Sam Koslowsky

Predictive analytics has become routine in a wide variety of disciplines. While models have become standard for many, I am not convinced that many analysts are appropriately evaluating the results of their efforts. Many, including novice analysts, believe that with the availability of gains or decile analysis, the evaluation standards are obvious. Users overvalue the reasonableness of the gains table. Are more responders identified in the top decile segments, fewer in the middle, and a minimum amount on the bottom? While this is important, it does not always lead to selecting the "best" model for a given situation.

Text mining has an illustrious history in the world of analytics. Investigators have used text mining to fight fraud, conduct anti-terrorist surveillance and analyze police interrogations in criminal investigations. Matters of national security, public safety, patent protections and trade secrets, and other high-minded topics all have made use of text-mining technology tools, and the wise minds of analysts behind them. But there's a very important role for text mining to play in direct marketing, as well.

In the world of marketing, text mining has gained greater awareness and favor as social media, inbound e-mail marketing, filtering and other "digitized" communication and documents in free text have flourished—and as the cost and quality of tools available to analysts and marketing departments have become more accessible.

By Lisa Yorgey Lester How to identify possible churn within your database. Customer defection should come as no surprise. Your customers are waving goodbye as they slowly walk out the door. If you learn to read the signs beforehand, you can launch a preemptive strike to potentially keep them from churning—before you need to spend additional money to win back customers in whom you've already invested. That is, if they are profitable. If not, you may simply want to let them keep walking. Suss Out Churn At its essence, segmentation divides a file into groups that behave differently. In this case, you want to

Driven by the need to be more compliant and more efficient, companies are cozying up to their databases like never before. According to a Forrester Research report released mid-2004, spending on data warehousing is expected to see double-digit growth this year. And because a stocked data warehouse is worthless unless you use it to glean business insight, The Data Warehousing Institute predicts a continued demand for data mining tools. If your data interests fit this national—if not global—trend, then this month's cover story, themed "Know Your Customer," can help you get the most out of your quest to better understand your interactions with

When to Use Regression and Neural Networks in Models By Sam Koslowsky The other day I made an unusual purchase on a credit card I seldom use. I found myself somewhat uncomfortable as I was told, "I'm sorry, but your charge has been rejected by your bank." The store associate was kind enough to connect me to the financial service organization responsible for maintaining my credit card. I knew the person to whom I spoke wasn't the same person who picked my transaction out of the millions that are being evaluated continually. It was, more than likely, a statistical model that proved to

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