CoolData blog

18 February 2010

Tracking event attendance in Banner, Part 2

Filed under: Banner, Event attendance — Tags: , , — kevinmacdonell @ 8:49 am

In Part 1, I showed you the data-entry side of the system – setting up new events and adding database constituents as RSVPs and/or attendees. Today I’ll show you how to access counts of RSVPs and attendees, right in Banner.

Let’s say you’re working at the registration desk for your annual on-campus alumni reunion event, and your Alumni Director or someone comes up and asks for an update on how many attendees have registered so far. You can provide him or her with up-to-the-minute numbers in a cinch. Here’s what you do.

Go directly to GEIATTD. This screen will allow you to view and count all IDs who attended an Event or any specific Function within an Event. This is a query form – you can’t change attendance information here.

If you’ve been signing people in, the first two fields in this screen should already be populated with the unique number (CRN) and description of the event. If not, just click on Search (down arrow beside the Event field) to call up SLQEVNT, and press F8 to populate the query table; double-click on the desired event and return to GEIATTD.

You’ve got several options for counting up your attendees. But first, a couple of notes:

  • If a person is registered for more than one function, their ID will be listed twice on this screen, but the ID will be counted only once, so you don’t have to worry about double-counting.
  • Non-constituent guests (noted in comment fields) cannot be counted.

The following options assume that the buttons “No Guest Criteria” (Guest Indicator area) and “Both” (Invitee/Guest Indicator area) are selected. This follows from the fact that I don’t use the guest registration features in Banner. If you decide to use them, make adjustments accordingly.

1. To count both RSVPs and attendees together:

  • Leave Function blank
  • Leave RSVP blank
  • Select “No Attendance Criteria”
  • Click in any field of the box below.
  • View number in the box “Count”, top right.

2. To count only attendees:

  • Same as above, but instead select “Attended” instead of “No Attendance Criteria”

3. To count RSVPs:

  • Same as (1), but double-click in RSVP field and choose the appropriate code (eg. “Attend”).

4. To count RSVPs or attendees for a specific function:

  • Fill in the desired Function in the Function field, using the search icon.
  • Fill in other fields as desired.

This screen is perfect for obtaining quick RSVP/attendee counts on the fly. Naturally, though, you’ll want to have some reporting options in place in order to make full use of the Event Module for attendance tracking.

17 January 2010

Proving ‘event attendance likelihood’ actually works

Filed under: Event attendance, Model building, Predictive scores, skeptics — Tags: , , , , — kevinmacdonell @ 6:56 pm

In an earlier post I talked about what you need to get started to build an ‘event attendance likelihood’ model. Today I want to provide some evidence to back up my claim that yes, you can identify which segment of your alumni population is most likely to attend your future event.

To recap: Every living, addressable alumnus/na in our database is scored according to how likely he or she is to attend an event, whether it be a President’s Reception or Homecoming, whether they’ve ever attended an event or not.

The scores can be used to answer these types of questions:

  • What’s the top 30% of alumni living in Toronto who should be mailed a paper invite to the President’s Reception?
  • Who are the 50 members of the Class of 2005 who are most likely to come to Homecoming for their 5th-year reunion?

I built our first event-attendance model last summer. As I always do, I divided all our alumni into deciles by the predicted values that are produced by the regression analysis (the ‘raw score’). The result is that all alumni were ranked from a high score of 10 (most likely to attend an event) to 1 (least likely).

At that time, alumni were sending in their RSVPs for that fall’s Homecoming event. Because I use only actual-attendance data in my models, these RSVPs were not used as a source of data. … That made Homecoming 2009 an excellent test of the predictive strength of the new model.

Have a look at this chart, which shows how much each decile score contributed to total attendance for Homecoming 2009. The horizontal axis is Decile Score, and the vertical axis is Percentage of Attendees. Almost 45% of all alumni attendees had a score of 10 (the bar highlighted in red).

(A little over 4% of alumni attendees had no score. Most of these would have been classified as ‘lost’ when the model was created, and therefore were excluded at that time. In the chart, they are given a score of zero.)

To put it another way, almost three-quarters of all alumni attendees have a score of 8 or higher. But those 10 scores are the ones who really stand out.

Let me anticipate an objection you might have: Those high-scoring alumni are just the folks who have shown up for events in the past. You might say that the model is just predicting that past attendees are going to attend again.

Not quite. In fact, a sizable percentage of the 10-scores who attended Homecoming had never attended an event before: 23.1%.

The chart below shows the number of events previously attended by the 10-scored alumni who were at Homecoming in 2009. The newbies are highlighted in red.

The majority of high-scoring attendees had indeed attended previous events (a handful had attended 10 or more!). But that one-quarter hadn’t – and were still identified as extremely likely to attend in future.

That’s what predictive modeling excels at: Zeroing in on the characteristics of individuals who have exhibited a desired behaviour, and flagging other individuals from the otherwise undifferentiated masses who share those characteristics.

Think of any ‘desired behaviour’ (giving to the annual fund, giving at a higher level than before, attending events, getting involved as an alumni volunteer), then ensure you’ve got the historical behavioural data to build your model on. Then start building.

14 January 2010

Building your ‘event attendance likelihood’ model

Filed under: Event attendance, Model building — Tags: , , , , — kevinmacdonell @ 12:20 pm

Photo courtesy of Alumnae Association of Mount Holyoke College (Creative Commons licence)

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.

Photo courtesy of Alumnae Association of Mount Holyoke College (Creative Commons licence)

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?

14 December 2009

Why you should capture alumni event attendance in your database

Filed under: Event attendance, Predictor variables — Tags: , , — kevinmacdonell @ 11:46 am

There are many reasons why it makes sense to have a system in place to capture alumni event attendance in your database. Here are a few:

  • Pre-entering RSVPs, if your database allows it, enables an efficient way of producing event bios (what in some shops are called ‘blurbs’), by pulling all the relevant data from your database for the IDs that have an RSVP in their record for the event.
  • Statistics for attendance at key annual events (Homecoming in particular) are much more easily generated when the data is stored in the database. Not just attendee counts, but breakdowns by class year, milestone reunion year, giving, and so on.
  • It’s easy to pull a mailing list of event attendees, for post-event surveying.
  • Event attendance history is a useful piece of information to incorporate in major-gift prospect profiles. Having it in one place (ideally as part of a report) will make it easier to retrieve quickly.
  • Event attendance is very highly correlated with giving. It is a valuable predictor in any propensity-to-give model.
  • You can turn that around, and say that giving is highly correlated with event attendance. Make “events attended” the predicted value in an event attendance likelihood model, to segment which group of alumni should receive a mailed invitation.

Naturally, I am most excited by the possibilities for building ever more robust predictive models, but any combination of these reasons is enough to proceed with some system for tracking attendance in your database.

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