Craig Wacaser Dewitt Dawkins Ryan Vaske Alex Fly
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- Elvin Elliott
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2 Presenters Craig Wacaser: Predictive Analytics Solutions, Channel Development Executive, IBM Dewitt Dawkins: Customer Analytics, NA Sales Leader, IBM Ryan Vaske: Principal, Embel Assist Alex Fly: Chief Architect - Predictive Marketing, Embel Assist
3 Why Predictive Marketing? Most customers think companies do NOT know them We have limited opportunities to impact a customer Multi-touch generates significant revenue
4 Overview Topics: Predictive Marketing Platform Overview Scenario Walk-Through Solution & Benefits Best Practices Points of Emphasis: A predictive marketing platform combines predictive and detected insights delivering real-time personalization across customer experiences A predictive marking platform leverages PCI to distill down vast amounts of rich contextual and behavioral data into actionable insights A predictive marketing platform is an adaptive segmentation approach eliminating the business rule driven campaign segmentation and strategies that no longer scale
5 Predictive Marketing Pyramid Leads Customer Responses Customer Preferences Real-time Detection and Responses Optimized Decisioning Optimized Response Models Leverage Rich Behavioral Data Real-time Personalization Segmentation and Cluster Models Demand Generation Tactics Interact Internal Learning
6 Predictive Marketing Building Blocks
7 Close-up Look: Data Infrastructure Performance & Operational Metrics Organization-generated customer/prospect & product purchase data IoT Data Social Data Transactional Data
8 Close-up: Data Integration Emit & receive key business events High volume data integration Continuous feeds of real-time data
9 Close-up: Insight Engine Prioritize high-volume transactional decisions before deployment Learn & understand the data Preliminary data preparation Initial variable reduction Distribute insights for usage in decision & execution engines Leverage or create statistical models to generate insights
10 Close-up: Marketing Execution Engines Provide real-time, relevant offers to customers in session Manage Marketing Projects Leverage contact optimization logic for effective batch campaigns
11 Close-up: Predictive Marketing Performance Management Understand the impact and ROI of each campaign Monitor to ensure statistically accurate Determine the effectiveness of offers
12 Building Blocks & IBM
13 Predictive Marketing Putting it into Practice
14 Flight Search (Customer View) Customer Interaction: - Travel Type: Family Vacation - Passengers: Young Kids - Destination: Cancun - Duration: 1 Week
15 Flight Search (Behind the Scenes) Past Behavioral Insights Personalized Results PCI Real-Time Insights Behavioral Data
16 Flight Confirmation (Customer View) Customer Interaction: - Flight Confirmation Page - Personalized Offers Display
17 Flight Confirmation (Behind the Scenes) Behavioral Data PCI Recent Insights Historical Insights Prescriptive Leads Offer Model Optimized Offer Interact Interact General Learning Engine Offer
18 Travel Reminder (Customer View) Customer Interaction: - Upcoming Travel Reminder - Real Time Contextual Offers - Location and Weather Aware
19 Travel Reminder (Behind the Scenes) Behavioral Data Real-Time Insights Real-Time Propensities Micro-batch Insight Push Travel Reminder PCI Real-Time Contextual Offers Campaign Interact
20 Solution
21 Solution Benefits Benefits: Inbound enrollment rate increase by 18% when compared to historical outbound rates; savings of $250K quarterly 20%+ increase in call center marketing effectiveness. 20%+ reduction in Call center average handle time 30% - 300% IBM Interact acceptance rates increase using PCI attribute compression model score deciles in the learning model $3 million/year+ saved in operating costs
22 Best Practices Highly tuned models Targeted models matching business offerings Deployment methodology Flexible reporting platform Common metrics across models to easily compare Single Offer Library for inbound & outbound marketing Leverage highly scalable database platforms Common consumer identifiers Impactful data from Near Real- Time sources Create offer life-cycle management Streamline tasks & resources Leverage multiple learning methods Event detection to discover issues Job stream design Strong Quality Control Measures
23 Contact Information Ryan Vaske Craig Wacaser Dewitt Dawkins
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