Data mining featured in the Chronicle of Philanthropy

Its not every day (literally) that data mining and analytics in support of fundraising and advancement gets the attention of the larger fundraising community, so this article about our colleagues at Memorial Sloan Kettering (including the very sharp Kate Chamberlain) and Josh Birkholz is a great chance to "sermonize" the benefits of analytics driven and supported planning and decision making.

note: a subscription is required to view the full article.

A New York Cancer Center Uses Technology to Predict Who Will Give
By Nicole Wallace

Almost every charity's pool of donors includes plenty of people who have both the means and the inclination to make a far bigger gift than they ever did in the past. The trick, of course, is to figure out just which people will make the leap.
To that end, Memorial Sloan-Kettering Cancer Center, in New York, has become...

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8 rules for Better Predictions: sage advice from Nate Silver

I was not able to attend the recent SPSS directions conference in Las Vegas, but my boss Josh Birkholz was. He returned with some great ideas regarding the new software developments, and also raved about keynote speaker Nate Silver.

Of course to many of us data junkies, Silver is a "household" name for his incredible prediction of 2008 election outcomes (at the presidential, and congressional, and state levels). Modeling something complex as voting choices so accurately has rightfully given Silver great respect within the analytics community. At SPSS Directions, he offered his 8 rules for data mining and modeling, regardless of the field or scope.

I was very happy to read that he stressed "knowing the truth". While many of us often enter projects with specfic goals or outcomes, it is always important to be honest with and true to, the data we have.

I also enjoyed his rule "visualize when in doubt". This is a simple rule I often forget in my own work, and it can provide opportunities for alternative and fresh perspectives on either problems encountered in the modeling process, or the results.

Nate Silver will be someone to keep an eye for years to come in the analytics industry. Be sure to keep an eye out for his book sometime in 2010.

8 Rules for Better Predictions SPSS Directions '09: Statistician Nate Silver shared his tips for successful data analysis predictions.

LAS VEGAS — Nate Silver dove headfirst into the world of data analysis -- and used SPSS, an IBM company's offerings -- at a young age. When he was nine years old, Silver and his father sat down during a rainy day while on vacation in Maine to figure out what attracted people to go to Major League Baseball games.

"Oddly enough, your chances of filling a stadium are greater if you have a good team," he quipped to the crowd on day two of SPSS Directions North American Conference, the predictive analytics company's annual user conference.

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Netflix prize awarded, a new challenge is made

Josh and I both have followed the Netflix challenge, an open-source style competition to beat out their movie matching algorithms, with a good deal of interest.

I hope that predictive analytics can have a more collaborative effort in other disciplines as well, allowing us to all benefit from insights and successes.

Note that Netflix has enlisted a new challenge, predicting movie selection based purely of bio-demographic and geographic data. This should be very intersting.

A $1 Million Research Bargain for Netflix, and Maybe a Model for Others

Even the near-miss losers in the Netflix million-dollar-prize competition seemed to have few regrets.

Netflix, the movie rental company, announced on Monday that a seven-man team was the winner of its closely watched three-year contest to improve its Web site’s movie recommendation system. That was expected, but the surprise was in the nail-biter finish.

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The "Naploeon Dynamite Problem"

In pondering my return to active posting on this blog, I came back to this article from late November concerning the Netflix challenge. Josh wrote a bit about this competition some months back—basically Netflix has created an “open source competition” to see if someone can improve upon on the accuracy of their movie matching algorithm. When you select one title, Netflix suggests others—and they want to increase the accuracy that you will enjoy their recommendation based upon pre-existing selections/tastes.

The competition has become an intense “hobby” for many interested in data mining and analytics (Josh downloaded the data set to work on it as well), and the sharing of these results has produced an issue contestants are calling the “Napoleon Dynamite Problem.” Basically, Napoleon Dynamite is a movie most everyone who reviews it loves, or hates, and while that rating has strong predictive power, there is little discernible pattern between who would love or who would hate the movie. One of the strongest predictors in the data set is displaying an almost random distribution. In other words, this powerful predictor appears to be an outlier.

How should a contestant proceed? As a very popular movie which elicits strong predictive responses (love or hate, not just like or dislike) Napoleon Dynamite is a significant point in the Netflix data landscape. However, the lack of pattern between those with similar ratings has rendered contestants' models fuzzy, or worse.

This brought me back to issues I encounter almost daily in my own analytics work: how to deal with outliers. Whether it is building a predictive model, or creating simple algorithmic projections of future giving, there always seem to be a dialog between myself and clients regarding what should be included or excluded.

Consider Example 1:

Total Giving
FY04 $14,000,000
FY05 $16,000,000
FY06 $15,500,000
FY07 $15,800,000
FY08 $26,500,000

This demonstrates a common issue seen in fundraising: how do you account for large gifts in projections (dramatic increase in FY08)? If this was a realized planned gift, or possibly even a major gift, some would argue to exclude it to not erroneously affect future projections. The gift was made though right? Is FY08 giving sustainable? How accurate can projections of future giving be, if you exclude historical realized giving?

For Example 2, lets consider building a predictive model where you may run into issues with deceased records, especially in relatively “younger” institutions. You can produce a model on living records (they are the only constituents that can still give major gifts!), but what if half or more of the major gifts at an institution came from records flagged as deceased? Is it necessary to lose roughly 50% of your sample? Is your model inaccurately skewed for not considering donors, many of whom have a data-rich profile, who made major gifts when they were alive, but have since passed? Does inclusion of deceased records produce “generational” predictive phenomenon with only minor relevance to today’s living donor pool?

It is difficult to produce “rules” on outlier issues like these—many times decisions on how to approach these situations can be relative to a specific institution or project goals. Consider though, the “Napoleon Dynamites” in your work, and continue to experiment with ideas, and challenge your own work by creating new ways to utilize the data at your finger tips to answer your own questions.

If You Liked This, You’re Sure to Love That
By CLIVE THOMPSON
Published: November 21, 2008

THE “NAPOLEON DYNAMITE” problem is driving Len Bertoni crazy. Bertoni is a 51-year-old “semiretired” computer scientist who lives an hour outside Pittsburgh. In the spring of 2007, his sister-in-law e-mailed him an intriguing bit of news: Netflix, the Web-based DVD-rental company, was holding a contest to try to improve Cinematch, its “recommendation engine.” The prize: $1 million.

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Analytics vs Instinct

Many thoughts I have introduced in the DonorCast NewsWatch cover the topic of “quality” in data mining and predictive modeling. I came across this article and realized that while I have made suggestions and raised questions about how to, for example, build a model predicting major donor likelihood, I have done little to discuss implementation of this work. I want to use this post to address one of the implementation challenges I encounter most: analytics (i.e. modeling scores) vs. instinct (i.e. VP's institutional experience).

While analytics and predictive modeling is not a completely fresh concept in the philanthropy world, it is young enough to be both misunderstood and mistrusted by some. After all, higher education, health care, and the arts were successfully completing ambitious campaigns long before RFM scores became a standard tool. Many in the philanthropic community still rely heavily on “gut feeling” or instinct for determining a donor's intention or affinity, prospect assignment, or more broadly, campaign readiness and viability.

The post I found discusses a summary of Ian Ayres' conclusion from his best-selling book, Super Crunchers, that “intuition and experiential expertise is losing out time and time again to number crunching.” I agree with the author who asserts that while data mining can offer concrete, and in some cases unforeseen insight, there is still an important role in business (or in our world, philanthropy) for experience, personal understanding, and basic qualitative characteristics.

Josh and I both often recommend that analytics be blended with organizational experience and environment. Achieving an effective balance may prove tricky. Convincing members of the “gut” society to buy into analytics integration may prove trickiest.

To show the value of analytics integration, try a simple control group. If you create an annual giving model, take 100 names at random and make your appeals. Then take the 100 highest scoring in the model not in the control group and offer the same appeal. Compare renewal rates and gift amounts. You may surprise people with the results.

Analytics versus Good, Old-Fashioned Creative Gut Feeling

I really enjoyed a recent post I found on the Precision Marketing online magazine. Jenny Hoffbrand discusses Ian Ayres' new book called Super Crunchers and a quote from the book that really summarizes the value of using analytics in the business as opposed to relying on your “intuition” or gut-feeling: “Intuition and experiential expertise is losing out time and time again to number crunching. Businesses and governments are relying more and more on databases to guide their decisions.”

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Profiling Your Donors: What Data Should You Append?

Here is thoughtful article that discusses some of the most common external data acquisitions that Josh and I encounter in our work. While Austin does a fair job laying out three basic sources of external data, I wish to add some specific examples where they might be used, as well as some thoughts to consider.

External data acquisition can be a powerful tool for any organization—but like most tools at our disposal—it should be applied strategically. Instead of starting with data, start with some program goals:

• Identify new major gift prospects
• Increase the participation rate in the annual fund
• Discover planned giving opportunities

Once a goal has been identified, review your database to determine which data points are present and which are missing in respect to your goals.

Using the example program goals from above, here are some data acquisition points to consider.

• Identify new major gift prospects (Wealth/Capacity Screening)
• Increase the participation rate in the annual fund (National Change of Address Screening)
• Discover planned giving opportunities (Deceased or Age Overlay)

What is a lesson that can be learned from this? Be very thoughtful when acquiring external data, as it may have more limited applicability than you might think.

Lastly, a development shop should never let external data be the band-aid to record keeping and data entry problems. No one should have better information or a deeper understanding of your donors than you do.

Demographics—Who Are They?
What you should know about profiling your donors
by Don Austin

At some point, most nonprofits ask the question, "Who are my donors?" It seems intuitive that if you know the characteristics of your donors you can market to them more successfully.

Answering this question usually means, "profiling" your donors. While this might sound easy, the process is not always straightforward. Profiling involves, first, overlaying demographic and lifestyle data on your donor file. Second, in the profiling step, you will have to choose between two methods to develop a picture, or pictures, of your donors.

Before you decide to begin this process you should ask yourself how you will specifically use the information and how you will justify the cost. You might find that a simple overlay of donor age will suit your needs.

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Partnerships and Brand Loyalty

Perhaps as a provider of services in the nonprofit community, it is impossible to write about all of the recent partnerships and brand loyalty campaigns without portraying a sense of bias. Nonetheless, I will make an attempt and encourage you to reach for that proverbial grain of salt. I am often asked to comment about these changes. The following is my brief attempt to do so.


As a resident of the Minneapolis / St. Paul area, I frequently fly Northwest airlines. Since I often need to work at airports, my membership with the WorldClub lounge more than pays for itself in saved internet costs and accessible work space. This membership also enables me to access Delta and Continental clubs. However, when I am in an airport that only has a Delta club, I am enormously frustrated. I have nothing against Delta. However, their club has a partnership with T-Mobile for internet access. I am required to pay additional for my internet access at the club through this arrangement.

My cell phone company has its own power cords made for the phone. The labeling says to use their brand of power cords. Generally, I find less expensive chargers made by other manufactures. These alternatives provide me with flexibility to plug and play other devices as well. There is no need to buy from the cell phone company when a better option exists.

How often do people use Mozilla instead of Internet Explorer because of features or even just principle? How many people have an Apple iPod even though they have a Windows computer? Do you only go to the dealer for the service on your car? Are all of your golf clubs the same brand?

I believe most people are intelligent when it comes to purchasing the right things for their situation. Whether it is for cost, services, convenience, or the overall best fit, people will set aside blind brand loyalty.

When it comes to your organization, do you exercise the same discernment? Do you choose services that are the best fit for you? Or, do you chose services that are the best fit for your software vendor? Do you build your predictive models to maximize the potential of your own existing data? Or, do you purchase models that seem conveniently interchangeably with the ones your peers purchased.

Among the most valuable contributions of analytics is allowing your data to guide your strategies. In this time of partnerships and brand loyalty campaigns, I only encourage you to exercise discernment. Do what is right for you. Do what is right for your organization. Your data is your most valuable asset. Leverage this asset as your advantage. This data, after all, is a reflection of your donors. When your donors are plugged into your decisions, you will make the right choices.

How would you prefer to be sliced and diced?

Analytics has been pushed to the foreground of American minds by the 2008 election cycle. TV and news media provide seemingly endless hours of pundits and commentators discussing data and predictions. This analysis is based off of complex modeling as well as basic segmentation; political analytics brought us the terms "Soccer Moms" and "NASCAR dads" after all. While not the professional specialty area of most that read this blog, analytics is getting a lot of attention, and in many cases being applied in increasingly prominent ways.

I recently finished the book Microtrends by Political Analyst Svengali Mark Penn. The book offers a provocative analysis of “undiscovered,” yet potentially important populations in America, and promoted strategies on how to engage them and effect change. This idea of almost hyper segmentation has forced me to consider the ways in which I segment data and the resulting application.

I fundamentally believe that studying a heterogeneous group on a more micro level has great benefits, but I believe there can be costs as well. I hope others in our field give thoughtful consideration to the ways we “slice and dice” our data, as well as how “fine” we choose too cut.

You can segment individuals in a variety of ways, but many of these ways may not be useful for the questions you seek to answer. I may be identified as a “mid-twenties jazz music buff,” an “urban chess student and wine lover,” or as someone who “drives American” because I own a Pontiac. These are all accurate segments that connect me with others and offer some snapshots into my interests and purchasing preferences—but is it helpful to you? I feel there is a normal distribution related to the amount of segmentation conducted—a natural sweet spot, after which further division can create more problems than answers, or more incorrect conclusions than accurate ones.

Following the questions of “how do we cut” as well as “how deep” lies the next step: how should we use this information? Does segmentation serve as the sign post for a new fundraising strategy? Or does it simply signal more research? There are successful applications of both I believe, but it depends on the segmentation process and the questions you are trying to answer.

Read this article, consider analytic's emerging seat at the table in our world, and then ask yourself this question:

“How would I want to be identified (segmented) by organizations or causes I care about?”

What’s for Dinner? The pollsters want to know

If there’s butter and white wine in your refrigerator and Fig Newtons in the cookie jar, you’re likely to vote for Hillary Clinton. Prefer olive oil, Bear Naked granola and a latte to go? You probably like Barack Obama, too. And if you’re leaning toward John McCain, it’s all about kicking back with a bourbon and a stuffed crust pizza while you watch the Democrats fight it out next week in Pennsylvania.

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Predictive versus Descriptive Modeling: some points to consider

This is a fantastic article which I think very clearly describes the difference between descriptive and predictive analytics; I often find these terms blurred and blended very casually when discussing our work.

As the article suggests, understanding the difference along with the appropriate applications is fundamental to any good analytics shop. I personally believe the author is a little too critical on historically based projections and forecasts (basic descriptive analytics), but does raise some important limitations, including resource scarcity (the infamous pipeline), economic influences, and even potential competitors.

Woods also suggests productive applications of descriptive performance metrics such as “identifying broken systems” (perhaps a gift officer portfolio analysis). Many of us invest a great amount of effort in building complex and nuanced predictive models. I find it useful (and sometimes efficient) to conduct some descriptive models (average growth rate formulas, logarithmic projections) at the same time to get a wide analytics perspective. You may surprise yourself with what you might find, or discover something is missing…

Many organizations use historical analytics data as a basis for forecasting future growth, and establishing performance goals and budgets. This applicaton for analytics data can blur the distinction between predictive and descriptive data. Understanding this difference is critical to an effective analytics program. It generally falls to the analytics professional to ensure that the difference is clearly understood within the organization.

I'm going to start out with a couple of definitions. What do I mean when I say predictive versus descriptive modeling?

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APRA Summit on Data Mining and Modeling

I would be negligent in my duties as promoting data mining and predictive modeling in the area of fundraising if I didn't promote this upcoming conference. This is a fantastic new forum that will feature many of the brightest and most creative minds in our field, including my boss Josh Birkholz. The conference also coincides with the release of his new book.

I will be there as well, and hope to connect with those who read this blog for in-person discussions about where data mining and modeling is today in fundraising, and where future directions may take us.

Hope to see you there!

Summit on Prospect Data Mining and Modeling April 3 – 4, 2008

Don’t miss the first-ever APRA Summit on Prospect Data Mining and Modeling - the year's best opportunity to interact with prospect researchers and analysts engaged at the cutting edge of the advancement research field. This two-day symposium will be divided into two groups of sessions: a beginners/management track, and an intermediate/advanced track. The beginners/management track will provide a solid grounding in the goals of, methods for and approaches to data mining. The intermediate/advanced track will showcase new technologies and present case studies of effective applications of statistical methods to prospecting and prospect management.

Whether you’re a proficient data miner, or a researcher or manager contemplating a foray into data mining, this summit will provide you with fresh insights, understanding and tools to help you better understand your constituent base. If you are engaged in building your prospect pool, looking for ways to prioritize and bring focus to an unwieldy database, or seeking to discover diamonds hidden in the rough of a broad annual base of support, this event is for you.

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Sentiment Analytics Opportunities

A colleague provided a link to this article and I loved the title: Sentiment Analysis. This article is another perspective on a theme I have been posting on this forum for some time—moving fundraising analytics beyond simply “who” and “how much” (which are important questions) into more analysis of giving motivations, or "why.”

Presented here is a more in-depth consideration of some of the inherent challenges in using text analytics. The most basic challenge discussed is that opinions (say for example affinity) are harder to describe than facts (I gave $100). This article touches on some basic concepts that may “boost” fuzzy opinions and statements into data with high utility and function. Some of these strategies include:

*Classifying the source for more tailored analysis (gift officer notes, institutional survey, donor pledge card).
*If you have the appropriate software-lexical choice analysis.
*Bayesian methods to identify matching patterns.
*Hybrids of sentiment and account fielded (primarily numeric) analysis to improve sentiment “accuracy.”
*Making “two passes” at text—using automated tools/software, then a set of human eyes to verify results.

This article poses more questions than answers, but I believe with sentiment analytics relatively absence in the fundraising world, questions are the best place to start.

Sentiment Analysis: Opportunities and Challenges

Sentiment analysis is one of the most exciting applications of text analytics today. It may also be the most challenging. The steps involved in sentiment analysis are easy enough to grasp: use automated tools to discern, extract, and process attitudinal information found in text; apply to sources as varied as articles, blog postings, e-mail, call-center notes, and survey responses that capture facts and opinions. What do customers, reviewers, the business community – thought leaders and the public – think about your company and your company's products and services – and about your competitors? What can you learn that will help you improve design and quality, positioning, and messaging and also respond quickly to complaints?

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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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Why Mathematical Models Just Don't Add Up

This article presents an interesting perspective on quantitative models of prediction vs qualitative models of prediction. Two main themes can be drawn from this article and applied to prospect research and its utilization of predictive modeling:

1) Be good consumers of research and research techniques. Not every model or technique is a good fit for your questions, or the information available to you (your data).
2) Ask questions outside the box. Instead of just "who is giving" and "how much they might give", ask "why are they giving", and "when might they give" (vs "when might we as an organization ask").

Don't be afraid of "what if" questions either. "What if we managed prospects by affinity rather than capacity, how might our campaign's opportunities for success change?"

Prospect research has barely scratched the surface in respect to analytics, and the opportunities it offers to inform and contribute to our abilities to maximize organizational fundraising potential. Being both critical and creative about what we do as researchers, as well as why we do it, is fundamental to this field reaching new frontiers of success.

Assurances by scientists that the outcome of nature's dynamic processes can be predicted by quantitative mathematical models have created the delusion that we can calculate our way out of our environmental crises. The common use of such models has, in fact, damaged society in a number of ways.

For instance, the 500-year-old cod fishery in the Grand Banks, off Newfoundland, was destroyed by overfishing. That happened in large part because politicians, unable to make painful decisions on their own to reduce fishing and throw thousands of people out of work, shielded themselves behind models of the cod population — models that turned out to be faulty. Now the fish are gone, and so are the jobs.

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Note: viewing the full article requires an online subscription to the Chronicle of Higher Education website.

CourseAdvisor Forms Data Mining Group to Increase Enrollments at Postsecondary Schools

Data mining is a tool being used throughout higher education. This article introduces a group recently formed by CourseAdvisor to identify prospective students. CourseAdvisor is co-presenting their approach with Eduventures through a webinar on Thursday, June 21, 2007. See the article below for more details.

CourseAdvisor, a marketing and lead generation company that operates one of the top online education directories (OED), today announced a newly formed Data Mining Group. The group works with educational institutions to learn more about their course offerings and understand the ideal profile for a successful enrollee. By employing advanced data capturing and filtering techniques to the institution’s inquiry data pool, the group identifies ideal prospective students – those with a high propensity to apply and enroll – and tailors the school’s campaign to target them. This process results in improved conversion rates of leads-to-enrollments. The announcement was made today at Career College Association Convention & Exposition.

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Netflix Prize Still Awaits a Movie Seer

Since its inception Netflix has employed analytics to drive growth and increase their competitive advantage. Lasf fall they launched a contest, seeking the brains and skills of analytics gurus outside their company. The goal: improve the accuracy of the existing Cinewatch movie recommendation system. The prize: $1 million.

The following article from the New York Times provides a summary of the contest results to date. Details about the contest are available at Netflix Prize.

Sometimes a good idea becomes a great one after it is set loose.

Last October, Netflix, the online movie rental service, announced that it would award $1 million to the first person or team who can devise a system that is 10 percent more accurate than the company’s current system for recommending movies that customers would like.

About 18,000 teams from more than 150 countries — using ideas from machine learning, neural networks, collaborative filtering and data mining — have submitted more than 12,000 sets of guesses. And the improvement level to Netflix’s rating system is now at 7.42 percent.

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Organization-wide Business Intelligence

Here is a cross-industry survey about integrating business intelligence and analytics from Optimize Magazine.

If business intelligence (BI) isn't in the hands of a majority of your organization's users, chances are it will be in the next two years. BI is expanding beyond its decade-long use by only handfuls of technically savvy employees. No longer will business and financial analysts own data mining, analysis, and reporting tools. Businesses are trying to get smarter about BI, and they're planning to expand use of these applications outside the historical confines of finance, sales and marketing, customer service, and IT departments. The idea is that spreading BI technology to more business functions—such as human resources, supply chains, and E-commerce—will eventually allow more data sharing and collaboration throughout the value chain.

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Viral Marketing Ideas

With the increase of innovations in nonprofit communications, many of you might be interested in this "Hall of Fame" of viral marketing ideas.

Includes creative samples and results data for viral efforts targeting organic moms, Hong Kong’s Gen Y, America’s Gen X, and tight-focus biz professionals. Plus, a nifty way to get celebrities more involved in fundraising.

Read the article

Opportunity Intelligence

A challenge for most prospectors in fundraising is the transfer of prospects and knowledge to the front lines. There have been many advances using data mining, screening, integrated surveys, efficient prospect research qualification, and profiling to filter and identify new names. However, we still face inefficiencies in realigning portfolios and engaging new prospects.

Umberto Milletti observes similar inefficiencies despite substantial advances in identification technologies. He has developed an approach called "Opportunity Intelligence." I believe it translates well to prospecting.

Opportunity intelligence solutions filter through large quantities of company, market and personnel data, business news, financial filings and other sources, employing techniques, such as natural language processing and semantic analysis to extract meaning from the data. They, then, assess relevance, applying algorithms tuned by expert industry knowledge, and present highly selective results that precisely identify top selling opportunities.

Check it out

Data Classification: Brains or Brawn

Elements of data classification may apply strongly to your data mining program. I recommend building consolidated classification coding systems for attribute, interest, and funding categories. For example engineering graduates may have an engineering interest, which rolls-up into a science interest, which rolls into the constituency pool for science and technology. When a person notes their interest in engineering on a survey, attends an engineering event, or gives to engineering, they join this pool as well.

By "smart-coding" your entire systems into these categories, you will multiply the availability of independent characteristics for predictive modeling. Similar work might be done for occupations and industries. The manual mapping is the most difficult step in these classification projects.

On a deeper level, Here is an article on data classification for the techies on the list.

The current state of data classification is largely a byproduct of historical, hierarchical storage management (HSM) implementations where data age is the primary classification criterion. Early visions of classifying data based on business value never fully came to fruition because it required a manual, brute force approach and was too hard to automate. Age-based classification enabled automation processes to be more easily applied to data classification initiatives and became the de facto standard.

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Is Predictive Analytics at the Tipping Point?

It has been my mantra that predictive analytics is within your reach. Although the science comes out of the academic community, the products are designed by the experts, and the development was ushered by math geeks, it is time for the user to step up. The tools are increasingly user-friendly and you know your data better than anyone.

It seems we are at the point where the leading fundraising organizations are building data mining programs rapidly. By the increasing size of the campaign totals, I can understand why. The tools are there. The necessity is clear.

The organizations that have built predictive analytics programs are likely more efficiently identifying prospects, applying effective segmentation strategies, understanding their constituents, making smart data enhancement decisions, and raising more money.

The business world agrees:
Business intelligence and decision support have been utilized by organizations for several decades. Their deployment continues to spread in both breadth and depth within many of these organizations while gaining new converts in others. However, data mining, and its application as predictive analytics, has often been characterized as a technology that could only be utilized by highly skilled technical practitioners with strong statistical backgrounds...This may have been true in the past, but is arguably no longer the situation today.

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Data Mining in Financial Services

This account of the integration of data mining with the insurance and financial services industry has much overlap with the approach to fundraising. Although many fundraising organizations are now using data mining to predict donor behavior, few are digging into their data to profile existing donor segments.

"We wanted to be able to analyze profiles and preferences of existing customers and then predict buying behavior," says Alt-Simmons. "And, we wanted to make sure our marketing efforts were what they needed to be."

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Communicating Data Mining Results

A challenge that faces almost every fundraising analytics professional is explaining models and analysis for non-technical front line staff. I generally use analogies and stories liberally. By comparing a propensity model to a credit score, or comparing the incorporation of many independent variables to TVs with more pixels, I find it easier to get over the deployment hurdles.

A Whole New Mind by Daniel Pink is a must read for all data miners. Andrew Sallee of William Jewell College pointed it out to me as we were discussing statistics at the SPSS users forum. We couldn't help but notice the varied backgrounds of analytics professionals. From our anecdotal observations, these backgrounds seemed heavy in the arts. This book will shed some light on not only the personalities predisposed to discovery, but also techniques for telling the story.

This article, "Science-speak 101: Researchers sometimes need help to explain complex work in simple terms" might also shed some light on the matter.

Some business counselors urge scientists to use allegories, metaphors and everyday images to make their technology understandable. "I don't want to say they should dumb down their stories, but that's what it amounts to sometimes," said Mark Long, president and chief executive of IU Research & Technology Corp., which runs a life-sciences business incubator near Downtown Indianapolis.

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Building a Predictive Model

In a previous post I described the standard process for building a predictive model. I found this older article, "The non predictive part of predictive modeling." It provides a bit more detail on the project outline. Based on my experience, I would agree that the actual modeling rarely exceeds 10% of the total project time.

Some catalogers may be intimidated by the techniques required to build a statistics-based predictive model. But actually generating the predictive model - that is, creating the scoring equation - makes up about only 10% of the entire six-step process. The remaining 90% encompasses the nonpredictive part of predictive modeling: developing a sound research design, creating accurate analysis files, performing careful exploratory data analysis, implementing the model, and creating ongoing quality control procedures.

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Boomers and Planned Giving

Increasingly, nonprofits are targeting planned giving appeals to younger audiences. Lately, when I have built custom planned giving models, my clients requested that I control for age as well as consistent giving. Older, regular donors have long been the population for targeting. Since those variables are constantly targeted, it is difficult to find other factors without controlling them.

Families with more than $10 million to give away often create family foundations rather than give to established charities, but taxes and paperwork discourage that for those who are wealthy but have smaller estates - in the $1 million to $2.5 million range. At the same time, only 42 percent of people have wills.

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More Fundraising Database Breaches

It seems that Universities are increasing as targets for hackers. Most of the analytics professionals in fundraising run their data mining projects in flat file environments. These continuous warnings should remind us to be extra careful in how we handle and transmit the modeling data sets.

The University of Idaho in Moscow yesterday began sending letters to more than 331,000 people warning them about the potential compromise of their personal data following the theft of three desktop computers in November. Meanwhile, in a separate incident, officials at the University of Arizona in Tucson are investigating a computer break-in that disrupted several school services this week and continued to keep an online procurement system offline even today.

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5 Key Steps to Prospect Relationship Management

I've seen references to the 5 key steps to Prospect Relationship Management published by Canoe Money. These were written from a business perspective. In fundraising, Prospect Relationship Management is generally used synonymously with Moves Management or PM&T. This methodology is a better fit with what I would call "Suspect Relationship Management."

Although often overlooked, these first interactions with an organization can set the tone for the life of the relationship. I took the 5 steps from the article and commented on them from a fundraising analytics perspective.

1. Knowing
Use profiling technologies to analyze the predominant segments in your database. By knowing the various ways individuals become donors or are motivated to give, you will be better equipped for the entire relationship.

2. Follow Up
Be aware of all touch points your prospects have with your organizations. Are they coming to events, sports, fine arts, or lecture series? Have they participated in alumni activities or reunions? Are their children applicants for admissions? Have they been patients recently? All of these are opportunities for follow-up. And, they contribute to the movement through the pipeline.

3. Communication
The take-away for fundraising is to customize earlier in the process. Even when prospects are unknown to you (anonymous records in your database) can you be managing the relationship? With analytics, you can segment these populations to provide custom messaging and cultivation.

4. Tracking
As with all scorecarding and dashboard methodologies, determining metrics that matter happens over time. As you track different events, activities, and strategies, always measure against the impact on increased production. Tracking also enables stronger modeling for future segmentation and prioritization.

5. Refining
Be willing to change your benchmarks. Don't let the metrics exist for their own sake. The purpose is to increase gift production and manage infrastructure efficiency.

Managing that process is the art of Prospect Relationship Management ("PRM"), the lesser-known cousin of CRM and a critical contributor to the business development process. When executed effectively, prospect relationship management achieves two important goals. First, it shortens the selling cycle, creating a positive financial impact in the short term. Second, it lengthens the life of the customer for your business - and that creates a positive financial impact in the long term.

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N.Y.U. Mines Personal Data for a Fund-Raising Edge

This New York Times article does not really specify any data mining activities, but it does cover a number of aspects of the prospecting process such as screening, prospect research, peer review, and qualification. Overall, it is an interesting description of prospecting and cultivation work.

The board’s campaign steering committee, made up of trustees, representatives from each N.Y.U. school, a parent representative, and the president of the alumni association, meets about three times a year, and at those meetings Ms. LaMorte also reads names of prospects to the trustees.

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Legacy Admissions and Giving

In many of the models I have built predicting annual and planned giving to universities, multiple alumni relationships within a family is a consistent predictor. Annual and planned gifts tend to be more loyalty-based than investment-based outright major gifts. These relationships are a strong sign of loyalty.

Catherine Rampell, a Princeton senior, discusses the controversial topic of legacy admissions. When a factor is consistently predictive, should there be an emphasis to broaden this factor? If multiple alumni-relationships are predictive of giving, accepting family of alumni over equivalent individuals with no such relationship seems like a natural application. Is this a way to go? Catherine thinks it might not be all bad. She says:

Contrary to popular characterizations, not all alumni are rich, and proponents of legacy preference do not expect a one-to-one financial return for each admit helped by his legacy status, since the school does not tell families whether their legacy status had any effect on their admissions results. But helping out a few alumni kids on the cusp is a benign gesture that can help grease annual giving's wheels nonetheless.

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Qualifying New Leads

The most common application of analytics in nonprofit fundraising is for lead generation. Usually, it takes place before research and field qualification (AKA discovery) stages of the process. This article on B2B sales translates well to the discovery process. For many organizations, having an efficient method for connecting with new leads separates the good from the great.

You’ve spent a great deal of time, effort and money putting together your business-to-business sales lead generation programs. How you handle B2B sales leads once you get them makes the difference between a happy sales team and new customers or an unhappy sales team and lost sales.

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