Predictive modelling enables an organization to focus its limited resources of time and money where they will earn the best return, using data. People who work at nonprofits can probably relate to the “limited resources” part of that statement. But is it a given that predictive analytics is possible or necessary for any organization?
This week, I’m in Kingston, Ontario to speak at the conference of the Association of Fundraising Professionals, Southeastern Ontario Chapter (AFP SEO). As usual I will be talking about how fundraisers can use data. Given the range of organizations represented at this conference, I’m considering questions that a small nonprofit might need to answer before jumping in. They boil down to two concerns, “when” and “what”:
When is the tipping point at which it makes sense to employ predictive modelling? And how is that tipping point defined — dollars raised, number of donors, size of database, or what?
What kind of data do we need to collect in order to do predictive modelling? How much should we be willing to spend to gather that data? What type of model should we build?
These sound like fundamental questions, yet I’ve rarely had to consider them. In higher education advancement, the questions are answered already.
In the first case, most universities are already over the tipping point. Even relatively small institutions have more non-donor alumni than they can solicit all at once via mail and phone — it’s just too expensive and it takes too much time. Prioritization is always necessary. Not all universities are using predictive modelling, but all could certainly benefit from doing so.
Regarding the second question — what data to collect — alumni databases are typically rich in the types of data useful for gauging affinity and propensity to give. Knowing everyone’s age is a huge advantage, for example. Even if the Advancement office doesn’t have ages for everyone, at least they have class year, which is usually a good proxy for age. Universities don’t always do a great job of tracking key engagement factors (event attendance, volunteering, and so on), but I’ve been fortunate in being able to have enough of this already-existing data with which to build robust models.
The situation is different for nonprofits, including small organizations that may not have real databases. (That situation was the topic I wrote about in my previous post: When does a small nonprofit need a database?) One can’t simply assume that predictive modelling is worth the trouble, nor can one assume that the data is available or worth investing in.
Fortunately the first question isn’t hard to answer, and I’ve already hinted at it. The tipping point occurs when the size of your constituency is so large that you cannot afford to reach out to all of them simultaneously. Your constituency may consist of any combination of past donors, volunteers, clients of your services, ticket buyers and subscribers, event attendees — anyone who has a reason to be in your database due to some connection with your organization.
Here’s an extreme example from the non-alumni charity world. Last year’s ALS Ice-Bucket Challenge already seems like a long time ago (which is the way of any social media-driven frenzy), but the real challenge is now squarely on the shoulders of ALS charities. Their constituency has grown by millions of new donors, but there is no guarantee that this windfall will translate into an elevated level of donor support in the long run. It’s a massive donor-retention problem: Most new donors will not give again, but retaining even a fraction could lead to a sizeable echo of giving. It always makes sense to ask recent donors to give again, but I think it would be incredibly wasteful to attempt reaching out to 2.5 million one-time donors. The organization needs to reach out to the right donors. I have no special insight into what ALS charities are doing, but this scenario screams “predictive modelling” to me. (I’ve written about it here: Your nonprofit’s real ice bucket challenge.)
None of us can relate to the ice-bucket thing, because it’s almost unique, but smaller versions of this dilemma abound. Let’s say your theatre company has a database with 20,000 records in it — people who have purchased subscriptions over the years, plus single-ticket buyers, plus all your donors (current and long-lapsed). You plan to run a two-week phone campaign for donations, but there’s no way you can reach everyone with a phone number in that limited time. You need a way to rank your constituents by likelihood to give, in order to maximize your return.
(About five years ago, I built a model using data from a symphony orchestra’s database. Among other things, I found that certain combinations of concert series subscriptions were associated with higher levels of giving. So: you don’t need a university alumni database to do this work!)
It works with smaller numbers, too. Let’s say your college has 1,000 alumni living in Toronto, and you want to invite them all to an event. Your budget allows a mail piece to be sent to just 250, however. If you have a predictive model for likelihood to attend an event, you can send mail to only the best prospective attendees, and perhaps email the rest.
In a reverse scenario, if your charity has 500 donors and you’re fully capable of contacting and visiting them all as often as you like, then there’s no business need for predictive modelling. I would also note that modelling is harder to do with small data sets, entailing problems such as overfitting. But that’s a technical issue; it’s enough to know that modelling is something to consider only at the point when resources won’t cover the need to engage with your whole constituency.
Now for the second question: What data do you need?
My first suggestion is that you look to the data you already have. Going back to the example of the symphony orchestra: The data I used actually came from two different systems — one for donor management, the other for ticketing and concert series subscriptions. The key was that donors and concert attendees were each identified with a unique ID that spanned both databases. This allowed me to discover that people who favoured the great Classical composers were better donors than those who liked the “pops” concerts — but that people who attended both were the best donors of all! If the orchestra intended to identify a pool of prospects for leadership gifts, this would be one piece of the ranking score that would help them do it.
So: Explore your existing data. And while you’re doing so, don’t assume that messy, old, or incomplete data is not useable. It’s usually worth a look.
What about collecting new data? This can be an expensive proposition, and I think it would be risky to gather data just so you can build predictive models. There is no guarantee that what you’re spending time and money to gather is actually correlated with giving or other behaviours. My suggestion would be to gather data that serves operational purposes as well as analytical ones. A good example might be event attendance. If your organization holds a lot of events, you’ll want to keep statistics on attendance and how effective each event was. If you can find ways to record which individuals were at the event (donors, volunteers, community members), you will get this information, plus you will get a valuable input for your models.
Surveying is another way organizations can collect useful data for analysis while also serving other purposes. It’s one way to find out how old donors are — a key piece of information. Just be sure that your surveys are not anonymous! In my experience, people are not turned off by non-anonymous surveys so long as you’re not asking deeply personal questions. Offering a chance to win a prize for completing the survey can help.
Data you might gather on individuals falls into two general categories: Behaviours and attributes.
Behaviours are any type of action people take that might indicate affinity with your organization. Giving is obviously the big one, but other good examples would be event attendance or volunteering, or any type of interaction with your organization.
Attributes are just characteristics that prospects happen to have. This includes gender, where a person lives, age, wealth information, and so on.
Of the two types, behavioural factors are always the more powerful. You can never go wrong by looking at what people actually do. As the saying has it, people give of their time, talent, and treasure. Focus on those interactions first.
People also give of something else that is increasingly valuable: Their attention. If your organization makes use of a broadcast email platform, find out if it tracks opens and click-throughs — not just at the aggregate level, but at the individual level. Some platforms even assign a score to each email address that indicates the level of engagement with your emails. If you run phone campaigns, keep track of who answers the call. The world is so full of distractions, these periods of time when you have someone’s full attention are themselves gifts — and they are directly associated with likelihood to give financially.
Attributes are trickier. They can lead you astray with correlations that look real, but aren’t. Age is always a good thing to have, but gender is only sometimes useful. And I would never purchase external data (census and demographic data, for example) for predictive modelling alone. Aggregate data at the ZIP or postal code level is useful for a lot of things, but is not the strongest candidate for a model input. The correlations with giving to your organization will be weak, especially in comparison with the behavioural data you have on individuals.
What type of model does it make sense for a nonprofit to try to build first? Any modelling project starts with a clear statement of the business need. Perhaps you want to identify which ticket buyers will convert to donors, or which long-lapsed donors are most likely to respond positively to a phone call, or who among your past clients is most likely to be interested in becoming a volunteer.
Whatever it is, the key thing is that you have plenty of historical examples of the behaviour you want to predict. You want to have a big, fat target to aim for. If you want to predict likelihood to attend an event and your database contains 30,000 addressable records, you can be quite successful if 1,000 of those records have some history of attending events — but your model will be a flop if you’ve only got 50. The reason is that you’re trying to identify the behaviours and characteristics that typify the “event attendee,” and then go looking in your “non-attendee” group for those people who share those behaviours and characteristics. The better they fit the profile, the more likely they are to respond to an event invitation. Fifty people is probably not enough to define what is “typical.”
So for your first foray into modelling, I would avoid trying to hit very small targets. Major giving and planned giving propensity tend to fall into that category. I know why people choose to start there — because it implies high return on investment — but you would be wise to resist.
At this point, someone who’s done some reading may start to obsess about which highly advanced technique to use. But if you’re new to hands-on work, I strongly suggest using a simple method that requires you to study each variable individually, in relation to the outcome you’re trying to model. The best beginning point is to get familiar with comparing groups (attendees vs. non-attendees, donors vs. non-donors, etc.) using means and medians, preferably with the aid of a stats software package. (Peter Wylie’s book, Data Mining for Fundraisers has this covered.) From there, learn a bit more about exploring associations and correlations between variables by looking at scatterplots and using Pearson Product-Moment Correlation. That will set you up well for learning to do multiple linear regression, if you choose to take it that far.
In sum: Predictive modeling isn’t for everyone, but you don’t need Big Data or a degree in statistics to get some benefit from it. Start small, and build from there.