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10 Tips for Tackling Predictive Modeling in Fundraising

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Predictive Modeling in Fundraising

By Caitlin Garrett, Sr. Statistical Analyst

Over the years, we’ve helped many organizations bring predictive modeling in-house and have learned a lot along the way. Below is a “best of” list of ten tips that our fundraising customers helped us put together to make the modeling process – from idea through to execution – go a little bit more smoothly.

1. Establish buy-in before your first modeling project

Establishing leadership support before your project gets off the ground will ensure that your modeling efforts have a strong foundation and will not flounder as you encounter challenges along the way. Communication during the modeling process is key and this is the first step. By openly communicating about your predictive modeling project from the start, you can be clear about expectations, outcomes, and the process before you begin.  

2. Pick the best person for the project

The person in charge of predictive modeling will have to collect, analyze, and work with the results from your data. This person should be a creative problem-solver who is willing to learn. Ideally, this person is someone who is already working with the data. A person who understands your data – how it is stored, labeled, and used – can learn any statistical techniques they might need to know, but it is harder to learn the data while doing so.  

3. Data preparation is 80% of the modeling process

You may have heard of the term GIGO – garbage in, garbage out – which is a creative way of saying that a model is only as good as the data you use to create it. That said, data preparation is crucial as you’re gearing up to build a model. This means creating new variables, checking for missing values, and making sure your dataset is “clean”. If possible, create a data preparation process that is repeatable, which will ensure that your time is well-spent. It’s worth the investment.

4. Avoid common mistakes

Luckily, fundraisers have a particularly supportive community, and many of them have some experience with predictive modeling. Talk to anyone who’s worked with models to see what has worked for them, what hasn’t and what advice they have. Accurate models mean better results, which is incentive to avoid any of the “easy” mistakes. There are lots of reputable sources online.

5. Incorporate as much data, from as many places, as possible

Gather as much information about each of your constituents as possible. In collecting data, strive for the most complete picture of your constituents that you can create from your data. This includes demographic data, giving history data, membership data, occupational data, event history data, swipe or visit data, and any other data you might have available.

6. Time-slice your data to get the most out of it

Time-slicing your data allows you to see what a person looked like before they gave they responded to a mailing, gave their first gift, or became a major donor. Once you establish what they looked like before an event, you can apply that knowledge to your current database to see who might be likely to exhibit the same behavior. Additionally, time-slicing your data will actually augment your dataset, making your modeling efforts more robust.

7. Communicate with stakeholders often

Staying in touch with stakeholders works both ways – making sure that they are informed of your progress keeps everyone’s expectations on the same page, and checking in with them keeps you engaged with the bigger picture and how modeling will support your organization’s objectives. When framing communication, keep your end audience in mind – in lots of cases, a dashboard or visualization will convey what you’ve learned better than a series of raw numbers.

8. Choose the right vendor

When you are using the results of a predictive model to make key decisions within your organization, it’s important that you can own the decisions and knowledge that went into the model. Additionally, make sure that you have the ability to change and update your models as needed without having to pay heavy fees. If you aren’t confident in your predictive modeling skillset, look for a knowledge partner who can teach you best practices and techniques. At the end of the day, you’ll need to be confident that you can explain the model scores to your team and how you got from raw data to the decisions you’ve made.

9. Measure your progress

As you’re implementing your model results, keep your KPI’s and organization-wide goals in mind. Track the impact that your model is having. For example, if you’re using the model to decide who to mail, track the responses that come in from people you would not have mailed prior to using the model. This will tell you the impact the model is having on your responses.

10. Reduce, reuse, recycle

The process of building a model shouldn’t be a single-use effort. Reuse the data cleansing and extracting processes for other analyses. Reduce stress by automating reports whenever possible to keep everyone on the same page. Finally, recycle the knowledge you gain through your organization – the value of a model isn’t limited to the scores. You’ll gain an understanding of your data, historical trends, and an idea of what to expect – all of which is valuable and not necessarily contained in a single model score.

… So, these are just some of tips that we’ve come up with, and I’m sure there are more out there. If you have tips, advice, or cautionary tales, we’d love to hear about them in the comments!

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