1. Data Refinement:
The missing link between data collection and decisions
Stephen H. Yu
Data Strategy & Analytics Consultant

2. What we will cover
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• Database Marketing Landscape
• Analytics and Models
• “Model-Ready” Environment
• Data Summarization & Categorization
• Delivery: Scoring & QC
• Closing the Loop

3. Big Data, Small Data, Neat Data, Messy Data
How is the "Big Data” working out for you?
2.5 quintillion bytes collected per “day”
1 quintillion (exabytes) = 1 billion gigabytes
• Did all this data improve your decision making process?
• Do you have the results to show for?
• Information Overload? You bet!
 Harness insights, drop the noise
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6. Raw Data
• Demographic / Firmographic
• RFM
• Products & Services Used
• Promotion / Response History
• Lifestyle / Survey Responses
• Delinquent history
• Call / Communication Log
• Movement Data
• Sentiments
Marketing Answers
• Likely to buy a luxury car
• Likely to take a foreign vacation
• Likely to response to free
shipping offer
• Likely to be a high value customer
• Likely to be qualified for credit
• Likely to upgrade
• Likely to leave
• Likely to come back
Refined Answers
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10. Custom Models for every stage of Marketing Lifecycle
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15. • Most modern databases optimized for massive storage and
rapid retrieval, not necessarily for predictive analytics
o Relational databases
o NoSQL databases
• Need “Analytical Sandbox” (or Database/Data-mart)
o Structured & de-normalized
o Variables as descriptors of model targets
o Common analytical language (SAS, R, SPSS)
o Must support “in-database” scoring
Unstructured to Structured
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47. It is not just about modeling, but all surrounding services as well
10 essential items to consider when outsourcing analytical projects
1. Consulting capability: Translate marketing goals into mathematics
2. Data processing: Conversion, edit, summarization, data-append, etc.
3. Pricing structure: Model development is only one part; hidden fees?
4. Track record in the industry: Not in rocket science, but in marketing
5. Types of models supported: Watch out for one-trick ponies
6. Speed of execution: Turnaround time measured in days, not weeks
7. Documentation: Full disclosure of algorithms, charts and reports
8. Scoring validation: Job not done until fully scored and validated
9. Back-end analysis: For true “Closed-loop” marketing
10. Ongoing Support: Periodic review and update
10 essential items to consider when outsourcing analytics
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