Why Nonprofits Should Be Building Predictive Models
Last fall, the Whitney Museum of American Art decided to take a different approach when deciding which of their prospective donors to mail. They built their first in-house predictive model from the ground up, and felt ready to use it. They shifted their focus away from some of their prospects who “made sense” but had never given, and used the model to inform a large part of their mailing list. Within the first six months of modeling, they received a $10k donation from a donor they would not have mailed using their previous methodology.
…And they aren’t the only ones. More and more nonprofits are turning to predictive modeling to drive their fundraising. For a more in-depth look at the ‘hows’ and ‘whys’, I sat down with a man who founded his own company to provide software so that nonprofits and for-profits alike could start building their own predictive models in-house. He also happens to be my boss and one of the smartest people I know – Mike Laracy:
Why would a nonprofit use predictive modeling? How can it drive fundraising?
The quest for any organization, whether a for-profit or non-profit, is to figure out how to achieve its goals and to do so in the most efficient and cost-effective manner possible. Predictive modeling allows an organization to make better decisions and become more efficient with its use of what are often limited resources. By using analytics, an organization can better determine who to contact, how often to contact, how much to ask for and how best to achieve their desired fundraising results.
Although driven by very different motivations, the relationship between a nonprofit and its donors is very similar to the relationship between a for-profit company and its customers. Customers choose whether to buy a product or not buy a product. They can become loyal customers or non-loyal customers. They can buy a lot or they can buy very little. It is much the same story for nonprofits and their donors. Donors can be loyal or not loyal. A prospect can choose to be a don r or not be a donor. They can give large gifts or small gifts. With accurate data and a modeling process that is easy to implement,a non-profit can begin to model a donor’s behavior using the exact same methodologies that are used to model a customer’s behavior.
What kinds of resources are needed to start building predictive models in-house?
Without quality data, predictive modeling isn’t possible. So let’s start with that. There needs to be a system in place that is capturing an organization’s historical data. Almost every organization is already capturing their data, so that’s usually not a problem. The data doesn’t necessarily need to be organized in a data warehouse. In fact, the data needs to be available in its raw form, so sometimes having data pre-aggregated in a warehouse can be a disadvantage. What’s important is that the data is accessible.
From a staffing perspective, you will need a person or people to collect information on the data, build the models, communicate the results and make sure the models are being used. There needs to be someone who is making sure the right information is being collected and the right information is being communicated. This can be a single person, but that person needs to make sure that others in the organization are on board with an understanding of why the models are being built and how they will be used.
What are good first steps for an institution looking to get into predictive modeling?
Like any new initiative, it’s vital to the success of your predictive modeling efforts that there is universal buy-in across the organization. If there isn’t buy-in, the models won’t be utilized. To get buy-in, start small. Go for the early win by building and implementing a single model. Make sure others in the organization have an understanding of what the model will do, how it will be utilized, and most importantly, how the model will benefit the organization. Once you get that first win, the interest and buy-in will usually spread quickly across the organization. As you share the results of those first few successes, begin to identify who the champions for this initiative will be. Work with them to help them communicate the success of the project organization-wide.
In your experience, how should an institution decide who should build the predictive models?
Ideally, you want someone who has an understanding of the data. If you don’t already have someone with that knowledge, you want a person who is willing to learn the data. Some understanding of statistics is a plus, but with current analytic software technology, there is no longer a need to rely on someone with programming skills or a PhD in statistics to be your data expert. The people you want to dedicate as resources for predictive modeling should be creative problem solvers who are willing to learn.
What modeling challenges might be unique to different types of nonprofits?
There are definitely different needs and different challenges depending on what type of fundraising entity you are. A college advancement office, for example, has an advantage in that they have information on the students who graduated with them. For example, age comes up as a predictor in many giving models. Whereas an organization like a museum might not have good info on the age of all of its members and donors, a college or university will at the very least have each student’s year of graduation, which is a great proxy for age. A college will also have great information like the major each student graduated with and whether or not the current year is a major reunion year. While a non higher-ed entity won’t have this type of information, they will have information that a college advancement office won’t have. A museum will have info on its members, how many times someone has visited the museum, and a lot of other great information for modeling that a college won’t have.
Another challenge that people may encounter is how spread out their data is. Some organizations have more sophisticated computer systems with everything centralized and others may have the information spread across multiple spreadsheets, databases and even outside sources. As you determine what your data needs to look like, keep in mind that you will need to pull it together and do cleanup before you can begin to model with it. This was actually one of the reasons we originally created our Veera Construct product. People were looking for an easier way to clean up and merge their data before they created their models.
Are there any common mistakes to avoid when gearing up to build a model?
I think the biggest mistake to avoid is building a model without buy-in from the rest of the organization. Another mistake is building a model without an implementation/utilization plan. Building and scoring a model is great, but by itself the model doesn’t do anything for you. Before building the model you should have a plan for how you are going to use the model. For example, if you are a nonprofit and you build a model to predict each donor’s probability of giving to the annual fund, you need to utilize the model in your annual fund outreach. You will need a plan to mail/call the top X% of your donors with the highest probability of giving, or you should have a plan to not mail donors that are below some probability threshold. Or perhaps you only want to mail to donors who are likely to give at least a $500 gift. There are many ways that these models can be used, but the key is that they have to be used.
Once you begin to use them, you can also begin the process of refining and measuring the effectiveness of your models. Then you can refine them to make them even better.
What kinds of resources/learning opportunities are out there for those looking to get started with predictive modeling?
In the fundraising world, APRA and the Data Analytic Symposium have a lot of extremely useful sessions. I’d also recommend Prospect DMM, which is a listserv where a lot of really smart people discuss modeling topics. We (Rapid Insight) put on a predictive modeling class not too long ago with Brown University and Chuck McClenon from the University of Texas – Austin. Classes like those are a great place to get started and we’re thinking about doing one again soon.
What strategies can you recommend so that a customer gets the most mileage possible out of their predictive modeling efforts?
To borrow a phrase, I’d say reduce, reuse, recycle.
Once you’ve set up a process for organizing, cleansing and analyzing your data for one model, you can use that same process for all of your models. In fact, you can even use that same process for scoring and testing all of your models. There’s no reason to reinvent the wheel each time.
Another important strategy is to make sure you set up a system for knowledge capture. Modeling is an iterative process; you don’t just build one and you’re done. You can learn a tremendous amount as you’re building models. A lot of that knowledge is actually knowledge about your data. That knowledge will accumulate very quickly over time and will make you smarter and smarter as an organization. This is one of the biggest advantages to bringing predictive modeling in-house: if you are not doing predictive modeling yourself, you run the risk of that knowledge escaping from your organization. Once it escapes, you miss out on an opportunity to grow your organization’s analytic intelligence.
Remember the old proverb about giving a man a fish and feeding him for a day versus a lifetime? The same thing is true with predictive modeling. If you give an organization a model; you’ve made them smart for a day. When you give them the tools to build their own models they become smarter and more competitive for a lifetime.
Besides being the Founder and CEO of Rapid Insight, Mike Laracy is a devoted Birkenstock fan, recently ran up Mount Washington, has an eclectic taste in music, loves talking about predictive modeling, is a sap for his two kids, and has pretty much always been a nerd. For those of you attending APRA, he’ll be giving a presentation – “Preparing Your Data for Modeling” – on Wednesday, August 7th at 1:30 pm.
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