This document provides 13 key takeaways for model-ready marketing databases. It stresses the importance of clean, well-organized data from all sources being in a consistent format from sampling to scoring. Non-numeric data must be properly categorized and numeric data converted to descriptors of the target. Missing values and back-end analysis cannot be ignored. The overall goals when designing marketing databases should be long-term.

1. 1
Thirteen
Key Takeaways Analytics is an integral part of
1-to-1 Marketing
for Model-Ready Marketing Databases
2 3 4
Analytical projects depend on Modeling is about “ranking” – Predictive modeling requires
good data feeds – Garbage in, Design analytical tables unique data structure that is not
garbage out accordingly necessarily a relational
database
5 6 7
Data variables must be in the Consider all your data sources, Numeric data must be
same format from sampling to but don’t wait for a perfect set converted into descriptors of
scoring the target through proper data
summarization
8 9 10
Non-numeric data must be Data hygiene opens doors to Missing values are meaningful
categorized properly external data sources too
11 12 13
Modeling work is not complete Do not skip back-end analysis Do not lose sight of long term
until scoring is done properly and start it with setting key ROI goals when designing marketing
metrics databases
For more information or to view a Stephen Yu, Vice President, Data Strategies
demo, visit us at booth #801 stephen.yu@infogroup.com · www.infogroup.com