Affinity Insight™ Models Focus on the Right Candidates.
Colleges, universities and other nonprofit organizations amass a great deal of information about the people they serve. What many organizations don’t realize is that they can use this information to gain valuable insight to improve their advancement efforts. Applying effective fundraising analytics, such as data mining and predictive modeling, can yield significant benefits in cost savings and more productive donor contact.
Start with the Information You Already Own.
At Data Description, we have teamed up with Peter Wylie, a well-known authority on data analytics for fundraisers, to develop our Affinity Insight Models™ and make data mining and predictive modeling an affordable opportunity for any institution.
While many data analysis services provide information about your prospects’ capacity to give, our customized predictive models rate your prospects’ inclination to give.
Affinity Insight Models start with the information you already own; your database. For every prospect in your database, your Affinity Insight Model will produce a score that predicts the likelihood that person will make a gift. This information becomes part of your database.
Arrive at Cost-Effective Insight.
With information about which prospect relationships will pay off, you can move immediately to:
- Create precisely targeted lists for annual fund and membership campaigns
- Identify new prospects for major and planned gifts
- Find new alumni volunteer candidates
- Save money and generate more revenue on specific appeals
- Incorporate data-driven decision making into your organization
Our Clients Get Results.
Our clients range from small private schools and colleges to major public universities and nonprofits. The results they’ve achieved with the Affinity Insight Model have made them believers in our approach to predictive analytics.
Affinity Insight™ Models for Major and Planned Gift Prospects.
It’s not always easy to determine whether a donor prospect has the capacity to make a major gift, and it’s even harder to figure out how likely that person is to actually make such a gift. At Data Description, we have teamed up with Peter Wylie, a well-known authority on data analytics for fundraisers, to help you do just that. The Affinity Insight Model will streamline your prospect research by showing you which candidates to concentrate on.
Prospect Insight Improves Your Performance.
If you are a prospect researcher you spend your time looking for new candidates to put in front of gift officers. Providing the right candidates results in more efficient use of the gift officer’s time and, hopefully, more gift money to the institution. So it’s important to find candidates who are not only capable of significant gifts but likely to make them. Wealth screening can show you a donor’s capacity to give, and Data Description’s Affinity Insight Model can assess that person’s inclination to give to your organization. These valuable insights will allow you and your colleagues to be more productive.
Insight Is within Your Reach.
Data Description’s Affinity Insight Model puts predictive data analytics within reach of any organization. It is an affordable analytical framework that is customized for your organization and for your role within that organization. It starts with your most valuable fundraising asset-one you already own-your internal database.
The Affinity Insight Model uses information in your database to produce an affinity score for each candidate in the database. Our clients report that these scores are very accurate predictors of giving.
Once you integrate the affinity scores in your database, you can use them to:
- Prioritize your prospect list
- Identify new prospects
- Maximize your gift goals
- Recruit elite volunteers
- Improve your performance
Affinity Insight™ Models for Annual Giving.
If you are a fundraising professional who directs an annual fund campaign or a membership drive your biggest challenge is probably one of sheer numbers: how to elicit optimum participation from the many, many people on whom your organization keeps information. At the same time, you have to wrestle with the risings costs of donor acquisition and retention.
The solution to the time-budget-yield equation is focus, and focus is the purpose of our customized Affinity Insight Model for Annual Giving. Our models allow you to concentrate your efforts and budget on the subsection of people in your database who are most likely to become donors.
You Can Afford Predictive Modeling.
Affinity Insight Models put predictive modeling within reach of any institution, and they analyze information that you already own.
Your Affinity Insight Models will start with the information in your database. By analyzing a selection of very simple predictors of giving, such as the presence of a home telephone number or membership in a sorority or fraternity, our model returns a likelihood score for every person in your database. These scores allow you to rank your prospects and to target your efforts on those people with the highest likelihood scores. In other words, you spend time and money only on those candidates who are likely to provide a return on your investment.
Affinity Insight Models start with the information you already; your database. For every prospect in your database, your Affinity Insight Model will produce a score that predicts the likelihood that person will make a gift. This information becomes part of your database.
In addition, the predictive scores from your Affinity Insight model will remain part of your database records.
Increase Annual Fund Productivity-Along with Your Own!
Our clients in annual fund campaigns have had great success when they put their likelihood scores to work for them. In some cases, they have been able to limit their outreach to 10 percent of the people in their database.
Want to Test Drive the Affinity Insight Model?
Seeing is believing. So why not find out how the Affinity Insight Model will work with your institution’s data to improve your performance? You will see first hand the predictive value of the data in your own database.
Exploratory Data Analysis
Today’s professionals in business, engineering, and science work in complex—often overwhelmingly complex—environments. To be effective, they must understand masses of data from a variety of sources. Traditional Nat mining tools employ blackbodies algorithms that generate complex predictive models. The models can be useful for predicting, but provide little to no insight into the data. Exploratory data analysis—the underlying premise of Data Desk software—is a statistical approach that allows the decision maker to not only see patterns and relationships in a dataset but to get at the causes and effects behind the relationships. EDA facilitates sophisticated understanding of what’s really going on in a body of data.
Most data arise as a byproduct of other activities. A business person may have data in a spreadsheet intended for tracking sales, data in a database for human resource management, or data that have been published by a government or trade organization. A researcher may collect data to sift a variety of alternatives, may want to look in a new way at data originally collected for a different purpose, or may want to check experiment data for errors or unexpected patterns. That is why the process of analyzing data needs to be wide open to the possibility.
About a hundred years ago, in its early days, statistics concentrated on analyses of data, considering effective ways to describe patterns, trends, and relationships. In the middle half of the 20th century, attention moved to developing a solid mathematical foundation, establishing the properties of various estimators to find the best methods. In 1962 Dr. John Tukey warned that mathematical statistics was ignoring real-world data analysis and called for a return to scientific statistics in which the value of the statistical description of the data was paramount. In subsequent work, Tukey defined Exploratory Data Analysis, a philosophy that returned to the original goals of statistics but used modern methods.
Traditional inferential statistics start from a hypothesis, performs an experiment, and then tests the hypothesis. EDA starts instead from the data and asks what patterns, relationships, or trends they might hold. In recent years EDA has gained wider acceptance. A large part of this growth is due to the availability of desktop computers and the explosion of data for which traditional statistics is just not suitable. Desktop computers have also made it possible to develop new graphical methods that support the EDA philosophy in strikingly effective fashion.
Because EDA relies heavily on data display, makes few assumptions about the structure of the data and emphasizes identifying and describing patterns, it is useful to a wide range of professionals who can recognize important patterns easily but may not wish to work with complex statistical techniques.
Data Description’s graphical analytical tools start from the EDA philosophy. They empower people who have data and want to discover the patterns hiding within.
Learn how to focus on the most likely candidates
Build your own Affinity Insight™ Model.
In addition to offering Affinity Insight models, we train fundraising professionals to use Affinity Insight’s conceptual framework to analyze their own donor data and build predictive models focused on your organization’s needs – whether those needs are prospect identification or annual fund or membership appeals. In conjunction with data mining expert Peter Wylie, we work with you personally via telephone and real-time web sessions to teach you how to build your own predictive models. This web-delivered approach to training saves your organization travel expenses and travel time and saves you from the fatigue of all-day, traditional training courses. Between training sessions, you and your staff can practice what you have learned.
You’ll get plenty of hands-on experience with your own data and guidance on how to apply the principals and techniques you learn during training to other challenges as they arise in your day-to-day fundraising work.
As you’ll see from their comments on this page, the professionals we’ve trained have high praise for our help. They come away with a new appreciation for data mining and how it can improve their work. And they are enthusiastic about applying their new model-building skills to fundraising questions they’ve always wanted to address.
Talk to Our Clients, Test Drive Your Data
If you are interested in talking with some of our current clients in institutional development, contact us for customer references.