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Using Machine Learning & Spark to Power Data-Driven Marketing

  1. Data Intelligence Using Machine Learning & Spark to Power Data-Driven Marketing February 13, 2018 Presented by: Joe Caserta Max Goldbas Co-Presented by:
  2. Big Data Warehousing Meetup • Knowledge Sharing: All things Data & Innovation • 4,800+ Members • Founded and hosted by Caserta
  3. About Caserta Data Intelligence and Strategic Consulting Data Lakes, Data Warehouses, Data Laboratories Award-winning company for Data Innovation Data Science, Machine Learning, Artificial Intelligence Internationally recognized work force Best Practices, Authors, Educators, Mentors Strategy, Governance, Architecture, Implementation
  4. The Customer Journey PR Radio TV Print Outdoor Word of Mouth Direct Mail Customer Service Physical Touchpoints Digital Touchpoints Search Paid Content email Website/ Landing Pages Social Media Community Chat Social Media Call Center Offers Mailings Survey Loyalty Programs email Agents Partners Ads Website Mobile 3rd Party Sites Offers Web self-service
  5. Learning the Path-to-Purchase Attribution Type Comments Single Touch Rules-Based Statistically Driven Assign the credit to the first or last exposure Assign the credit to each interaction based on business rules Assign the credit to interactions based on data-driven model Ad-Click Mailing MailingE-mail E-mailAd-Click Ad-Click 100% 33% 33% 33% 27% 49% 24% - Last touch only - Ignores bulk of customer journey - Undervalues other interactions and influencers - Subjective - Assigns arbitrary values to each interaction - Lacks analytics rigor to determine weights  Looks at full behavior patterns  Consider all touch points  Can apply different models for best results  Use data to find correlations between touch points (winning combinations)
  6. Data Science in Practice Source:
  7. Data Science for the Enterprise CRISP-DM: Cross Industry Standard Process for Data Mining 1. Business Understanding • Solve a single business problem 2. Data Understanding • Discovery • Data Munging • Cleansing Requirements 3. Data Preparation • ETL 4. Modeling • Evaluate various models • Iterative experimentation 5. Evaluation • Does the model achieve business objectives? 6. Deployment • PMML; application integration; data platform; Excel Business Understanding Data Understanding Data Preparation Modeling Evaluation Deployment Data
  8. S3 Ingest Storage ETL Presentation VisualizationData Sources • OPRA • Equifax • CDS • Moody’s • BlackBox Relational Datasets • Barclay • Eureka • Hedge Fund Intelligence • Hedge Fund Research • Lipper • Morningstar • MF Holdings • BD/ ADV Flat File Datasets S/ FTP Push Kinesis • CAT Landing Data Lake (Tier 1) Data Lake (Tier 2) Data Science (Ephemeral) Redshift Spark (Streaming* / Batch) Lambda Data Science • Python • SQL • Scala • Predic ve Analy cs • Text Analy cs • Business Intelligence Structured Data Redshift Metadata Repository • Data Marketplace • Clean • Match • Derive • Aggregate • Mllib • CoreNLP • Prepare • Deliver Streaming Data Sets Data Analytics Ecosystem Campaigns Sales Netezza Relational DBs Salesforce RESTful APIs Cloud DBs Adobe Weblogs Web Data DMP Streaming Data Redshift
  9. Governing Data Innovation
  10. Customer Journey Dashboard
  11. Thank You @Joe_Caserta Joe Caserta President, Caserta Concepts

Notas del editor

  1. Teaching half-day class on this at the Data Summit in Boston in May 22nd