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TechConnectr's Big Data Connection. Digital Marketing KPIs, Targeting, Analytics, & Optimization
This document provides an overview of the big data and predictive analytics ecosystem by mapping out the key components. It shows how data flows from various sources through data management, processing, analytics and applications. It outlines different types of analytics and examples of applications across various vertical industries. The document also includes examples of real-time bidding and different players in the digital advertising ecosystem.
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TechConnectr's Big Data Connection. Digital Marketing KPIs, Targeting, Analytics, & Optimization
- 1. Deep Dive Marketing BigData and Predictive Analytics Bob Samuels TechConnectr.com TechConnectr@gmail.com @techconnectr Graphic Source: Gleanster - An Intro to Big Data for Marketers
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- 3. BI Platform /Reporting OSS Visualizations Unstructured / Search Indexing / Metadata Search NLP Hadoop Analytics Hadoop Dev Platforms / Automation HDFS Predictive Analytics “Big Data” EcoSystemAPPLICATIONSTOOLSDATAMANAGEMENT STRUCTURED UNSTRUCTURED Transactional DB OSS High Performance Analytical DB NewSQL Enhancement Distributed NoSQL Graph Document Key Value / Column Enterprise Apps Internet Apps Social Media Web Content Mobile Devices Camera / DVR Sensors / RFID Logfiles Hadoop aaS HDFS Alternatives DBaaS HANA GraphDB Filesystem EMR Text / Sentiment Analysis Data as a Service Data Warehouses vFabricL Drill Vertical Market Applications Impala Messaging Optimization Data Integration / CEP OSS IMDG Redshift Based on Source: Perella Weinberg Partners AI
- 4. Applications & VerticalSolutions Big Data / Analytics (PATTERNS) Verticals (& Horizontals) Solution - Predictive / Prescriptive Finance & Insurance * Fraud Detection / Risk Management IT & Operations * Resource Optimization Manufacturing & Tracking * Resource Optimization Telecom & Utilities Resource Optimization Transportation & Logistics * Resource Optimization Product Management * Resource Optimization Retail * Revenue & Customer Experience Investment Management Optimization / Risk Management Government & Defense * Risk Management eCommerce / Digital Marketing * Targeting / Personalization Media & Entertainment Targeting / Personalization Mobile & Location * Targeting / Personalization Natural Resources & Exploration Patterns / Causalities Healthcare & Life Sciences * Patterns / Causalities
- 5. Bob Samuels The TechConnectr– www.techconnectr.com Cell: 408-206-5858 Strategic * Marketing Analytics * Client & Partner Door Opening * Demand Generation & Nurturing * Financial ROI Optimization Real-Time-Bidding eMail Recommendation Engine Search Demand Side Platforms CRM Loyalty Programs Display Web Analytics Games Customer Experience Mobile / Location SEO Video Targeting / Personalization Community / Social Marketing Automation Yield Optimization Re-Targeting Data Management Platform Sharing Tools Integrated Marketing Management Feedback / Surveys Corporate Structured Data Structured / Unstructured Content Management Data as a Service Web Content / Search Social Media Images / Video Mobile / Location Sensors / RFID / Satellite Machine / Log Files Customer Personalization Digital Mktg / eCommerce Healthcare / Bioscience Insurance / Risk Mgmt Investment Management Telecom / Utilities IT & Operations Manufacturing / Logistics Oil & Gas Exploration Government & Defense Business Intelligence Dashboards / KPIs Data Discovery Descriptive Analytics Statistical Packages Predictive Analytics Machine Learning Prescriptive Analytics Decision Management Graphs / Visualization Hardware & Infrastructure Natural Language Processing ETL / ELT Data Integration Data Governance Marshalling MapReduce Databases Hadoop / In-Memory Distributed File Systems Digital Marketing Applications DATA SOURCES DATA PROCESSING DATA ANALYTICS APPLICATIONS
- 6. Multi-channel two-way messaging Website Mobile site Mobile app CRM / ERP POS Call Center / IVR Email Display Social DATA LAYER Onsite Online Offline Customer History & Profile Credits to Ensighten for graphics
- 7. Analytics Types • Dashboards /KPIs • Business Intelligence • Data Discovery • Descriptive Analytics • Statistical Packages • Machine Learning • Predictive Analytics • Prescriptive Analytics • Decision Management • Graphs / Visualization Examples Examples
- 8. Increasing Value ofData Data- BI – Predictive - Prescriptive PrescriptivePredictiveBiz IntelligenceData Mining
- 9. Another way tolook at Analytics Levels Dash Boards Analytics Prescriptive Pivots Predictive http://practicalanalytics.wordpress.com/2011/05/01/the-vendor-landscape-of-bi-and-analytics/
- 11. Business Intelligence Analytics/ Visualization Big Data BI & Analytics/Visualization Solution Providers Oracle Essbase Laurén
- 12. *(NICE) *(SAP) Predictive Analytics Solution Providers PredictiveAnalytics Solution Providers
- 13. Source: http://www.xmind.net/m/LKF2/ Statistical Analytics SkillSets & Data Sources - R
- 14. Applications Vertical / Horizontal •Customer Personalization • Digital Marketing / eCommerce • Healthcare / Bioscience • Insurance / Risk Management • Investment Management • Telecom / Utilities • IT & Operations • Manufacturing / Logistics • Oil & Gas Exploration • Government & Defense Examples Examples Source: http://cloudtimes.org/wp-content/uploads/2011/11/Clouds.cloudtimes.png
- 15. Example: Recommend Engine TargetedeMail & Web Messaging & Timing • Provide Recommended Action: re: Predictive Analytics and Patterns: – Marketing spend effectiveness (how much to spend) – Targeting (who to target; when, with what message; what medium) – Promotion differentiation (how to differentiate offers) – Contact strategy (how to contact customers over time) • Targeting Precision by Groups / Clusters: (examples below) – New: Predict lead conversion, welcome second offer; high predicted LTV – Growth: Based on product interest browsing; Cart – Behavioral, Brand, Need Clustering & AOV – At Risk – high value at risk; disengaged; high returns, complaints – Lapsed – need-based cluster re: reactivation; Focus: high value-high size of wallet • Use Collaborative Filtering & Clustering; Propensity Modeling – Use in different contexts to solve different problems – Start grouping by product behavior. And build in range. • i.e. Shoe Retailer – distinguish moms from jocks from execs – clusters – Can start contextualizing the email or the website for the individual • Relevant, personalized eMail & web benefits include: – Increase open and click rates while minimizing unsubscribe rates – Predict which customers are most likely to engage, reactivate or complain – Customize email frequency and content by customer segment – Measure and optimize the ROI of email campaigns for specific customers – Maximize email revenue and campaign performance
- 16. Unique Selling Proposition •Relevance.. Key to e-marketing success • Help identify which data us useful and which isn’t • Help identify which algorithms are most useful • Customer-focused & Marketing-focused analytics – Better Relations with Customers (Satisfaction; Up-sell; Retention, Targeting to specific actions & interests; Risk Management) – Spend Marketing Money Wisely - Customer Acquisition; SEM, SEO, etc) • Multi-media Sources – ex: are they up for renewal; which emails are they responsive to; what pages are they looking at on website; any calls / complaints / inquiries; • Predictive Analytics: – Detect Changes of Behavior; Sources; Trends – quantity, quality – risks & opportunities – Group – Buying Pattern; look at DNA; ie based on what they buy.. Old vs athletes, region • With that, may merchandisestore, email differently • clusteringmodels for products,brand and behavior. – Predicting what is going to happen – what is likelihood of coming back to store, buy – Correlative – if bought this, what is next thing to buy… look at similar person, neighbors • Support • UI / Ease of Use – run reports & analyses – answer questions • Customer Metrics, Advanced Clustering, & Predictive Analytics Models • Interfaces & APIs – social, web, email, POS, CRM, ERP, ESPs
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- 21. Ad ecosystem-slides -by Eric Picard, CEO at Rare Crowds on Mar 17, 2012
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- 24. Ad Exchanges &DSPs Online Ad Exchanges DSPs Examples: Yahoo! bought Right Media in April, Google bought DoubleClick in May and Microsoft bought AdECN in August , all in 2007 Examples: DataXu, Invite Media (acquired by Google in 2010), Turn, Mediamath, Xplusone, AppNexus, Acuity Ads, (Rocket Fuel) Enable bid-based ad “trades” between buyers and sellers on their platforms. In this case, media buyers have to use a different system to access each exchange. DSPs allow media buyers to buy from multiple biddable media sources through a single interface, which gives buyer access to more liquid inventory. Buying from multiple exchanges is time consuming and inefficient from companies. Manage, optimize, and execute bid-based buys. DSPs also feature algorithmic optimization capabilities that dynamically alter bid prices based on performance data. Ad Exchanges is a layer below DSP. DSP is a layer on top of AD exchanges. These companies can access inventory from multiple exchanges with no need to aggregate inventory through relationships with publishers. Typical campaign buys from multiple ad exchange so it is difficult to achieve unique reach or optimal frequency. Reach and frequency can be better controlled using one interface. Use of DSPs is constantly growing, but is still a small share in Overall Display Media Buying Source: http://www.shilpagupta4.com/2011/09/09/quick-guide-to-demand-side-platform-dsp/
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- 31. Source: CapGemini: http://www.capgemini.com/sites/default/files/technology-blog/files/2012/09/big-data-vendors.jpg 4Main Buckets: Data Acquisition; Structuring/Indexing; Analytics; Applications DATA SOURCES DATA PROCESSING DATA ANALYTICS APPLICATIONS
- 32. Source: http://blog.softwareinsider.org/wp-content/uploads/2013/04/Screen-Shot-2013-04-25-at-2.48.29-PM.png - Applications& Tools DATA SOURCES DATA PROCESSING DATA ANALYTICS APPLICATIONS
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- 34. Big Data Landscape http://www.bigdatalandscape.com/ DATAANALYTICS DATA PROCESSING DATA SOURCES APPLICATIONS
- 35. Source: http://www.bigdatalandscape.com/ APPLICATIONS DATAANALYTICS DATA SOURCES DATA PROCESSING
- 36. Source: http://wikibon.org/wiki/v/Big_Data:_Hadoop,_Business_Analytics_and_Beyond More Slicesof the Key Technologies Involved .. * Next Gen Data Warehouse DATA PROCESSING APPLICATIONSDATA ANALYTICS
- 37. Source: Sqrrl: http://blog.sqrrl.com/post/46306669352/sqrrls-take-on-the-big-data-ecosystem DATAANALYTICS DATA PROCESSING
- 38. Big Data OpenSource Tools Source: http://www.bigdata-startups.com/open-source-tools/
- 39. Source: http://www.forbes.com/video-specials/industry-atlas.html?VID=24891553 &Wikibon The Big Boys will eat up the pure-play Big Data providers soon Forbes & Wikibon
- 40. Visualization Bob Samuels TechConnectr.com TechConnectr@gmail.com @techconnectr Source: http://inmaps.linkedinlabs.com/ Ibroke LinkedIn’s Custom Network Visualization map Bob Samuels’
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