DonorCast NewsWatch

Sig and Annie Talk Annual Giving

2009-07-10T18:05:00.000-05:00

Some fun with self-promotion.

Other great blogs to be aware of

2009-06-23T15:16:00.003-05:00

Just a quick note to NewsWatch readers. I have yet to add links of other blogs I like, but I definitely think everyone should check out Amanda Jarman and Dan Allenby and their thoughts.

Fundraising Nerd (Amanda's blog) covers a wide array of topics and she always brings ideas and news to my attention that I had never heard of, or had even considered. Plus for being a "nerd" her blog has a ton of humor. She might be the only person I know that has as much energy and passion for data mining as Josh Birkholz.

Ideas for Annual Giving (Dan's blog) is more conventional like the Newswatch, but updated far more frequently and I really appreciate Dan's selection of topics and commentary as well. Some blogs slap and paste anything with the word "fundrasing" in it online. His thoughtful approach and research can be appreciated by any one in fundraising.

I should also point out Dan likes to use the "recipe" analogy for describing predictive modeling that is a favorite of myself and Josh as well. However, while Dan suggests leaving the cooking to the chef's, Josh and I prefer to steal a line from my favorite Pixar film Ratatouille: "Anyone can cook!".

Or if the humor is lost, anyone can create good models.

Happy blog reading everyone.

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

2009-06-23T15:10:00.004-05:00

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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Analytics Versus Creativity

2009-05-20T08:33:00.002-05:00

This is an interesting blog posting by Mark Ritson of Branding Strategy Insider. It dives into the debate of "are we going to far with data-driven decision making?" Are organizations such as Google ignoring the qualitative when up against the quantitative?

In the fundraising industry, qualitative strategies have historically guided the process. Even campaign goals are more often set by gut instinct an in reaction to peer institutions. They may or may not be grounded in reality.

In recent years, there is a pronounced shift to incorporating data into the decision making process. From prospect identification, to staff management, to direct marketing segmentation, nonprofits are finding data is making the difference and especially so in this uncertain economy. Efficiencies and program productivity are more important now than ever. I am excited that analytics is providing hope and optimism. I am not convinced we are erring on the side of data yet in fundraising as is suggested of these other organizations.

From the article:

30 Seconds On…Analytics Versus Creativity
    * 'A big part of our innovation process is iteration. We try something, get a lot of feedback, then try something new. We let the maths and the data govern how things look and feel.' Marissa Mayer, vice-president of search, Google
    * 'You can't just ask customers what they want and then try to give that to them. By the time you get it built, they'll want something new.' Steve Jobs, chief executive, Apple
    * 'Long-gone is the day of the gut-instinct management style. Today's business leaders are adopting algorithmic decision-making techniques and using highly sophisticated software to run their organisations.' Ian Davis, worldwide managing director, McKinsey
    * 'Let me suggest an alternative trend - the rise of heuristics over algorithms; qualitative over quantitative research; judgement over analytics; creativity over crunching. Smart executives are recognising that the analytic approach to business has overreached.' Professor Roger Martin, dean, Rotman School of Management
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How to Use Database-Driven Analytics to Flourish Despite Economic Crisis

2009-05-14T16:01:00.004-05:00

Bill Franks writes a good article on using analytics to succeed during economic troubles. I especially agree with the initial thoughts on the "analytics power player." I have observed the most successful analytics professionals in fundraising have understood major and annual gift fundraising strategy, IT/advancement services, and also data mining skills. The ability to serve a cross-functional capacity is critical for not only adoption, but also measurable implementation.

Two fundamental shifts are occurring in the commercial world: the data environment is integrating and gravitating to the center of every business, and the roles of the analytics practitioner, the business analyst and the IT specialist are fusing. The emerging skill set is a multidimensional one, and the role might be described as that of "analytics power player" or one who knows how to best exploit a company's dynamic information assets for competitive advantage—while serving as a catalyst and bellwether for teams of his or her associates.

Read the article by Bill Franks

Average Salaries for Data Mining Professionals

2009-05-14T15:59:00.001-05:00

The results of KDnuggets 2009 Data Mining Salary / Income Poll show that data mining continues to be well compensated. The highest reported annual salary/income is in the US/Canada - about US $110,000 followed by Australia / NZ (US $84,000), and W. Europe (US $75,000).

See the KDnuggets Survey

Investment in your loyalty strategy will pay dividends

2009-04-27T17:07:00.003-05:00

This article translates well to the nonprofit sector. Focusing on those donors with the most promise is more important now than ever. Analytics is an effective way to efficiently target in on these populations. From the article:

Invest in customer insight. The current economic slowdown has produced a new urgency among U.S. retailers to extract additional value from customer data. One of the best investments during a downturn is in internal or external resources who can help you dig deeper into customer data to deliver actionable insight to get the right offer to the right customer, through the right channel.

Dedicated analytics experts can help you pinpoint your most promising customers and connect with them effectively. Using loyalty-program and transactional data, overlaid with publicly available demographic information, a skilled analyst can zero in on buying patterns and lifestyle traits to determine what products and brands your customers value most—and which customers will deliver the most value to your business.

Strategies for donors in the recession: "Giving When It Hurts"

2009-04-27T16:46:00.002-05:00

As readers of the DonorCast Newswatch focus on identifying donors for their nonprofits, I thought it might be helpful to read about strategies Betsy Brill of Forbes is recommending for the donors.

Some key points I might stress after reading. 1) Focus on your donor relations. It is important to understand how major donors view giving as an investment. From the Forbes article:"There are ways to mitigate some of the risk in your philanthropic investments while still achieving your overall charitable objectives." 2) Consider "give while you live" strategies. 3) Identify those donors that are still giving. I have found building "prospect resiliency models" can be helpful. Simply use donors since January (perhaps by level) as a dependent variable.

Read the article

DonorCast Book Review: Microtrends

2009-03-03T16:27:00.006-06:00

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"

2009-01-06T15:25:00.013-06:00

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

2008-08-28T16:10:00.007-05:00

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?

2008-08-05T12:27:00.010-05:00

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.

Book Review—Fundraising Analytics: Using Data to Guide Strategy

2008-08-05T12:21:00.007-05:00

Here is a very in-depth and thoughtful review of Josh's book. The feedback for this work has been tremendous—it has even become standard reading for MBA programs.

If you have not yet had a chance to pick up a copy, read this review, and see if it might be useful in your work/professional development (I am betting it will be).

Fundraising Analytics: Using Data to Guide Strategy
Review by: Gayle L. Gifford, ACFRE, CharityChannel

Fundraising Analytics is a gift to the masses ... a lens into the world of the sophisticated fundraising operations that pump the big bucks into major US institutions. Written by Joshua M. Birkholz, the director of the analytics division of Bentz Whaley Flessner, a major fundraising consulting firm, the book’s subtitle is “Using Data to Guide Strategy” and that’s what the book delivers.

I read this book from the perspective of the majority of US charities (82%) – the ones with the budgets below $1 million. At first glance, this book might seem an irrelevant fantasy fit only for the top strata of charities. None of these nonprofits have the legions of prospect researchers, major gifts officers, data analysts, and annual fund managers discussed in this book. Heck, it’s a lucky find to encounter a small organization that has even one fundraising professional and/or a functioning donor database from which one might extract the kind of information that Birkholz discusses.

But don’t ignore this book. Ease your way. Try jumping ahead to Chapter 5, Data-Driven Prospect Management, and you’ll find a wealth of easily comprehensible wisdom on running a fundraising program that is worth the price of the book.

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Partnerships and Brand Loyalty

2008-07-15T10:35:00.003-05:00

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.

Data vs. Science

2008-07-02T11:00:00.006-05:00

It has been awhile since I have posted on the DonorCast NewsWatch. Alex is so in-tune with the data mining world, I have had little to add. However, as a long-time Wired subscriber, I could not go without mentioning the latest issue, "The End of Science."

Chris Anderson sets a premise followed by several other contributors regarding the modern use of data. One of the most provocative points of the feature is that the scientific method can actually get in the way of data exploration. I believe Chris is correct.

However, as with most debates (endogeneity vs. exogeneity, in-house vs. outsourcing, prospect-based tracking vs. project-based tracking) it is not as simple as one or the other. James Cheng, the brilliant data miner at MIT, presented a compelling case for the scientific method at the APRA data mining symposium this past April. Before changing strategies based on analysis, I use control group tests whenever possible. Kate Chamberlin and Michelle Paladino at Memorial Sloan-Kettering very effectively use controlled study principles in testing the validity and effectiveness of both models and development strategies.

There are times when the method can get in the way, too. Chris Anderson points out that Google does not try and understand "why" before implementing the results of the analysis. It simply moves ahead with it. In the writers own words:

Google's founding philosophy is that we don't know why this page is better than that one: If the statistics of incoming links say it is, that's good enough. No semantic or causal analysis is required. That's why Google can translate languages without actually "knowing" them (given equal corpus data, Google can translate Klingon into Farsi as easily as it can translate French into German). And why it can match ads to content without any knowledge or assumptions about the ads or the content.

When I first began to build predictive models, I always started with a hypothesis. This would influence my data selection as well as my model selection. The more I build, the more I move to allowing the data to guide the process. In fact, the most challenging part of CRISP-DM is the "data understanding" step. If I let my data decisions be guided entirely by what I understand the business question to be, I might miss a hidden pattern.

What is the risk of letting the data guide the process? Well, let me use a major giving model as an example. As I have discussed before, if your goal is to predict giving likelihood to risk-manage a gift pyramid, you may wish to have a large degree of endogeneity. Like a credit score, you really would want to know the probability of the behavior. If your goal is to find new people that might be good major giving prospects, you might choose to minimize endogeneity. The result would be less predictive, but would serve to minimize the identification of names already known to you.

In this scenario, if you were to allow too much endogeneity in the identification model, the risk is small. You would likely exclude researched and assigned names before starting your qualification process anyway. What remains are not known names. But, you might have missed some names that would fit the profile if you had more data about them.

Sometimes, I have Marianne Pelletier's voice in my head, "Well, did you find more prospects?...That's good--isn't it!?" It is very similar to Google's "Did we make more revenue on that ad?...That's good--isn't it!?" Sometimes "why" can get in the way.

Maybe, this is my long way of saying, "Buy this magazine and read the feature." It can be confusing at times since it references "models" in the context of "ways of doing things" as opposed to statistical models. But, I think you will see Chris Anderson's point. It is worth the read.

Read The End of Theory
Link goes to the first essay from the feature by Chris Anderson. See the links on the left side of the page for the other brief essays.

The World of Phone Service is Changing...

2008-06-09T14:44:00.006-05:00

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"

2008-06-09T14:38:00.010-05:00

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?

2008-05-14T11:22:00.012-05:00

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

2008-04-25T15:39:00.004-05:00

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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Exciting week - Josh's book is out and the Data Mining Summit

2008-03-31T11:17:00.005-05:00

Josh's book, Fundraising Analytics: Using Data To Guide Strategy, has been formally released. He has already received wonderful feedback from people who have purchased it and read it in one afternoon. It is currently sold-out on Amazon last I checked, but you can order a copy that will be delivered once it is in stock again or you can try ordering it directly from the publisher. Pick it up!

Also, I will be in Nashville at the end of the week, attending the inaugural APRA Summit on Data Mining and Modeling. I am very excited to meet people who also have a interest, or even passion, for the work we do. Feel free to say hi, and comments/questions/critiques of this blog are also welcome. Hope to meet you there!

-Alex

P.S. Josh will have a few copies of his book for sale at the APRA Summit.

Can we Build a Better Zip Model?

2008-03-31T11:14:00.003-05:00

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.

Predictive Modeling the 2008 Elections...

2008-03-19T16:32:00.000-05:00

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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APRA Summit on Data Mining and Modeling

2008-02-25T16:09:00.006-06:00

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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Why Demographic Data Just Won't Die

2008-02-25T15:50:00.007-06:00

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?

Sentiment Analytics Opportunities

2008-02-14T11:13:00.006-06:00

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?