CoolData blog

26 August 2015

Exploring associations between variables

Filed under: Book, CoolData, Predictor variables — Tags: , , , — kevinmacdonell @ 6:57 pm

 

CoolData has been quiet over the summer, mainly because I’ve been busy writing another book. (Fine weather has a bit to do with it, too.) The book will be for nonprofit and higher education advancement professionals interested in learning how to use multiple regression to build predictive models. Over the next few months, I will adapt various bits from the work-in-progress as individual posts here on CoolData.

 

I’ll have more to say about the book later, so if you’re interested, I suggest subscribing via email (see the box to the right) to have the inside track on this project. (And if you aren’t familiar with the previous book, co-written with Peter Wylie, then have a look here.)

 

A generous chunk of the book is about the specifics of getting your hands dirty with cleaning up your messy data, transforming it to make it suitable for regression analysis, and exploring it for interesting patterns that can strengthen a predictive model.

 

When you import a data set into Data Desk or other statistics package, you are looking at more than just a jumble of variables. All these variables are in a relation; they are linked by patterns. Some variables are strongly associated with each other, others have weaker associations, and some are hardly related to each other at all.

 

What is meant by “association”? A classic example is a data set of children’s weights, heights, and ages. Older children tend to weigh more and be taller than younger children. Heavier children tend to be older and taller than younger children. We say that there is an association between age and weight, between height and weight, and between age and height.

 

Another example: Alumni who are bigger donors tend to attend more reunion events than alumni who give more modestly or don’t give at all. Or put the other way, alumni who attend more events tend to give more than alumni who attend fewer or no events. There is an association between giving and attending events.

 

This sounds simple enough — even obvious. The powerful consequence of these truths is that if we know the value of one variable, we can make a guess at the value of another, as long as the association is valid. So if we know a child’s weight and height, we can make a good guess of his or her age. If we know a child’s height, we can guess weight. If we know how many reunions an alumna has attended, we can make a guess about her level of giving. If we know how much she has given, we can guess whether she’s attended more or fewer reunions than other alumni.

 

We are guessing an unknown value (say, giving) based on a known value (number of events attended). But note that “giving” is not really an unknown. We’ve got everyone’s giving recorded in the database. What is really unknown is an alum’s or a donor’s potential for future giving. With predictive modeling, we are making a guess at what the value of a variable will be in the (near) future, based on the current value of other variables, and the type and degree of association they have had historically.

 

These guesses will be far from perfect. We aren’t going to be bang-on in our guesses of children’s ages based on weight and height, and we certainly aren’t going to be very accurate with our estimates of giving based on event attendance. Even trickier, projecting into the future — estimating potential — is going to be very approximate.

 

Still, our guesses will be informed guesses, as long as the associations we detect are real and not due to random variation in our data. Can we predict exactly how much each donor is going to give over this coming year? No, that would be putting too much confidence in our powers. But we can expect to have plenty of success in ranking our constituents in order by how likely they are to engage in whatever behaviour we are interested in, and that knowledge will be of great value to the business.

 

Looking for potentially useful associations is part of data exploration, which is best done in full hands-on mode! In a future post I will talk about specific techniques for exploring different types of variables.

 

28 June 2015

Data mining in the archives

Filed under: Data, Predictor variables — Tags: , , , — kevinmacdonell @ 6:24 pm

 

When I was a student, I worked in a university archives to earn a little money. I spent many hours penciling consecutive index numbers onto acid-free paper folders, on the ultra-quiet top floor of the library. It was as dull a job as one can imagine.

 

Today’s post is not about that kind of archive. I’m talking about database archive views, also called snapshots. They’re useful for reporting and business intelligence, but they can also play a role in predictive modelling.

 

What is an archive view?

 

Think of a basic stat such as “number of living alumni”. This number changes constantly as new alumni join the fold and others are identified as deceased. A straightforward query will tell you how many living alumni there are, but that number will be out of date tomorrow. What if someone asks you how many living alumni you had a year ago? Then it’s necessary to take grad dates and death dates into account in order to generate an estimate. Or, you look the number up in previously-reported statistics.

 

A database archive view makes such reporting relatively easy by preserving the exact status of a record at regular points in time. The ideal archive is a materialized view in a data warehouse. On a given schedule (yearly, quarterly, or even monthly), an automated process adds fresh rows to an archive table that keeps getting longer and longer. You’re likely reliant on central IT services to set it up.

 

“Number of living alumni” is an important denominator for such key ratios as the percentage of alumni for whom you have contact information (mail, phone, email) and participation rates (the proportion of alumni who give). Every gift is entered as an individual transaction record with a specific date, which enables reporting on historical giving activity. This tends not to be true of contact information. Even though mailing addresses may be added one after another, without overwriting older addresses, the key piece of information is whether the address is coded ‘valid’ or ‘invalid’. This changes all the time, and your database may not preserve a history of those changes. Contact information records may have “To” and “From” dates associated with them, but your query will need to do a lot of relative-date calculations to determine if someone was both alive and had a valid address for any given point in time in the past.

 

An archive table obviates the need for this complex logic, and ours looks like the example below. There’s the unique ID of each individual, the archive date, and a series of binary indicator variables — ‘1’ for “yes, this data is present” or ‘0’ for “this data is absent”.

 

archive

 

Here we see three individuals and how their data has changed over three months in 2015. This is sorted by ID rather than by the order in which the records were added to the archive, so that you can see the journey each person has taken in that time:

 

  • A00001 had no valid email in the database in February and March, but we obtained it in time for the April 1 snapshot.
  • A00002 had no contact information at all until just before March, when a phone append supplied us with a new number. The number proved to be invalid, however, and when we coded it as such in the database, the indicator reverted to zero.
  • A00003 appeared in our data in February and March, but that person was coded deceased in the database before April 1, and was excluded from the April snapshot.

 

That last bullet point is important. Once someone has died, continuing to include them as a row in the archive every month would be a waste of resources. In your reporting software, a simple count of records by archive date will give you the number of living alumni. A simple count of ‘Address Indicator’ will give you the number of alumni with valid addresses. Dividing the number of valid addresses by the number of living alumni (and multiplying by 100) will give you the percentage of living alumni that are addressable for that month. (Reporting software such as Tableau will make very quick work of all this.)

 

Because an archive view preserves changing statuses over time at the level of the individual constituent, it can be used for reporting trends along any slice you choose (age bracket, geography, school, etc.), and can play a role in staff activity/performance reporting and alumni engagement scoring.

 

But enough about archive views themselves. Let’s talk about using them for predictive modelling.

 

In the archive example above, you see a bunch of 0/1 indicator variables. Indicator variables are common in predictive modelling. For example, “Mailing address present” can have one of two states: Present or not present. It’s binary. A frequency breakdown of my data at this point in time looks like this (in Data Desk):

 

freq1

 

About 78% of living alumni have a valid address in the database today — the records with an address indicator of ‘1’. As you might expect, alumni with a good address are more likely to have given than alumni without, and they have much higher lifetime giving on average. In the models I build to predict likelihood to give (and give at higher levels), I almost always make use of this association between contact information and giving.

 

But what about using the archive view data instead? The ‘Address Indicator’ variable breakdown above shows me the current situation, but the archive view adds depth by going back in time. Our own archive has been taking monthly snapshots since December of last year — seven distinct points in time. Summing on “Address Indicator” for each ID shows that large numbers of alumni have either never had a valid address during that time (0 out of 7 months), or always did (7 out of 7). The rest had a change of status during the period, and therefore fall between 0 and 7:

 

freq2

 

A few hundred alumni (387) had a valid address in one out of seven months, 143 had a valid address in two out of seven — and so on. Our archive is still very young; only about 1% of alumni have a count that is not 0 or 7. A year from now, we can expect to see far more constituents populating the middle ground.

 

What is most interesting to me is an apparent relationship between “number of months with valid address” (x-axis) and average lifetime giving (y-axis), even with the relative scarcity of data:

 

chart1

 

My real question, of course, is whether these summed, continuous indicators really make much of a difference in a model over simply using the more familiar binary variables. The answer is “not yet — but someday.” As I noted earlier, only about 1% of living alumni have changed status in the past seven months, so even though this relationship seems linear, the numbers aren’t there to influence the strength of correlation. The Pearson correlation for “Address Indicator” (0/1) and “Lifetime Giving” is 0.186, which is identical to the Pearson correlation for “Address Count” (0 to 7) and “Lifetime Giving.” For all other variables except one, the archive counts have only very slightly higher correlations with Lifetime Giving than the straight indicator variables. (Email is slightly lower.)

 

It’s early days yet. All I can say is that there is potential. Have a look at this pair of regression analyses, both using Lifetime Giving (log-transformed) as the dependent variable. (Click on image for larger view.) In the window on the left, all the independent variables are the regular binary indicator variables. On the right, the independent variables are counts from our archive view. The difference in R-squared from one model to the other is very slight, but headed in the right direction: From 12.7% to 13.0%.

 

regressions

 

Looking back on my student days, I cannot deny that I enjoyed even the quiet, dull hours spent in the university archives. Fortunately, though, and due in no small part to cool data like this, my work since then has been a lot more interesting. Stay tuned for more from our archives.

 

7 January 2015

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):

 

 

new_expec

(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.

22 September 2014

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.

15 January 2013

The cautionary tale of Mr. S. John Doe

A few years ago I met with an experienced Planned Giving professional who had done very well over the years without any help from predictive modeling, and was doing me the courtesy of hearing my ideas. I showed this person a series of charts. Each chart showed a variable and its association with the condition of being a current Planned Giving expectancy. The ultimate goal would have been to consolidate these predictors together as a score, in order to discover new expectancies in that school’s alumni database. The conventional factors of giving history and donor loyalty are important, I conceded, but other engagement-related factors are also very predictive: student activities, alumni involvement, number of degrees, event attendance, and so on.

This person listened politely and was genuinely interested. And then I went too far.

One of my charts showed that there was a strong association between being a Planned Giving expectancy and having a single initial in the First Name field. I noted that, for some unexplained reason, having a preference for a name like “S. John Doe” seemed to be associated with a higher propensity to make a bequest. I thought that was cool.

The response was a laugh. A good-natured laugh, but still — a laugh. “That sounds like astrology!”

I had mistaken polite interest for a slam-dunk, and in my enthusiasm went too far out on a limb. I may have inadvertently caused the minting of a new data-mining skeptic. (Eventually, the professional retired after completing a successful career in Planned Giving, and having managed to avoid hearing much more about predictive modeling.)

At the time, I had hastened to explain that what we were looking at were correlations — loose, non-causal relationships among various characteristics, some of them non-intuitive or, as in this case, seemingly nonsensical. I also explained that the linkage was probably due to other variables (age and sex being prime candidates). Just because it’s without explanation doesn’t mean it’s not useful. But I suppose the damage was done. You win some, you lose some.

Although some of the power (and fun) of predictive modeling rests on the sometimes non-intuitive and unexplained nature of predictor variables, I now think it’s best to frame any presentation to a general audience in terms of what they think of as “common sense”. Limiting, yes. But safer. Unless you think your listener is really picking up what you’re laying down, keep it simple, keep it intuitive, and keep it grounded.

So much for sell jobs. Let’s get back to the data … What ABOUT that “first-initial” variable? Does it really mean anything, or is it just noise? Is it astrology?

I’ve got this data set in front of me — all alumni with at least some giving in the past ten years. I see that 1.2% percent of all donors have a first initial at the front of their name. When I look at the subset of the records that are current Planned Giving expectancies, I see that 4.6% have a single-initial first name. In other words, Planned Giving expectancies are almost four times as likely as all other donors to have a name that starts with a single initial. The data file is fairly large — more than 17,000 records — and the difference is statistically significant.

What can explain this? When I think of a person whose first name is an initial and who tends to go by their middle name, the image that comes to mind is that of an elderly male with a higher than average income — like a retired judge, say. For each of the variables Age and Male, there is in fact a small positive association with having a one-character first name. Yet, when I account for both ‘Age’ and ‘Male’ in a regression analysis, the condition of having a leading initial is still significant and still has explanatory power for being a Planned Giving expectancy.

I can’t think of any other underlying reasons for the connection with Planned Giving. Even when I continue to add more and more independent variables to the regression, this strange predictor hangs in there, as sturdy as ever. So, it’s certainly interesting, and I usually at least look at it while building models.

On the other hand … perhaps there is some justification for the verdict of “astrology” (that is, “nonsense”). The data set I have here may be large, but the number of Planned Giving expectancies is less than 500 — and 4.6% of 500 is not very many records. Regardless of whether p ≤ 0.0001, it could still be just one of those things. I’ve also learned that complex models are not better than simple ones, particularly when trying to predict something hard like Planned Giving propensity. A quirky variable that suggests no potential causal pathway makes me wary of the possibility of overfitting the noise in my data and missing the signal.

Maybe it’s useful, maybe it’s not. Either way, whether I call it “cool” or not will depend on who I’m talking to.

13 November 2012

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.

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