Using Machine Learning & Spark to Power Data-Driven Marketing

Using Machine Learning & Spark to Power Data-Driven Marketing

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  Data Intelligence Using MachineLearning & Spark to Power Data-Driven Marketing February 13, 2018 Presented by: Joe Caserta Max Goldbas Co-Presented by:
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  Big Data WarehousingMeetup • Knowledge Sharing: All things Data & Innovation • 4,800+ Members • Founded and hosted by Caserta
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  About Caserta Data Intelligenceand 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
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  The Customer Journey PR Radio TV Print Outdoor Wordof 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
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  Learning the Path-to-Purchase Attribution Type Comments SingleTouch 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)
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  Data Science inPractice Source: https://www.collaberatact.com/data-science-stay/
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  Data Science forthe 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
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  S3 Ingest Storage ETLPresentation 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
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  Governing Data Innovation
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  Customer Journey Dashboard
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  Thank You [email protected] @Joe_Caserta Joe Caserta President,Caserta Concepts