CoolData (the book) beta testers needed

 

UPDATE (Jan 5): 16 people have responded to my call for volunteers, so I am going to close this off now. I have been in touch with each person who has emailed me, and I will be making a final selection within a few days. Thank you to everyone who considered taking a crack at it.

 

Interested in being a guinea pig for my new handbook on predictive modelling? I’m looking for someone (two or three people, max) to read and work through the draft of “CoolData” (the book), to help me make it better.

 

What’s it about? This long subtitle says it all: “A how-to guide for predictive modelling for higher education advancement and nonprofits using multiple linear regression in Data Desk.”

 

The ideal beta tester is someone who:

 

• has read or heard about predictive modelling and understands what it’s for, but has never done it and is keen to learn. (Statistical concepts are introduced only when and if they are needed – no prior stats knowledge is required. I’m looking for beginners, but beginners who aren’t afraid of a challenge.);
• tends to learn independently, particularly using books and manuals to work through examples, either in addition to training or completely on one’s own;
• does not have an IT background but has some IT support at his or her organization, and would not be afraid to learn a little SQL in order to query a database him- or herself, and
• has a copy of Data Desk, or intends to purchase Data Desk. (Available for PC or Mac).

 

It’s not terribly important that you work in the higher ed or nonprofit world — any type of data will do — but the book is strictly about multiple linear regression and the stats software Data Desk. The methods outlined in the book can be extended to any software package (multiple linear regression is the same everywhere), but because the prescribed steps refer specifically to Data Desk, I need someone to actually go through the motions in that specific package.

 

Think of a cookbook full of recipes, and how each must be tested in real kitchens before the book can go to press. Are all the needed ingredients listed? Has the method been clearly described? Are there steps that don’t make sense? I want to know where a reader is likely to get lost so that I can fix those sections. In other words, this is about more than just zapping typos.

 

I might be asking a lot. You or your organization will be expected to invest some money (for the software, sales of which I do not benefit from, by the way) and your time (in working through some 200 pages).

 

As a return on your investment, however, you should expect to learn how to build a predictive model. You will receive a printed copy of the current draft (electronic versions are not available yet), along with a sample data file to work through the exercises. You will also receive a free copy of the final published version, with an acknowledgement of your work.

 

One unusual aspect of the book is that a large chunk of it is devoted to learning how to extract data from a database (using SQL), as well as cleaning it and preparing the data for analysis. This is in recognition of the fact that data preparation accounts for the majority of time spent on any analysis project. It is not mandatory that you learn to write queries in SQL yourself, but simply knowing which aspects of data preparation can be dealt with at the database query level can speed your work considerably. I’ve tried to keep the sections about data extraction as non-technical as possible, and augmented with clear examples.

 

For a sense of the flavour of the book, I suggest you read these excerpts carefully: Exploring associations between variables and Testing associations between two categorical variables.

 

Contact me at kevin.macdonell@gmail.com and tell me why you’re interested in taking part.

 

 

 

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New finds in old models

 

When you build a predictive model, you can never be sure it’s any good until it’s too late. Deploying a mediocre model isn’t the worst mistake you can make, though. The worst mistake would be to build a second mediocre model because you haven’t learned anything from the failure of the first.

 

Performance against a holdout data set for validation is not a reliable indicator of actual performance after deployment. Validation may help you decide which of two or more competing models to use, or it may provide reassurance that your one model isn’t total junk. It’s not proof of anything, though. Those lovely predictors, highly correlated with the outcome, could be fooling you. There are no guarantees they’re predictive of results over the year to come.

 

In the end, the only real evidence of a model’s worth is how it performs on real results. The problem is, those results happen in the future. So what is one to do?

 

I’ve long been fascinated with Planned Giving likelihood. Making a bequest seems like the ultimate gesture of institutional affinity (ultimate in every sense). On the plus side, that kind of affinity ought to be clearly evidenced in behaviours such as event attendance, giving, volunteering and so on. On the negative side, Planned Giving interest is uncommon enough that comparing expectancies with non-expectancies will sometimes lead to false predictors based on sparse data. For this reason, my goal of building a reliable model for predicting Planned Giving likelihood has been elusive.

 

Given that a validation data set taken from the same time period as the training data can produce misleading correlations, I wondered whether I could do one better: That is, be able to draw my holdout sample not from data of the same time period as that used to build the model, but from the future.

 

As it turned out, yes, I could.

 

Every year I save my regression analyses as Data Desk files. Although I assess the performance of the output scores, I don’t often go back to the model files themselves. However, they’re there as a document of how I approached modelling problems in the past. As a side benefit, each file is also a snapshot of the alumni population at that point in time. These data sets may consist of a hundred or more candidate predictor variables — a well-rounded picture.

 

My thinking went like this: Every old model file represents data from the past. If I pretend that this snapshot is really the present, then in order to have access to knowledge of the future, all I have to do is look at today’s data stored in the database.

 

For example, for this blog post, I reached back two years to a model I created in Data Desk for predicting likelihood to upgrade to the Leadership level in Annual Giving. I wasn’t interested in the model itself. Rather, I wanted to examine the underlying variables I had to work with at the time. This model had been an ambitious undertaking, with some 170 variables prepared for analysis. Many of course were transformations of variables or combinations of interacting variables. Among all those variables was one indicating whether a case was a current Planned Giving expectancy or not, at that point in time.

 

In this snapshot of the database from two years ago, some of the cases that were not expectancies would have become so since then. In other words, I now had the best of both worlds. I had a comprehensive set of potential predictors as they existed two years ago, AND access to the hitherto unknowable future: The identities of the people who had become expectancies after the predictors had been frozen in time.

 

As I said, my old model was not intended to predict Planned Giving inclination. So I built a new model, using “Is an Expectancy” (0/1) as the target variable. I trained the regression model on the two-year-old expectancy data — I didn’t even look at the new expectancies while building the model. No: I used those new expectancies as my validation data set.

 

“Validation” might be too strong a word, given that there were only 80 or so new cases. That’s a lot of bequest intentions, for sure, but in terms of data it’s a drop in the bucket compared with the number of cases being scored. Let’s call it a test data set. I used this test set to help me analyze the model, in a couple of ways.

 

First I looked at how new expectancies were scored by the model I had just built. The chart below shows their distribution by score decile. Slightly more than 50% of new expectancies were in the top decile. This looks pretty good — keeping in mind that this is what actual performance would have looked like had I really built this model two years ago (which I could have):

 

 

(Even better, looking at percentiles, most of the expectancies in that top 10% are concentrated nicely in the top few percentiles.)

 

But I didn’t stop there. It is also evident that almost half of new expectancies fell outside the top 10 percent of scores, so clearly there was room for improvement. My next step was to examine the individual predictors I had used in the model. These were of course the predictors most highly correlated with being an expectancy. They were roughly the following:

• Year person’s personal information in the database was last updated
• Number of events attended
• Age
• Year of first gift
• Number of alumni activities
• Indicated “likely to donate” on 2009 alumni survey
• Total giving in last five years (log transformed)
• Combined length of name Prefix + Suffix

 

I ranked the correlation of each of these with the 0/1 indicator meaning “new expectancy,” and found that most of the predictors were still fine, although they changed their order in the rank correlation. Donor likelihood (from survey) and recent giving were more important, and alumni activities and how recently a person’s record was updated were less important.

 

This was interesting and useful, but what was even more useful was looking at the correlations between ALL potential predictors and the state of being a new expectancy. A number of predictors that would have been too far down the ranked list to consider using two years ago were suddenly looking much better. In particular, many variables related to participation in alumni surveys bubbled closer to the top as potentially significant.

 

This exercise suggests a way to proceed with iterative, yearly improvements to some of your standard models:

• Dig up an old model from a year or more ago.
• Query the database for new cases that represent the target variable, and merge them with the old datafile.
• Assess how your model performed or, if you created more than one model, see which model would have performed best. (You should be doing this anyway.)
• Go a layer deeper, by studying the variables that went into those models — the data “as it was” — to see which variables had correlations that tricked you into believing they were predictive, and which variables truly held predictive power but may have been overlooked.
• Apply what you learn to the next iteration of the model. Leave out the variables with spurious correlations, and give special consideration to variables that may have been underestimated before.

Don’t worry, just do it

People trying to learn how to do predictive modelling on the job often need only one thing to get them to the next stage: Some reassurance that what they are doing is valid.

Peter Wylie and I are each just back home, having presented at the fall conference of the Illinois chapter of the Association of Professional Researchers for Advancement (APRA-IL), hosted at Loyola University Chicago. (See photos, below!) Following an entertaining and fascinating look at the current and future state of predictive analytics presented by Josh Birkholz of Bentz Whaley Flessner, Peter and I gave a live demo of working with real data in Data Desk, with the assistance of Rush University Medical Center. We also drew names to give away a few copies of our book, Score! Data-Driven Success for Your Advancement Team.

We were impressed by the variety and quality of questions from attendees, in particular those having to do with stumbling blocks and barriers to progress. It was nice to be able to reassure people that when it comes to predictive modelling, some things aren’t worth worrying about.

Messy data, for example. Some databases, particularly those maintained by non higher ed nonprofits, have data integrity issues such as duplicate records. It would be a shame, we said, if data analysis were pushed to the back burner just because of a lack of purity in the data. Yes, work on improving data integrity — but don’t assume that you cannot derive valuable insights right now from your messy data.

And then the practice of predictive modelling itself … Oh, there is so much advice out there on the net, some of it highly technical and involving a hundred different advanced techniques. Anyone trying to learn on their own can get stymied, endlessly questioning whether what they’re doing is okay.

For them, our advice was this: In our field, you create value by ranking constituents according to their likelihood to engage in a behaviour of interest (giving, usually), which guides the spending of scarce resources where they will do the most good. You can accomplish this without the use of complex algorithms or arcane math. In fact, simpler models are often better models.

The workhorse tool for this task is multiple linear regression. A very good stand-in for regression is building a simple score using the techniques outlined in Peter’s book, Data Mining for Fundraisers. Sticking to the basics will work very well. Fussing with technical issues or striving for a high degree of accuracy are distractions that the beginner need not be overly concerned with.

If your shop’s current practice is to pick prospects or other targets by throwing darts, then even the crudest model will be an improvement. In many situations, simply performing better than random will be enough to create value. The bottom line: Just do it. Worry about perfection some other day.

If the decisions are high-stakes, if the model will be relied on to guide the deployment of scarce resources, then insert another step in the process. Go ahead and build the model, but don’t use it. Allow enough time of “business as usual” to elapse. Then, gather fresh examples of people who converted to donors, agreed to a bequest, or made a large gift — whatever the behaviour is you’ve tried to predict — and check their scores:

• If the chart shows these new stars clustered toward the high end of scores, wonderful. You can go ahead and start using the model.
• If the result is mixed and sort of random-looking, then examine where it failed. Reexamine each predictor you used in the model. Is the historical data in the predictor correlated with the new behaviour? If it isn’t, then the correlation you observed while building the model may have been spurious and led you astray, and should be excluded. As well, think hard about whether the outcome variable in your model is properly defined: That is, are you targeting for the right behaviour? If you are trying to find good prospects for Planned Giving, for example, your outcome variable should focus on that, and not lifetime giving.

“Don’t worry, just do it” sounds like motivational advice, but it’s more than that. The fact is, there is only so much model validation you can do at the time you create the model. Sure, you can hold out a generous number of cases as a validation sample to test your scores with. But experience will show you that your scores will always pass the validation test just fine — and yet the model may still be worthless.

A holdout sample of data that is contemporaneous with that used to train the model is not the same as real results in the future. A better way to go might be to just use all your data to train the model (no holdout sample), which will result in a better model anyway, especially if you’re trying to predict something relatively uncommon like Planned Giving potential. Then, sit tight and observe how it does in production, or how it would have done in production if it had been deployed.

1. Observe, learn, tweak, and repeat. Errors are hard to avoid, but they can be discovered.
2. Trust the process, but verify the results. What you’re doing is probably fine. If it isn’t, you’ll get a chance to find out.
3. Don’t sweat the small stuff. Make a difference now by sticking to basics and thinking of the big picture. You can continue to delve and explore technical refinements and new methods, if that’s where your interest and aptitude take you. Data analysis and predictive modelling are huge subjects — start where you are, where you can make a difference.

* A heartfelt thank you to APRA-IL and all who made our visit such a pleasure, especially Sabine Schuller (The Rotary Foundation), Katie Ingrao and Viviana Ramirez (Rush University Medical Center), Leigh Peterson Visaya (Loyola University Chicago), Beth Witherspoon (Elmhurst College), and Rodney P. Young, Jr. (DePaul University), who took the photos you see below. (See also: APRA IL Fall Conference Datapalooza.)

Click on any of these for a full-size image.

What predictor variables should you avoid? Depends on who you ask

People who build predictive models will tell you that there are certain variables you should avoid using as predictors. I am one of those people. However, we disagree on WHICH variables one should avoid, and increasingly this conflicting advice is confusing those trying to learn predictive modeling.

The differences involve two points in particular. Assuming charitable giving is the behaviour we’re modelling for, those two things are:

1. Whether we should use past giving to predict future giving, and
2. Whether attributes such as marital status are really predictors of giving.

I will offer my opinions on both points. Note that they are opinions, not definitive answers.

1. Past giving as a predictor

I have always stressed that if you are trying to predict “giving” using a multiple linear regression model, you must avoid using “giving” as a predictor among your independent variables. That includes anything that is a proxy for “giving,” such as attendance at a donor-thanking event. This is how I’ve been taught and that is what I’ve adhered to in practice.

Examples that violate this practice keep popping up, however. I have an email from Atsuko Umeki, IT Coordinator in the Development Office of the University of Victoria in Victoria, British Columbia*. She poses this question about a post I wrote in July 2013:

“In this post you said, ‘In predictive models, giving and variables related to the activity of giving are usually excluded as variables (if ‘giving’ is what we are trying to predict). Using any aspect of the target variable as an input is bad practice in predictive modelling and is carefully avoided.’  However, in many articles and classes I read and took I was advised or instructed to include past giving history such as RFA*, Average gift, Past 3 or 5 year total giving, last gift etc. Theoretically I understand what you say because past giving is related to the target variable (giving likelihood); therefore, it will be biased. But in practice most practitioners include past giving as variables and especially RFA seems to be a good variable to include.”

(* RFA is a variation of the more familiar RFM score, based on giving history — Recency, Frequency, and Monetary value.)

So modellers-in-training are being told to go ahead and use ‘giving’ to predict ‘giving’, but that’s not all: Certain analytics vendors also routinely include variables based on past giving as predictors of future giving. Not long ago I sat in on a webinar hosted by a consultant, which referenced the work of one well-known analytics vendor (no need to name the vendor here) in which it seemed that giving behaviour was present on both sides of the regression equation. Not surprisingly, this vendor “achieved” a fantastic R-squared value of 86%. (Fantastic as in “like a fantasy,” perhaps?)

This is not as arcane or technical as it sounds. When you use giving to predict giving, you are essentially saying, “The people who will make big gifts in the future are the ones who have made big gifts in the past.” This is actually true! The thing is, you don’t need a predictive model to produce such a prospect list; all you need is a list of your top donors.

Now, this might be reassuring to whomever is paying a vendor big bucks to create the model. That person sees names they recognize, and they think, ah, good — we are not too far off the mark. And if you’re trying to convince your boss of the value of predictive modelling, he or she might like to see the upper ranks filled with familiar names.

I don’t find any of that “reassuring.” I find it a waste of time and effort — a fancy and expensive way to produce a list of the usual suspects.

If you want to know who has given you a lot of money, you make a list of everyone in your database and sort it in descending order by total amount given. If you want to predict who in your database is most likely to give you a lot of money in the future, build a predictive model using predictors that are associated with having given large amounts of money. Here is the key point … if you include “predictors” that mean the same thing as “has given a lot of money,” then the result of your model is not going to look like a list of future givers — it’s going to look more like your historical list of past givers.

Does that mean you should ignore giving history? No! Ideally you’d like to identify the donors who have made four-figure gifts who really have the capacity and affinity to make six-figure gifts. You won’t find them using past giving as a predictor, because your model will be blinded by the stars. The variables that represent giving history will cause all other affinity-related variables to pale in comparison. Many will be rejected from the model for being not significant or for adding nothing additional to the model’s ability to explain the variance in the outcome variable.

To sum up, here are the two big problems with using past giving to predict future giving:

1. The resulting insights are sensible but not very interesting: People who gave before tend to give again. Or, stated another way: “Donors will be donors.” Fundraisers don’t need data scientists to tell them that.
2. Giving-related independent variables will be so highly correlated with giving-related dependent variables that they will eclipse more subtle affinity-related variables. Weaker predictors will end up getting kicked out of our regression analysis because they can’t move the needle on R-squared, or because they don’t register as significant. Yet, it’s these weaker variables that we need in order to identify new prospects.

Let’s try a thought experiment. What if I told you that I had a secret predictor that, once introduced into a regression analysis, could explain 100% of the variance in the dependent variable ‘Lifetime Giving’? That’s right — the highest value for R-squared possible, all with a single predictor. Would you pay me a lot of money for that? What is this magic variable that perfectly models the variance in ‘Lifetime Giving’? Why, it is none other than ‘Lifetime Giving’ itself! Any variable is perfectly correlated with itself, so why look any farther?

This is an extreme example. In a real predictive model, a predictor based on giving history would be restricted to giving from the past, while the outcome variable would be calculated from a more recent period — the last year or whatever. There should be no overlap. R-squared would not be 100%, but it would be very high.

The R-squared statistic is useful for guiding you as you add variables to a regression analysis, or for comparing similar models in terms of fit with the data. It is not terribly useful for deciding whether any one model is good or bad. A model with an R-squared of 15% may be highly valuable, while one with R-squared of 75% may be garbage. If a vendor is trying to sell you on a model they built based on a high R-squared alone, they are misleading you.

The goal of predictive modeling for major gifts is not to maximize R-squared. It’s to identify new prospects.

2. Using “attributes” as predictors

Another thing about that webinar bugged me. The same vendor advised us to “select variables with caution, avoiding ‘descriptors’ and focusing on potential predictors.” Specifically, we were warned that a marital status of ‘married’ will emerge as correlated with giving. Don’t be fooled! That’s not a predictor, they said.

So let me get this straight. We carry out an analysis that reveals that married people are more likely to give large gifts, that donors with more than one degree are more likely to give large gifts, that donors who have email addresses and business phone numbers in the database are more likely to give large gifts … but we are supposed to ignore all that?

The problem might not be the use of “descriptors,” the problem might be with the terminology. Maybe we need to stop using the word “predictor”. One experienced practitioner, Alexander Oftelie, briefly touched on this nuance in a recent blog post. I quote, (emphasis added by me):

“Data that on its own may seem unimportant — the channel someone donates, declining to receive the mug or calendar, preferring email to direct mail, or making ‘white mail’ or unsolicited gifts beyond their sustaining-gift donation — can be very powerful when they are brought together to paint a picture of engagement and interaction. Knowing who someone is isn’t by itself predictive (at best it may be correlated). Knowing how constituents choose to engage or not engage with your organization are the most powerful ingredients we have, and its already in our own garden.”

I don’t intend to critique Alexander’s post, which isn’t even on this particular topic. (It’s a good one — please read it.) But since he’s written this, permit me scratch my head about it a bit.

In fact, I think I agree with him that there is a distinction between a behaviour and a descriptor/attribute. A behaviour, an action taken at a specific point in time (eg., attending an event), can be classified as a predictor. An attribute (“who someone is,” eg., whether they are married or single) is better described as a correlate. I would also be willing to bet that if we carefully compared behavioural variables to attribute variables, the behaviours would outperform, as Alexander says.

In practice, however, we don’t need to make that distinction. If we are using regression to build our models, we are concerned solely and completely with correlation. To say “at best it may be correlated” suggests that predictive modellers have something better at their disposal that they should be using instead of correlation. What is it? I don’t know, and Alexander doesn’t say.

If in a given data set, we can demonstrate that being married is associated with likelihood to make a donation, then it only makes sense to use that variable in our model. Choosing to exclude it based on our assumption that it’s an attribute and not a behaviour doesn’t make business sense. We are looking for practical results, after all, not chasing some notion of purity. And let’s not fool ourselves, or clients, that we are getting down to causation. We aren’t.

Consider that at least some “attributes” can be stated in terms of a behaviour. People get married — that’s a behaviour, although not related to our institution. People get married and also tell us about it (or allow it to be public knowledge so that we can record it) — that’s also a behaviour, and potentially an interaction with us. And on the other side of the coin, behaviours or interactions can be stated as attributes — a person can be an event attendee, a donor, a taker of surveys.

If my analysis informs me that widowed female alumni over the age of 60 are extremely good candidates for a conversation about Planned Giving, then are you really going to tell me I’m wrong to act on that information, just because sex, age and being widowed are not “behaviours” that a person voluntarily carries out? Mmmm — sorry!

Call it quibbling over semantics if you like, but don’t assume it’s so easy to draw a circle around true predictors. There is only one way to surface predictors, which is to take a snapshot of all potentially relevant variables at a point in time, then gather data on the outcome you wish to predict (eg., giving) after that point in time, and then assess each variable in terms of the strength of association with that outcome. The tools we use to make that assessment are nothing other than correlation and significance. Again, if there are other tools in common usage, then I don’t know about them.

Caveats and concessions

I don’t maintain that this or that practice is “wrong” in all cases, nor do I insist on rules that apply universally. There’s a lot of art in this science, after all.

Using giving history as a predictor:

• One may use some aspects of giving to predict outcomes that are not precisely the same as ‘Giving’, for example, likelihood to enter into a Planned Giving arrangement. The required degree of difference between predictors and outcome is a matter of judgement. I usually err on the side of scrupulously avoiding ANY leakage of the outcome side of the equation into the predictor side — but sure, rules can be bent.
• I’ve explored the use of very early giving (the existence and size of gifts made by donors before age 30) to predict significant giving late in life. (See Mine your donor data with this baseball-inspired analysis.) But even then, I don’t use that as a variable in a model; it’s more of a flag used to help select prospects, in addition to modeling.

Using descriptors/attributes as predictors:

• Some variables of this sort will appear to have subtly predictive effects in-model, effects that disappear when the model is deployed and new data starts coming in. That’s regrettable, but it’s something you can learn from — not a reason to toss all such variables into the trash, untested. The association between marital status and giving might be just a spurious correlation — or it might not be.
• Business knowledge mixed with common sense will help keep you out of trouble. A bit of reflection should lead you to consider using ‘Married’ or ‘Number of Degrees’, while ignoring ‘Birth Month’ or ‘Eye Colour’. (Or astrological sign!)

There are many approaches one can take with predictive modeling, and naturally one may feel that one’s chosen method is “best”. The only sure way to proceed is to take the time to define exactly what you want to predict, try more than one approach, and then evaluate the performance of the scores when you have actual results available — which could be a year after deployment. We can listen to what experts are telling us, but it’s more important to listen to what the data is telling us.

//////////

Note: When I originally posted this, I referred to Atsuko Umeki as “he”. I apologize for this careless error and for whatever erroneous assumption that must have prompted it.

Alumni engagement scoring vs. predictive modelling

Alumni engagement scoring has an undeniable appeal. What could be simpler? Just add up how many events an alum has attended, add more points for volunteering, add more points for supporting the Annual Fund, and maybe some points for other factors that seem related to engagement, and there you have your score. If you want to get more sophisticated, you can try weighting each score input, but generally engagement scoring doesn’t involve any advanced statistics and is easily grasped.

Not so with predictive modelling, which does involve advanced stats and isn’t nearly as intuitive; often it’s not possible to really say how an input variable is related to the outcome. It’s tempting, too, to think of an engagement score as being a predictor of giving and therefore a good replacement for modelling. Actually, it should be predictive — if it isn’t, your score is not measuring the right things — but an engagement score is not the same thing as a predictive model score. They are different tools for different jobs.

Not only are engagement scoring schemes different from predictive models, their simplicity is deceptive. Engagement scoring is incomplete without some plan for acting on observed trends with targeted programming. This implies the ability to establish causal drivers of engagement, which is a tricky thing.

That’s a sequence of events — not a one-time thing. In fact, engagement scoring is like checking the temperature at regular intervals over a long period of time, looking for up and down trends not just for the group as a whole but via comparisons of important subgroups defined by age, sex, class year, college, degree program, geography or other divisions. This requires discipline: taking measurements in exactly the same way every year (or quarter, or what-have-you). If the score is fed by a survey component, you must survey constantly and consistently.

Predictive models and engagement scores have some surface similarities. They share variables in common, the output of both is a numerical score applied to every individual, and both require database work and math in order to calculate them. Beyond that, however, they are built in different ways and for different purposes. To summarize:

• Predictive models are collections of potentially dozens of database variables weighted according to strength of correlation with a well-defined behaviour one is trying to predict (eg. making a gift), in order to rank individuals by likelihood to engage in that behaviour. Both Alumni Relations and Development can benefit from the use of predictive models.
• Engagement scores are collections of a very few selectively-chosen database variables, either not weighted or weighted according to common sense and intuition, in order to roughly quantify the quality of “engagement”, however one wishes to define that term, for each individual. The purpose is to allow comparison of groups (faculties, age bands, geographical regions, etc.) with each other. Comparisons may be made at one point in time, but it is more useful to compare relative changes over time. The main user of scores is Alumni Relations, in order to identify segments requiring targeted programming, for example, and to assess the impact of programming on targeted segments over time.

Let’s explore key differences in more depth:

The purpose of modelling is prediction, for ranking or segmentation. The purpose of engagement scoring is comparison.

Predictive modelling scores are not usually included in reports. Used immediately in decision making, they may never be seen by more than one or two people. Engagement scores are included in reports and dashboards, and influence decision-making over a long span of time.

The target variable of a predictive model is quantifiable (eg. giving, measurable in dollars). In engagement scoring, there is no target variable, only an output – a construct called “engagement”, which itself is not directly measurable.

Potential input variables for predictive models are numerous (100+) and vary from model to model. Input variables for engagement scores are limited to a handful of easily measured attributes (giving, event attendance, volunteering) which must remain consistent over time.

Variables for predictive models are chosen primarily using statistical methods (correlation) and only secondarily using judgment and “common sense.” For example, if the presence of a business phone number is highly correlated with being a donor, it may be included in the model. For engagement scores, variables are chosen by consensus of stakeholders, primarily according to subjective standards. For example, event attendance and giving would probably be deemed by the committee to indicate engagement, and would therefore be included in the score. Advanced statistics rarely come into play. (For more thoughts on this, read How you measure alumni engagement is up to you.)

In predictive models, giving and variables related to the activity of giving are usually excluded as variables (if ‘giving’ is what we are trying to predict). Using any aspect of the target variable as an input is bad practice in predictive modelling and is carefully avoided. You wouldn’t, for example, use attendance at a donor recognition event to predict likelihood to give. In engagement scoring, though, giving history is usually a key input, as it is common sense to believe that being a donor is an indication of engagement. (It might be excluded or reported separately if the aim is to demonstrate the causal link between engagement indicators and giving.)

Modelling variables are weighted using multiple linear regression or other statistical method which calculates the relative influence of each variable while simultaneously controlling for the influence of all other variables in the model. Engagement score variables are usually weighted according to gut feel. For example, coming to campus for Homecoming seems to carry more weight than showing up for a pub night in one’s own city, therefore we give it more weight.

The quality of a predictive model is testable, first against a validation data set, and later against actual results. But there is no right or wrong way to estimate engagement, therefore the quality of scores cannot be evaluated conclusively.

The variables in a predictive model have complex relationships with each other that are difficult or impossible to explain except very generally. Usually there is no reason to explain a model in detail. The components in an engagement score, on the other hand, have plausible (although not verifiable) connections to engagement. For example, volunteering is indicative of engagement, while Name Prefix is irrelevant.

Predictive models are built for a single, time-limited purpose and then thrown away. They evolve iteratively and are ever-changing. On the other hand, once established, the method for calculating an engagement score must not change if comparisons are to be made over time. Consistency is key.

Which is all to say: alumni engagement scoring is not predictive modelling. (And neither is RFM analysis.) Only predictive modelling is predictive modelling.

Making a case for modeling

Guest post by Peter Wylie and John Sammis

(Click here to download post as a print-friendly PDF: Making a Case for Modeling – Wylie Sammis)

Before you wade too far into this piece, let’s be sure we’re talking to the right person. Here are some assumptions we’re making about you:

• You work in higher education advancement and are interested in analytics. However, you’re not a sophisticated stats person who throws around terms like regression and cluster analysis and neural networks.
• You’re convinced that your alumni database (we’ll leave “parents” and “friends” for a future paper) holds a great deal of information that can be used to pick out the best folks to appeal to — whether by mail, email, phone, or face-to-face visits.
• Your boss and your boss’s bosses are, at best, less convinced than you are about this notion. At worst, they have no real grasp of what analytics (data mining and predictive modeling) are. And they may seem particularly susceptible to sales pitches from vendors offering expensive products and services for using your data – products and services you feel might cause more problems than they will solve.
• You’d like to find a way to bring these “boss” folks around to your way of thinking, or at least move them in the “right” direction.

If we’ve made some accurate assumptions here, great. If we haven’t, we’d still like you to keep reading. But if you want to slip out the back of the seminar room, not to worry. We’ve done it ourselves more times than you can count.

Okay, here’s something you can try:

1. Divide the alums at your school into ten roughly equal size groups (deciles) by class year. Table 1 is an example from a medium sized four year college.

Table 1: Class Years and Counts for Ten Roughly Equal Size Groups (Deciles) of Alumni at School A

2. Create a very simple score:

EMAIL LISTED(1/0) + HOME PHONE LISTED(1/0)

This score can assume three values: “0, “1”, or “2.” A “0” means the alum has neither an email nor a home phone listed in the database. A “1” means the alum has either an email listed in the database or a home phone listed in the database, but not both. A “2” means the alum has both an email and a home phone listed in the database.

3. Create a table that contains the percentage of alums who have contributed at least $1,000 lifetime to your school for each score level for each class year decile. Table 1 is an example of such a table for School A.

Table 2: Percentage of Alumni at Each Simple Score Level at Each Class Year Decile Who Have Contributed at Least $1,000 Lifetime to School A

 

4. Create a three dimensional chart that conveys the same information contained in the table. Figure 1 is an example of such a chart for School A.

In the rest of this piece we’ll be showing tables and charts from seven other very diverse schools that look quite similar to the ones you’ve just seen. At the end, we’ll step back and talk about the importance of what emerges from these charts. We’ll also offer advice on how to explain your own tables and charts to colleagues and bosses.

If you think the above table and chart are clear, go ahead and start browsing through what we’ve laid out for the other seven schools. However, if you’re not completely sure you understand the table and the chart, see if the following hypothetical questions and answers help:

Question: “Okay, I’m looking at Table 2 where it shows 53% for alums in Decile 1 who have a score of 2. Could you just clarify what that means?”

Answer. “That means that 53% of the oldest alums at the school who have both a home phone and an email listed in the database have given at least $1,000 lifetime to the school.”

Question. “Then … that means if I look to the far left in that same row where it shows 29% … that means that 29% of the oldest alums at the school who have neither a home phone nor an email listed in the database have given at least $1,000 lifetime to the school?”

Answer. “Exactly.”

Question. “So those older alums who have a score of 2 are way better givers than those older alums who have a score of 0?”

Answer. “That’s how we see it.”

Question. “I notice that in the younger deciles, regardless of the score, there are a lot of 0 percentages or very low percentages. What’s going on there?”

Answer. “Two things. One, most younger alums don’t have the wherewithal to make big gifts. They need years, sometimes many years, to get their financial legs under them. The second thing? Over the last seven years or so, we’ve looked at the lifetime giving rates of hundreds and hundreds of four-year higher education institutions. The news is not good. In many of them, well over half of the solicitable alums have never given their alma maters a penny.”

Question. “So, maybe for my school, it might be good to lower that giving amount to something like ‘has given at least $500 lifetime’ rather than $1,000 lifetime?”

Answer. Absolutely. There’s nothing sacrosanct about the thousand dollar level that we chose for this piece. You can certainly lower the amount, but you can also raise the amount. In fact, if you told us you were going to try several different amounts, we’d say, “Fantastic!”

Okay, let’s go ahead and have you browse through the rest of the tables and charts for the seven schools we mentioned earlier. Then you can compare your thoughts on what you’ve seen with what we think is going on here.

(Note: After looking at a few of the tables and charts, you may find yourself saying, “Okay, guys. Think I got the idea here.” If so, go ahead and fast forward to our comments.)

Table 3: Percentage of Alumni at Each Simple Score Level at Each Class Year Decile Who Have Contributed at Least $1,000 Lifetime to School B

 

Table 4: Percentage of Alumni at Each Simple Score Level at Each Class Year Decile Who Have Contributed at Least $1,000 Lifetime to School C

Table 5: Percentage of Alumni at Each Simple Score Level at Each Class Year Decile Who Have Contributed at Least $1,000 Lifetime to School D

Table 6: Percentage of Alumni at Each Simple Score Level at Each Class Year Decile Who Have Contributed at Least $1,000 Lifetime to School E

Table 7: Percentage of Alumni at Each Simple Score Level at Each Class Year Decile Who Have Contributed at Least $1,000 Lifetime to School F

Table 8: Percentage of Alumni at Each Simple Score Level at Each Class Year Decile Who Have Contributed at Least $1,000 Lifetime to School G

Table 9: Percentage of Alumni at Each Simple Score Level at Each Class Year Decile Who Have Contributed at Least $1,000 Lifetime to School H

Definitely a lot of tables and charts. Here’s what we see in them:

• We’ve gone through the material you’ve just seen many times. Our eyes have always been drawn to the charts; we use the tables for back-up. Even though we’re data geeks, we almost always find charts more compelling than tables. That is most certainly the case here.

• We find the patterns in the charts across the seven schools remarkably similar. (We could have included examples from scores of other schools. The patterns would have looked the same.)

• The schools differ markedly in terms of giving levels. For example, the alums in School C are clearly quite generous in contrast to the alums in School F. (Compare Figure 3 with Figure 6.)

• We’ve never seen an exception to one of the obvious patterns we see in these data: The longer alums have been out of school, the more money they have given to their school.

• The “time out of school” pattern notwithstanding, we continue to be taken by the huge differences in giving levels (especially among older alums) across the levels of a very simple score. School G is a prime example. Look at Figure 7 and look at Table 8. Any way you look at these data, it’s obvious that alums who have even a score of “1” (either a home phone listed or an email listed, but not both) are far better givers than alums who have neither listed.

Now we’d like to deal with an often advanced argument against what you see here. It’s not at all uncommon for us to hear skeptics say: “Well, of course alumni on whom we have more personal information are going to be better givers. In fact we often get that information when they make a gift. You could even say that amount of giving and amount of personal information are pretty much the same thing.”

We disagree for at least two reasons:

Amount of personal information and giving in any alumni database are never the same thing. If you have doubts about our assertion, the best way to dispel those doubts is to look in your own alumni database. Create the same simple score we have for this piece. Then look at the percentage of alums for each of the three levels of the score. You will find plenty of alums who have a score of 0 who have given you something, and you will find plenty of alums with a score of 2 who have given you nothing at all.

We have yet to encounter a school where the IT folks can definitively say how an email address or a home phone number got into the database for every alum. Why is that the case? Because email addresses and home phone numbers find their way into alumni database in a variety of ways. Yes, sometimes they are provided by the alum when he or she makes a gift. But there are other ways. To name a few:

• Alums (givers or not) can provide that information when they respond to surveys or requests for information to update directories.
• There are forms that alums fill out when they attend a school sponsored event that ask for this kind of information.
• There are vendors who supply this kind of information.

Now here’s the kicker. Your reactions to everything you’ve seen in this piece are critical. If you’re going to go to a skeptical boss to try to make a case for scouring your alumni database for new candidates for major giving, we think you need to have several reactions to what we’ve laid out here:

1. “WOW!” Not, “Oh, that’s interesting.” It’s gotta be, “WOW!” Trust us on this one.

2. You have to be champing at the bit to create the same kinds of tables and charts that you’ve seen here for your own data.

3. You have to look at Table 2 (that we’ve recreated below) and imagine it represents your own data.

Table 2: Percentage of Alumni at Each Simple Score Level at Each Class Year Decile Who Have Contributed at Least $1,000 Lifetime to School A

Then you have to start saying things like:

“Okay, I’m looking at the third class year decile. These are alums who graduated between 1977 and 1983. Twenty-five percent of them with a score of 2 have given us at least $1,000 lifetime. But what about the 75% who haven’t yet reached that level? Aren’t they going to be much better bets for bigger giving than the 94% of those with a score of 0 who haven’t yet reached the $1,000 level?”

“A score that goes from 0 to 2? Really? What about a much more sophisticated score that’s based on lots more information than just email listed and home phone listed? Wouldn’t it make sense to build a score like that and look at the giving levels for that more sophisticated score across the class year deciles?”

If your reactions have been similar to the ones we’ve just presented, you’re probably getting very close to trying to making your case to the higher-ups. Of course, how you make that case will depend on who you’ll be talking to, who you are, and situational factors that you’re aware of and we’re not. But here are a few general suggestions:

Your first step should be making up the charts and figures for your own data. Maybe you have the skills to do this on your own. If not, find a technical person to do it for you. In addition to having the right skills, this person should think doing it would be cool and won’t take forever to finish it.

Choose the right person to show our stuff and your stuff to. More and more we’re hearing people in advancement say, “We just got a new VP who really believes in analytics. We think she may be really receptive to this kind of approach.” Obviously, that’s the kind of person you want to approach. If you have a stodgy boss in between you and that VP, find a way around your boss. There’s lots of ways to do that.

Do what mystery writers do; use the weapon of surprise. Whoever the boss you go to is, we’d recommend that you show them this piece first. After you know they’ve read it, ask them what they thought of it. If they say anything remotely similar to: “I wonder what our data looks like,” you say, “Funny you should ask.”

Whatever your reactions to this piece have been, we’d love to hear them.