CoolData blog

8 November 2011

An opportunity not to screw up

Filed under: predictive analytics, Private sector — Tags: , , , — kevinmacdonell @ 1:18 pm

It’s taken me a while to catch my breath after my recent return from Seattle, where I attended the inaugural DRIVE Conference, hosted by the University of Washington on Oct 26-27. DRIVE stands for Data, Reporting, Information and Visualization Exchange, which gives you an idea how diverse the group of 80 or 90 attendees was. I had conversations with people working in IT/Info Management, annual giving, prospect research, reporting, advancement services, data analysis — a real cross-section of disciplines that rarely meet in one room even within their own institutions.

I found an interesting thread weaving through the career histories of the people I met, one that I haven’t encountered in Canada so much: A lot of these people came to the non-profit world from the for-profit sector. Some of them were squeezed out by the recession; some didn’t feel secure in their jobs and fled of their own accord.

Meeting people who used to work as analysts for banks and telecom companies, I asked myself, “Wow, is this not an amazing opportunity?”

Hear me out, and tell me if I’m wrong about this. I’d honestly like to know.

Downsized or not, these are people who have taken a pay cut to work with us. As the economy recovers, some of them will return to the private sector. But I’m optimistic we can retain a lot of them, because it might not hinge on paying them high salaries so much as paying them our attention.

What do nonprofits have in spades? Meaning. We have meaningful work to do. Anyone who has cares that extend beyond getting a paycheque derives happiness from knowing that their work has real results in people’s lives, regardless of the sector they work in.

But a warning: This is a time-limited offer! These people have had enough time to realize that the nonprofit sector is stuck in the 1950s. The “innovations” that excite us make them yawn. They are restless to make investments and changes that will enable organizations to be effective in carrying out their missions. And we’d better listen, or frustrate them into leaving. The stakes are rather high.

I’d like to make a bold prediction, a prediction not based on modelling or statistics. I think our sector is about to undergo a transformation which will bring more progress in data-driven decision making in the next five years than we’ve seen in the last twenty. Provided, that is, we do not flush this opportunity down the toilet.


17 April 2010

Models outside of fundraising

Filed under: Model building, Private sector — Tags: , , , — kevinmacdonell @ 2:13 pm

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

Today’s post strays well outside the realm of nonprofit fundraising, so feel free to give it a miss. It’s all about predictive modeling, though, so if you’re still with me, please indulge me while I stray. (And toward the end, I will bring the discussion back to nonprofit fundraising.)

I work in university advancement, but most of my working life (so far) was spent in newspapers and magazines, as a writer and editor. When my mind returns to my old work these days, I think more about the business side of things, in particular the subscription department, more properly known as circulation.

It was while working for a magazine publisher that I encountered my first database. I developed and maintained a very simple database (more of a flat file, really) for indexing editorial content for later retrieval. Another database, a more sophisticated one which I had nothing to do with, was used to manage subscriptions and mailings.

Having rediscovered the database in a whole new way via predictive modeling for nonprofits, I can’t help wonder what sorts of things I could do for a business such as my former employer. What about a magazine (or newspaper) subscription predictive model? Why not?

Some things a publisher might want to predict include:

  • Who is most likely to renew. Or most likely to commit to a multi-year subscription.
  • Who is most at risk of letting their subscription lapse.
  • Which lapsed subscribers are most likely to be enticed back.
  • Who is most at risk of cancelling their subscription.
  • Who is most likely to purchase a gift subscription.
  • Who is most likely to be successfully cross-promoted to another magazine put out by the same publisher.
  • Who is most likely to purchase books or other promoted products.

Applied to promotions, a renewal-propensity model would help target dollars in the most cost-effective way. The publisher could cut back on expensive incentives to the most loyal subscribers, who are going to renew anyway. As well, former subscribers who are least likely to return can be proactively identified as a dead loss, and cut loose. That would free up dollars to target current at-risk subscribers, and to lapsed subscribers who are most likely to return.

Potential predictor variables for a subscriber model include:

  • Geography (from address)
  • Rural/urban (from postal code)
  • Payment method
  • Gift subs purchased
  • Source of original sub (newsstand sale, referred by friend, received gift sub)
  • Lapses in subs
  • Phone number present
  • Books purchased
  • Letters to the magazine
  • Surveying data
  • Complaints on file
  • Gender (imputed from first name or name prefix)
  • Subscribes to other magazines
  • Has placed an ad
  • Free-text comment fields
  • Various patterns in subscription behaviour
  • Has ever had an issue returned undeliverable

Let me bring this back to fundraising for non-profits. Think about all the different kinds of magazines you see on the newsstand. Imagine how different the subscriber bases are from each other! The characteristic attributes of those database constituents are incredibly specific to geography, lifestyle, hobbies, political leanings – you name it. It isn’t hard to imagine that having a rural postal code is predictive of renewing a subscription to a gardening magazine, but no one would ever leap to the conclusion that therefore a rural code is predictive for, say, The New Yorker, or any other type of magazine.

So why would we accept any assumptions about what is predictive of giving to our institutions? Are we all alike? Nope!

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