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.
Data miners continually search for ways to improve the predictive accuracy of their marketing models. During the last few years, there have been improvements in the technology tools available that do just that. Additionally, new data sources periodically are introduced that further enhance the modeling result. Processes for employing data and tools have progressed, as well. One approach frequently overlooked by analysts involves developing a series of models, and then combining their outcomes to improve the overall prediction. Creating a set of models can be accomplished in a variety of ways, which I’ll explain later. But first, a little background. When individuals make decisions,
In the data mining world, many would have you believe that refining the technical aspects of the analytic process is key for improving the performance of mining exercises and the insight gained from them. While there is some truth to this, the critical area for progress lies in the non-technical procedures that are so vital to a powerful outcome. Here are five low-tech ways to improve your data mining exercises, with a view to preventing all-too-common errors that can keep marketers from optimizing customer relationships. 1. Take time to prepare your data. We’ve all heard the “garbage in, garbage out” slogan that suggests the quality of the
By Sam Koslowsky The Uses and Abuses of Cross Tabs The old timers among us remember the days when the primary tool for analysis was the trusted cross tab. While many of us have abandoned this approach for more complex analyses, and assigned it to extinction, researchers still employ this simple and appropriate method to provide key statistics and valuable insight. With the emergence of spreadsheets as a managerial tool, cross tabs became more readily available. For those marketers not familiar with the term "cross tab," a simple example, shown in Table 1, will demonstrate that this technique is used widely in one way
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