November 29, 2007

DELTA Force

Perhaps a misleading, if not corny title. The "spirit" however is relevant to this article.

Thomas Davenport, a respected leader in the field of predictive analytics, spoke at the SPSS Directions conference last month in Orlando, Florida.

DELTA is an acronym Davenport created to capture the life cycle, as well as the environment necessary, for successful predictive analytics ventures. If you have read his book "Competing on Analytics: The New Science of Winning," the concepts will be familiar. If you have not picked up a copy, I strongly suggest you purchase it.

Either way this review of his keynote is informative.

ORLANDO, FLA. -- Walking on stage here yesterday at SPSS's Directions 2007 North American Conference, author Tom Davenport sported a Boston Red Sox cap and used the 2007 World Series Champions as an example of how predictive analytics can give organizations a competitive advantage.

"The Oakland A's had analytics and no money," Davenport said, referring to A's general manager Billy Beane, who introduced the power of mathematics and statistical analysis to the day-to-day operations of running a major league baseball team. "The Yankees had money and no analytics," he added. "The Red Sox have both money and analytics," which he believed contributed to the team's second championship in four years. Not without taking a few additional jabs at Yankees fans in the audience, Davenport, as part of his presentation, "Competing on Analytics: How Fact-Based Decisions and Business Intelligence Drive Performance," proceeded to emphasize the importance of predictive analytics. His formula, he said, could be broken down using the acronym DELTA:


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Online Fundraising - How is Behavior Different?

With its relative ease of operation, low overhead costs, and the increasing role of the Internet replacing previously in-person transactions in our daily lives, online fundraising is now a major player in fundraising. While working on a recent project regarding various giving channels I asked myself this question:

How is online giving behavior different from offline?

While this might not satisfy any requirements as "breaking news" (it is nearly a year old), I found this study regarding online fundraising behavior incredibly informative.

Some interesting findings:
  • The Internet can serve as an effective acquisition source
  • Online donors tend to be younger and wealthier than offline donors
  • Online donors have lower renewal rates than offline donors
  • Multiple channel donors (online and phone or mail or personal solicitation) have higher revenue and retention rates

This article does a fantastic job summarizing the study, and I suggest you read it.

Online Fundraising on the Rise - Target Analysis Group and Donordigital Report finds

While the Internet, broadband networks and email have grown to be the new fundraising tools for non-profits over the past several years, their potential has not been reached - in terms of the amount of money raised and the number of organizations fundraising online.

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SPSS Directions Panelist Notes

The SPSS Directions conference, held in Orlando last month-featured predictive analytics industry leaders, including Bentz Whaley Flessner's Josh Birkholz and the "Grandfather" of predictive analytics, Thomas Davenport.

This article reviews a keynote panelist discussion, revolved not around statistical techniques, but the presence of predictive modeling in the business industry today.

Understanding how predictive modeling is viewed within your organization, and developing ways for further integration of your work were central themes; from "simple" language to helping your organization where your predictive resources can be applied to where there might be limitations that are not obvious to others.

Finally:

"You can never have enough data" - Thomas Davenport

ORLANDO, FLA. -- As part of SPSS's Directions North American Conference here Monday, all of the keynote panelists portrayed themselves as the visionaries of their respective companies. Each speaker strongly described predictive analytics as a means to elevate a company above its competition -- and, ultimately, to better serve its customers -- regardless of any corporate obstacles.

"If you know this is right, you need to just take [other executives'] criticism. Don't let them win the battle!" said Mike Hayes, senior vice president of The Bon-Ton Stores, a Pennsylvania-based operator of over 200 department stores.


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November 12, 2007

(Re)-emerging strategies for the “narrative” or “unstructured data” problem.

This article discusses a re-emerging field in predictive analytics called Text Analytics. I say re-emerging, because as the author points out, narrative analysis was a cornerstone of the earliest business intelligence strategies. Today this concept may have utility especially when combined with segmentation or donor-targeting strategies. From prospect management report sheets, phone-a-thon caller logs, to the infamous “other” box on a simple survey question, Text Analytics can provide opportunities for more nuanced insight into the “narrative” data we do have—as well as applications to quantitative models we construct.

One of the fundamental problems of using mathematics to analyze human behavior is the unstructured, or as I like to call it, “narrative” data problem. The amount of purely numerical or quantifiable information available to those in the predictive analytics field is limited—and what this quantifiable information available can tell you is variable as well. I consider non-profit or fundraising analytics to be more opaque than for-profit sectors in respect to this reality. Individuals, on a basic level, need to purchase goods and services. Therefore intent and preference are more transparent. In for-profits, purchasing a product can imply a variety of affinity relationships; this product is a necessity, I prefer this product to other similar products, etc.

Philanthropic giving, monetary or in-kind, is less clear in respect to quantifiable variables producing specific affinity. Attitudes towards institutions or missions may often be more personal than the type of soap you buy, so a donation may imply high affinity. The source of affinity however, can differ greatly: I am an alumnus, my child was a patient, the institution is important to the community, I like the sports teams, etc. Also the absence of immediately available options (there are no supermarkets to choose between charitable organizations) makes comparisons difficult as well. Giving data, capacity rating, alumni classification are all quantifiable values, but some more “narrative” fields like the basic question, “why is giving to us important to you” are more complex.

While the technology for Text Analysis may be more complex and costly than many organizations care to absorb, I believe this represents a very exciting frontier; making predictive modeling more accurate, dynamic, and relevant.

Text analytics is a new IT discipline that has already proved itself in applications ranging from pharmaceutical drug discovery to counter-terrorism to survey analysis, in science, government, and industry. It is poised to break out into the broader analytics market, in workbench form, integrated with business intelligence solutions, embedded in line-of-business applications, and enabling semantic search.

Text analytics is an answer to the “unstructured data” problem, which is best expressed by the truism that eighty percent of enterprise information originates and is locked in “unstructured” form. That problem has been recognized for decades. In fact, the first definition of business intelligence (BI) itself, in an October 1958 IBM Journal article by H.P. Luhn, A Business Intelligence System, describes a system that will:

“…utilize data-processing machines for auto-abstracting and auto-encoding of documents and for creating interest profiles for each of the ‘action points’ in an organization. Both incoming and internally generated documents are automatically abstracted, characterized by a word pattern, and sent automatically to appropriate action points.”

So we see that the earliest BI focus was on text – on extraction, categorization, and classification rather than on numerical data!


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