Can You Predict the Future?
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 be the culprit. The structure of these "rules," or models, that drive credit purchase acceptance more than likely was a regression- or neural network-supported architecture.
How did this model recognize that this particular transaction was unusual? Having previously examined transactions of millions of other people—including transactions that were eventually found to be fraudulent—the model generated an algorithm, or a set of rules, that permitted it to separate valid transactions from bad ones. Of course, a model only can select transactions that appear to be fraudulent. That's why a human typically gets involved to make the final determination. Fortunate for me, I remembered my mother's maiden name and the last four digits of my Social Security Number. The transaction eventually was approved.
Thirty years ago, it was difficult to find marketers developing formal targeted models for their mailing campaigns. Rather, a team of managers would decide what criteria it made sense to employ for list selection—more hunch and what's-worked-before thinking than anything scientific.
A statistical model removes the subjectivity from this analysis. The mathematical rigor applied to solving direct marketing problems guarantees objectivity in name/list selection. Regression analysis became the 1970's direct marketer's tool of choice.