CoolData blog

5 May 2015

Predictive modelling for the nonprofit organization

Filed under: Non-university settings, Why predictive modeling? — Tags: , , — kevinmacdonell @ 6:15 pm

 

Predictive modelling enables an organization to focus its limited resources of time and money where they will earn the best return, using data. People who work at nonprofits can probably relate to the “limited resources” part of that statement. But is it a given that predictive analytics is possible or necessary for any organization?

 

This week, I’m in Kingston, Ontario to speak at the conference of the Association of Fundraising Professionals, Southeastern Ontario Chapter (AFP SEO). As usual I will be talking about how fundraisers can use data. Given the range of organizations represented at this conference, I’m considering questions that a small nonprofit might need to answer before jumping in. They boil down to two concerns, “when” and “what”:

 

When is the tipping point at which it makes sense to employ predictive modelling? And how is that tipping point defined — dollars raised, number of donors, size of database, or what?

 

What kind of data do we need to collect in order to do predictive modelling? How much should we be willing to spend to gather that data? What type of model should we build?

 

These sound like fundamental questions, yet I’ve rarely had to consider them. In higher education advancement, the questions are answered already.

 

In the first case, most universities are already over the tipping point. Even relatively small institutions have more non-donor alumni than they can solicit all at once via mail and phone — it’s just too expensive and it takes too much time. Prioritization is always necessary. Not all universities are using predictive modelling, but all could certainly benefit from doing so.

 

Regarding the second question — what data to collect — alumni databases are typically rich in the types of data useful for gauging affinity and propensity to give. Knowing everyone’s age is a huge advantage, for example. Even if the Advancement office doesn’t have ages for everyone, at least they have class year, which is usually a good proxy for age. Universities don’t always do a great job of tracking key engagement factors (event attendance, volunteering, and so on), but I’ve been fortunate in being able to have enough of this already-existing data with which to build robust models.

 

The situation is different for nonprofits, including small organizations that may not have real databases. (That situation was the topic I wrote about in my previous post: When does a small nonprofit need a database?) One can’t simply assume that predictive modelling is worth the trouble, nor can one assume that the data is available or worth investing in.

 

Fortunately the first question isn’t hard to answer, and I’ve already hinted at it. The tipping point occurs when the size of your constituency is so large that you cannot afford to reach out to all of them simultaneously. Your constituency may consist of any combination of past donors, volunteers, clients of your services, ticket buyers and subscribers, event attendees — anyone who has a reason to be in your database due to some connection with your organization.

 

Here’s an extreme example from the non-alumni charity world. Last year’s ALS Ice-Bucket Challenge already seems like a long time ago (which is the way of any social media-driven frenzy), but the real challenge is now squarely on the shoulders of ALS charities. Their constituency has grown by millions of new donors, but there is no guarantee that this windfall will translate into an elevated level of donor support in the long run. It’s a massive donor-retention problem: Most new donors will not give again, but retaining even a fraction could lead to a sizeable echo of giving. It always makes sense to ask recent donors to give again, but I think it would be incredibly wasteful to attempt reaching out to 2.5 million one-time donors. The organization needs to reach out to the right donors. I have no special insight into what ALS charities are doing, but this scenario screams “predictive modelling” to me. (I’ve written about it here: Your nonprofit’s real ice bucket challenge.)

 

None of us can relate to the ice-bucket thing, because it’s almost unique, but smaller versions of this dilemma abound. Let’s say your theatre company has a database with 20,000 records in it — people who have purchased subscriptions over the years, plus single-ticket buyers, plus all your donors (current and long-lapsed). You plan to run a two-week phone campaign for donations, but there’s no way you can reach everyone with a phone number in that limited time. You need a way to rank your constituents by likelihood to give, in order to maximize your return.

 

(About five years ago, I built a model using data from a symphony orchestra’s database. Among other things, I found that certain combinations of concert series subscriptions were associated with higher levels of giving. So: you don’t need a university alumni database to do this work!)

 

It works with smaller numbers, too. Let’s say your college has 1,000 alumni living in Toronto, and you want to invite them all to an event. Your budget allows a mail piece to be sent to just 250, however. If you have a predictive model for likelihood to attend an event, you can send mail to only the best prospective attendees, and perhaps email the rest.

 

In a reverse scenario, if your charity has 500 donors and you’re fully capable of contacting and visiting them all as often as you like, then there’s no business need for predictive modelling. I would also note that modelling is harder to do with small data sets, entailing  problems such as overfitting. But that’s a technical issue; it’s enough to know that modelling is something to consider only at the point when resources won’t cover the need to engage with your whole constituency.

 

Now for the second question: What data do you need?

 

My first suggestion is that you look to the data you already have. Going back to the example of the symphony orchestra: The data I used actually came from two different systems — one for donor management, the other for ticketing and concert series subscriptions. The key was that donors and concert attendees were each identified with a unique ID that spanned both databases. This allowed me to discover that people who favoured the great Classical composers were better donors than those who liked the “pops” concerts — but that people who attended both were the best donors of all! If the orchestra intended to identify a pool of prospects for leadership gifts, this would be one piece of the ranking score that would help them do it.

 

So: Explore your existing data. And while you’re doing so, don’t assume that messy, old, or incomplete data is not useable. It’s usually worth a look.

 

What about collecting new data? This can be an expensive proposition, and I think it would be risky to gather data just so you can build predictive models. There is no guarantee that what you’re spending time and money to gather is actually correlated with giving or other behaviours. My suggestion would be to gather data that serves operational purposes as well as analytical ones. A good example might be event attendance. If your organization holds a lot of events, you’ll want to keep statistics on attendance and how effective each event was. If you can find ways to record which individuals were at the event (donors, volunteers, community members), you will get this information, plus you will get a valuable input for your models.

 

Surveying is another way organizations can collect useful data for analysis while also serving other purposes. It’s one way to find out how old donors are — a key piece of information. Just be sure that your surveys are not anonymous! In my experience, people are not turned off by non-anonymous surveys so long as you’re not asking deeply personal questions. Offering a chance to win a prize for completing the survey can help.

 

Data you might gather on individuals falls into two general categories: Behaviours and attributes.

 

Behaviours are any type of action people take that might indicate affinity with your organization. Giving is obviously the big one, but other good examples would be event attendance or volunteering, or any type of interaction with your organization.

 

Attributes are just characteristics that prospects happen to have. This includes gender, where a person lives, age, wealth information, and so on.

 

Of the two types, behavioural factors are always the more powerful. You can never go wrong by looking at what people actually do. As the saying has it, people give of their time, talent, and treasure. Focus on those interactions first.

 

People also give of something else that is increasingly valuable: Their attention. If your organization makes use of a broadcast email platform, find out if it tracks opens and click-throughs — not just at the aggregate level, but at the individual level. Some platforms even assign a score to each email address that indicates the level of engagement with your emails. If you run phone campaigns, keep track of who answers the call. The world is so full of distractions, these periods of time when you have someone’s full attention are themselves gifts — and they are directly associated with likelihood to give financially.

 

Attributes are trickier. They can lead you astray with correlations that look real, but aren’t. Age is always a good thing to have, but gender is only sometimes useful. And I would never purchase external data (census and demographic data, for example) for predictive modelling alone. Aggregate data at the ZIP or postal code level is useful for a lot of things, but is not the strongest candidate for a model input. The correlations with giving to your organization will be weak, especially in comparison with the behavioural data you have on individuals.

 

What type of model does it make sense for a nonprofit to try to build first? Any modelling project starts with a clear statement of the business need. Perhaps you want to identify which ticket buyers will convert to donors, or which long-lapsed donors are most likely to respond positively to a phone call, or who among your past clients is most likely to be interested in becoming a volunteer.

 

Whatever it is, the key thing is that you have plenty of historical examples of the behaviour you want to predict. You want to have a big, fat target to aim for. If you want to predict likelihood to attend an event and your database contains 30,000 addressable records, you can be quite successful if 1,000 of those records have some history of attending events — but your model will be a flop if you’ve only got 50. The reason is that you’re trying to identify the behaviours and characteristics that typify the “event attendee,” and then go looking in your “non-attendee” group for those people who share those behaviours and characteristics. The better they fit the profile, the more likely they are to respond to an event invitation. Fifty people is probably not enough to define what is “typical.”

 

So for your first foray into modelling, I would avoid trying to hit very small targets. Major giving and planned giving propensity tend to fall into that category. I know why people choose to start there — because it implies high return on investment — but you would be wise to resist.

 

At this point, someone who’s done some reading may start to obsess about which highly advanced technique to use. But if you’re new to hands-on work, I strongly suggest using a simple method that requires you to study each variable individually, in relation to the outcome you’re trying to model. The best beginning point is to get familiar with comparing groups (attendees vs. non-attendees, donors vs. non-donors, etc.) using means and medians, preferably with the aid of a stats software package. (Peter Wylie’s book, Data Mining for Fundraisers has this covered.) From there, learn a bit more about exploring associations and correlations between variables by looking at scatterplots and using Pearson Product-Moment Correlation. That will set you up well for learning to do multiple linear regression, if you choose to take it that far.

 

In sum: Predictive modeling isn’t for everyone, but you don’t need Big Data or a degree in statistics to get some benefit from it. Start small, and build from there.

 

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18 February 2014

Save our planet

Filed under: Annual Giving, Why predictive modeling? — Tags: , , — kevinmacdonell @ 9:09 pm

You’ve seen those little signs — they’re in every hotel room these days. “Dear Guest,” they say, “Bed sheets that are washed daily in thousands of hotels around the world use millions of gallons of water and a lot of detergent.” The card then goes on to urge you to give some indication that you don’t want your bedding or towels taken away to be laundered.

Presumably millions of small gestures by hotel guests have by now added up to a staggering amount of savings in water, energy and detergent.

It reminds me of what predictive analytics does for a mass-contact area of operation such as Annual Giving. If we all trimmed down the amount of acquisition contacts we make — expending the same amount of effort but only on the people with highest propensity to give, or likelihood to pick up the phone, or greatest chance of opening our email or what-have-you — we’d be doing our bit to collectively conserve a whole lot of human energy, and not a few trees.

With many advancement leaders questioning whether they can continue to justify an expensive Phonathon program that is losing more ground every year, getting serious about focusing resources might just be the saviour of a key acquisition program, to boot.

15 April 2013

What do we do about Phonathon?

Filed under: Alumni, Annual Giving, Phonathon — Tags: , , , — kevinmacdonell @ 5:41 am

I love Phonathon. I love what it does, and I love the data it produces. But sad to say, Phonathon may be the sick old man of fundraising. In fact some have taken its pulse and declared it dead.

A few weeks ago, a Director of Annual Giving named Audra Vaz posted this question to a listserv: “I’m writing to see if any institutions out there have transitioned away from their Phonathon program. If so, how did it affect your Annual Giving program?”

A number of people immediately came to the defence of Phonathon with assurances of the long-term value of calling programs. The responses went something like this: Get rid of Phonathon?? It’s a great point of connection between an institution and its alumni, particularly its younger alumni. It’s the best tool for donor acquisition. It’s a great way to update contact and employment information. Don’t do it!

Audra wasn’t satisfied. “As currently run, it’s expensive and ineffective,” she wrote of her program at Florida Atlantic University in Boca Raton. “It takes up 30% of my budget, brings in less than 2% of Annual Fund donations and only has a 20% ROI. I could use that money for building societies, personal solicitations, and direct mail which is much more effective for us. In a difficult budget year, I cannot be nostalgic and continue to justify the bleed for a program that most institutions do yet hardly any makes money off of. Seems like a bad business model to me.”

I can’t disagree with Audra. Anyone following fundraising listservs knows that, in general, contact rates and productivity are declining year after year. And out of the contacts it does manage to make, Phonathon generates scads of pledges that are never fulfilled, entailing the additional cost of reminder mailings and write-offs. There are those who say that Phonathon should be viewed as an investment and not an expense. I have been inclined to that view myself. The problem is that yes, it IS an expense, and not a small one. If Phonathons create value in all the other ways that the defenders say they do, then where are the numbers to prove it? Where’s the ROI? Audra had numbers; the defenders did not. At strategic planning time, numbers talk louder than opinions.

When I contacted Audra recently to get permission to use her name, she told me she has opted to keep her Phonathon program for now, but will market its services to other university divisions to turn it into a revenue generator (athletics and arts ticket sales, admissions welcome calls, invitations to events, and alumni membership renewals). That sounds like a good idea. I can think of a number of additional ways to keep Phonathon alive and relevant, but since this is a data-related blog I will focus on just two.

1. Stop calling everybody!

At many institutions, Phonathon is used as a mass-contact tool for indiscriminately soliciting anyone the Annual Fund believes might have a pulse. This approach is becoming less and less sustainable. The same question is asked repeatedly on the listservs: “How many times, on average, do you attempt to call alumni non-donors before you retire their call sheet?” And then people give their one-size-fits-all answers: five times, seven times, whatever times per record. Given how graduating classes have increased in size for most institutions, I am not surprised to read that some programs are stretched too thin to call very deeply. As one person wrote recently: “Because of time and resources constraints, we’re lucky to get two attempts in with nondonor/long lapsed alumni.”

I just don’t get it.

We know that people who have attended events are more likely to pick up the phone. We know that alumni who have shared their job title with us are more likely to pick up the phone. We know that alumni who have given us their email address are more likely to pick up the phone. So why in 2013 are schools still expending the same amount of energy on each of their prospective donors as if they were all exactly alike? They are NOT all alike, and these schools are wasting time and money.

If you’ve got automated calling software, you should be adding up the number of times you’ve successfully reached individual alumni over the years (regardless of the call result), and use that data to build predictive models for likelihood to answer the phone. If you don’t have that historical data, you should at least consider an engagement-based scoring system to focus your efforts on alumni who have demonstrated some of the usual signs of affinity: coming to events, sharing contact and employment information, having other family members who are alumni, volunteering, responding to surveys and so on.

A phone contact propensity score (and related models such as donor acquisition likelihood) will allow you to make cuts to your program when and if the time comes. You can feel more confident that you’re trimming the bottom, cutting away the least productive slice of your program.

2. Think outside Phonathon!

Your phone program is a data generation machine, granting you a wide window view on the behaviours of your alumni and donors. I’m not talking just about address updates, as valuable as those are. You know how many times they’ve picked up the phone when they see your ID come up on the display, and you might also know how long they’ve spent on the phone with your student callers. This is not trivial information nor is it of interest only to Phonathon managers.

Relate this behavioural data to other desired behaviours: Are your current big donors characterized by picking up more often? Do your Planned Giving expectancies tend to have longer conversations on average? What about volunteering, mentoring, and other activities? Phone contact history is real, affinity-related data, delivered fresh to you daily, lifting the curtain on who likes you.

(When I say real data, I mean REAL. This is a record of what individuals have actually DONE, not what they’ve stated as a preference in a survey. This data doesn’t lie.)

A few closing thoughts. …

I said earlier that Phonathon has been used (or misused) as a mass-contact tool. Software and automation enables a hired team of students to make a staggering number of phone calls in a very short time. The bulk of long-lapsed and never-donors are approached by phone rather than mail: The cost of a single call attempt seems negligible, so Phonathon managers spread their acquisition efforts as thinly as possible, trying to turn over every last stone.

There’s something to be said about having adequate volume in order to generate new donors, but here’s the problem: The phone is no longer a mass-contact medium. In fact it’s well on its way to becoming a niche medium, handled by a whole new type of device. Some people answer the phone and respond positively to being approached that way, and for that reason phone will be important for as long as there are phones. But the masses are no longer answering.

These days some fundraisers think of email as their new mass-contact medium of choice. Again they must be thinking in terms of cost, since it hardly matters whether you’re sending 1,000 emails or 100,000 emails. And again they’re mistaken in thinking that email is practically free — they’re just not counting the full cost to the institution of the practice of spamming people.

The truth is, there is no reliable mass-contact medium anymore. If email (or phone, or social media) is a great fundraising channel, it’s not because it’s a seemingly cheap way to reach out to thousands of people. It’s a great fundraising channel when, and only when, it reaches out to the right people at the right time.

  1. Alumni and donors are not all the same. They are not defined by their age, address or other demographic groupings. They are individual human beings.
  2. They have preferred channels for communicating and giving.
  3. These preferences are revealed only through observation of past behaviours. Not through self-reporting, not through classification by age or donor status, not by any other indirect means.
  4. We cannot know the real preferences of everyone in our database. Therefore, we model on observed past behaviours to make intelligent guesses about the preferences we don’t already know.
  5. Our models are an improvement on current practice, but they are imperfect. All models are wrong; we will make them better. And we will keep Phonathon healthy and productive.

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.

20 September 2012

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.

26 January 2012

More mistakes I’ve made

Filed under: Best practices, Peter Wylie, Pitfalls, Validation — Tags: , , , — kevinmacdonell @ 1:38 pm

A while back I wrote a couple of posts about mistakes I’ve made in data mining and predictive modelling. (See Four mistakes I have made and When your predictive model sucks.) Today I’m pleased to point out a brand new one.

The last days of work leading up to Christmas had me evaluating my new-donor acquisition models to see how well they’ve been working. Unfortunately, they were not working well. I had hoped — I had expected — to see newly-acquired donors clustered in the upper ranges of the decile scores I had created. Instead they were scattered all along the whole range. A solicitation conducted at random would have performed nearly as well.

Our mailing was restricted by score (roughly the top two deciles only), but our phone solicitation was more broad, so donors came from the whole range of deciles:

Very disappointing. To tell the truth, I had seen this before: A model that does well predicting overall participation, but which fails to identify which non-donors are most likely to convert. I am well past the point of being impressed by a model that tells me what everyone already knows, i.e. that loyal donors are most likely to give again. I want to have confidence that acquisition mail dollars are spent wisely.

So it was back to the drawing board. I considered whether my model was suffering from overfit, whether perhaps I had too many variables, too much random noise, multicolinearity. I studied and rejected one possibility after another. After so much effort, I came rather close to concluding that new-donor acquisition is not just difficult — it might be darn near impossible.

Dire possibility indeed. If you can’t predict conversion, then why bother with any of this?

It was during a phone conversation with Peter Wylie that things suddenly became clear. He asked me one question: How did I define my dependent variable? I checked, and found that my DV was named “Recent Donors.” That’s all it took to find where I had gone wrong.

As the name of the DV suggested, it turned out that the model was trained on a binary variable that flagged anyone who had made a gift in the past two years. The problem was that included everybody: long-time donors and newly-acquired donors alike. The model was highly influenced by the regular donors, and the new donors were lost in the shuffle.

It was a classic case of failing to properly define the question. If my goal was to identify the patterns and characteristics of newly-acquired donors, then I should have limited my DV strictly to non-donors who had recently converted to donors!

So I rebuilt the model, using the same data file and variables I had used to build the original model. This time, however, I pared the sample down to alumni who had never given a cent before fiscal 2009. They were the only alumni I needed to have scores for. Then I redefined my dependent variable so that non-donors who converted, i.e., who made a gift in either fiscal 2009 or 2010, were coded ‘1’, and all others were coded ‘0’. (I used two years of giving data instead of just one in order to have a little more data available for defining the DV.) Finally, I output a new set of decile scores from a binary logistic regression.

A test of the new scores showed that the new model was a vast improvement over the original. How did I test this? Recall that I reused the same data file from the original model. Therefore, it contained no giving data from the current fiscal year; the model was innocent of any knowledge of the future. Compare this breakdown of new donors with the one above:

Much better. Not fan-flippin-tastic, but better.

My error was a basic one — I’ve even cautioned about it in previous posts. Maybe I’m stupid, or maybe I’m just human. But like anyone who works with data, I can figure out when I’m wrong. That’s a huge advantage.

  • Be skeptical about the quality of your work.
  • Evaluate the results of your decisions.
  • Admit your mistakes.
  • Document your mistakes and learn from them.
  • Stay humble.
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