Campaign Pyramids: Brick by Brick

Recently, I got to chat with Chelsea Drake and James Dye, who are both Data Analysts at the College of William & Mary, about the work they've been doing on campaign pyramids. For a more in-depth look at the functions that their campaign pyramids serve, and their process for building them, be sure to check out their presentation at our user conference or stay tuned for a webinar rebroadcast in July. 

What is a campaign pyramid’s function in your office?

CD: Right now we’re using the pyramids as a donor-centric list of prospects. To give some background on the pyramids, we did a massive data mining project to determine where our donors’ interests were. The end result is a dynamic pyramid that updates as new gifts come in and as we get new information about where their philanthropic interest lie. We use them as accurate prospect lists.

JD: We had a bunch of people in our prospect pool and needed to know where their interests were. For example, if they’re into Athletics but graduated from the Business school, do we want to go after a split gift, or do we say that their primary interest is athletics, so they should be doing the ask? The pyramids help us decide which one we should try to raise money for. They also help to set goals for each department and each school. So we’ll set a goal and ask a question like ‘how many gifts do we need at different levels, and prospects do we need to make up that pool and reach our goal?’

How do you set the goals for each pyramid?

CD: We’re able create pyramids to test high, medium, and low goals to see which one is most feasible for each unit and each campaign overall.

JD: Each unit has three pyramids – they have a high goal, say 120M if 100M is the medium or mid-range goal, and a low goal, which might be something like 80M. The mid-range goal should be something they can accomplish without too much effort and the low goal is what we think they’d get if they only asked people we already knew. This allows us to see how much stretch we need to do and how many people we need to identify in order to hit certain monetary goals. The idea behind the project was to figure out where our prospect pool’s interests were and where we need to do work and identify new prospects to fill in gaps and holes.

What triggered your interest in campaign pyramids?

CD: We started last summer, our Assistant VP of Operations wanted to make sure we were being as donor-centric as possible. She knew we had some information on interests but that we didn’t have a reporting tool that identified which prospects should go with each interest. She knew I had an analytical background and that’s how she chose to bring the project to me.

JD: Previous pyramids had been done at the university level. For the college, we wanted to know who we had out there and how much money that would bring in with specific gift ratings. But we were also asking things like ‘How much can we get for athletics?’ and ‘Who are the people who are interested in athletics?’. That’s where it spawned into a donor-centric thing. We wanted to know what our donors’ interests were, what they’ve given to in the past, and on a program and unit based levels, who were the donors for each area.

Who builds the pyramids in your office? How did you decide that?

CD: James and I do, and that was decided based on our backgrounds. James has a programming and computer science background and I have a background in research and analytics.

JD: We’re the programming and analytic people in our office and were already working on data pools, but were brought onto this project based on our skillset. Within our department, we’re the ones who generally work with the data.

What’s your administration’s take on the pyramids?

JD: They like them a lot. It gives them an idea of monetary goals for each unit and school to stretch for and concrete lists of names. We can show them the people we’ve identified, and if we sum up all of things we have in a pyramid, we can see if the goal set for a department is realistic. It helps them to see who’s out there and who’s in our database. They also use it to present to a board of visitors in a slideshow on where we stand in a campaign and how our numbers are at any given point. They can tell how many people we’ve already identified and how many new people we need to identify to meet a goal.

What advice would you have for someone looking to undertake a project like this?

CD: One of the things that was really helpful for us as the project started was having a good relationship with IT to fine tune what the data files we get from them would look like. The key to doing this type of analysis effectively is to have the best data that you’re able to get from your system in the most consistent way possible. Also, you should absolutely plan out what your goals are for the project before you get started.

JD: You have to know which data points out there you can pull from and what would be relevant for your goal. Depending on the size of the school, you might want to focus on a single unit pyramid to narrow down the scope of what you want to do. You could start with a major gifts or annual fund pyramid, for example. It’s about first defining your question, then looking at the data to figure out which people to target and looking at the numbers to establish what your monetary goals should be. It helps to nail out a template of what you want the end result to look like before you start programming.  We knew what we wanted our end result to be, so then when we were programming forward, the question became ‘how do I fill out these blanks where these numbers should be?’. This way, when you start building, you’re able to visualize how to compile everything correctly according to your template. Also make sure that you have a good team working on the project, and that team members know what their role in the project is.

CD: Anytime you’re taking on a project like this, you want to have the ability to talk to the managers or executives of your department to make sure that your end result matches what they feel they need.

JD: Make sure it’s helpful for them. We’re numbers people. We can make a page full of numbers and look at it and understand it, but management might need something a little bit more nice looking. So the sheet we create for them outputs to a single page with colors so that when we turn it over to them, the information is logical and easy to read. It comes down to knowing your audience. 

Customer Webinar: Predictive Modeling for SEM

Our next customer webinar, "Strategic Enrollment Management: St. Michael's College and Predictive Analytics" will be given by Bill Anderson, CIO of Saint Michael's College today at 2pm EDT and will be re-broadcasted on Tuesday, March 26th, and Thursday, May 2nd. 

I got the chance to ask him a couple of questions about his session, which will describe the ways in which Veera and Analytics are utilized on campus to produce predictions and other analyses for the scoring team. 

What types of models have you been building?
Almost entirely enrollment management - mostly apply to enroll. We've been building them on and off for about five years now. I have someone on campus that I collaborate with and when we first started, she was using SPSS for the statistical analysis, but we've since abandoned that. 

How has model building changed your Enrollment and/or Financial Aid practices?
There have been a number of ways that we've used the models - one as a sort of verification of what our consultant has been doing, two to be able to do some sensitivity and what-if analysis (and suggest different practices or emphases on where the aid awards should go), and three to help confirm in-semester and in-process prediction on where the class is going to end up. 

In some occasions, this has impacted size of waiting list or the way we thought about awarding wait list spots, including the total number of admits. This last year, our model suggested that we could be more selective than we had been in the past. 

What do you hope attendees will learn from your presentation?
One thing is that you can do it on your own - it's not that hard. You have to have a background that supports responsible interpretation of the results, but you can sit down and do it. That's one element: just do it. I think there's another element that says once you start thinking this way, it can become infectious. In our enrollment management meetings, we have the opportunity to appeal to the data or look at a Veera job that identifies the applicants we could avoid accepting. This changes the internal conversation - from a culture of anecdote, you can change the conversation with data. The use of the products has been fabulous in terms of making the data accessible to people. 

Four Years of Predictive Modeling and Lessons Learned

I recently got the chance to talk with Dr. Michael Johnson, Director of Institutional Research at Dickinson College, about his experiences with predictive modeling over the past four years. Dr. Johnson will be presenting a free webinar, "Four Years of Predictive Modeling and Lessons Learned", on Thursday, January 31st at 2pm EST in which he'll provide a more in-depth look at his experiences. 

Can you give us an example of a lesson you’ve learned through your experiences with predictive modeling?

I’ve learned that predictive modeling is good but predictive modeling in real time is just more extremely beneficial. When we picked up Rapid Insight, we moved a five day turnaround time to an eight minute turnaround. That’s one of the biggest changes we’ve made, and the effects have been very apparent.

If there was another thing I’ve learned, it is to automate absolutely every process possible to remove the opportunity for human error.

What types of predictive models will you be discussing during your webinar?

The enrollment management model is our primary model but a close cousin to that is the one we’ve been using for retention. The dataset is basically the same only slightly enhanced. It’s good to use essentially the same dataset to solve two different problems.

How has predictive modeling changed the way you operate?

It is the primary tool that we use to make decisions on the incoming class. This last week has been an incredible example of that. We’re taking a look at our early action pool and asking questions: What does it look like? How does it compare with previous years? What if we swap out some people; how does that change our incoming class?

What do you hope attendees will take away from your webinar?

There’s really no need to reinvent the wheel, so I’ll share some ideas that I’ve picked up. I hope that others come on board and share their successes as well. We all have the same problem set, so it will be nice for others to take away a few things that I’ve seen that have given me success. It would be great if they had ideas that they wanted to share with others as well. 

To register for Dr. Johnson's free webinar, "Four Years of Predictive Modeling and Lessons Learned", or for more information, please click here. 

To read a case study about how Dickinson College uses predictive modeling for strategic enrollment management, please click here. 

Thoughts from a Registrar

Dan Wilson, Registrar at Muskingum University, recently talked with us  about some of the reports he's been working on, how he's using Veera, and his upcoming webinar. 
CG - What types of reports are usually on your plate?

DW - Some of the reports I'll be talking about in the webinar include:

• historical registrations by date,
• historical majors by semester,
• number of students still needing to take specific general education courses, 
• graduation persistence by major, 
• retention rates by various factors, and
• DWF (drop/withdraw/fail) rates by course.

I have one report for each of these and I make minor adjustments to it each time a new question is asked. 

CG - How has Veera helped with your reporting?

DW - It's helped me develop complex reports that would have taken me 4-6 hours each to get all the data, build, and run. Now I pull up a report and run it in about a minute. It's especially useful for complicated and repetitious reports. 

All of the reports I've mentioned have been automated in Veera. Everything that I can, I automate. I anticipate that people might be asking for DWF rate for the first year students, or by division, or by course, or by phase of the moon. Veera is good at pulling that data together and allowing me to tweak it and make adjustments as needed. I usually choose to work with Veera when I think I'll see a lot of revisions, need to do some digging around, or can see similar questions being framed differently. 

The year-over-year registrations by date report was one of the first I created using Veera. It's proven to be one of the most valuable to our administration in helping improve our retention rates. It literally takes two minutes to run - it actually takes longer to download the data file than it does to run the report in Veera.

CG - What do you hope attendees will take away from your webinar?

DW - I hope each person will find something that is a spark moment for them. Some people need exposure to Veera and to see what it can do. For users, I'm hoping to spark a brainstorm on how and why to use Veera - as the way to achieve what they need faster and easier. 

CG- Anything else you'd like to add?

DW - Anyone who knows what a small college registrar does will understand that I wear many, many hats. Though I'm not an institutional researcher, some of the work that I do is borderline IR. Typically I'm asked a question and need to get someone the answer quickly. That's what I use Veera for the most. 


For more information or to register for Dan's upcoming webinar, Digging Deep into Data: How a Small University's Registrar Develops Complex and Repeatable Mission-Critical reports, click here. 

Thoughts from a Reporting Wiz

Scott Alessandro of the MIT Sloan School of Management is a lover of ad-hoc reporting and coffee ice cream.  In anticipation of his webinar on Friday, we asked him a few quick questions about his day-to-day analytic life. 

CG - What types of analytic requests do you handle?

SA - Some ad-hoc requests and some internal reports for my office, including degree requirement completion, grade distribution reports, enrollments by programs, GPA comparisons among courses or programs, impact of student population on enrollment/availability, etc. 

CG - What is your typical response time?

SA - Much faster now [with Veera] than beforehand. In the past, it would take me at least a couple of hours of unbroken time to create a report - which means a while. With Veera, unbroken time doesn't make a bit of difference. 

CG - Have you seen your decision-making become more data-driven with Veera?

SA- Most definitely. I hoped that it was always data-driven, but now because I have such easy access to data, it allows me to answer more questions, or anticipate more questions. 

CG - What do you hope attendees will take away from your webinar?

SA - That we have a lot of data and the problem was not having the time to use it or go through it. That's what Veera allows us to do. Since it's a visual tool, it becomes that much more accessible for people who are not as data-inclined. 

CG - Anything else you'd like to add?

SA - When I have Veera on at home, even my kids are impressed. It looks really neat. There's something artistic about it and that's why I like it. 

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We are pleased to present Scott's webinar, From Data to Decisions: Ad Hoc Analytics and Reporting with Rapid Insight Veera, on how he is utilizing Veera efficiently to respond to the wide range of analytic demands confronting him daily. The webinar will take place on Friday, December 14th from 11am - 12pm EST. 

For more information about Scott's webinar, or to register, click here. 

For more information about Scott, read on:

Scott Alessandro is the Associate Director of Educational Services at MIT Sloan School of Management. His main responsibilities entail overseeing the Registration Team, managing MIT Sloan’s course bidding system, and reacting to various data requests. In previous lives, he has worked at Boston University (running summer pre-college programs), Temple University (in the Honors Program and the Undergraduate Admissions Office), and the College Board (coordinating AP workshops and data reporting). All of his jobs have combined numbers and people, which has made Scott quantifiably and qualitatively happy. Outside of work, Scott satisfies his curiosity and finds entertainment in hiking, woodworking, playing sports, watching sports (though has not found as much happiness lately rooting for the Chicago Bears and New York Mets), and trying to stay one step ahead of his two young children.