For the last two years, we've started a new Christmas tradition.....a seriously intense Cranium match after dinner on Christmas day. This year was a rematch between "the boys" and "the girls." I'm not going to say who won because it's still being contested. But, I had to share this great image drawn by my mother-in-law for "bun in the oven." Just wait til next year!
Have you seen "With Their Whole Lives Ahead of Them?" It's the first of three reports describing students' views on higher education and college completion. This report includes very interesting myths and realities about why students fail to finish college. Check this out:
Myth # 1: Most students go to college full-time. If they leave without a degree, it's because they're bored with their classes and don't want to work hard.
Reality #1: Most students leave college because they are working to support themselves and going to school at the same time. At some point, the stress of work and study just becomes too difficult.
Myth #2: Most college students are supported by their parents and take advantage of a multitude of available loans, scholarships, and savings plans.
Reality #2: Young people who fail to finish college are often going it alone financially. They're essentially putting themselves through school.
Myth #3: Most students go through a meticulous process of choosing their college from an array of alternatives.
Reality #3: Among students who don't graduate, the college selection process is far more limited and often seems happenstance and uninformed.
Myth #4: Students who don't graduate understand fully the value of a college degree and the consequences and trade-offs of leaving school without a diploma.
Reality #4: Students who leave college realize that a diploma is an asset, but they may not fully recognize the impact dropping out of school will have on their future.
Every high-ed marketer needs to get on our new YouTube Channel and watch the TV ads that are posted. It's an extremely valuable exercise to get a sense of the various strategies colleges are using to communicate about themselves. Compare Loyola Chicago's ads to the University of Cincinnati. Loyola is quiet, slow, and thoughtful while UC is loud and booming. Great examples of each institution communicating their brands.
What will really strike you (if you watch them all) is the common use of certain words: innovative, community, hands-on, global. You will want to create a little list for youself of words to ban from your marketing vocabulary.
SimpsonScarborough did not create ANY of these ads. We are a research and strategy company. But, we think it is so useful to see this large repository of TV ads all in one place. Probably the best way to get a sense of the approaches that colleges are using for marketing.
Happy viewing!
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SimpsonScarborough discussed with Lo de Janvry of ASquared Consulting, how data mining and modeling can assist you to drive greater returns from your school's development, alumni relations, admissions, athletic, and student services programs.
Q. What is data mining and modeling?
A. Data mining is an investigative process, similar to that of mining for gold. But instead of sifting through soil for precious nuggets of gold, you rigorously analyze (mine) your database to identify possible predictors of your desired outcome, such as annual or major gifts to your school or positive responses to your invitations or appeals (aka nuggets of gold!). For example, there tend to be a greater percentage of major gift donors among alumni who are married or divorced, have certain wealth indicators, live in certain wealthy zip codes, and have already made 9+ gifts to your school.
Modeling is a statistical process by which you determine the true relationship and statistical validity of each possible predictor in combination with each other, and the overall strength of your model to predict your desired outcome. In other words, the statistical modeling process will determine whether each of hte possible predictors of major gifts mentioned above are still true predictors once considered in combination with each other. The statistical modeling will also determine the relevant strength of each individual variable so that you can assign different weights (scores) to each variable. For example, we can determine if being married is more or less related to the likelihood of making a major gift than ones wealth screening score or zip code.
Ultimately, the outcome of a data mining and modeling engagement is a predictive 'scoring' model that helps you to segment your population according to their likelihood of positively responding to your offer (i.e. ask, invitation, appeal, etc.)
Q. How will data mining and modeling help my school's fundraising/alumni relations programs, particularly during these tough budgetary and economic times?
A. Especially during these tough budgetary and economic times, a predictive 'scoring' model can assist you to prioritize and target your resources more strategically towards the most likely of donors, members, or responders so you can maximize your return on investments.
For example:
* An annual donor acquisition model can help you cut down your mailings and prioritize your tele-fundraising campaigns towards the most likely of non-donors to give.
* A $1K+ special donor model can help you to prioritize your frontline fundraising efforts during reunion years towards the most likely of donors to upgrade to a $1K+ gift.
* A major or planned giving model can likewise help your staff identify new prospects and allocate their time most efficiently and effectively.
* An alumni association membership and travel package model can help target your precious marketing dollars for the greatest returns.
Q. Who provides data mining and modeling services, and how do these service providers differ?
A. There are several providers of data mining and modeling services in the higher education space that vary significantly in size, methodology, and price. The larger consulting firms typically sell you on purchasing external data sets, conducting a campaign assessment, and will ultimately provide you with a 'blackbox' that mysteriously provides you with a final score for each individual on your database that you must continuously contract their services to refresh. There are a few smaller players, like myself, who are in the business of developing highly customized scoring models for your school that you own so that you know exactly what the models consist of. Furthermore, you can program the model into your database as a dynamic, formulaic field so that your scores are always live and fresh, eliminating ongoing consulting fees to update your scores. Finally, there are a few of us that actually prefer to train some of your staff on how to mine your database and develop predictive models so that your school can internalize this expertise so taht you are empowered to revamp existing models and develop additional ones.
Q. How is data mining and modeling different from market research?
A. Data mining and modeling is an exercise that utilizes existing data on your population to help predict a future behavior. The data used for data mining and modeling reflect your population's current characteristics, such as whether one is married, is a lawyer or CEO, or lives in certain zip codes, and past behaviors, such as what degree(s) one has from your school, whether one has already given to your school or attended homecoming. While there is no new data collection process, external data can be purchased and utilized, such as wealth indicators and socio-demographic and purchasing information, but is often unnecessary.
Market research on the other hand aims to collect new data on your population, or confirm existing data. Typically market research studies aim to collect data that is not easily attainable, including data points on the perceptions and interests of your population to make strategic marketing, branding, and messaging decisions. Market research data can be utilized within a data mining and modeling engagement, but is not typically done since market research studies are often conducted on a sample population to reduce the costs associated with data collection.
For additional quesitons or inquiries on data mining and modeling, please contact:
Laurent "Lo" de Janvry
ASquaredConsulting
M: 510.589.5944
Lo@ASquaredConsulting.com
ASquaredConsulting.com
