5 reasons every nonprofit should use analytics for fundraising

1. Identify Prospects
Anyone working in major gift development realizes, despite all the benefits, wealth screening has its limitations. Most wealth data is not public information, matching is never perfect, and being wealthy alone does not make one a prospect. Predictive analytics can find the individuals fitting your organization’s donor profile. It can help you find the people with the connections, potential passion for mission, and likely wealth. By adding dimensions to your prospect identification, you can find more and better potential major donors.

2. Do more with existing staff
Among the most immediate benefits of predictive analytics is staff efficiencies. Prospect researchers can look at fewer names to find prospects for assignments. This can bring up to double the output from research at many nonprofits. You can contact fewer individuals and net at-or-above previous direct marketing efforts. Analytics is also helpful in tuning processes such as prospect management and engagement strategies.

3. Make better decisions
When I meet with the fundraising staff at most of my clients, they tell me that their executives are not data people. When I meet with the executives, they often say, “No one gives me data around here.” Modern, effective fundraising leadership desires and requires more thoughtful decision support than ever before. Analytics can point out gaps, reveal opportunities, and clarify production clogs better than any tools we’ve had to date. Arming the experienced fundraiser with timely and relevant data can be transformative for nonprofits.

4. Manage data more effectively
Organizations with an eye towards analytics gather and store data in their donor management system differently than other organizations. Initially, they make sure every touch point between constituent and organization is captured. Then, they make strides in capturing process data in a more granular fashion. Rather than simply record contact reports as free text data, analytics enterprises will record where the meetings took place, what steps were taken, adjustments to strategies, and targeting refinements as codes. This enables them to learn how to best cultivate new prospects, engage cold relationships, and bring about sustainable involvement.

5. Raise more money
Productivity is the bottom line of any nonprofit fundraising program. To raise more money, an organization needs to ask more and ask smarter. From setting campaign ask amounts to determining solicitation readiness, and from staffing analysis to measuring return on investment, analytics can help your nonprofit raise more money. And, that’s why we’re here. Isn’t it?

2011 Analytics for Fundraising survey results infographic

We’ll be at the APRA Data Analytics Symposium in Austin, Will you?

Kate Chamberlin was telling me about the program for this year’s APRA’s analytics symposium. It sounds really cool. I especially like the section of case studies they’ve lined up. Several great institutions will be highlighting aspects of their own real projects. This conference will hardly be just “theory.” Here is the official blurb:

Explore the potential of data analytics and learn from an expert faculty during the Data Analytics Symposium, July 27 – 28 in Austin, Texas.

Designed for analysts, development IT professionals, business intelligence professionals and fundraising managers the Data Analytics Symposium — held in conjunction with APRA’s 24th Annual International Conference — will help you identify new and exciting ways to approach fundraising and organizational growth with minimal investment. Educational sessions will review in-depth case studies to understand what works (and what doesn’t) when tackling real-world solutions. The fundamental track will teach you how to start a successful analytics program, while the intermediate/advanced track will share the latest in ideas, presentation models and analytics methods.

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The "Netflix prize" model...only this time more serious

Josh and I have both posted about the Netflix Prize...drawn to both the idea of creating a very accurate preference/choice models with a very large menu of outcomes, as well as the crowd-sourced approach to solving the problem (and there was in fact a winner).

Well I am very excited to share this approach has been applied to a more "serious" problem: build a model that will predict upcoming hospitalizations. The end result is far more lofty than a movie pairing to "Young Frankenstein". This project hopes to identify individuals at greatest risk for imminent adverse events before they happen, creating an "early detection" system to save lives and reduce overall costs.

A $3mil prize is also a great incentive...keep an eye on this content.

Netflix Prize-Style Competition Predicts Hospitalizations
What if you could predict if a given patient were at a higher risk for hospitalization in the coming year? You could potentially save money, and lives, by pulling out all the stops to prevent that hospital visit, if possible. And that's why the Heritage Provider Network (HPN) has put up $3 million for a Netflix Prize-style competition that will pit coders against each other to devise the most effective predictive algorithm for incipient hospitalizations. HPN will be announcing a launch date for the prize this week.

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CRISP-DM: does this really capture our work?

The Cross Industry Standard Process for Data Mining--the time honored road map for building data-mining, evaluation and deployment, and ultimately building a self-sustaining cycle of new information, new insight and new analysis. As a construct it is both intuitive and transformational; identifying small steps as processes and linking them to in a larger approach to successful predictive analysis.

However I question how often it is ever truly realized? Is there merely an aspirant blue print? The white whale of our data-mining efforts? In the non-profit space I have a difficult time thinking of organizations where this has become organic.

I reviewed this after reading a recent posting suggesting many projects and even organizations start at very different points in this process. Sometimes the same organizations may start at a different place depending on the project design, data available, timeline, resources available etc.

Just curious what others think of CRISP-DM. Is it a firm road map to successful data-mining, or does it suggest merely an outline of processes that is malleable?


Doing Data Mining Out of Order
I like the CRISP-DM process model for data mining, teach from it, and use it on my projects. I commend it to practitioners and managers routinely as an aid during any data mining project. However, while the process sequence is generally the one I use, I don't always; data mining often requires more creativity and "art" to re-work the data than we would like; it would be very nice if we could create a checklist and just run through the list on every project! But unfortunately data doesn't always cooperate in this way, and we therefore need to adapt to the specific data problems so that the data is better prepared.


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Fantasy Football analytics

I think a fantasy football draft is a "prediction/planning experience" most people can relate to and understand. This is a wonderful article for sharing with colleagues who may not have a solid understanding of predictive analytics. This article does a great job discussing a combination of different data sources (quantitative - performance stats, and qualitative - text like injury reports) and how these tools can effectively outperform "hunches" or "rules of thumb". With three "titles" in the last 5 years, its hard to argue with her results too.

Fantasy Football Guru Ignores Her Instincts, Trusts Analytics

IBM's Hetal Thaker bucks a couple of common stereotypes regarding football viewership and fantasy football leagues -- and uses predictive analytics to draft her way to success. Here's her advice on analyzing data on running backs and running your business.

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Nice Predictive Analytics Write-Up

While this forum primarily concentrates on sharing analytics concepts and trends, one "area" of analytics I am passionate about promoting is a greater understanding of uses/efficacy of analytics and data-mining. I will freely admit I often find myself, along with many other practitioners, focusing on topics you might call "inside baseball" to the analytics world; bemoaning limitations related to data quality, or frustrated trying to adapt the powerful software we use to meet our specific needs.

Every so often however, I step back and take a longer view of my work, and our industry. In doing so, I see the biggest barrier to success and growth in this field are not these important yet narrowly focused issues. The larger challenge is an understanding, acceptance, and ultimately, utilization of our efforts. I remind myself people dont trust or use what they don't understand very often.

I find articles like this to be helpful. While it does not mention fundraising, it also does not discuss technical details or statistics: it talks about end-target impact. It talks about how positive it is for customers. I firmly believe that more stories like these will be the entry way to more interest and understanding in our work. Please feel free to distribute to friends and colleagues and add a note saying "and ask me what I can do like this for donors..."

Sam’s Club Personalizes Discounts for Buyers

By Andrew Martin

SECAUCUS, N.J. — For years, hotels, airlines, banks, online retailers and other data-driven businesses have turned to powerful computers to help determine the optimal price for their products, or to find ways to recommend items that groups of customers with similar tastes might want to buy.

The big retail chains have been slower to adapt, in part because of the sheer volume of customers they serve and products they sell. But now, Sam’s Club, Wal-Mart’s warehouse chain, is offering a program called eValues that strives to offer bargains tailored to each member, based on that member’s buying history.

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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.