DonorCast NewsWatch

Predictive Modeling for Direct Mail at Children's Mercy Kansas City

2011-10-28T14:16:00.005-05:00

The direct mail program at Children’s Mercy Hospitals and Clinics in Kansas City has been raising more money and is costing the hospital less to implement. Why? Because David Logan‘s annual fund team has been paying attention to the numbers and using predictive modeling to better target the mail they send out. Check out the video below for a more complete explanation of David and team’s work…

"Data Hoarders" A New Parody from the BWF Team

2011-10-06T14:23:00.007-05:00

Are you a data hoarder? Do you have to parse through mountains of relatively useless information to find those oh-so-valuable tidbits that can make or break your fundraising efforts? If so, help is available...

The Donorcast team joined forces with the BWF Social crew to bring you "Data Hoarders" - a parody of the A&E series "Hoarders."

Should I go into fundraising analytics?

2011-09-19T10:55:00.007-05:00

5 reasons every nonprofit should use analytics for fundraising

2011-06-08T13:00:00.000-05:00

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

2011-06-01T15:23:00.005-05:00

FREE Webinar - The Role of Data in Fundraising Strategy

2011-05-19T10:11:00.006-05:00

To Join the Online Briefing: click HERE Entry Code: strategy

Perhaps e.e. cummings best described the prevailing approach to fundraising strategy in Seeker of Truth. But sometimes what is common is not what is best.

To celebrate Analytics month at Bentz Whaley Flessner, our three in-house fundraising economists will explore the role of data in fundraising strategy development and decision-making. From predictive analytics in prospecting to cultivation modeling, and from risk-adjustment to campaign forecasting, expect a practical exploration of data-driven approaches for your fundraising operation. Join Alex Oftelie, Jeff Lockhart, and Joshua Birkholz in this unique online discussion. We may not know where all paths lead, but truth is here and the price is right.

Date: Thursday, May 19, 2011

Time: 12:00 Noon-1:00 pm Central; 1:00-2:00 pm Eastern

BWF Consultant: Joshua Birkholz, Principal, Bentz Whaley Flessner
Guest Presenters: Alexander Oftelie, Director of Operations and Analytics, and Jeff Lockhart, Senior Analyst, Bentz Whaley Flessner

Top Chef DonorCast Parody (read "math nerds make funny video!" - funny awesome!!)

2011-05-13T18:48:00.003-05:00

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

2011-04-27T10:43:00.003-05:00

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.

From BWF news today. Welcome Justin Ware. BWF Director of Social Media

2011-04-11T15:15:00.005-05:00

Friends,

At Bentz Whaley Flessner, we appreciate the confidence and loyalty you have in our innovative specialty consulting services. From development operations to prospecting and from analytics to engagement strategies, we are proud of the ways we’ve partnered with you to transform philanthropy. Now, in response to considerable demand for counsel and services in new media, we are pleased to announce a new addition to our specialty consulting team, Justin Ware.

Justin Ware is the Director of Social Media for Bentz Whaley Flessner. Justin provides our clients with his proven experience in new media technologies, specifically with diagnostic assessments of their social media strategies; content production guidance, counsel and training in social graph development and video communication, and coaching for nonprofit leadership in press and media interaction.

Justin brings nearly a decade of video communication and new media experience to Bentz Whaley Flessner. He began in television news as a reporter, anchor, and producer; continued on to higher education at the University of Minnesota, where he developed the top ten, most-viewed YouTube site in higher education, and went on to found the social media company, Warehouse Media, where he provided media services in higher education, youth development, environment and green energy, convention and event management, and financial services. Justin puts his Emmy-winning experience to work producing and leveraging video and written communication strategies to help nonprofits achieve their goals and communicate their message using new media. He has a BA in Journalism and Mass Communication from the University of Minnesota.

We look forward to partnering with you once again, that together we might transform philanthropy. Justin can be reached at jware@bwf.com and at our website, www.bwf.com.

Joshua M. Birkholz
Principal, Bentz Whaley Flessner

The Popularity of Data Analysis Software: R vs SAS vs SPSS

2011-03-28T12:35:00.002-05:00

As posted on KDDnuggets. A paper written by Robert A. Muenchen.

Among more interesting observations

R has the largest number of email discussions (by 2-to-1 margin), followed by Stata and SAS
the number of R packages published on CRAN continues to grow exponentially
R is leading in blogs (170), with only 31 blogs for 2nd place SAS
However, jobs mentioned SAS and SPSS have a big lead over jobs mentioning R

Click to see the KDD page with the article

2nd Survey on Analytics for Fundraising

2011-03-21T15:04:00.001-05:00

The 2nd Survey on Analytics for Fundraising is here! The goal of this survey is to better understand our industry, our colleagues, and trends in analysis and software.

All results will be public. Anyone can request a raw file of the results yourself for your own data-mining!

Please feel free to send a link of this survey to anyone you feel has something to share.

Click here to begin the survey

The Last 12 Months of DonorCast Newswatch Posts Visualized in Many Eyes

2011-03-21T00:00:00.007-05:00

Click for interactive view

Hear me on AFP/wiley Radio with Ted Hart noon EST today http://bit.ly/enkGmO

2011-03-15T10:33:00.002-05:00

AFP/ Wiley Radio The Nonprofit Coach w/ Ted Hart (Joshua M. Birkholz - Fundraising Analytics Expert) Noon EST Today, March 15, 2011

Click to join

Big Data mining: Who owns your social network data?

2011-03-11T17:56:00.001-06:00

An attractive application of Hadoop and other Big Data technologies is to analyze users' social activities, sometimes without their express knowledge

By Paul Krill | InfoWorld

The now-trendy concept of Big Data usually implies ever-growing hordes of data, including unstructured info posted on Facebook and Twitter, and ways of gleaning intelligence from all of it to create business opportunities. The concept, however, also carries with it risks for anyone opening up about themselves on the Internet and raises questions about who exactly owns all this data.

Big Data is associated with technologies such as the Apache Hadoop distributed computing platform and is prompting some technology companies, including IBM, to make major acquisitions. But the term "Big Data," claims GigaOm analyst Derrick Harris, is a bit of a misnomer; it's really about data from different sources, including social networks and even cell phones. "It's coming from sensors, it's coming from computers, it's coming from the Web," he says.

Read more

Statistics in golf can yield great benefits

2011-03-11T16:06:00.004-06:00

Following up on our sports-related posts a few months back, the golf fan in me loved this article. Now I can tell my wife I have to watch golf for work.

In response to such trends, the Massachusetts Institute of Technology's Sloan Sports Analytics Conference held the first Golf Analytics panel in the conference's 5-year history to discuss potential applications of advanced statistical analysis in golf. We've seen numbers-crunchers change baseball, basketball, and football. Golf could be next.

And the Oscar goes to...

2011-02-23T14:49:00.003-06:00

...Ryan Kavanaugh? In the spirit of Sunday's Oscars, I wanted to pass along an article I read the other day discussing Ryan Kavanaugh, a movie producer, and his company's use of analytics to manage risk.

When deciding which films to make, Relativity Media executives rely heavily on data. They use what they call a regression analysis—a computer deep-dive into dozens of variables that could affect a film's potential performance, such as genre, release date, and actor's appeal—before deciding to greenlight a film. The goal: limiting the company's risk.

Read more

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

2011-02-04T17:35:00.000-06:00

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.

Read more

A interwebs dialog!

2011-02-01T09:14:00.001-06:00

Mark Egge, a very sharp prospect researcher and data-miner, in addition to being my favorite classical saxophonist, posted a response to my CRISP-DM post. It only took about two years but we have an actual interwebs dialog on data-mining!

I know Josh posted a link to the CoolDataBlog (Kevin MacDonnell has some great ideas and since he is Canadian, all the American readers can say they consult "international sources"). Well Mark is another colleague to pay close attention too.

Maybe this dialog can put the pressure on both of us to add content more frequently

Check out Mark's blog, Managing Prospect Research

RFM: Not a substitute for predictive modeling

2011-01-29T11:14:00.003-06:00

Just a note to thank Kevin MacDonell for the mention on his CoolData blog. If by chance there are still a few of you that don't follow his blog, please do so. You will be smarter.

One encouragement about software in response: If you are able to explore the world of statistics software (SPSS, SAS, others), you may find that it makes many things much easier than using Excel or other more generic products. They've come a long way from the syntax-only days. Some of us still like the syntax (we may even dream about it--did I just write that?!?). But, really, you can point & click through seemingly complex calculations. Maybe DIY learning would seem daunting, but the Prospect-DMM and Fundraising Analytics Forum communities are a very sharing bunch.

I've worked with several clients who were able to build regression models for major and planned giving by day two of working together. We could usually get to producing an RFM score in a morning.

Read Kevin's post on RFM

CRISP-DM: does this really capture our work?

2011-01-24T16:50:00.000-06:00

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.

Read more

Some great statistics reads...

2011-01-19T14:10:00.000-06:00

If you are into analytics or even basic statistics Andrew Gelman is a guy you should be aware of. He has some similarities to Steven Levitt of Freakenomics fame (both award winning professors under the age of 40) but Gelman is more focused on stats than the self professed math-novice Levitt. Gelman is also a social scientist while Levitt is proudly homo-economicus. They both love to ask and answer questions using data.

Gelman has also produced what is in my mind the best definition of what statistics is: "the study of uncertainty and variation".

Here is a link to Gelman's stats reading list--a pretty broad selection and titles any quant-head should have on their shelves.

As a personal note I would add "Against the Gods - the remarkable story of risk" to this list as the DonorCast selection.

Andrew Gelman on Statistics
Award-winning statistician and political scientist Andrew Gelman says that uncertainty is an important part of life, and recognition of that uncertainty is itself an important step. This is where statistics can help us

Read more

DonorCast is looking to add to our team

2010-10-19T10:43:00.000-05:00

DonorCast has had a wonderful 2010 with the addition of deeply skilled analyst (Jeff Lockhart) and our new online dashboard tool (DonorCast Discovery).

The DonorCast team is so busy that we have decided to add a new position to help lead our marketing and sales efforts. Please click on the link below to learn about this new opportunity.

DonorCast Account Manager

Moneyball

2010-09-20T11:23:00.002-05:00

Building on the sports-related theme of our last post, I thought several of you may be interested in the attached article. While Fans of "Moneyball" are familiar with the use of analytics to build a baseball team, we're now seeing NBA teams employ similar tactics to evaluate basketball players.

"Think of Milton Lee as a technical investor. Except instead of analyzing stocks or commodities, he is analyzing basketball players.

A former Wall Street equities trader who has done stints at ING and S.A.C Capital, Lee has joined the refurbished New Jersey Nets as the team’s director of basketball operations. His job is to crunch the statistics of Nets players, looking not just at their scoring percentages but also at their defensive efforts and where on the court they are most successful at hitting the net."

Fantasy Football analytics

2010-09-10T18:22:00.003-05:00

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.

Read more

Fundraising: Art or Science?

2010-09-02T09:50:00.002-05:00

I recently came across this article on the AFP website. Noah Drezner does an excellent job explaining how data can enhance the strategic thinking required in fundraising.

(Aug. 31, 2010) Philanthropy, fundraising and higher education scholar Noah Drezner, Ph.D., discusses the value of collecting data about your prospects and donors and using that data to raise funds more effectively and efficiently.

Read more

10 ways data is changing how we live

2010-08-26T15:39:00.002-05:00

I recently found this interesting article from our friends across the pond. It provides a good overview of the ways data is being efficiently employed across industries.

The availability of new sets of data has changed the way we live our lives: here are 10 examples of data which have changed everything from how we assess wars to how companies deliver milk.

While non-profit fundraising is not explicitly listed, I'm excited about the potentials of the last section: linked data.

Read more.

IBM buys Unica

2010-08-13T16:17:00.003-05:00

I generally don't post many press releases here, but this brings me back. When I took some of my first data mining classes, we used Model One by Unica. Now, I am a rather ardent SPSS user. IBM has bought both SPSS and Unica, so these worlds have collided.

From the article:

IBM made it clear Friday it wants to own the online marketing and advertising analytics space. The company reported a deal to acquire software automation company Unica in an effort to better support Madison Avenue marketing and ad agencies, from campaign launch through execution and measurement.

BWF Fundraising Video Series: Facilities

2010-07-22T19:10:00.000-05:00

Webinar on Fundraising Analytics: Dive into Predictive Modeling and Analytics

2010-07-16T08:45:00.004-05:00

I just gave this WealthEngine Thursday workshop webinar with Jason Boley of Purdue University. Follow this link to view the webinar. It is free but requires filling out a contact form.

Here are the details:
Analytics is a science used by leading corporations for understanding customer behavior, prioritizing sales teams, and bringing efficiency to business processes. This session will highlight the application of this science to nonprofit fundraising and how it integrates your screening data with your existing prospect data. Want to learn more about how predictive modeling is accomplished? During the workshop, we will discuss how analytics are used for campaign decision making and the steps for building an in-house analytics capacity. Explore, why a “screen-then-model” method is the most effective approach, and why understanding these concepts are beneficial for both large and small fundraising organizations.

Presenters:
Joshua Birkholz, Principal at Bentz Whaley Flessner
Jason Boley, Director of Prospect Management at Purdue University

Follow this link to view the webinar.

Exploding Manhole Covers?

2010-07-13T16:33:00.005-05:00

I recently stumbled across what I had previously considered an urban legend: the exploding manhole cover. I had heard several stories of this occurrence playing out across Washington on the news but had never seen the aftermath in person.

See picture

Due to the nature of our work, any article containing the word "predict" instantly catches my attention. The following cross-industry article discusses how predictive models are being developed to better identify manhole covers that may need to be inspected or repaired.

Every so often in New York City, a disk of cast iron weighing up to 300 pounds will burst out of the street and fly as high as several stories before clattering back to the blacktop. Flames, smoke or both may issue from the breach, as if somebody had pulled hell’s own pop-top.

Read more

Hailing a Donor

2010-06-16T13:15:00.007-05:00

As is often the case, we recently came across another data mining article that does not mention fundraising directly but that still provides useful parallels for where the industry is headed.

The following write-up discusses a free smartphone application that uses predictive algorithms to identify the most likely place to find a cab in New York City. To those who have spent time in NYC and also worked in the fundraising industry, you understand that finding an empty cab and finding a donor with capacity can be similarly difficult. While donors are not armed with GPS trackers, the tools we are developing aim to better identify likely donors in the traffic.

"Forget the so-called paper trail. Wherever you go these days, you're creating a path of digital data, thanks to GPS technology and an ever-expanding network of location-aware smartphones."

Read more

Nice Predictive Analytics Write-Up

2010-05-31T12:45:00.000-05:00

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.

Fundraising Analytics Survey - Video Summary

2010-05-24T16:53:00.004-05:00

Results from the 2010 DonorCast survey on analytics in fundraising in video summary format.

Fundraising Analytics Survey Results

2010-05-20T14:15:00.005-05:00

Dear fundraising analytics professionals,

The results of our 2010 survey are now available. Links to an executive summary or full presentation can be found here:

Executive Summary
Full Presentation

Thank you to those that participated!

Joshua Birkholz
Alex Oftelie
Jeff Lockhart

Welcome Jeff Lockhart

2010-05-14T14:36:00.001-05:00

Many of you know that Jeff Lockhart joined the DonorCast team earlier this year. I have invited him to be a contributer to our new and improved NewsWatch. Here is his bio.

Jeff Lockhart is a senior analyst in the DonorCast practice. In this role, Jeff provides economic analysis and research for campaign and development program studies. Additionally, he provides a unique perspective on predictive analytics methodologies.

Before joining Bentz Whaley Flessner, Jeff served in various analytical and client service capacities for the University of Maryland, Cambridge Associates, Vanderbilt University, and the Dell Corporation. Jeff is a skilled economist and thought-leader in data-driven strategies. He has earned his MBA in Decision Sciences and Strategy from the Fuqua School of Business at Duke University and his Bachelor of Arts in Economics from Vanderbilt University.

Competitive Advantage with Predictive Analytics

2010-05-14T14:27:00.004-05:00

It is quite clear that the fundraising landscape has become more competitive for non-profits. As a result, there’s been a growing emphasis on the use of technology and analytics to grab new donors.

Given that several of the models used in the non-profit world are logical extensions of those developed in the for profit world, it’s no surprise that we frequently find the same land grab in the business world. The accompanying article discusses the use of predictive analytics in the insurance world to improve profitability. Transcending industries, one simple statement from the article underscores DonorCast’s main sense of purpose in helping clients:

“Predictive analytics are on track to become a major enabler in the success of commercial lines insurers, providing a competitive advantage over those that are either late-coming adopters or non-users all together.”

One could easily replace commercial line insurers with non-profit organizations to highlight the challenges facing organizations today.

Read more

Identifying Capital Prospects

2010-05-13T11:46:00.002-05:00

A little video fun...

NewsWatch is back

2010-05-13T10:34:00.002-05:00

Thanks for your patience as we migrated the NewsWatch from our server to blogspot. We are back.

We look forward to resuming our coverage of interesting developments in analytics and their potential applications to nonprofit fundraising.

Joshua Birkholz

Fundraising Analytics Survey

2010-03-02T10:08:00.000-06:00

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!

2010-03-01T16:24:00.005-06:00

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.

Read More

Man vs Machine?

2010-02-05T09:01:00.005-06:00

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.

Read More

Data mining featured in the Chronicle of Philanthropy

2010-01-19T07:39:00.001-06:00

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...

Read More

8 rules for Better Predictions: sage advice from Nate Silver

2009-12-03T10:50:00.011-06:00

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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Best Practices for Prospect Research in Higher Education Fundraising

2009-11-30T22:17:00.000-06:00

WealthEngine recently released Best Practices for Prospect Research in Higher Education Fundraising, the latest in their series on best practices reports. This 60+ page report is the culmination of a 5-month research initiative which included an in-depth survey of colleges and universities and qualitative interviews with development professionals.

The report outlines eight best practices, details five university case studies, and includes helpful return on investment worksheets to help you achieve success in your prospect research and overall development efforts.

You may download a free copy of the report at http://www.wealthengine.com/pub/bpEDUorder.jsp.

WealthEngine and BWF Working Together

2009-10-21T15:24:00.002-05:00

WealthEngine, a leading provider of sophisticated prospect research tools and screening services, announced that it has teamed up with fundraising consulting firm, Bentz Whaley Flessner, to provide the market's most comprehensive and effective analysis of giving capacity and propensity.

For more than 18 years, WealthEngine has been helping higher education, healthcare and nonprofit organizations understand the giving capacity of their donors and identify prospective future donors through its comprehensive wealth screening capabilities. Today, more than 2000 organizations rely on WealthEngine's wealth identification and verification tools for their donor and prospect research.

Bentz Whaley Flessner's analytics division, DonorCast, founded by Joshua Birkholz, principal of Bentz Whaley Flessner and author of Fundraising Analytics, specializes in custom-engineered analytics and data mining training that provide nonprofit organizations with important insights into the giving patterns among their own donors across a spectrum of fundraising programs. DonorCast uses predictive modeling and statistical analysis to increase the sophistication of fundraising programs and to point their efforts to the greatest opportunities for success.

"We have proven to be a solid combination as our clients already respect the tremendous value WealthEngine and Bentz Whaley Flessner deliver within our own respective disciplines of prospect research and modeling," said Tony Glowacki, chief executive officer of WealthEngine. "In fact, working together, we recently won engagements at two major universities in the Midwest. By formalizing our partnership going forward, institutions can benefit from our integrated approach to wealth identification, prospect segmentation and predictive modeling."

Through this integrated approach, WealthEngine will comprehensively screen an institution's records for wealth qualification and giving capacity. WealthEngine's screening across 30 databases provides the capability to quickly identify and focus on the best major gift prospects. It also provides the capability to further segment the results to identify opportunities for annual appeal upgrades. DonorCast will then incorporate the wealth screening results and constituent information from the client's database to perform highly customized predictive modeling on all individual records in the database.

"The goal is to identify and segment the best prospects for each institution by using the latest in capacity and propensity analysis, thereby providing transformative change to our clients' prospect development efforts," said Joshua Birkholz director of DonorCast. "Our unique "screen then model" approach arose from the many requests of prospect research and development professionals uncomfortable with pre-filtering screening by demographic modeling. Together with WealthEngine, we're answering the call for these proven approaches that yield measurable results."

About WealthEngine
WealthEngine is a leading provider of sophisticated prospect research and analytics to nonprofit organizations and institutions of higher education, financial services firms, and other for profit organizations. More than 2000 clients use WealthEngine's products for comprehensive prospect research on individuals, companies and foundations. Headquartered in Bethesda, Md., WealthEngine offers products in both the United States and the United Kingdom. For more information, visit www.wealthengine.com.

About Bentz Whaley Flessner
Bentz Whaley Flessner is a full-service consulting firm dedicated to helping leading nonprofit organizations build strong institutional advancement programs and execute successful campaigns. Since 1983, Bentz Whaley Flessner has provided counsel and assistance for every step of the development process. DonorCast, the firm's analytics division, offers the most comprehensive custom analytics services for fundraising. For more information, visit www.bwf.com.

Netflix prize awarded, a new challenge is made

2009-09-23T15:19:00.004-05:00

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.

Read more

Linked Data: World Wide Web for Data

2009-09-22T10:34:00.003-05:00

Tim Berners-Lee gave an interesting talk on linking data through the web for public use. Tim was the one who had the idea for HTTP and the actual web concept we all use now. This is a worthwhile talk to at least consider as an analytics professional.

From TED
20 years ago, Tim Berners-Lee invented the World Wide Web. For his next project, he's building a web for open, linked data that could do for numbers what the Web did for words, pictures, video: unlock our data and reframe the way we use it together.

See the video

Sig and Annie Talk Annual Giving

2009-07-10T18:05:00.003-05:00

Some fun with self-promotion.

Sig explains to Annie how predictive modeling can help her be more productive in her annual giving work. See the video on YouTube

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?

Segmentation and Shakespeare

2008-01-18T15:04:00.000-06:00

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

2008-01-07T08:39:00.000-06:00

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"

2007-12-20T15:38:00.000-06:00

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.

DELTA Force

2007-11-29T08:11:00.001-06:00

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

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

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

Either way this review of his keynote is informative.

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

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

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Online Fundraising - How is Behavior Different?

2007-11-29T07:41:00.000-06:00

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.

SPSS Directions Panelist Notes

2007-11-29T07:26:00.000-06:00

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

2007-11-12T15:10:00.000-06:00

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

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

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

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

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

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

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

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

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Lets not forget about the Annual Fund; Behavioral Targeting

2007-10-26T14:56:00.000-05:00

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

2007-09-27T10:30:00.000-05:00

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

2007-08-09T11:02:00.000-05:00

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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Why Mathematical Models Just Don't Add Up

2007-08-09T10:42:00.000-05:00

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.

Can Data Mining and Predictive Modeling Really Make Us Safe?

2007-08-09T08:27:00.001-05:00

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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CourseAdvisor Forms Data Mining Group to Increase Enrollments at Postsecondary Schools

2007-06-14T08:18:00.000-05:00

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

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

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Netflix Prize Still Awaits a Movie Seer

2007-06-04T11:05:00.000-05:00

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

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

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

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

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

Fundraising and Philanthropy: Yesterday, Today, and Tomorrow

2007-05-14T18:01:00.000-05:00

Many of you may have seen this OnPhilanthropy article discussing the history and future of fundraising and philanthropy. As it points out, even though the dollars to nonprofits are increasing rapidly, competition for these dollars are also increasing. This will challenge us to grow our capacity for understanding our donors and relating to them more effectively.

The world of tomorrow will not only be one of increased public and political pressure for nonprofit accountability, but it will also see increasing reliance upon financial sustainability and impact measurement. Donors will move away from making "gifts," to creating philanthropic contracts in which the donor expectations are legally binding, often held in perpetuity by their recipient.

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Artificial Intelligence enters the Mainstream

2007-05-14T17:46:00.000-05:00

...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

2007-05-14T17:40:00.000-05:00

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

2007-05-14T17:22:00.000-05:00

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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Basic Data Mining Definitions

2007-04-26T21:08:00.000-05:00

I am often asked to define some of the commonly used analytics terms. I have heard "data mining" used to refer to things from screening, to sorting names in excel, to querying from data marts. Below are a handful of some very common terms and how I define them.

Analytics
A broad set of mathematical tools used to reveal trends and patterns and harvest additional value from existing data. Analytics departments generally have the following services:

Descriptive Analytics: Analyzing constituencies to understand core segments according to behaviors and demographics. Also, analyzing programs to understand performance and the key factors and metrics impacting this performance.
Predictive Analytics: Using internal and/or external data to predict behaviors and segment constituents according to probabilities.
Decision logic / Decision Support: Metrics-based forecasting and simulation studies to determine database potential, capacity or philanthropic potential of constituent segments, and investment priorities.

Data Mining
Finding useful information by identifying patterns and trends within data--typically in large databases. Often this statistical pattern recognition is married with predictive analytics to produce predictive models.

Predictive Modeling
An outcome of predictive analytics, predictive models are formulas producing probability scores predicting future behaviors. Typically, these are built using statistical tools such as regression analysis, decision trees, and neural networks.

Competing on Analytics

2007-04-26T21:05:00.000-05:00

Here is a book I am eagerly anticipating. Thomas Davenport wrote an article of the same name for Harvard Business Review in January of 2006. I found the article enormously helpful for equipping researchers to make the case for building internal analytics programs. I will circle back to write a review in upcoming months.

The New York Police Department does it. The Harrah's casinos in Las Vegas do it. And businesses like Netflix are built entirely on the basis of it. It, in this case, is using the sophisticated analysis of data -- or "analytics" -- to drive decisions. As a concept, analytics is neither new nor complicated. Any dieter standing on the bathroom scale can attest that numbers are a more reliable source of information than intuition or a spouse's kind opinion. You might feel fit, but the numbers don't lie.

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Does Data Mining = Privacy Concerns?

2007-04-26T20:36:00.000-05:00

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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Viral Marketing Ideas

2007-04-26T20:29:00.000-05:00

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

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

Read the article

Good Data and Experienced Analysts

2007-04-13T12:47:00.000-05:00

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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"Anonymizing" Technology

2007-04-13T12:38:00.000-05:00

I find many non-profits all to comfortable in the transmission of sensitive data. When I work with clients on analytics projects, I ask for anonymous records and use secure transmission methods. As you build your data mining programs in fundraising, it is important to take donor privacy very seriously.

This article describes an advance in "anonymizing" technology. I expect these advances will show up in screening and data enhancement services for fundraising in the near future.

Banks typically send customer information to data aggregators, which match it against demographic and lifestyle data about those customers, such as which magazines they subscribe to. The information is sent back to the banks, which use it to profile their customers to help with product up-selling and retention. Banks that use Anonymous Resolution are protecting their customers from hackers and unscrupulous employees of data aggregators, Jonas says.

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

2007-04-13T11:59:00.000-05:00

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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Opportunity Intelligence

2007-04-13T11:43:00.000-05:00

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

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

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

Check it out

Data Classification: Brains or Brawn

2007-03-30T15:31:00.000-05:00

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

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

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

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

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Data Mining for Airfares

2007-03-30T15:30:00.000-05:00

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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