Big Data & Technology at Billabong


Published on

Jason Millett, Head of Technology at Billabong

Published in: Education, Technology, Business
  • Be the first to comment

  • Be the first to like this

No Downloads
Total views
On SlideShare
From Embeds
Number of Embeds
Embeds 0
No embeds

No notes for slide

Big Data & Technology at Billabong

  1. 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. 2. 22 Agenda 1.  Context for Billabong 2.  What we did to get to the solution 3.  What we have found – so far
  3. 3. 3 19 September 2013 3 Context for Billabong
  4. 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. 5. 55 To unlock the strategic potential of the business we refocused; an integrated approach
  6. 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. 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. 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. 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. 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. 11. 11 19 September 2013 11 What we did to get to the Solution
  12. 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. 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. 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. 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. 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. 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. 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. 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  
  20. 20. 20 19 September 2013 20 What we have found – So far
  21. 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. 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%  
  23. 23. 2323 Outputs & Insights
  24. 24. 2424 Outputs & Insights
  25. 25. 2525 Sample Market Basket
  26. 26. 2626 Outputs & Insights
  27. 27. 2727
  28. 28. 28 21 August 2013 28 Questions? Thank you