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Digitale Disruptie - BI slaat terug!

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Presentaties van het BICC Congres editie 2015

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Digitale Disruptie - BI slaat terug!

  1. 1. Inleiding Digital Disruptie en BI Dave Vanhoudt, BICC Thomas More
  2. 2. Onze missie Een neutraal en onafhankelijk platform voor samenwerking met de bedrijfswereld met als hoofddoel kennisdeling en innovatie te stimuleren
  3. 3. Onze missie
  4. 4. Onze missie
  5. 5. Onze missie
  6. 6. Onze missie “A time where technology and society are evolving faster than the ability of many organizations to adapt”
  7. 7. Onze missie
  8. 8. Agenda • Sessie 1: Open source, meer dan disruptieve software? Bart Maertens, Managing Partner, know.bi • Sessie 2: M&S Analytics: Join the Big Data revolution! Carl Sablon, Senior Consultant, Keyrus Peter Poppe, Principal Consultant, Keyrus • Sessie 3: Hoe het overzicht bewaren? Jörgen Jacob, Business Unit Manager, Fit IT • Sessie 4: Een versie van de waarheid: een achterhaald idee? Tobias Temminck, Teradata, Benelux Technology Officer • Sessie 5: Nood aan meer strategisch management? Dries Van Nieuwenhuyse, Onderzoeker, BICC Thomas More
  9. 9. Before lift-off • Mogelijkheid tot korte interactie (vragen) na elke sessie • Netwerk: TM_BICC met als key BICC1 • Twitter hastag: #BICongres15 • Twitter user: @BICC_ThomasMore
  10. 10. Sessie 1 Open Source Meer dan disruptieve software? Bart Maertens, Managing Partner know.bi
  11. 11. Open Source Business Intelligence
  12. 12. know.bi: • Founded in 2012 • OSBI consultancy in Benelux and UK • 5 consultants
  13. 13. What is OSS? • “Open-source software (OSS) is computer software with its source code made available with a license in which the copyright holder provides the rights to study, change and distribute the software to anyone and for any purpose” • Free “as in speech” rather than “as in beer”
  14. 14. What is OSS?
  15. 15. OSS Licenses
  16. 16. OSS Licensing • Moving from copyleft (GPL family) to permissive (e.g. Apache v2) • 2015: Apache v2 (do wtf you want) • Professional/Commercial OSS: • Dual licensing/Open Core: • Free Community Edition: • go your own way • Only free (as in beer) to download, use has a cost • Paid Enterprise Edition: • Pro approach (consultancy, support, training…) • Enhanced functionality • Beekeeper model
  17. 17. Evolution of OSS • Infrastructure: Linux, OS on low level hardware • Middleware: • databases (PostgreSQL, MariaDB) • application servers (JBoss, Tomcat) • Applications: Firefox, LibreOffice, GIMP • OSS is ubiquitous: increased need for standardization forces towards OSS
  18. 18. OSS Market Share Open Source dominates in • Supercomputing (485 of top 500 run Linux) • Cloud computing (75% Linux) • Web servers (65% Apache) • Mobile • Embedded • IoT • …
  19. 19. Open Everything
  20. 20. Why OSBI? • Frequent releases (+/- 6 months) • Support for cutting edge technologies (Big Data!) • Cost • Flexibility to integrate in/with other platforms • Easy to extend (plugin interfaces) • Community ecosystem • Avoid vendor lock in
  21. 21. OSBI Landscape• Data Integration: • Talend • Kettle/Pentaho Data Integration • Reporting • Eclipse BIRT • Jasper Reports • JFreeReport/Pentaho Reporting • OLAP • Palo • Mondrian/Pentaho Analysis + Saiku • Data Mining, Statistics: • R • RapidMiner • Weka • Platforms • SpagoBI • Pentaho • Jedox
  22. 22. • Pentaho components: • Data Integration (Kettle) • Reporting OLAP (Mondrian) • Data Mining (Weka) • Dashboarding (CTools) • BA server (security, scheduling) • Community contributions (marketplace)
  23. 23. • Only complete OSBI platform in the market • Founded in 2004 • (to be) acquired by HDS in 2015 • Open core: • CE: OSS engines • EE: CE + support, enhanced functionality • Strong community in (ao) EU
  24. 24. Community involvement • Marketplace: • Kettle/PDI • BA server • Forums • IRC (##pentaho) • Social media • Events: PBUG, PCM
  25. 25. The Power of OSS •
  26. 26. Use Cases
  27. 27. Use Case: Cipal • AthenaWeb: data warehouse solution for local governments • application-based data marts • multitenant • cloud-based • flexible
  28. 28. Use Case: Cipal Built end-to-end using Pentaho: • PDI for extract, transfer, ETL • Static reporting • OLAP (Analyzer) • Dashboards (EE dashboards + CTools) • Role know.bi: coaching, infrastructure, development • Community involvement: PCM13, PBUG13, PCM14, …
  29. 29. Thank You!
  30. 30. Use Cases
  31. 31. Use Cases
  32. 32. Use Cases
  33. 33. Sessie 1 Open Source Meer dan disruptieve software? Bart Maertens, Managing Partner know.bi
  34. 34. Sessie 2 Sales & Marketing Analytics Join the Big Data revolution! Carl Sablon, Senior Consultant, Keyrus Peter Poppe, Principal Consultant, Keyrus
  35. 35. AGILITY I COLLABORATIVE INTELLIGENCE I INNOVATION I PERFORMANCE CONSULTING I TECHNOLOGY SALES AND MARKETING ANALYTICS / JOIN THE BIG DATA GENERATION! PIETER VANDAMME MARCH 2015
  36. 36. ©Keyrus–Tousdroitsréservés / THE KEYRUS APROACH – STEPWISE AND AGILE SOLUTIONS FROM DATA TO PRESCRIPTIVE INSIGHTS AND PERSONALIZED MARKETING Understand (context) Model (stakes) Embed (insights) from intuition to business issue formulation in a data-centric problem « data-analytic thinking » from modeling to deployment « analytic action » Continuous monitoring based on KPI’s and « enrichment of the data environment »
  37. 37. ©Keyrus–Tousdroitsréservés / PRACTICAL ANALYTICS CASES IN MARKETING FROM DATA TO PRESCRIPTIVE INSIGHTS AND PERSONALIZED MARKETING CASE STUDY – SOLIDSTORE.COM / INTERNATIONAL RETAILER / CONSUMER ELECTRONICS, BOOKSTORE, FASHION, LIFESTYLE / ONLINE SALES / PROMO DRIVEN
  38. 38. ©Keyrus–Tousdroitsréservés / SALES FIGURES 2013 vs 2014 UNDERSTAND CUSTOMER BEHAVIOR SOLIDSTORE.COM Limited Analytical Capabilities
  39. 39. ©Keyrus–Tousdroitsréservés / FIGURES PER COUNTRY UNDERSTAND CUSTOMER BEHAVIOR -25% -20% -15% -10% -5% 0% 5% 10% 0% 2% 4% 6% 8% 10% 12% Sales Per Country Weight Country Evol vs N-1 SOLIDSTORE.COM Reliability issues
  40. 40. ©Keyrus–Tousdroitsréservés / VALUE SEGMENTATION: 2014 vs 2013 UNDERSTAND CUSTOMER BEHAVIOR • +350 EUR/yearTier 1 •120 – 350 EUR/yearTier 2 •40 – 120 EUR/year Tier 3 •- 40 EUR/year Tier 4 18 k -4% 15,2M -5% 20 k -2% 4,1M -3% 19 k -4% 1,4 M -4% 21 k -1% 0,4 M -3% € 27% 24% 25% 23% 2% 7% 20% 72% SOLIDSTORE.COM Labour intensive Not Flexible
  41. 41. ©Keyrus–Tousdroitsréservés / SOLIDSTORE.COM SALES AND MARKETING DATA ANALYTICS REQUIREMENTS UNDERSTAND CUSTOMER BEHAVIOR Extra capabilities required on top of traditional Enterprise Business Intelligence to improve and guarantee flexibility Reporting ► Business and data expertise instead of technology focus Analytics ►Visual Story Telling with latest techniques ► Access to basic statistical algorithms Data Integration ► Sandbox for advanced query ► Volatile and unstructured data access High Performance ► Real-Time data exploration on granular data
  42. 42. ©Keyrus–Tousdroitsréservés / THE KEYRUS APROACH – STEPWISE AND AGILE SOLUTIONS FROM DATA TO PRESCRIPTIVE INSIGHTS AND PERSONALIZED MARKETING Understand (context) Model (stakes) Embed (insights) from intuition to business issue formulation in a data-centric problem « data-analytic thinking » from modeling to deployment « analytic action » Continuous monitoring based on KPI’s and « enrichment of the data environment » ► Business Case Creation (Intuition) ► Data Discovery ► Data Acquisition
  43. 43. ©Keyrus–Tousdroitsréservés Decreasing Sales  How does the different countries perform compared to each other ?  Which product categories increase in sales compared to last year ?  Does my different sales channels perform as expected ? Customer Loss  Do I lose high value customers compared to last year ?  Are my customers loyal ? Realize full potential of Online Channel  How is the conversion of my web campaigns ?  How can I optimize my campaign strategy approach ? / BUSINESS CASE (INTUITION) UNDERSTAND CUSTOMER BEHAVIOR
  44. 44. ©Keyrus–Tousdroitsréservés / THE KEYRUS APROACH – STEPWISE AND AGILE SOLUTIONS FROM DATA TO PRESCRIPTIVE INSIGHTS AND PERSONALIZED MARKETING Understand (context) Model (stakes) Embed (insights) from intuition to business issue formulation in a data-centric problem « data-analytic thinking » from modeling to deployment « analytic action » Continuous monitoring based on KPI’s and « enrichment of the data environment » ► Business Case Creation (Intuition) ► Data Discovery ► Data Acquisition
  45. 45. ©Keyrus–Tousdroitsréservés Decreasing Sales  How does the different countries perform compared to each other ?  Which product categories increase in sales compared to last year ?  Does my different sales channels perform as expected ? / BUSINESS CASE (INTUITION) UNDERSTAND CUSTOMER BEHAVIOR
  46. 46. ©Keyrus–Tousdroitsréservés / BUSINESS CASE (INTUITION) UNDERSTAND CUSTOMER BEHAVIOR • How can I explain the decreasing sales ? • Channel specific performance: online perform better than stores, but below expectations • Different performance between countries: often product related • Align marketing 2015 budgets per country based on sales facts per product category • Extend dashboard with ROI and budget figures Question Insight Action Plan
  47. 47. ©Keyrus–Tousdroitsréservés Decreasing Sales  How does the different countries perform compared to each other ?  Which product categories increase in sales compared to last year ?  Does my different sales channels perform as expected ? Customer Loss  Do I lose high value customers compared to last year ?  Are my customers loyal ? / BUSINESS CASE (INTUITION) UNDERSTAND CUSTOMER BEHAVIOR
  48. 48. ©Keyrus–Tousdroitsréservés / BUSINESS CASE (INTUITION) UNDERSTAND CUSTOMER BEHAVIOR • How can I explain the decreasing sales ? • Channel specific performance: online perform better than stores, but below expectations • Different performance between countries: often product related • Align marketing 2015 budgets per country based on sales facts per product category • Extend dashboard with ROI and budget figures • Do I lose high value customers ? • Customer trend towards lower segments • Positive inflow of new customers in Tier 1 • Customer value segmentation strongly related to products bought (Hardware) • Review customer segmentation • Campaign Optimization: right offer to the right customer at the right time Question Insight Action Plan
  49. 49. ©Keyrus–Tousdroitsréservés Decreasing Sales  How does the different countries perform compared to each other ?  Which product categories increase in sales compared to last year ?  Does my different sales channels perform as expected ? Customer Loss  Do I lose high value customers compared to last year ?  Are my customers loyal ? Realize full potential of Online Channel  How is the conversion of my web campaigns ?  How can I optimize my campaign strategy approach ? / BUSINESS CASE (INTUITION) UNDERSTAND CUSTOMER BEHAVIOR
  50. 50. ©Keyrus–Tousdroitsréservés / BUSINESS CASE (INTUITION) UNDERSTAND CUSTOMER BEHAVIOR • How can I explain the decreasing sales ? • Channel specific performance: online perform better than stores, but below expectations • Different performance between countries: often product related • Align marketing 2015 budgets per country based on sales facts per product category • Extend dashboard with ROI and budget figures • Do I lose high value customers ? • Customer trend towards lower segments • Positive inflow of new customers in Tier 1 • Customer value segmentation strongly related to products bought (Hardware) • Review customer segmentations • Campaign Optimization: right offer to the right customer at the right time • Do I realize the full potential of my Online Channel ? • Customers do not find easily relevant products (Select No) • Customers fall out too often at Check Out (Time / Transportation Costs) • Credit Card process: time outs • Recommendation Engine • Renegotiate conditions with delivery companies: calculate potential lost sales • Improve site performance on peak moments Question Insight Action Plan
  51. 51. ©Keyrus–Tousdroitsréservés / THE KEYRUS APROACH – STEPWISE AND AGILE SOLUTIONS FROM DATA TO PRESCRIPTIVE INSIGHTS AND PERSONALIZED MARKETING Understand (context) Model (stakes) Embed (insights) from intuition to business issue formulation in a data-centric problem « data-analytic thinking » from modeling to deployment « analytic action » ► Business Case Creation (Intuition) ► Data Discovery ► Data Acquisition Continuous monitoring based on KPI’s and « enrichment of the data environment »
  52. 52. ©Keyrus–Tousdroitsréservés / DATA ECOSYSTEM ASSESSMENT FROM DATA TO PRESCRIPTIVE INSIGHTS AND PERSONALIZED MARKETING Enterprise ERP Data Enterprise Data Warehouse Analytical Applications Public Open Data Web Data Social Data CRM Data In-Store Terminal Data enriched data space of the digital ecosystem Availability Accessibility Costs Insights Availability Accessibility Costs Insights Availability Accessibility Costs Insights Availability Accessibility Costs Insights Availability Accessibility Costs Insights Availability Accessibility Costs Insights Availability Accessibility Costs Insights Availability Accessibility Costs Insights
  53. 53. ©Keyrus–Tousdroitsréservés / THE KEYRUS APROACH – STEPWISE AND AGILE SOLUTIONS FROM DATA TO PRESCRIPTIVE INSIGHTS AND PERSONALIZED MARKETING Understand (context) Model (stakes) Embed (insights) from intuition to business issue formulation in a data-centric problem « data-analytic thinking » from modeling to deployment « analytic action » Continuous monitoring based on KPI’s and « enrichment of the data environment » ► Model ► Obtain Collateral Insights ► Enrich Data Sources
  54. 54. ©Keyrus–Tousdroitsréservés / ACTIONS FOR ANALYTICS TEAM MODEL CUSTOMER BEHAVIOR • How can I explain the decreasing sales ? • Channel specific performance: online perform better than stores, but below expectations • Different performance between countries: often product related • Align marketing 2015 budgets per country based on sales facts per product category • Extend dashboard with ROI and budget figures • Do I lose high value customers ? • Customer trend towards lower segments • Positive inflow of new customers in Tier 1 • Customer value segmentation strongly related to products bought (Hardware) • Review customer segmentations • Campaign Optimization: right offer to the right customer at the right time • Do I realize the full potential of my Online Channel ? • Customers do not find easily relevant products (Select No) • Customers fall out too often at Check Out (Time / Transportation Costs) • Credit Card process: time outs • Recommendation Engine • Renegotiate conditions with delivery companies: calculate potential lost sales) • Improve site performance on peak moments Question Insight Action Plan
  55. 55. ©Keyrus–Tousdroitsréservés / PERSONALIZATION LEVEL MODEL CUSTOMER BEHAVIOR Mass ACCURACY COMPLEXITY Segmentation Personalised
  56. 56. ©Keyrus–Tousdroitsréservés / COMPARE PREDICTIVE APPROACHES MODEL CUSTOMER BEHAVIOR Propensity model offer Recommendation engine offer Event Based event/behaviour change A customer does not think in channels or campaigns ... Right offer for the right customer in the right time
  57. 57. ©Keyrus–Tousdroitsréservés / PLATFORM AS A SERVICE KEYRUS EXPERTISE & KNOW-HOW / Data Integration / Transforming data into models / Publication of auto-scalable services Data Integration Management models Development models Models made ​​by services Exported Models Test/validation Interface Interface service call Interface data export Integrate Generate Keyrus ServicesClient Services Test a recommendation Ask for a recommendation
  58. 58. ©Keyrus–Tousdroitsréservés / PLATFORM AS A SERVICE KEYRUS EXPERTISE & KNOW-HOW / Comparison of several possible methodologies and configurations of algorithms / Enabling publishing services in one click. Analytics as a service / Auto scale-up of the cluster to start the treatments and auto - scale down . Cost and performance optimization / Contains an API able to absorb a variable load to 20 million customers / Two modes , Wizard and Flow Designer offer assisted or customized modelling / Methods developed on the basis of open source algorithms / Service-oriented and modular Architecture / Customizable extensions / Easy to test and deploy
  59. 59. ©Keyrus–Tousdroitsréservés MODEL MANAGEMENT KEYRUS EXPERTISE & KNOW-HOW /Model Testing Customer list Customer’s information Recommandation list for the customer
  60. 60. ©Keyrus–Tousdroitsréservés / THE KEYRUS APROACH – STEPWISE AND AGILE SOLUTIONS FROM DATA TO PRESCRIPTIVE INSIGHTS AND PERSONALIZED MARKETING Understand (context) Model (stakes) Embed (insights) from intuition to business issue formulation in a data-centric problem « data-analytic thinking » from modeling to deployment « analytic action » Continuous monitoring based on KPI’s and « enrichment of the data environment » ► Deploy ► Monitor ► Integrate
  61. 61. ©Keyrus–Tousdroitsréservés / THE CAMPAIGN MANAGEMENT PROCESS EMBED INSIGHTS IN DAY TO DAY PROCESSES Communication and Campaign Policy • Budget • Platform Capacity • Contact Rules CustomerEligibility Channel Product Reduction Validity Lay Out Mail Books 5% 1 day Traditional SMS Hardware 10% 1 week Fancy Email DVD 15% 2 weeks Grouped Coupon Toys 20% 1 month Multimedia Accessories Allocation Process • Map the proposed offer to the customer • Allocate taking into account eligibility and campaign policies
  62. 62. ©Keyrus–Tousdroitsréservés / SOLIDSTORE DATA ECOSYSTEM EMBED INSIGHTS IN DAY TO DAY PROCESSES Applications CRM Data Terminals ExternalExisting Analytics Data Sources Ecosystem Data Acquisition & Qualification Behavioral & Predictive Modeling Data Ecosystem Insights ► opportunities ► constraints ► limitations ► acquisition costs <<enrich>> <<get>> machine learning algorithms library 1 Model Performance Assessment power, business performance KPI’s 2 3 <<tune>> <<enrich>> technical and technology services experimental data lab data pre-processing routines and algorithms Deployment Feasibility & Impact <<finalize>> 4 ITERATIVE BUILD-UP
  63. 63. ©Keyrus–Tousdroitsréservés Approach ► Self service flexibility on top of traditional Business Intelligence (volatile and unstructured data integration) ► Skilled consultants in Marketing Management, Data Science and Campaign Management ► Based on Smart Visualization ► Advanced Analytical Capabilities beyond traditional models (Statistics, Data Mining, Text Mining, Machine Learning) ► Iterative and collaborative approach ► Actionable / WRAP UP FROM DATA TO PRESCRIPTIVE INSIGHTS AND PERSONALIZED MARKETING KEY SUCCESS FACTORS Understand (context) Model (stakes) Embed (insights)
  64. 64. ©Keyrus–Tousdroitsréservés Platform ► Scalable solutions ► Analytics on Big Data High Performance platforms (Volume/Time 2 Market/Structured and Unstructured) ► Skilled in both open source as traditional vendor platforms and products ► Available in private and public cloud ► Embedded in the operational processes / WRAP UP FROM DATA TO PRESCRIPTIVE INSIGHTS AND PERSONALIZED MARKETING KEY SUCCESS FACTORS Understand (context) Model (stakes) Embed (insights)
  65. 65. Sessie 2 Sales & Marketing Analytics Join the Big Data revolution! Carl Sablon, Senior Consultant, Keyrus Peter Poppe, Principal Consultant, Keyrus
  66. 66. One more thing... Enterprise community of students 1 IMS
  67. 67. “Education is not the piling on of learning, information, data, facts, skills or abilities – that’s training or instruction – but is rather a making visible what is hidden as a seed...” - Thomas More
  68. 68. One more thing...
  69. 69. Sessie 3 The Data Gods must be crazy... Hoe het overzicht behouden? Natalie Beernaert, Business Unit Manager, Fit IT
  70. 70. Predictive Analytics Creating Business Value from your Data Nathalie Beernaert – 26 Maart 2015
  71. 71. Agenda ●Introduction ●Value for the Customer ●Data Mining vs Predictive Analytics ●Learn from Experience ●Use Cases by Function ●Use Cases by Market ●Return on Investment Predictive Analytics
  72. 72. Fit IT at a glance Predictive Analytics Strategic PartnersFacts & Figures ● 3 Activities  Business Analytics  Systems Engineering  Business Applications ● > 14 Mio Revenue ● 90 FTE’s Locations  Ghent  Brussels  Antwerp
  73. 73. Predictive Analytics Value for the Customer Reporting Query, Search, Reporting COMPLEXITY BUSINESS VALUE What happened Why did it happen What’s happening now What might happen Analysis OLAP Visualisation Monitoring Dashboards, scorecards Prediction Predictive Analytics
  74. 74. Predictive Analytics Data Mining versus Predictive Analytics “Which products are bought together” => CORRELATIONS “Who buys a certain product and why” => INFLUENCE Predictive Analytics Data Mining
  75. 75. Predictive Analytics Learn from Experience Male, Ghent, Married, Children, ...
  76. 76. Predictive Analytics Use Cases by Function Product Mix Marketing Predicting Life Time Value Up Selling Channel Optimization Reactivation Likelyhood Customer Churn Risk Credit Risk Accounts Payable Recovery Fraud Detection Anti-Money Laundering Treasury or Currency Risk Churn HR Resume Screening Training Recommendation Talent Management Employee Churn
  77. 77. Predictive Analytics Use Cases by Market Product Mix Life Science Drug/chemical Discovery & Analysis Diagnostic Targeting (CRM) Predicting prescription adherence with different approaches to reminding patients Predicting drug demand in different geographies for different products Churn Retail Merchandising Shrinkage Analytics Location of New Stores Pricing Market Basket Analysis Next Best Offer Analysis Warranty Analytics Insurance Claims Prediction Investments Product Mix Agent and Brand Performance Price Sensitivity
  78. 78. Predictive Analytics Return on Investment Mailing to all customers (1.000.000)  Cost: €2  Profit/Conversion: €220  Response rate 1%  Profit: €200.000 Mailing to segment customers (250.000)  Cost: €2  Profit/Conversion: €220  Response rate 3%  Profit: € 1.150.000
  79. 79. Thank you ! Axians - Fit IT nv Guldensporenpark 35 9820 Merelbeke nathalie.beernaert@axians.com 0476/27.66.03 jorgen.jacob@axians.com 0475/60.42.27
  80. 80. Sessie 3 The Data Gods must be crazy... Hoe het overzicht behouden? Jörgen Jacob, Business Unit Manager, Fit IT
  81. 81. Sessie 4 Een versie van de waarheid Een achterhaald idee? Tobias Temmink, BeNeLux Technology Officer, Teradata
  82. 82. INTEGRATED DATA IN THE BIG DATA ERA Tobias Temmink – Technology Officer March 2015
  83. 83. 95 © 2014 Teradata
  84. 84. 96 © 2014 Teradata The data-driven business puts data and analytics at the center
  85. 85. 97 Data and Analytics Evolution Application Centric Integration Centralized Decentralized Capability Rigid Agile Data and Analytic Centric
  86. 86. 98 Organization has a full fledged analytic architecture that is enterprise wide, fully automated, integrated into processes, and sophisticated Organization has high quality data. An enterprise wide analytics plan, governance principles, and some automated analytics Proliferation of BI tools and data marts but most data remains unintegrated. Non standardized, and inaccessible Organization collects transaction data efficiently but often lacks the right data for better decision making Organization is plagued by missing or poor quality data, multiple definitions of its data, and poorly integrated systems Stages of Analytic Maturity Source: Davenport Harris, Competing on Analytics, Harvard Business School Press, 2007, pp156
  87. 87. 99 FINANCE Revenue Expenses Customers CUSTOMER CARE Customer Products Orders Case History SALES Orders Customers Products MARKETING Customers Orders Campaign History OPERATIONS Inventory Returns Manufacturing Supply Chain Which plants are using which suppliers for EV batteries? How many EV batteries are in inventory by manufacturing plant? What is the trend of warranty costs? What is the Year-Over- Year growth in hybrid sales? How many people reported an issue with EV batteries last month? How many people made a warranty claim on Hybrid cars last week? How many sales of hybrid cars have been made quarter to date? What % of after market accessories are sold to hybrid customers? Which customers should get upcoming email communication on hybrid car extended warranties? Which of our customers are likely to buy a hybrid car in the next 3 months? 54 32 29 49 66
  88. 88. 100 2954 32 49 41
  89. 89. 101 2855 Given the rise in warranty costs, isolate the problem to be a specific plant, then isolate to a specific battery lot. Communicate with affected customers, who have not already made a warranty claim on batteries, through Marketing and Customer Service channels to recall cars with batteries. Inventory Returns Manufacturing Supply Chain Customer Service Orders Revenue Expenses Case History Customers Products Pipeline Customers Campaign History FINANCE SALESMARKETING OPERATIONS CUSTOMER EXPERIENCE 2855
  90. 90. 102 2855 Inventory Returns Manufacturing Supply Chain Customer Service Orders Revenue Expenses Case History Customers Products Pipeline Customers Campaign History FINANCE SALESMARKETING OPERATIONS CUSTOMER EXPERIENCE Tightly Coupled
  91. 91. 103 Is not about Volume, Velocity and Variety anymore…. It is about how you use the data and analytics
  92. 92. 104 BIG DATA WEB Petabytes CRM Terabytes Gigabytes ERP Exabytes INCREASING Data Variety and Complexity User Generated Content Mobile Web SMS/MMS Sentiment External Demographics HD Video Speech to Text Product/ Service Logs Social Network Business Data Feeds User Click Stream Web Logs Offer History A/B Testing Dynamic Pricing Affiliate Networks Search Marketing Behavioral Targeting Dynamic Funnels Payment Record Support Contacts Customer Touches Purchase Detail Purchase Record Offer Details Segmentation DECREASING Value Density in the Data Big Data: From Transactions to Interactions
  93. 93. 105 105 2855
  94. 94. 106 2855 SENSOR DIGITAL ADVERTISING CLICKSTREAM INTERACTIONS RATINGS & REVIEWS CUSTOMER PORTAL INTERACTIONS EXTERNAL INTERACTIONS SOCIAL MEDIA IVR Routing RFID ELECTRONIC COMMERCE FINANCE SALES MARKETING Inventory Returns Manufacturing Supply Chain Customer Service Orders Revenue Expenses Case History Customers Products Pipeline Customers Campaign History OPERATIONS CUSTOMER CARE – AUDIO RECORDINGS Maps Telemetry SERVER LOGS CUSTOMER EXPERIENCE
  95. 95. 107 2855 Tightly Coupled Loosely Coupled Non Coupled
  96. 96. 108 108 28556350
  97. 97. 109 109 2855 FINANCE SALES MARKETING OPERATIONS CUSTOMER EXPERIENCE By combining customer care , warranty, and supply chain data with battery sensor data, it is discovered that excessive heating on cells from a specific manufacturer is the root cause. CASE HISTORY COSTSSENSOR SUPPLY CHAIN
  98. 98. 110 110 2855 MANUFACTURING CAMPAIGN HISTORY COSTS PRODUCTS FINANCE SALES MARKETING OPERATIONS CUSTOMER EXPERIENCE Given the rise in warranty costs, isolate the problem to be a specific plant, then isolate the specific lot. Result is two-thirds of the bad battery lot are fine, and exclude them from the recall. Communicate with affected customers, who have not already made a warranty claim on batteries, through Marketing and Customer Service channels to recall cars with batteries. CUSTOMERS CASE HISTORY SENSOR
  99. 99. 111 © 2014 Teradata Customer Segments Product Affinity Predictive Part Customer Churn Customer LTV Non Coupled Loosely Coupled Tightly Coupled Business Generated Human Generated Machine Generated Interaction Generated Operational Intelligence Predictive Analytics Graph Analytics Path Analytics Machine Learning Customer Risk E-Mail Offer Reporting Data Business Decision Degrees of Integration Analytic Analytic Processes Business Process Customer Product Email Product Offer Customer Care Treatment
  100. 100. 112112
  101. 101. Sessie 4 Een versie van de waarheid Een achterhaald idee? Tobias Temminck, BeNeLux Technology Officer, Teradata
  102. 102. Afsluitende sessie & wrap-up Nood aan meer strategische management? Dries Van Nieuwenhuyse, BICC Thomas More
  103. 103. Strategie = TOP DOWN Meestal start men met een schitterende missie en een veelbelovend visie en zet men alles in het werk om die te realiseren…
  104. 104. Strategie = TOP DOWN
  105. 105. Strategie = TOP DOWN
  106. 106. Strategie = BOTTOM UP • De vraag die zich stelt is echter of het allemaal zo helder was van bij het begin… • Hebben succesvolle bedrijven niet altijd een goede strategie? • Komt het niet van onderen naar boven?
  107. 107. Strategisch management • Combinatie van ontdekken en ontwikkelen • Van mogelijkheden om op herhaalbare wijze • Waarde te creëren
  108. 108. Strategisch management • Huwelijk tussen gehoopte toekomst, haalbare toekomst en noodzakelijke toekomst • Hoe kunnen we het verschil maken en blijven maken?
  109. 109. Strategisch management • Een zinvol business model kan maar worden gerealiseerd via een passend organisatie model
  110. 110. Performance Management en Strategisch Management • Kan Performance MANAGEMENT hier een bijdrage leveren? • Kunnen we iets leren van Strategisch MANAGEMENT? • Natuurlijk, wat had je gedacht?
  111. 111. Strategieformulering • Waar zijn we nu goed in? • Waar zijn onze concurrenten goed in? • Waar liggen nog opportuniteiten en in welke mate? Ansoff Product- Markt matrix BCG product portfolio
  112. 112. Strategieopvolging • Balanced Scorecard: visualisatie van de realisatie van de strategie • Actual versus Target • Stapje achteruit en zien
  113. 113. Strategie(bij)sturing • Faciliteren van de kwantitatieve beleidsprocessen door PDCA- cyclus heen o Plan  Budgettering  Opvolging o Do  Operationele ondersteuning o Check  Actual versus budget  Balanced Scorecard
  114. 114. Strategie(bij)sturing • (Re)Act o Performance MANAGEMENT heeft ervaring met het formuleren en opvolgen van strategie o Continu bijsturen van de strategie o Terugkoppeling naar de strategieformulering o Target setting om strategische doelen ook effectief op te volgen en te realiseren o Evaluatie van hoe goed we wel bezig zijn o Predictie van potentieel o Forecasting van wat mogelijk resultaat van onze strategie zou kunnen zijn
  115. 115. Strategie = TOP-DOWN Strategie = BOTTOM-UP • Performance MANAGEMENT heeft ervaring met het formuleren en opvolgen van strategie • Strategisch MANAGEMENT helpt creatieve vragen te stellen van wat mogelijk zou kunnen zijn • Beide zijn met elkaar getrouwd, en dat is maar goed ook…
  116. 116. Afsluitende sessie & wrap-up Nood aan meer strategische management? Dries Van Nieuwenhuyse, Onderzoeker, BICC Thomas More
  117. 117. #BICongres16
  118. 118. Change is the law of life. Those who look only to the past or present are certain to miss the future. - John F. Kennedy

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