In marketing, statistical approaches are often used to target potential customers and increase retention. Marketing companies employ demographic information coupled with consumer behaviors and cluster analysis to create several customer profiling tools. PRIZM from Nielsen Claritas, was one of the successful segmentation products that were employed by the analytic challenge winners at the 2006 Direct Marketing Association (DMA) annual conference. This leaves the question: what is the proper way to use cluster analysis data in modeling? What are some common mistakes used when modeling with cluster analysis results? This presentation will discuss the challenges of working with segmentation in custom models. You will walk away with:This presentation aims to increase your predictive modeling skills. If you are not currently using cluster analysis results (segmentation) in marketing custom models, you should attend this presentation. Similarly, if you have not benefited from using segmentation in custom models in the past, you may benefit from this presentation. First, learn the skills used by the pros, and then be walked through a step-by-step example. At the end, walk away with a "best practices" understanding of modeling with segmentation. The Top 5 Things Attendees will Learn:Title of this section should be the same for all seminars. If a bulleted list, the first line may be coded as follows, optional.
Speaker:Erin McClintic Tanenbaum is a Senior Statistician at Nielsen Claritas. Erin has brought a keen consulting eye to Nielsen Claritas’ segmentation products: PRIZM, P$YCLE, ConneXions, IPA, and Net Worth. Prior to working with Nielsen Claritas, Erin was a statistical consultant working on a variety of problems including Fair Lending Analysis and Automated Value Models. Erin enjoys teaching applied statistics and presents frequently within Nielsen. Registration Rates
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