Nice Predictive Analytics Write-Up
While this forum primarily concentrates on sharing analytics concepts and trends, one "area" of analytics I am passionate about promoting is a greater understanding of uses/efficacy of analytics and data-mining. I will freely admit I often find myself, along with many other practitioners, focusing on topics you might call "inside baseball" to the analytics world; bemoaning limitations related to data quality, or frustrated trying to adapt the powerful software we use to meet our specific needs.
Every so often however, I step back and take a longer view of my work, and our industry. In doing so, I see the biggest barrier to success and growth in this field are not these important yet narrowly focused issues. The larger challenge is an understanding, acceptance, and ultimately, utilization of our efforts. I remind myself people dont trust or use what they don't understand very often.
I find articles like this to be helpful. While it does not mention fundraising, it also does not discuss technical details or statistics: it talks about end-target impact. It talks about how positive it is for customers. I firmly believe that more stories like these will be the entry way to more interest and understanding in our work. Please feel free to distribute to friends and colleagues and add a note saying "and ask me what I can do like this for donors..."
Sam’s Club Personalizes Discounts for Buyers
By Andrew Martin
SECAUCUS, N.J. — For years, hotels, airlines, banks, online retailers and other data-driven businesses have turned to powerful computers to help determine the optimal price for their products, or to find ways to recommend items that groups of customers with similar tastes might want to buy.
The big retail chains have been slower to adapt, in part because of the sheer volume of customers they serve and products they sell. But now, Sam’s Club, Wal-Mart’s warehouse chain, is offering a program called eValues that strives to offer bargains tailored to each member, based on that member’s buying history.
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