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.

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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.

How to learn data analysis: Focus on the business

A few months ago I received an email from a prospect researcher working for a prominent theatre company. He wanted to learn how to do data mining and some basic predictive modeling, and asked me to suggest resources, courses, or people he could contact. 

I didn’t respond to his email for several days. I didn’t really have that much to tell him — he had covered so many of the bases already. He’d read the  book “Data Mining for Fund Raisers,”  by Peter Wylie, as well as “Fundraising Analytics: Using Data to Guide Strategy,” by Joshua Birkholz. He follows this blog, and he keeps up with postings on the Prospect-DMM list. He had dug up and read articles on the topic in the newsletter published by his professional association (APRA). And he’d even taken two statistics course — those were a long time ago, but he had retained a basic understanding of the terms and concepts used in modeling.

He was already better prepared than I was when I started learning predictive modeling in earnest. But as it happened, I had a blog post in draft form (one of many — most never see the light of day) which was loosely about what elements a person needs to become a data analyst. I quoted a version of this paragraph in my response to him:

There are three required elements for pursuing data analysis. The first and most important is curiosity, and finding joy in discovery. The second is being shown how to do things, or having the initiative to find out how to do things. The third is a business need for the work.

My correspondent had the first element covered. As for the second element, I suggested to him that he was more than ready to obtain one-on-one training. All that was missing was defining the business need … that urgent question or problem that data analysis is suited for.

Any analysis project begins with formulating the right question. But that’s also an effective way to begin learning how to do data analysis in the first place. Knowing what your goal is brings relevance, urgency and focus to the activity of learning.

Reflect on your own learning experiences over the years: Your schooling, courses you’ve taken, books and manuals you’ve worked your way through. More than likely, this third element was mostly absent. When we were young, perhaps relevance was not the most important thing: We just had to absorb some foundational concepts, and that was that. Education can be tough, because there is no satisfying answer to the question, “What is the point of learning this?” The point might be real enough, but its reality belongs to a seemingly distant future.

Now that we’re older, learning is a completely different game, in good ways and bad. On the bad side, daily demands and mundane tasks squeeze out most opportunities for learning. Getting something done seems so much more concrete than developing our potential. 

On the good side, now we have all kinds of purposes! We know what the point is. The problems we need to solve are not the contrived and abstract examples we encountered in textbooks. They are real and up close: We need to engage alumni, we need to raise more money, we need, we need, we need.

The key, then, is to harness your learning to one or more of these business needs. Formulate an urgent question, and engage in the struggle to answer it using data. Observe what happens then … Suddenly professional development isn’t such an open-ended activity that is easily put off by other things. When you ask for help, your questions are now specific and concrete, which is the best way to generate response on forums such as Prospect-DMM. When you turn to a book or an internet search, you’re looking for just one thing, not a general understanding.

You aren’t trying to learn it all. You’re just taking the next step toward answering your question. Acquiring skills and knowledge will be a natural byproduct of what should be a stimulating challenge. It’s the only way to learn.

 

When less data is more, in predictive modelling

When I started doing predictive modelling, I was keenly interested in picking the best and coolest predictor variables. As my understanding deepened, I turned my attention to how to define the dependent variable in order to really get at what I was trying to predict. More recently, however, I’ve been thinking about refining or limiting the population of constituents to be scored, and how that can help the model.

What difference does it make who gets a propensity score? Up until maybe a year ago, I wasn’t too concerned. Sure, probably no 22-year-old graduate had ever entered a planned giving agreement, but I didn’t see any harm in applying a score to all our alumni, even our youngest.

Lately, I’m not so sure. Using the example of a planned gift propensity model, the problem is this: Young alumni don’t just get a score; they also influence how the model is trained. If all your current expectancies were at least 50 before they decided to make a bequest, and half your alumni are under 30 years old, then one of the major distinctions your model will make is based on age. ANY alum over 50 is going to score well, regardless of whether he or she has any affinity to the institution, simply because 100% of your target is in that age group.

The model is doing the right thing by giving higher scores to older alumni. If ages in the sample range from 21 to 100+, then age as a variable will undoubtedly contribute to a large chunk of the model’s ability to “explain” the target. But this hardly tells us anything we didn’t already know. We KNOW that alumni don’t make bequest arrangements at age 22, so why include them in the model?

It’s not just the fact that their having a score is irrelevant. I’m concerned about allowing good predictor variables to interact with ‘Age’ in a way that compromises their effectiveness. Variables are being moderated by ‘Age’, without the benefit of improving the model in a way that we get what we want out of it.

Note that we don’t have to explicitly enter ‘Age’ as a variable in the model for young alumni to influence the outcome in undesirable ways. Here’s an example, using event attendance as a predictor:

Let’s say a lot of very young alumni and some very elderly constituents attend their class reunions. The older alumni who attend reunions are probably more likely than their non-attending classmates to enter into planned giving agreements — for my institution, that is definitely the case. On the other hand, the young alumni who attend reunions are probably no more or less likely than their non-attending peers to consider planned giving — no one that age is a serious prospect. What happens to ‘event attendance’ as a predictor in which the dependent variable is ‘Current planned giving expectancy’? … Because a lot of young alumni who are not members of the target variable attended events, the attribute of being an event attendee will be associated with NOT being a planned giving expectancy. Or at the very least, it will considerably dilute the positive association between predictor and target found among older alumni.

I confirmed this recently using some partly made-up data. The data file started out as real alumni data and included age, a flag for who is a current expectancy, and a flag for ‘event attendee’. I massaged it a bit by artificially bumping up the number of alumni under the age of 50 who were coded as having attended an event, to create a scenario in which an institution’s events are equally popular with young and old alike. In a simple regression model with the entire alumni file included in the sample, ‘event attendance’ was weakly associated with being a planned giving expectancy. When I limited the sample to alumni 50 years of age and older, however, the R squared statistic doubled. (That is, event attendance was about twice as effective at explaining the target.) Conversely, when I limited the sample to under-50s, R squared was nearly zero.

True, I had to tamper with the data in order to get this result. But even had I not, there would still have been many under-50 event attendees, and their presence in the file would still have reduced the observed correlation between event attendance and planned giving propensity, to no useful end.

You probably already know that it’s best not to lump deceased constituents in with living ones, or non-alumni along with alumni, or corporations and foundations along with persons. They are completely distinct entities. But depending on what you’re trying to predict, your population can fruitfully be split along other, more subtle distinctions. Here are a few:

• For donor acquisition models, in which the target value is “newly-acquired donor”, exclude all renewed donors. You strictly want to have only newly-acquired donors and never-donors in your model. Your good prospects for conversion are the never-donors who most resemble the newly-acquired donors. Renewed donors don’t serve any purpose in such a model and will muddy the waters considerably.
• Conversely, remove never-donors from models that predict major giving and leadership-level annual giving. Those higher-level donors tend not to emerge out of thin air: They have giving histories.
• Looking at ‘Age’ again … making distinctions based on age applies to major-gift propensity models just as it does to planned giving propensity: Very young people do not make large gifts. Look at your data to find out at what age donors were when they first gave $1,000, say. This will help inform what your cutoff should be.
• When building models specifically for Phonathon, whether donor-acquisition or contact likelihood, remove constituents who are coded Do Not Call or who do not have a valid phone number in the database, or who are unlikely to be called (international alumni, perhaps).
• Exclude international alumni from event attendance or volunteering likelihood models, if you never offer involvement opportunities outside your own country or continent.

Those are just examples. As for general principles, I think both of the following conditions must be met in order for you to gain from excluding a group of constituents from your model. By a “group” I mean any collection of individuals who share a certain trait. Choose to exclude IF:

1. Nearly 100% of constituents with the trait fall outside the target behaviour (that is, the behaviour you are trying to predict); AND,
2. Having a score for people with that trait is irrelevant (that is, their scores will not result in any action being taken with them, even if a score is very low or very high).

You would apply the “rules” like this … You’re building a model to predict who is most likely to answer the phone, for use by Phonathon, and you’re wondering what to do with a bunch of alumni who are coded Do Not Call. Well, it stands to reason that 1) people with this trait will have little or no phone contact history in the database (the target behaviour), and 2) people with this trait won’t be called, even if they have a very high contact-likelihood score. The verdict is “exclude.”

It’s not often you’ll hear me say that less (data) is more. Fewer cases in your data file will in fact tend to depress your model’s R squared. But your ultimate goal is not to maximize R squared — it’s to produce a model that does what you want. Fitting the data is a good thing, but only when you have the right data.

For agile data mining, start with the basics

Lately I’ve been telling people that one of the big hurdles to implementing predictive analytics in higher education advancement is the “project mentality.” We too often think of each data mining initiative as a project, something with a beginning and end. We’d be far better off to think in terms of “process” — something iterative, always improving, and never-ending. We also need to think of it as a process with a fairly tight cycle: Deploy it, let it work for a bit, then quickly evaluate, and tweak, or scrap it completely and start over. The whole cycle works over the course of weeks, not months or years.

Here’s how it sometimes goes wrong, in five steps:

1. Someone has the bright idea to launch a “major donor predictive modelling project.” Fantastic! A committee is struck. They put their heads together and agree on a list of variables that they believe are most likely to be predictive of major giving.
2. They submit a request to their information management people, or whomever toils in extracting stuff from the database. Emails and phone calls fly back and forth over what EXACTLY THE HECK the data mining team is looking for.
3. Finally, a massive Excel file is delivered, a thing the likes of which would never exist in nature — like the unstable, man-made elements on the nether fringes of the Periodic Table. More meetings are held to come to agreement about what to do about multiple duplicate rows in the data, and what to do about empty cells. The committee thinks maybe the IT people need to fix the file. Ummm — no!
4. Half of the data mining team then spends considerable time in pursuit of a data file that gleams in its cleanliness and perfection. The other half is no longer sure what the goal of the project was.
5. Somehow, a model is created and the records are scored by the one team member left standing. Unfortunately, a year has passed and the person for whom the model was built has left for a new job in California. Her replacement refers to the model as “astrology.”

Allow me a few observations that follow from these five stages:

1. Successful models are rarely produced by committee, and variables cannot be pre-selected by popular agreement and intuition — although certainly experience is a valuable source of clues.
2. Submitting requests to someone else for data, having to define exactly what it is you want, and then waiting for the request to be fulfilled — all of that is DEATH to creative data exploration.
3. A massive, one-time, all-or-nothing data suction job is probably not the ideal starting point. Neither is handling an Excel file with 200,000 rows and a hundred columns.
4. Perfect data is not a realistic goal, and is not a prerequisite for fruitful data mining.
5. A year is too long. The cycle has to be much, much tighter than that.

And finally, here are some concrete steps, based on the observations, again point-for-point:

1. If you’re interested in data mining, try going it alone. Ask for help when you need it, but you’ll make faster progress if you explore on your own or in a team of no more than two or three like-minded people. Don’t tell anyone you’re launching a “project,” and don’t promise deliverables unless you know what you’re doing.
2. Learn how to build simple queries to pull data from your database. Get IT to set you up. Figure out how to pull a file of IDs along with sum of all their hard-credit giving. Then, pull that AND something else — anything else. Email address, class year, marital status, whatever. Practice, get comfortable with how your data is stored and how to limit it to what you want.
3. Look into stats software, and learn some of the most common stats terms. Read up on correlation in particular. Build larger files for analysis in the stats software rather than in Excel. Read, read, read. Play, play, play.
4. Think in terms of pattern detection, and don’t get hung up on the validity of individual data points.
5. If you’ve done steps 1 to 4, you have the foundations in place for being an agile data miner.

Mind you, it could take considerable time — months, maybe even years — to get really comfortable with the basics, especially if data mining is a sideline to your “real” job.  But success and agility does depend on being able to work independently, being able to snag data on a whim, being able to understand a bit of what is going on in your software, having the freedom to play and explore, and losing notions about data that come from the business analysis and reporting side. In other words, the basics.

Call attempt limits? You need propensity scores

A couple of weeks ago I shared some early results from our calling program that showed how very high-scoring alumni (for propensity to give by phone) can be counted on to give, and give generously, even after multiple attempts to reach them. If they have a high score, keep calling them! Yes, contact rates will decline, for sure. But these prospects are still likely to give if you can get them on the phone, making the extra effort worthwhile.

For the other three-quarters of your prospects, it’s a different story. You may still want to call them, but keeping those phones ringing all year long is not going to pay off, even if you have the luxury of being able to do so.

This is ground I’ve already covered, but I think it bears repeating, and I’ve created some charts that illustrate the point in a different way. Have a look at this chart, which shows pledge percentage rates for the 6th, 7th, 8th, 9th and 10th decile score, at four stages of call attempts:

This chart is based on data from more than 6,600 phone conversations. How are we to interpret it? Let’s start with the top line, in blue, which represents prospects in the top 10% (decile) of alumni for propensity to give by phone, as determined by the predictive model:

• Almost 38% of 10th-decile alumni who were contacted on the very first call attempt made a pledge.
• Moving to the next dot on the blue line, we see that almost 37% of the 10th-decile alumni who were contacted on the 2nd or 3rd attempt made a pledge.
• The pledge rate slips a little more, to 36%, if the prospect picked up the phone on attempts 4 through 7.
• And finally, almost 26% of them pledged if it took more than 7 attempts to reach them.

That’s the first line. The other lines take different paths. The 9s and 8s start much lower than the 10s, but pledge percentages actually rise with the number of call attempts. They will fall back to earth — just not yet! As for the lower deciles, the 7s and 6s, they start relatively low and dwindle to zero.

So what does all this tell me? I am less interested in how each decile ranks at the start of calling (one or two attempts), because it’s no surprise to me that the 10th decile gives at twice the rate as the 9th decile, and that pledge rates fall with each step down in the score. I’ve seen that before.

What really interests me is what happens when we’ve made many repeated attempts to call. That the 8s and 9s have pledge rates that increase with the number of call attempts is pretty strange stuff, but the fact is: 26 alumni with a score of 9 made a pledge only after we called them 8 or 9 or maybe 15 times.

Whether it’s worth it to make that many call attempts is up to you. It depends on contact rates, and what it costs to make all those calls. But one thing is certain: If I’m going to call repeatedly, I’d better be calling the top three deciles, because if I keep on flogging the segments with a score of 6, I’m not going to do very well.

So what about contact rates?

Here’s another chart that shows what percentage of each score decile’s prospects have been successfully reached at the same four levels of call attempts. (Click on chart for full size.)

What does it mean? Compare the lowest decile called so far (Decile 6) with the highest decile (10). About 14% of 6s answered the phone on the first try, compared with about 19% of the 10s. That’s not a big difference: In fact, contact rates are similar across the scores for the first attempt. But the similarity ends there. After the first attempt, the lower scoring alumni have steadily decreasing rates of contact. The same is true of the higher-scoring alumni, but the difference is that some of them are still answering their phones on the 8th call. More than 4% of 10s were reached on the 8th call or greater.

The bottom line is, the propensity score is your biggest asset in setting appropriate call attempt limits. Yes, Renewal prospects are more likely to give than Acquisition prospects. But that’s not enough to go by. Are you going to call every last Renewal prospect before thinking about acquiring new donors? I wouldn’t recommend it — not if you care about long-term growth and not just this year’s totals. And because contact rates decline as attempts increase (regardless of score), you’re going to end up making a LOT of phone calls to find those gifts that will make up your goal.

My earlier post on the same subject is here. I am spending a lot of time on this, because I don’t see any of this being written about by the well-known experts in Phonathon fundraising. Why that is, I do not know.

Turning people into numbers?

(Image used via Creative Commons license. Click image for source.)

I tend to hear the same objections from presentation audiences, my own and others’. They’re not objections so much as questions, and very good questions, and always welcome. But no one yet has voiced a reservation that I know some must be thinking: This predictive modeling stuff, it’s all so … impersonal.

We already work in a profession that refers to human beings as “prospects” and “suspects”. Doesn’t sticking scores and labels on people perpetuate a certain clinical coolness underlying how fundraising is carried out today? Predictive modeling sounds like bar-coding, profiling, and commodifying people as if they were cattle destined for the table. Maybe we can be so busy studying our numbers and charts that we lose our connection with the donor, and with our mission.

Apologies in advance for setting up a straw man argument. But sometimes I imagine I see the thought forming behind someone’s furrowed brow, and wish it would be brought into the open so we can discuss it. So here we go.

(First of all, how many fundraising offices do you know that carry out their work with “clinical coolness”? We should be so lucky!)

More seriously: Data mining and predictive modeling will never interfere with the human-to-human relationship of asking for a gift, whether it’s a student Phonathon caller seeking an annual gift, or a Planned Giving Officer discussing someone’s ultimate wishes for the fruit of a lifetime of work. It’s a data-free zone.

What predictive modeling does is help bring fundraiser and would-be donor together, by increasing the odds (sometimes dramatically increasing the odds) that the meeting of minds will successfully converge on a gift.

Here’s how I frame it when I talk about predictive modeling to an audience that knows nothing about it. If all we know about a constituent is their giving history (or lack of it), we’re treating everyone the same. Is one non-donor just as likely as another to be convinced to make an annual gift? Is one $50-a-year donor just as likely as another to respond to an appeal to double their pledge this year, or be receptive to having a conversation with a Planned Giving Officer?

The answers are No, No, and NO!

What I say is, “Everyone is an individual.” If they played sports as a student, if they lived on campus, if they attended an event — we can know these things and act accordingly, based on what they tell us about their engagement with our institution. We just have to tune in and listen.

“Everyone is an individual.” Catchy, eh? Well, it’s trite, but it’s true — and it’s no less true for data miners than it is for anyone else.