Digital-Warriors-Marketing Roadmap with Big Data Analytics
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This document outlines a presentation on driving marketing programs with big data analytics. It discusses the evolution of analytics, provides a 4-step roadmap for implementing big data marketing, and reviews some key risks. The 4 steps are: 1) proof of concept, 2) datatification, 3) governance, and 4) consumerization. Each step provides critical people, process, and technology considerations. The presentation also reviews big data enablers and provides references for further information.
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Digital-Warriors-Marketing Roadmap with Big Data Analytics
- 1. 4 Key Steps to Drive your Marketing Program with Big Data Analytics Jason Bowden – Chief Warrior & Consultant www.digital-warriors.com IT Consulting - Business Intelligence Analytics - Digital Marketing @DigitalWarrior8 Copyright Digital-Warriors 2014
- 2. Jason Bowden Chief Warrior & Consultant www.digital-warriors.com @DigitalWarrior8 Presenter Experience: • 17 years of experience with roles specializing in Enterprise Intelligence & Analytics • Fortune 500 experience: UPS, T- Mobile, and Cox • Successful execution of 200+ Analytics Project Implementations • CFY Technology Board Member (Non-Profit) • Influential publisher for socialmediatoday.com • Known as an very helpful GEEK! Contact: jasonbowden1@gmail.com Or 404-966-7676 Free eBook: http://www.digital-warriors.com/big- data-marketing-analytics/ Copyright Digital-Warriors 2014
- 3. What you will Learn in this Session….. Big Data Marketing Phenomenon you care about…. How did we get here? The Evolution of Analytics 4 Key Steps for your Big Data Marketing Roadmap Key Risks to think about…. Free eBook: http://www.digital-warriors.com/big-data-marketing-analytics/ Copyright Digital-Warriors 2014
- 4. Big Data Marketing Phenomenon you care about…. Copyright Digital-Warriors 2014
- 5. The NEW 4P’s of Marketing! Performance Near Real Time Decisions and Treatment High Fidelity Enrichment of ALL the Data! Personalization & Preference Focus on Localization and Environment factors Focus on Relationships Focus on Individual Behavior Prediction Why vs. What or Causality vs. Correlation Machine Learning & Probabilities Privacy Great Power has Great Responsibilities Copyright Digital-Warriors 2014
- 7. Foresight Understanding the Evolution of AnalyticsBusinessValue Complexity High High Big Data Enables these… PredictiveExploratoryDescriptive InsightHindsight Copyright Digital-Warriors 2014 Free eBook: http://www.digital-warriors.com/big-data-marketing-analytics/
- 8. Auto Discovery of Unknowns • Things I don’t understand • I can ask questions • Things I Know • I can ask questions • Things I don’t know • I can Discover Copyright Digital-Warriors 2014 • Things I don’t know • I can’t ask questions • Very difficult to discover Big Data Value PropositionTraditional Analytics
- 9. How to get there? 4 Key Steps….. Proof of Concept Datatification Governance Consumerization Copyright Digital-Warriors 2014 Free eBook: http://www.digital-warriors.com/big-data-marketing-analytics/
- 10. Big Data Analytics Marketing Roadmap Proof of Concept Critical Steps People: • Recruit Open Minded Executive • Find/Grow and Dedicate a Techno-Functional Resource Process: • Invest in Use Cases! • Listen for Unsolved Problems • Develop ROI Case Technology: • Setup Low Cost Sandbox • Cloud Based-Amazon Redshift • Use Open Source • Experiment with APIs Use Case Definition Datatification Governance Consumerization Copyright Digital-Warriors 2014 Free eBook: http://www.digital-warriors.com/big-data-marketing-analytics/
- 11. 4 Steps: Big Data Marketing Roadmap Datatification Critical Steps People: • Position User/Customer Incentives • Turn Intangible Behavior into Tangible data Process: • Focus on Unique Identifiers • Top Down Metric Definition • Understand Data Exhaust & Reuse Technology: • Map Collection Channels: • Proprietary, Public, Purchase • Create a Data Lake Use Case Defition Datatification Governance Consumerization Data Lake Copyright Digital-Warriors 2014 Free eBook: http://www.digital-warriors.com/big-data-marketing-analytics/
- 12. 4 Steps: Big Data Marketing Roadmap Critical Steps: People: • Establish a Customer Health Organization Process: • Integration into Current Process and Systems • Focus on Unique Identifiers and matching logic Technology: • Meta-Data Management • Business Process & Statistical Modeling Proof of Concept Datatification Governance Consumerization Copyright Digital-Warriors 2014 Free eBook: http://www.digital-warriors.com/big-data-marketing-analytics/
- 13. 4 Steps: Big Data Marketing Roadmap Critical Steps: People: • Enable Internal Adoption by many levels of Employees • High Touch or White Glove Customer Service Process: • Prescriptive Actions • Automation of Decisions • Internal Training Technology: • Mobile Enablement • Visualization & Infographics • Self-Service Analytics & Exploration • Security Proof of Concept Datatification Governance Consumerization Copyright Digital-Warriors 2014 Free eBook: http://www.digital-warriors.com/big-data-marketing-analytics/
- 14. Big Data Marketing Risks to Consider Privacy Paradox Human Freewill Copyright Digital-Warriors 2014
- 15. Big Data Enablers Cloud Infrastructure and Cheap Storage Distributed and In Memory Processing Complex Data Visualization Business Process & Statistical Modeling Copyright Digital-Warriors 2014
- 16. Presentation Services Platform DW Big Data Services Information Reference Model Traditional ODS Replication Integration Platform MicroBatchEnrichment Master Data Traditional EDW Structured Transactions In Memory Data ComplexNightlyBatch Data Marts OLAP Services SOA: Web Services Security, Data Quality Interface, Meta Data Day or GreaterIntra-DayNear Real-Time Structured Data Master Entities & Hierarchies Alerting/Notification Treatment Engines Structured to Unstructured Interface Internal Sources Real-Time CRM Billing Projects SFDC Financials Others Social Media SDFC External Sources Internal Apps Direct Connections Enrichment Services Web Portals Visualization Note Key Understanding • Data Collection from Internal and External Sources; Datatification, Data Exhaust, Data Reuse • Matching Algorithm to Identify and Enrich Internal Business Entities: Customers, Vendors, Assets, Brands etc. • Data Science Environment: Statistical Modeling and Rapid access with In memory database architecture • Enrichment to business processes, products and service offerings. Powerful Presentation services should provide embeddable content • Outline items are new logical architecture required to support Big Data Analytics and Services 1 2 3 4 Mobile Machines • HDFS (Storage) • Pig (Scripting) • Hive (Query) • MapReduce (MPP) Data Lake Platform Mobile Apps Machine Learning 1 2 3 1 Advanced Analytics API: Restful 4 4 Near Real-Time Near Real-Time Copyright Digital-Warriors 2014
- 17. Follow me on Twitter: @DigitalWarrior8 Download my Free eBook: www.digital-warriors.com/big- data-marketing-analytics Contact: Skype: jbowden2005 Email: info@digital-warriors.com Phone: 888-988-7567 Copyright Digital-Warriors 2014
- 18. I am Board Member for a Non-Profit Org MISSION CFY helps students, together with their parents and teachers, use digital learning to improve educational outcomes. Our goal is to help improve student engagement and academic achievement in Atlanta’s low-income communities, where there is a tremendous need for educational programs and resources to help close the achievement gap. Our signature Digital Learning Program is a whole-school/community initiative that provides school leadership support along with technology tools and training to increase family involvement in learning, strengthen teachers’ ability to use digital learning to personalize instruction, and help students become self-directed learners. To date, CFY-Atlanta has helped and inspired over 6,500 families across metro Atlanta. With community support —your support— we can continue to provide much needed training and resources to ensure that all children are able to succeed in our digital world. Please contact me if you have old computer your company can donate CFA‘s Website-http://www.cfy.com/atlanta Copyright Digital-Warriors 2014
- 19. • http://hortonworks.com/customers/ • http://www.pentaho.com/customers • http://searchconsumerization.techtarget.com/definition/IT-consumerization-information-technology-consumerization • http://www.forbes.com/sites/gregsatell/2013/09/13/confused-about-big-data-here-are-5-things-you-need-to-do • http://www.sas.com/content/dam/SAS/en_us/doc/conclusionpaper1/align-marketing-it-to-drive-business-results-106431.pdf • http://acxiom.com/resources/big-data-big-deal/ • http://www.techrepublic.com/blog/big-data-analytics/big-data-analysts-do-you-hire-or-train-for-it/#. • http://www.emc.com/collateral/white-papers/idg-bigdata-storage-wp.pdf • http://zimmer.csufresno.edu/~sasanr/Teaching-Material/MIS/Automation/automated-decision-making-comes-of-age.pdf • http://www.slideshare.net/Management-Thinking/the-deciding-factor-big-data-decision-making • http://www.sap.com/bin/sapcom/en_us/downloadasset.2012-08-aug-28-19.the-roi-of-sap-netweaver-decision-service-management-pdf.html • http://www.bigdata-startups.com/open-source-tools/ References Copyright Digital-Warriors 2014
Notes de l'éditeur
- Big Data and Personalization: Understanding individual customer preferences & behaviors Understanding individual customer local environment ROI understanding (data and case studies) Big Data and Performance: External Data enrichment Social Listening Sentiment Analysis Social Signals Competitor Analysis ROI understanding (data and case studies Big Data and Predictive Applications Moving from Descriptive Analytics to Exploratory & Predictive Analytics Machine Learning& Recommendation Engines: Effective lead targeting Discovering upsell opportunities Proactive customer retention Customer Lifetime value ROI understanding (data and case studies)
- Big Data and Personalization: Understanding individual customer preferences & behaviors Understanding individual customer local environment ROI understanding (data and case studies) Big Data and Performance: External Data enrichment Social Listening Sentiment Analysis Social Signals Competitor Analysis ROI understanding (data and case studies Big Data and Predictive Applications Moving from Descriptive Analytics to Exploratory & Predictive Analytics Machine Learning& Recommendation Engines: Effective lead targeting Discovering upsell opportunities Proactive customer retention Customer Lifetime value ROI understanding (data and case studies)
- Proof of Concept Critical Steps People: Recruit Open Minded Executive Sponsorship/Budget Find and Dedicate a Techno-Functional Resource Process: Listen for Unsolved Problems Questions to ask: Product Recommendation Personalized Care Sentiment Analysis Lead Conversion Increase Customer Retention & Decrease Churn Develop ROI Case Why vs. What or Causality vs. Correlation What is it good enough? Technology: Setup Low Cost Sandbox Virtualization Cloud Based Storage Redshift Start Collecting data ASAP! Spin up it up in minutes Use Open Source Experiment with APIs-Get the data Outline a Real Case Study: Travian Games Overview: Travian Games is one of Germany’s most successful games companies, with more than 120 million registered users. The company specializes in award-winning browser games and MMOGs (Massive Multiplayer Online Games), which gamers can access from any computer or smartphone Challenges: Travian Games provides basic versions of games for free and must entices players to convert to paid versions by offering advanced features. Key Use Case: Understand Consumer behavior
- Datatification Critical Steps People: Position User/Customer Incentives Turn Intangible Behavior into Tangible data Process: Focus on Unique Identifiers Top Down Metric Definition Understand Data Exhaust & Reuse Technology: Map Collection Channels: Proprietary, Public, Purchase Create a Data Lake
- People: Establish a Customer Health Organization Process: Integration into Current Process and Systems Focus on Unique Identifiers and matching logic Technology: Meta-Data Management
- 1. Use Case Investment: Lead Conversion Retention & Loyalty Customer Lifetime Value Product Recommendation 2. Sources: Datafication Consumerization 3. Data Collection: Data Valuation Data Exhaust 4. Existing process integration Customer Health Organization Key Identifiers & MDM
- Privacy Paradox (Robert needs some graphic ideas here. Wrote some summary statements to give context) Give your data to get great valuable services. Many will want services that are innovative and are provided based on data you have already given. Companies will generate massive innovation, jobs and prosperity in this space At the same time….Give your data and never know exactly how it will be used again. Against you, or in ways you never dreamed that could be bad…. Human Free Will Propensity Maps will be developed from your historical actions that will be able to predict with great accuracy your propensity to behave. Left unchecked this could erase the idea that we have the ability to shape our own future rather we are destined for certain outcomes that we can predict