Insights
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Sample Market Basket
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Outputs & Insights
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21 August 2013
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Questions? Thank you