Fundraising Analytics Survey

Dear fundraising analytics professionals,

As a service to the data mining and fundraising community, we are conducting a non-scientific survey of this emerging field. We are posting to the main fundraising analytics lists and forums. We hope you will participate. Your responses are fully anonymous and we will post our report back to these same channels.

We ask that you coordinate with your development program so that we receive only one response per institution.

Thank you in advance for your participation!

Alex Oftelie and Joshua Birkholz
Click here to take the survey

Individual Giving Model--real time philanthropic forecasts!

Analysis of philanthropy and giving trends, as a discipline, has been primarily historical in nature. While researchers have gained a general understanding of the impact of certain economic factors on giving overall (income up = giving goes up), there have been very few "real time" models that can incorporate our shifting economic climate and create accurate predictions and forecasts. Rules of thumb and general trend directions lack precision, and with our mercurial economic climate, lack consistency as well.

Here is some interesting research Center on Wealth and Philanthropy at Boston College. They have designed a model to predict individual household giving in as "real time" as we have ever seen. They call it simply the "Individual Giving Model". They have beta tested"the model on previous years and showed formidable accuracy.

The ability to calculate, within any given year, the impact of economic change on giving can be a wonderful tool in our collective tool box. Please give this paper a review and keep an eye on the IGM.

If the IGM proves successful, the next frontier would be to accurately and timely predict participation, not just total dollars.

Household giving expected to fall
February 17, 2010

When all numbers are in, charitable giving by U.S. households is expected to have fallen by as much as 9 percent in 2009 after adjusting for inflation, a new model predicts.

Individual giving typically correlates to income and wealth, and given the continued challenges Americans face, even the rosiest scenario calls for a drop in donations, says the Individual Giving Model, created by the Center on Wealth and Philanthropy at Boston College.

Assuming slower growth during 2009, the model predicts income will drop at an annual rate of 6.4 percent after adjusting for inflation, and that net worth will grow 4.6 percent.

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Man vs Machine?

This recent article by Gary Kasparov may not touch directly on data-mining or fundraising/development analytics, but I do believe it addresses some larger themes I often see in my work:

Who is better at predicting our next major gift prospects--our most seasoned gift officer or a model built by someone who has never met any of our prospects?

My response is both! But only together...

The theme from Kasparov's article that compelled me to share it was the story about a recent "open" chess tournament. Anyone, or any machine could play. A significant purse was posted so many players from all over the world were drawn, including some of the worlds strongest grand masters, as well as the most advanced chess "computers" (similar Deep Blue).

The winner was a relative chess amateur with three laptops running inexpensive chess software.

The lesson for Kasparov, was that the strongest human minds/intuition/talent, and the strongest computational power from computers, was no match for moderate talent and moderate technology integrated effectively.

In fundraising, I am starting to see more of a natural blending of these two "worlds" that until even recently seemed to be sometimes be in friction with each other.

In my own work I still strive better to integrate other forms of information/analysis and perspective outside of my data sets. Doing so will make my own work better.

The Chess Master and the Computer
By Garry Kasparov

In 1985, in Hamburg, I played against thirty-two different chess computers at the same time in what is known as a simultaneous exhibition. I walked from one machine to the next, making my moves over a period of more than five hours. The four leading chess computer manufacturers had sent their top models, including eight named after me from the electronics firm Saitek.

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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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Demographic shifts: Is your data-mining approach as well?

Actuaries are some of the most successful analytics practitioners when it comes to predicting future events in very specific or individual ways, so this article describing the difficulties many actuaries are having given demographic shifts and the mercurial economic climate reminds me that my own analytics ideas, whether “standard” for all projects or custom tailored to a specific model, should be reviewed and where necessary revised.

Many models I have built have excluded age as a discreet or continuous independent over concerns regarding sensitivity to outliers (sometimes it is however include with grad decade as a proxy). This article presents some interesting information that while I already knew, had never considered in respect to my work: U.S. population is working longer, pushing retirement age higher and higher.

The implications I believe are both explicit and implicit. Directly, the trend towards working longer may necessitate a change in traditional assumptions of major gift work. Often there are general demographic “sweet spots” in age, relatively consistent from institution to institution. Certainly donors in their 70’s and 80’s have different giving behavior than those in their 30’s and 40’s. You may consider these “stages” in a donor’s life where they may have different attitudes towards making a major gift, or a planned gift, etc.

I have not observed a “rule of thumb” regarding major gifts and retirement age. Some individuals like to give while still working full time, others wait until retirement “settles in”, and some even use a major gift as a “kick off” to their transition from employment to retirement. American’s working longer on average impacts all three phenomenons.

Less directly, it may be important to consider the effect of older Americans working longer on younger generations of the American work force. Certainly with a glut of highly experienced employees choosing to remain past the average age for retirement, it may be suppressing the career growth opportunities of younger generations. The boomers will retire however, and this may also produce a vacuum effect of leadership and experience. Younger generations, who may have felt stalled by the logjam “at the top” may suddenly find themselves advancing at a rate greater than predecessors.

Maybe its time I reconsider how to use this most consistent and measureable longitudinal variables in my work.

Demographic Shifts Present Actuaries With Challenges And Opportunities
New Orleans, LA – Demographic changes are impacting the underwriting and pricing of many insurance products and the implications of these changes are creating new challenges and opportunities for property/casualty insurers, a panel of experts told attendees at the Casualty Actuarial Society’s 2009 Spring Meeting.

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DonorCast Book Review: Microtrends

For those that follow the field of political or opinion polling closely, Mark Penn is known as both legendary (he literally coined the term “soccer mom”) and polarizing (he rubs many other pollsters the wrong way, both personally and methodologically). Putting aside all that I knew of him—I found myself drawn to the premise of his book Microtrends: The Small Forces Behind Tomorrow’s Big Changes.

Penn was a pioneer of the process of micro targeting, particularly in the political sphere, under the hypothesis that small numbers of like-minded people may be the future moving forces behind our world. In Microtrends, Penn identifies 70 groups that make up 1% of the population of the United States (roughly 3 million per group). He explains why they are important to identify (or “micro target”), as well as suggestions for responding to their interests and harnessing their energy. Some examples include “Extreme Commuters” (Josh is one), “Young Knitters,” “Vegan Children,” “Archery Moms,” and even “Numbers Junkies” (where I self-identify).

Some of the groups sound like they have transformative potential (the “High School Moguls” for example) where others sound more like just narrow interest groups (“New Luddites”). Still I think there are some important lessons, and perhaps the seeds of provocative questions, that can be taken from Penn’s work if you examine his premise from a higher altitude.

In fundraising, the idea of micro targeting may sound second nature to many of us. Development professionals spend a lot of time segmenting and targeting folks by broad interest groups (athletics, arts, alumni) and by giving capacity (major giving, annual fund). But have you stopped to consider a perhaps more complex, and certainly smaller segment of your donor database? Do you closely follow former members of a campus group from a certain decade, or people with certain double majors, or maybe even those who give money just to increase their standing for better tickets to athletic events (I might fall under all three).

Certainly many databases might not have 70 groups lying within, just waiting to have their passions and interests spoken to, and energy harnessed. I will challenge you however, to step outside the traditional segments in the fundraising canon, pick different selection criteria, or identifying characteristics, and see if you can find Microtrends for your own organizations.

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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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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The World of Phone Service is Changing...

A new study says 3 in 10 get all or most calls on cell phones, and I am certain that number will only rise in the near future.

Nearly 1/3 of those under the age of 30 have cell phones only.

In general, people are more private with their cell phone use. They are often more reserved with giving out this number, and enjoy the decrease of direct marketing calls compared to landlines. There is no "directory" for cell numbers-which is both good and bad (depends on who you are and what you want).

Keeping aware of this technology shift is important for those who do modeling and use "preferred channel" type categories as independent variables. It may also be important to the annual fund folks, where phone solicitation is still a tried and true method of raising money. Perhaps this shift might imply an increase in email or online solicitations to targeted groups as opposed to trying to reach them on the phone? Or a comprehensive program to acquire cell numbers of recent grads?

All the wonderful messaging and strategy in the world is useless if we have no way of contacting our donors. Being aware of trends like these is vital.

For nearly three in 10 households, don't even bother trying to call them on a landline phone. They either only have a cell phone or seldom if ever take calls on their traditional phone.

The federal figures, released Wednesday, showed that reliance on cells is continuing to rise at the expense of wired telephones. In the second half of last year, 16 percent of households only had cell phones, while 13 percent also had landlines but got all or nearly all their calls on their cells.

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Man vs Machine, or "Numbers" vs "Guts"

Through my analysis and recommendations for a variety of clients, I have seen first hand the tension and complex relationship of what I like to call the “pre-analytics world” and the “post-analytics world.” This convergence of two almost fundamentally different perspectives on organizational and campaign planning is still very fresh in the world of fundraising. Analytics represents progress to many in our industry—insights and capabilities based upon a new process of information gathering and analysis. Unfortunately, this evolution (or some might say revolution) has been strained at times.

Many appreciate the technical ability and metrical sophistication gained from analytics and modeling. For some, it is difficult to grasp the concepts used and understand opportunities for application. For others, it is difficult to embrace and trust the insights gained.

Provided with a reasonably well-stocked database, I could offer not only predictions on an institution's future, but also “blind” insights and analysis on what has been happening to-date. Without knowing the information, I could tease out the shift in annual fund messaging strategy, suggest which gift officers were performing well and why, and even reveal strategy for prospecting and solicitation. Impressive? Perhaps. But what happened to good old fashion “gut feelings.”

In the example I present, experience, the strongest factor used in “gut” decision making, is completely absent. I have never spent an hour inside the institution whose profile I could construct. I may offer new insights and perspectives—but don’t really know XYZ University like the VP does. The VP knows the shop and the donors, and feels the campaign is a “go” despite the reservations I might provide.

I can understand why a VP might feel hesitant to plan campaign strategy around analytics work he/she barely understands from someone who doesn’t know the institution as well as he/she does. It’s the institution's campaign, but ultimately his/her job on the line. Beyond campaign success, part of that job is also embracing new ideas and technologies. While he/she may never want to have a fully analytics-driven campaign—rejecting these tools may brand you as a fundraiser from the “20th century,” a wholly undesirable title.

What is the future for “gut decisions” in our world? I truly hope they never go away—and I doubt they ever will. All the modeling in the world could never replace a highly skilled gift officer, or savvy VP. Yet these two groups: pre-analytics (gut and intuition decision-making) and post-analytics (metrics and analytically rooted strategy) are more and more seen as clashing, especially when considering the increased respect and weight given to analytics in fundraising.

What can we do to bridge this divide, and to integrate the best qualities both these approaches have to offer?

This article posits a similar question. While the author does not attempt a thesis-like response, she does offer one sobering and often overlooked factor: “You can't predict emotion with a machine.”

Last week's episode of The Apprentice, filmed at Ogilvy, proved that marketing does not come naturally to everyone. Which is why decades of admen have been held in great esteem for possessing an instinctive ability to produce great campaigns. But, increasingly, the traditional reliance on intuition as the basis for a successful campaign is being surpassed by evidence-based decision making and 'creative experts' should be on their guard.

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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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Can we Build a Better Zip Model?

Lately I have had a keen interest in demographic data and how it best fits with the tools we have and goals we seek in fundraising analytics. Certainly a plethora of affinity metrics and giving behavior makes our statistical mouths “water,” but demographic data still presents relevance and unique relationships (some good and some bad) when attempting to predict giving behavior.

I have recently posted articles suggesting another long look at demographic data (Why Demographic Data Just Won’t Die) and its benefits (Predictive Modeling the 2008 Elections…) in capturing difficult or complex decisions or choices. This article suggests some of the limitations of a zip model. While many of you may not use them regularly, I think zip-driven models may have utility for annual giving segmentation and mailings, and for institutions that rely heavily on a broad base of public and community support (urban public universities for example).

This article discusses some of the largest issues with zip-focused modeling, including aggregation, and the “self-fulfilling prophecy” phenomenon. It also offers some general but effective advice for anyone considering a zip model as an additional analytical tool.

How to Build a Better Zip Model

The May 2007 postal rate increase sent every direct retailer scrambling. It’s hard to argue the hike’s effectiveness as a catalyst for renewed analytical vigor.

Our clients have been analyzing everything from the impact of page count reductions and co-mailing programs to the most appropriate tools to optimize circulation. And for one, preliminary research indicated that a new zip model might be the right solution at the right time.

Zip modeling is not new. It remains a data-based tool that requires in-the-mail validation, but the postal rate increase was as good a time as any for many retailers to test it.

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Predictive Modeling the 2008 Elections...

In my content research for this blog, I look for specific articles relating to fundraising analytics, broader articles on analytics, or theory that provide either lessons or questions transferrable to our work, as well as other examples of creative minds using past behavior to predict future behavior. Without politicizing this blog, I want to share this article on Ken Strasma, a political analytics guru for a current presidential hopeful.

I was generally unaware of the depth and nuance of this pursuit of analytics. Particularly attractive I believe is the ability to model what are fundamentally just opinions (not financial transactions, such as charitable giving or consumer spending as opinions by proxy). I considered the lack of explicit numeric metrics to be a difficult obstacle to overcome, but Strasma and his colleagues have developed techniques to model not only complex preferences, but also predict what is essentially non-regular behavior (ie voting).

Strasma says:
“..there are a number of basic questions predictive analytics tries to answer for any campaign. These include how likely it is a voter is undecided, what issues undecided voters care about, how likely it is that a voter supports a certain candidate and how likely it is that an individual will contribute if asked.”

For our work, I considered this analysis to be similar to who has interest in giving, what causes do they support, how likely are they to support our organization, how much would they contribute to our organization, or more simply, who is a suspect, a prospect, what is the target, and what is the actual ask amount?

I hope this article enlightens your assumptions of predictive modeling, as it did for me.

Candidates Use Predictive Analytics To Seek Votes

As the primary race grinds on, the candidates are turning to predictive analytics tools to help find voters ready to support them.

A company called VisualCalc provides a free Web site that helps citizens analyze the presidential race through a series of dashboards that chart the status and trends of the primary election.

On the flip side, candidates in this year's historical race for the White House—for the first time a woman and a black man are vying for the Democratic Party nomination alongside a single presumptive Republican nominee—have similar tools to provide information that may help them attract those key undecided voters.

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Why Demographic Data Just Won't Die

This is a really interesting perspective on what many, myself included, may now consider one of the relic's of predictive modeling: basic demographic data. This data is basic, sometimes clumsy--the data we used in college to learn the techniques of statistics, regression analysis, and econometrics. As analytics junkies today, we all strive to build models and tools to help us fit the contours of the populations we study and to levels much more precise than a zip code or an age group. In modeling, there is “power in numbers,” but there is also an aggregation danger at play when using broad metrics which capture individual behavior and preferences.

I have been posting for some time now on this blog about the frontiers of text-analytics and the raw potential inherent in such custom data mining approaches, that I fear I may have become too nano in my purview.

Behavioral modeling is definitely one of the sharper tools in our toolbox, but read this article and you may find yourself having a similar reaction that I did: reconsidering the benefits and devising new applications for using demographic data.

Demographics: The Targeting Construct That Wouldn't Die

Recently, our customers have communicated a message to us loud and clear. It is a message that may seem counterintuitive here in the 21st century, in the all-digital, micro-targeting, behavioral targeting, contextual targeting age.

Demographics, they tell us, are of paramount importance.

No, seriously. Demographics. Like age, gender, household income. I know; it’s as if I told you I was converting all my MP3s to 8-track, right?

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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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Segmentation and Shakespeare

Interesting news release out of Stratford England—The Royal Shakespeare Company has developed a successful partnership with an American analytics firm to successfully segment their database to identify and engage different ticketing behavior.

DonorCast has been moving into the ticketing side of predicting modeling and this technique looks promising given adequate data (isn’t that always the case though…)

The Two-Step Cluster feature in SPSS is very powerful—our practice has only just touched the surface of application possibilities. This technique can be used as a finishing “sorting” of records, or can do a sort based on key variables pre-modeling (it can handle both categorical and continuous variables).

I will find some more relevant articles to share about clustering and segmentation techniques in the next edition. In the meantime, play around with this SPSS feature and consider how it might be applied in your work.

Advanced Analytics Move Centre Stage at the Royal Shakespeare Company

SAN FRANCISCO & LONDON--(BUSINESS WIRE)--Analytics software from KXEN is helping boost audiences at Royal Shakespeare Company (RSC) productions in a pioneering arts marketing move. The initiative, an Accenture-led program to segment audiences, has seen a 50% rise in ticket buyers at RSC's Stratford-upon-Avon theatre, a more than 70% increase in regular attendees and significantly earlier sell-outs for London bookings.

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Netflix Contest has Produced Prizes for the Analytics Community

In June 2007, we posted about the "Netflix Prize" - a contest promoted by analytics savvy movie-rental-house Netflix.

The goal: improve the accuracy of the existing Cinewatch movie recommendation system.

The prize: $1 million

Fifteen months along, and no model has come forward meeting the victory threshold of 10% improvement on matching accuracy. Fortunately, for everyone that doesn't work at Netflix, this contest has produced something of value.

The discussions and attempts conceived from this contest have provided those interested in analytics new perspectives and questions to ponder as we seek to analytically quantify and predict preference and behavior.

This article discusses some of the most interesting insights thus far:

"Open Questions" (text mining) has emerged as a theme to "fine-tune" the specificity of predictive models. Allowing individuals an opportunity to express, instead of forcing them to conform entirely to a pre-defined format, is really emerging as a more nuanced and "high-touch" approach. As I have posted previously, there is software emerging that is making great strides towards allowing text mining to be a pragmatic tool. Discriminate choice models of "ultimate" giving destination preference (athletics, fine arts, brick and mortar) for example, could be greatly enhanced by appropriately applied text mining.

Another model suggested that information about tastes as related genre, language, actors, directors etc, was surprisingly powerless compared to the star ranking of the movie itself. Perhaps this suggests that second tier "affiliation" data (I love Tom Hanks, or in the fundraising field, I was a Sociology major) may be more ambiguous than standard industry assumptions. At minimum, this revelation suggests that more consideration should be given to the importance of the top preference metric (for movies its a star rating, for fundraising, it is giving to the institution).

The $1,000,000 Netflix Prize competition has produced interesting results, even if no winner, 15 months in. Some of those results are a bit surprising; others we should have expected but didn't anticipate. So while participants haven't yet bettered the accuracy of Netflix's Cinematch recommendation algorithm by 10%, the threshold to win the $1 million prize, we can still take away lessons about predictive-analytics fundamentals.

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Web Analytics Primer from an "Evangelist"

This is a great new article by Avinash Kaushik, the “Analytics Evangelist” for Google. I have posted a few other articles that touch on the topic of web analytics because I consider this a relatively untapped, but potentially rich source of information.

This is a very good primer for web analytics. Kaushik describes basic concepts in how website usage, or “visit” data, has utility. These concepts are fundamental, but certainly are still the most widely used in website analytics.

The applications for analysis for the six basic measures mentioned:

■ Visits
■ Page views
■ Pages/visit
■ Bounce rate
■ Average time on site
■ % new visits

These are universal in creating core metrics for a website—you need to have some place to start to know where you are going.

Basic ideas off the top of my head for these simple applications include:
1) Basic web stats for an online donations page—what is the “close” rate of those who visit?
2) Tracking sourcing from online pages—what are the most effective and least effective “links” sending people to your online donations page?
3) Identifying other interest areas through usage stats—are there other surprising sources on your site that have generated strong interest? Special events, news, messages? Possible affinities or, at the very least, interests may lie undetected.

And this is just a start. Obviously, as you layer and link pages, data, etc., the specificity of the analysis can increase sharply. This is a basic start.

Try it out. Show a colleague—see if they are interested…

New to Web Analytics? Confused about Web Analytics? Think it is too hard? Scared of tools and consultants?

This post is for you, its goal: Web Analytics Demystified! Yeah!

Web Analytics is complex. That is what it is. Complex.

Get the nuance? Complex. Mysterious. Inviting. Come in. Sit down. See what’s there. No free rides. You’ll do your part, your efforts will have a rich payback.

Complex holds the promise that you’ll get it. Nay, you can get it. Come in, welcome.
Start with this post.

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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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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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Lets not forget about the Annual Fund; Behavioral Targeting

This article comes from a for-profit sector perspective and discusses an optimization technique using web data, commonly referred to as "behavioral targeting." Many of the articles and techniques shared here relate to predictive modeling, primarily for major giving—what about other giving populations who may yield smaller dollar amounts, but have more consistent patterns of philanthropy? Obviously the high-reward potential of accurately identifying transformative or major gift prospects is very attractive. There are other opportunities however, in which to apply predictive modeling techniques to support increasing the effectiveness of your giving programs at all gift levels.

The requirements (significant longitudinal data) and benefits (modeling of consistently stated preferences) of behavioral targeting make it an interesting technique when applied to the examination of annual giving behavior.

For example, if your institution had a well-developed online annual giving program, elements and principles of behavioral targeting could be applied. Inserting one simple, but well-designed affinity question into the process of submitting an online annual giving donation could produce some informative trends. From these trends, the annual giving program could be more specialized in targeting and messaging, as they seek to engage new constituents or increasing giving levels of current annual giving donors by identifying effective priorities and factors for giving.

The other benefit about these strategies is that they are relatively simple, when compared to complex major gift models with cluster analysis, etc.; the time invested may just be appropriately proportional to increase in dollars from focusing on populations of "base givers."


IN MY PAST FEW COLUMNS, I have set out to clarify optimization, a term that is often bandied about and regularly misunderstood.

I first covered testing, the most frequently used method of improving consumer response, which includes A/B testing and multivariate testing. With the targeting article, I covered how systems based on rules can be used to create more relevant experiences with better outcomes.

The third type is perhaps the most seductive -- and misunderstood -- form of optimization, behavioral targeting. (The fourth, social optimization, I will explain in the near future.)
What Is Behavioral Targeting?

The holy grail of direct marketing has been a system that detects consumer behavior and changes offers. The first incarnation of this approach was called data mining, and was focused on using data to drive strategic planning. There is an apocryphal story about Wal-Mart: "By scanning each sale into a data warehouse, grocery stores have determined that men in their 20s who purchase beer on Fridays after work are also likely to buy a pack of diapers. Thus, a display of Pampers or another brand might be set up in the beer aisle, or merchants will put one (but not both) of the products on sale on Friday evenings."

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Predictive Analytics frontiers: Web Analytics

Predictive Modeling and Analytics is a new and exciting tool in the development and advancement services world. Many organizations are now not only integrating these powerful tools into their current work, but also seek new opportunities to improve their organizations capacity to understand relationships and model behavior. By implementing new strategies for data collection and information, organizations recognize the gains which could increase Predictive Modeling’s utility.

The newest frontier in Predictive Analytics is Web Analytics; information gathered from website traffic is considered by many a fertile and relatively un-tapped resource of behavioral data. Applications for Universities, Colleges, Hospitals, and other non-profits vary from gauging the effectiveness of a campaign message, to the success of online giving solicitation versus more traditional methods.

This is a very informative article from Avinash Kaushik, the Analytics Evangelist at Google.com. Kaushik considers the opportunities in Web Analytics, in addition to current struggles in its application, along with ideas on how to improve Web Analytics data in the future.

Data Mining and Predictive Analytics on Web Data Works? Nyet!

Strong Russian word: Nyet (No). By the end of this post I hope you’ll agree. Worst case you’ll have food for thought.

This in-depth post covers a complex topic that might not apply to everyone, but it covers an area where companies have struggled to try to show return on the investments made in skills, technology and time. The post promises clarity and guidance that hopefully will result in you saving tons of aggravation and yes even a nice chunk of change.

Data Mining and Predictive Analytics have promised a the earth, the moon and the sun for sometime now, in all channels we do business in. My personal point of view is that on the web they fall far short of even the most pessimistic promises. For now…

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Data Mining: Three Steps To Mining Unstructured Data

This article presents some informative perspectives on effective ways to capture and integrate non-static, or "unstructured" data. The term unstructured data is often applied to information regarding an individuals preferences or tastes. This information is viewed as more susceptible to variation and more difficult to predict, yet powerful as a predictor.

One field of non-static data in prospect research may be rating an individuals willingness to give during a campaign. This rating may be tied to a series of factors which often have uncorrelated relationships to each other (affinity for the institution and stock market performance for example) and can change independently. However if they are appropriately aggregated and correctly understood, can be dynamic informers towards predicting the likelihood of giving at all levels.

In our journey of discovery, we have seen one mistake made repeatedly. We have seen static business models and static data models try to be used to model inherently dynamic business processes, particularly at the point of interaction. For example, virtually every customer relationship management system we have come across has a manual classification scheme (or taxonomy) that is meant to be used by the service agent to classify the nature of the customer interaction. This approach has two major flaws.

First, as soon as the classification scheme is published, it is out of date, because interactions with your customers are unpredictable and continually changing. Second, even if the classification scheme was representative of your customer interactions, it is unreasonable to expect any number of service agents to classify their interactions with their customers in a consistent way and with high quality. This very often makes such classification data completely useless, or, more dangerously, misleading. This issue is true throughout the business ecosystem where unstructured information exists.

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Can Data Mining and Predictive Modeling Really Make Us Safe?

This article which came out last summer, is particularly relevant given the discussions in recent weeks regarding the Federal Government and data-mining practices. What is data mining useful for? What is it not useful for? These are questions you may ask yourself in your own organizations and projects. There is also a useful link in this article to the comprehensive data mining report produced by the GAO on the governments data mining and predictive modeling projects.

While the scope of your projects may not include National Security concerns, it can be useful to see how others use data mining and predictive modeling techniques to model behavior and forecast future events, from purchasing a house to committing crimes.

In the post-9/11 world, there's much focus on connecting the dots. Many believe data mining is the crystal ball that will enable us to uncover future terrorist plots. But even in the most wildly optimistic projections, data mining isn't tenable for that purpose. We're not trading privacy for security; we're giving up privacy and getting no security in return.

Most people first learned about data mining in November 2002, when news broke about a massive government data mining program called Total Information Awareness. The basic idea was as audacious as it was repellent: suck up as much data as possible about everyone, sift through it with massive computers, and investigate patterns that might indicate terrorist plots.

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Artificial Intelligence enters the Mainstream

...One has to understand that the massive amounts of data being generated today cannot possibly be analysed effectively enough either by humans or traditional software, if one wishes to derive all the knowledge inherent in that data or ascertain the intrinsic hidden patterns. It is here that AI is playing a big role, whether in biotechnology or in banking.

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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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Data Mining at the New York Times

In a previous posting I pointed out the common negative associations with data mining. This article about data mining at the New York Times is another good example of this.

Prospect research has dealt with managing public perceptions of its work for decades. APRA, the professional association serving the prospect research field, put together a fine code of ethics for prospect information. It is important for data miners to adopt similar standards. Using data mining to identify prospects for philanthropic purposes is a very positive use of the technology. It locates individuals with strong affinity for the work of nonprofits. And, it helps segment out unattached or uninterested individuals.

Barely a year after their reporters won a Pulitzer prize for exposing data mining of ordinary citizens by a government spy agency, New York Times officials had some exciting news for stockholders last week: The Times company plans to do its own data mining of ordinary citizens, in the name of online profits.

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Does Data Mining = Privacy Concerns?

Data mining has many people concerned about privacy considerations. However, data mining as a technique should not be the concern. Instead, the data used for data mining should be held to the highest legal and ethical standards. Nonprofits are governed by legislation preventing use of sensitive information such as private credit data. Prospect researchers generally follow strict ethical guidelines about using data only where relevant to the relationship.

The majority of information in a nonprofit database is transactional giving data, relationship history, organizational activities, and contact information. By using this internal data, most organizations can very powerfully identify closer constituents and those most likely to respond positively to engagement activities. Additionally, it is possible to identify constituents preferring not to receive our communications.

Although the use of data mining in fundraising is less of a privacy concern from my perspective, it is important to stay current on the debates. Here is an article discussing this privacy debate.

The future of computing will feature devices that monitor you, anticipate your actions and chronicle your life. The problem: Privacy concerns.

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Good Data and Experienced Analysts

Custom-engineered models, created internally or by data mining professionals with thorough understanding of the specific context, are superior because of two factors:

• Knowledge of the data
• The human element

Analytics requires a good deal of art. To say a data miner is a "technical person," overlooks a much of their value. These professionals use statistics as a means. What drives them is, "Why do people give to us?" and "Who else fits this profile?" They are fundraising strategists with a unique perspective. Soon, they will be irreplaceable to your organization.

This article discusses the value of good data and the human element.

"One extravagant claim is that experienced human analysts will no longer be required," Wheaton says. "The problem is that it is easy to write software to identify statistical patterns in the data. But, it is a lot more difficult to figure out which of these patterns makes business sense and will hold up over time."

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Why Can't A Computer Be More Like A Brain?

I think most data miners would find this an interesting read. I did.

For 50 years, computer scientists have been trying to make computers intelligent while mostly ignoring the one thing that is intelligent: the human brain. Even so-called neural network programming techniques take as their starting point a highly simplistic view of how the brain operates.

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Data Mining for Airfares

A University of Washington professor used data mining to predict when to buy airfares. As these tools become increasingly accessible to people, I expect we will see the pricing sophistication of companies increase. Similarly, in fundraising, we might use data from our database to understand when a person is most likely to give the largest amounts, make certain types of gifts, or be willing to volunteer.

In 2003, Etzioni and colleagues published a paper showing that they could predict the fluctuation in airline-ticket prices surprisingly well. By sifting through the history of more than 12,000 airfares for nonstop flights from Seattle to Washington, D.C., and from Los Angeles to Boston, the researchers could predict with 62 percent accuracy whether or not those ticket prices would rise or fall in the future.

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The Next Wave of Business Analytics

Here is a great overview of the current state and future of analytics in the market.

Although Albert Einstein said, "Not everything that counts can be counted and not everything that can be counted counts," organizations in all industries are collecting and storing an increasing amount of data generated by internal transactional systems as well as external content sources. The challenge of what to measure and how to agree on key performance indicators (KPIs) is a point of frustration for both IT and business. However, most organizations are willing to err on the side of caution and deal with more data rather than discarding it.

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Data Miners Can Be Funny... Well, they can try.

On a light-hearted note, Here is the Data Mining Limericks contest at KDnuggets. It was from a few years back. Below is one of my favorites by Ross Bettinger:

There once was a data miner
Who claimed, "I'm a Forty-Niner."
His main obsession
Was logistic regression
But neural networks predicted finer.

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Lifetime Value and Fundraising

Most prospecting strategies concentrate on the capacity rating. This is generally an amount a person can give in an ideal scenario if your organization is their top philanthropic priority. This amount is generally compared to gift officer yield rates and/or target ask amounts to project portfolio performance.

In the for-profit arena, lifetime value is the preferred metric of customer rating. From an annual giving perspective, it makes sense to consider lifetime value in segmentation. However, if an annual giving directors only goal is the participation rate, are they likely to risk overall participation for the sake of high lifetime value acquisition? It is likely preferable for the big picture.

Customer lifetime value is a way of measuring how much your customers are worth to you, over the length of time that they remain your customers. The lifetime for customers will vary from industry to industry, and from brand to brand. The lifetime of customers should come to an end when their contribution ceases to be profitable unless steps are taken to revitalize them.

Here is an article with methodology for determining lifetime value

You've Got Data: Now What Do You Do With It?

This article from one of my favorite web sites, CRMguru, presents a four step process for making the most of your data. I am glad it points out predictive models don't predict customers. Rather, it points out they identify groups most likely to be customers. Similar to fundraising, predictive modeling will identify and prioritize your prospects to bring efficiency to your high-touch prospect research area or bring efficiency to your broad-based solicitation strategies.

For example, Toyota took customer data from previous repurchase campaigns and developed simple statistical models to predict which customers are more likely to repurchase in the future. The models identified the characteristics of customers who repurchased before and used this insight to identify, say, the 30 percent of customers most likely to repurchase in any given month in the future. The models were used to target customers in a number of repurchase campaigns to great success: The campaigns doubled the repurchase rate but at only one half of the volume (and cost) of mailings, a net 400 percent increase in campaign ROI.

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Data Mining and Video Games

This article about a video game data mining tool reminds me of a KDD conference session I attended five or six years ago. In this session, a data miner teamed up with a video game designer on a project. They sought out to see if they could predict click-through, customer behavior based on how the players maneuvered their way the game. Their results were compelling. We are just scratching the surface of understanding the predictability and interrelationships of human behavior.

You may have heard of Emergent Technologies Gamebryo engine, but you have probably never heard of their data mining tool called Metrics. It allows you to instrument and visually see data sets in your game letting you figure out problems hopefully before they are a problem.

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Data Mining with Java

Here is an introduction to data mining using Java. It provides some interesting descriptions, tables, and examples that will apply to you non-Java folks as well.

This article, an excerpt from Java Data Mining: Strategy, Standard, and Practice by Mark F. Hornick, Erik Marcade, Sunil Venkayala (Morgan Kaufman, 2007), introduces data mining concepts for those new to data mining, and will familiarize data mining experts with data mining terminology and capabilities specific to the Java Data Mining API (JDM).

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Is Data Mining Fraught with Peril?

This fun piece from ABC news describes the use and abuse of data mining. Often, the abuse surfaces when the analytics professional is overly interested in desired results over natural results. If there is not a clear business understanding and the model evaluation is not circled back to the original premise, error will result.

In fundraising, I see many people wanting unique and interesting factors to be the "key" for predicting giving. Instead, our goals should be identifying prospects, prioritizing prospects, predicting behaviors, segmenting our lists, and so on. In prospect identification, data mining is typically followed by prospect research. This hand verification will catch Type I errors. However, the deployment of models to annual giving does not have a safety net. I recommend testing a model like you might a survey or a direct mail piece to control for this potential of error.

I believe the science of data mining is strong and evolving, but I definitely recommend reading the ABC article to introduce some of the "cons."

That's been a good thing in some ways, because it has helped researchers spot trends in everything from politics to the stock market to long range weather patterns. But it's probably also why you get advertisements for stuff you don't want, and why sometimes it rains when it's supposed to be sunny. According to Austin and his colleagues, data mining is fraught with peril.

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Model Selection for Major Giving

This past couple weeks have been active on Prospect-DMM, the discussion group for data mining in fundraising. One discussion called into question the use of certain modeling techniques for major giving. I thought my brief response might be useful to this forum.

From Prospect-DMM:

For identifying major giving prospects, it makes sense to try various avenues.

A reason binary logistic regression or a decision tree on a binary variable (C5) maybe effective is because of the nature of major giving donors.

Certainly, a pathway to major giving via increasing annual support is a pattern found among major donors. These donors might be making gifts informed by cash flow (what can I afford to give this year?). And, using different ordinal or linear techniques makes sense.

However, there tends to be a large group of major donors--usually the majority of many files I review--that have very inconsistent "pre-major giving" gift behaviors. They tend to give gifts out of assets and are motivated by an investment frame of mind. Since they are very different from the overall donor population and tend to be a small pool, categorizing these large outright donors as a 1/0 makes a lot of sense.

Only using factors related to levels may miss many new opportunities (it also may not - each data file is different). Likewise, undocumented planned gift donors and future volunteers are difficult to predict using other modeling techniques.

I would rather be equipped with a large arsenal of techniques and cater my approach/es to the specific modeling need. In major gift modeling, this may require diversifying the models to find as many new leads as possible.

Putting the Business Intelligence Puzzle Together

Analytics is a key to understanding constituent behaviors, tune processes, and project future behaviors across many industries. The following article is a bit one-sided in promoting Advizor Solutions, but the presentation of business intelligence is worth a the quick read.

Combining data mining, statistical and numerical analysis and data visualization, the use of business intelligence applications is, as a result, spreading fast. What is differentiating competitors in the field is the BI application's ability to perform compound, ad hoc queries on large data sets on the fly and graphically display and distribute results in an intuitive, easily comprehensible format.

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Process Analytics and ASU Advising

Many fundraising analytics professionals use modeling for prospect identification. However, a new opportunity is tuning business processes.

Arizona State University is building a program for managing the process of advising. A goal of advising is to help see the student to graduation. By tuning this path and providing simulation for changing paths, advising would offer better service to the students.

Moves management processes see prospects along various paths as well. By tuning these paths and providing simulations for altering areas of interest, capacity, and affinity levels, development would offer better service to the prospects.

Provost Betty Capaldi, the university’s top academic official, is building a computer system that will tell students what classes they need to graduate, when they need to take them and, if they want to change direction, how to switch their major.

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Marketing to Algorithms

Data mining and modeling is integral to our everyday lives. Many everyday behaviors, conversations, and transactions can be tracked. Max Kalehoff points out in his piece that models need to be much more multidimensional in their approaches. He says:

This concept is terribly important to marketers that must now rebuild their consumer decision-making models. The old linear decision models are becoming irrelevant, and must be replaced with new ones that incorporate not only overt word-of-mouth behaviors, such as face-to-face discussions or online consumer discussions, but all behaviors that create halos of metadata, which algorithms process, mediate and disperse to others.

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Three Strategies for Successful Data Mining

I would start by establishing the metrics for success. The business understanding should guide the entire project. See my post regarding CRISP-DM.

Which data models are worthwhile? What are the best predictors? Which metrics work? A panel of catalogers and list pros provided simple tactics to help mailers improve the quality of their databases at the “Trick Out Your Data and Kick Up Your Revenue” session held during the List Vision conference earlier this month in New York.

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Why do your donors give to you?

This is an important question every fundraising analytics professional should consider. It is not sufficient to determine who will give to you. We should understand why. I have found that this is rarely one simple answer. Often times, there is a combination of a few predominant types of donors. For example:

• They love you
• They love what you do
• What you do helps them or their business
• They have confidence you will succeed at something they find valuable
• They want to give back (most common self-reported reason--but not always the actual motivator)

The following article provides one example of a giving motivation:

DR. BILL VAN DYK is straightforward about why he serves on the Contra Costa College Foundation board of directors and is now in his second term as its president.
He supports CCC because his San Pablo dental practice is dependent upon graduates from the college's dental assisting program. His personal donations to the CCC Foundation are designated for the dental assisting program, and he urges other West County dentists to follow his lead.

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Why do your donors give to you?

Top 10 IT Trends for 2007

If your nonprofit fundraising organizations are not pursuing these trends today, they will.

WELLESLEY, MA -- (MARKET WIRE) -- December 05, 2006 -- Nucleus Research today announced its Top 10 IT Predictions for 2007. The annual report has accurately predicted major IT trends for enterprise end users and vendors for the past three years. Nucleus predictions are based on analysis of both vendors and thousands of corporate end-user case studies.

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RFM vs. Predictive Modeling

RFM has long been a measure of customer value. Many nonprofits are using it to describe prospect or donor value. It can be an effective measure for a segment of the donor pool. I have even used the measure as a dependent variable in a linear model to understand the factors and predict future value. In this article from DM News, Melissa Campanelli compares RFM to predictive modeling in a back-to-school campaign.

"Names selected using predictive modeling had a four times higher average monthly spending rate than people selected with RFM, and a three times higher purchase rate. Also, consumers selected with predictive modeling spent 2.5 times more per direct mail piece than those chosen through RFM. "

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Process Analytics

I enjoyed this blog posting by Phil Ayres. Although most of the analytics applications in fundraising are limited to prospecting or direct marketing segmentation, I believe business processes are next. The strong interest in refining performance metrics will lead to descriptive and predictive applications. This will likely start in the deployment of major giving staff. However, managing truly lifelong (yes, even before they graduate higher ed) constituent cycles, and understanding the factors impacting increased engagement will have more sophisticated approaches in years to come.

"In many cases it will make sense for the analytics to focus on purely systems-based business services in the SOA. An example is an online loan customer inquiry. In a call center environment, process analytics would typically focus on attributes like the time to answer, abandon rate, loan value, type of request, all divided by class of customer. In an online world, similar information should be provided to the business to assess the effectiveness of their website, marketing and backend application processing, but the website analytics may not be the place to most manageably gather it, especially since much of the required information would not be exposed at this level."


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Analytics in Political Fundraising

Although it is generally referred to as "microtargeting," data mining has become an effective tactic for political fundraising strategy. In this feature piece about Bloomberg, John Heilemann writes:

What kind of campaign would Sheekey run? He isn’t saying. But judging from the one he devised for Bloomberg in 2005, it would be extremely sophisticated. Schoen points out that Bloomberg’s operation in 2001 was ahead of the Bush team’s now-famous use in 2004 of microtargeting—the new political science of combining consumer-database information with voter rolls to target people likely to be receptive to your message. And in 2005, Sheekey cranked up the tactic up another notch. In both elections, the Bloomberg campaign applied new technology, plus a boatload of cash, to the task of identifying and turning out independent and unenrolled voters. Hence the model that Sheekey would surely try to duplicate on a national scale.

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Analytics in Auction Fundraising

As yet another sign of the strength of analytics in fundraising applications, cMarket is integrating analytics in their online auctions.

cMarket has added an analytics feature to its charity-auction fundraising service for non-profits. cMarket's new SmarterAuction feature draws from data collected from over 1,500 completed cMarket auctions in more than a dozen cause categories to help its nonprofit clients run more successful fundraising auctions.

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