Four Deep Track Themes at Predictive Analytics World, Oct in New York
Predictive Analytics World for Business New York’s (Oct. 29-Nov. 2) rich program of brand name case studies and industry leaders covers deployed machine learning — across these topics: business, tech, marketing, and case studies.
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Predictive Analytics - Get Up to Speed on These Topics
Track 1: BUSINESS Analytics strategy & operationalization – Project management, project leadership, and organizational process — example sessions:
Addressing a Public Health Crisis in the NYC Mayor's Office
Just-In-Time Skills Training at LinkedIn
Data Science Driven Insights at The Clorox Company
Value Creation Through Analytics Innovation at Prudential Financial
Accelerating Data Science Innovation at Comcast
Operationalizing Analytics at Honeywell
Winning the Right Marketplace Talent with Analytics at Intel
Project Management for Data Scientists at Citigroup
Operationalizing Analytics at John Hancock
Track 2: TECH Predictive modeling methods – Core analytical and machine learning techniques, in-depth — example sessions:
Getting the Best Out of Hand-Tagged Training Data at Bloomberg
Time Series Crime Prediction with Twitter in New York City
Random Forests and Gradient Boosting Machines at Citigroup
Demand Forecasting with Machine Learning at Micron Technology
Machine Learning vs. Feature Engineering
Three Steps for Improving Data Quality for Predictive Analytics
Ask the Experts about Best Practices
Understanding Complex Predictive Models
Solving for the Conflict Between Laws and Analytics
Track 3 (Day 1): MARKETING Marketing & market research analytics – Machine learning applications such as targeting customer acquisition, optimizing retention, and uplift modeling — example sessions:
Retention Modeling in Uncertain Economic Times at Paychex
Predicting Customer Preferences at Walmart
Predicting Brand Love with Wireless Behaviors at Verizon Wireless
Acquisition Funnel for Higher Education at Becker College
Real-Time Automation to Build Relationships & Retain Customers
Which Predictive Model Will Best Help Increase Retention?
Using Rapid Experiments and Uplift Modeling to Optimize Outreach at Scale
Track 3 (Day 2): CASE STUDIES Varied business applications – Examples from the front lines where deployed machine learning taps into the most powerful value propositions, across business functions — example sessions:
What Makes TV Content Work at BBC Worldwide?
Implementing a Risk Model in the Maritime Industry at RightShip
The Limits of Surveys and the Power of Google Search Data
Customer Journey Analytics: Blazing Paths to Customer Success
Advancing Hydroponics through IoT Analytics
Leveraging Machine Learning for Realtime Pricing in Truck Logistics
