Your model’s predicted value doesn’t always have to be ‘giving’. Once you’ve discovered the power of predictive modeling for your fundraising efforts, you can direct that power into other Advancement functions.
How about alumni event attendance?
I’ve had great success with this new model, which scores all of our alumni according to how likely they are to attend an event. I’ll show you what we use it for, and then I’ll bounce a cool idea off you for your thoughts.
If you’ve read some earlier posts, you will already know that event attendance is highly correlated with giving (for our institution – but probably yours as well). Event attendance is an excellent predictor of giving, but it works the other way too: giving is a predictor of propensity to attend events.
We can say this because when we build our models we’re concerned only with correlation, not causation. It would be incorrect for me to say that attending events causes an alum to give, or vice-versa. I don’t know enough to make a statement either way. It could be that both behaviours spring from other influences. It’s enough for our purposes to say that they’re linked in a meaningful way.
To create an event attendance likelihood model you need at least a few years of actual attendance data. I was lucky – I had Homecoming data going back to 1999, as well as a few years of data for alumni receptions across the country. (Gathering this data pays off in many ways besides predictive modeling. See my earlier post, Why you should capture alumni event attendance in your database.)
I gave a lot of thought as to whether I should consider Homecoming and off-campus receptions separately. Clearly they are not the same thing, and perhaps should not have been weighted equally. However, for the sake of simplicity, I regarded all events as the same when I calculated my predicted value (‘number of events attended’). As long as an alumnus/na had to RSVP for the event AND showed up, they got a point for that event.
Another consideration is opportunity. To validly count off-campus events, ALL alumni should have at least had the option to attend an event. It is true that there are many cities where we have yet to host an event. However, I reasoned, we’ve hosted events in many of the towns and cities where the majority of our alumni live (or can reasonably travel to). Therefore I chose to include receptions along with Homecoming. Was I wrong? Not sure!
(Events I chose to leave out were of the exclusive, invite-only type. Because not all alumni were given the opportunity to attend, those events are not suitable to use in this model.)
You create a new model whenever you change the predicted value. Whether you use Peter Wylie’s simple-score method or multiple regression to create your model, when you make “number of events attended” your predicted value, your resulting score set will help to rank all alumni by how likely they are to show up to your event.
Here’s how we use those scores.
Let’s say the Alumni Office wants to send out invitations for Homecoming or for a reception in a city somewhere. Email is a no-brainer. It’s cheap and fast, and alumni of all ages seem very receptive to receiving communications that way.
Naturally we still mail out paper invitations, but for various reasons (cost being supreme), we have to be more selective. Some criteria we use for selecting who will get a mailing are included in the list below. The criteria are adjusted to be more or less restrictive, depending on what our target for mail pieces is.
- Lifetime household and business giving $x and up
- Member of donor recognition group in a recent year
- Has a Planned Giving commitment
- Identified as an ‘involved’ young alumnus/na
- Attended Homecoming once in past ‘x’ years
- Attended a previous event in region
The problem with these criteria is that so many alumni (particularly young alumni) might attend our event but aren’t donors and have never attended an event before. If the goal is attracting new faces to your event, you need some way to segment the ‘willing’ from the disinterested masses, and give them the extra attention they deserve.
This is where predictive modeling shines. I’ll have more to say about building this model later.
Now I want to bounce a cool idea off you. Let’s say you’ve created your model, scored all your alumni, and have since then put on several large events. Those events have generated actual attendance data. Let’s say you use this attendance data to work out the ‘percentage attended’ for each score level. Would that not provide you with a rough estimate of projected attendance for any given invitation list in the future? With incremental adjustments over time, and perhaps for different event types, would this be a valid tool your event planners could use?
I want to know!
An example. Let’s say you have an event coming up in Los Angeles, and your invitation list for that city includes 200 alumni who have a score of 10 in the Event Likelihood Model. You know from past events that 20% of alumni with that score will show up. Therefore you expect to see about 40 of them in Los Angeles. You add in 12% for the next level, 8% for the next level, and so on, and sum it all up to get your total projected attendance.
Valid? Not valid?