"Data Mining for Dummies"

Today as I work through a data-mining proposal, I am going down "memory lane" as I rethink of my own experiences managing & doing these projects, and in turn to the varying approaches and degrees of success that association membership, marketing, and conference departments take with this critical function. I'd like to share with you a few case studies and lessons learned.

I am somewhat odd, in that I actually started my professional career as a computer programmer/analyst in the Consumer Price Index. The old personnel line in the federal government was "we can't afford techies, so we train them." Apparently my background as a failed PhD candidate in economics and editor of my college newspaper gave me a nice combination of left-brain/right-brain aptitude to make me worth training and quickly promoting: I have to say that I learned and did more in database analytics in two short years than I ever did again in many years of association-related work.

What does this have to do with associations? Plenty, I should think. The CPI gave me a great tool set and a perspective that works to some extent with every association I worked with as staff.

• At CPI, infrastructure, mission, and support network all contributed to and benefited from data mining. Our explicit mission was to collect and data on product characteristics and their prices to develop and report a series of monthly indices, so our small army trooped around retail outlets to collect 200,000 price quotes a month; forty of my colleagues would review their work through exception reports; a smaller team of us were engaged in ongoing quality initiatives to ensure that we were measuring inflation properly. On any given day there would be 3 or 4 staff accessing the central database running simulations of alternative methodologies or writing academic-quality papers, and many more reviewing the individual data points to question odd changes or to accept/reject new models and other substitutes in our sample.
• At NAHB I/we did large dataset analysis in support of their Housing Economics journal as we monitored patterns in housing sales, design trends, etc. but the thought of applying this brainpower to marketing didn’t really work for our very large annual meeting or our grassroots/federated structure of membership.
• At ASHP, I worked with many pharmacists on staff who grew up reading the professional literature and many were editors for our AHFS drug information product—a several thousand page annual subscription service that served as a guide for drug use in most hospitals in the world. Yet I was the first person to convert their aging AS/400 database into something we could use to segment the audience to drive marketing plans, budgeting, results and penetration analysis. With a database of 180,000 names but only 30,000 members and maybe 15,000 other customers, we needed this service—we just didn't have it.
• At CRS, we used Pledgemaker to manage a database with 350,000 active donors; another 700,000 former donors;  millions of historical donations; and 40 million contact records created by our acquisition program. Unlike membership organizations, in disaster relief operations, there are donors who lie dormant for years but when a tsunami or earthquake or colossal famine strikes, these donors are assets who don't find it strange to be contacted and who generously give again if you're among the first to contact them. Once we hired staff, purchased FirstLogic and SAS, created a duplicate donor file for analysis, we were able to do far more, saving $250,000 a year on data processing bills, spending some of that money to collaborate with an outside firm to create scoring models for each acquisition campaign to save several million more and improve performance. 

When ASAE released 7 Measures of Success and rightly named data driven decision-making as one of the 7 Measures, I was glad to read it and not surprised at all to see it not have much of an effect at all. I still vividly remember being asked to speak for the Texas Medical Association at Digital Now! a few years ago, as they couldn't travel that year to tell their own story. In my case I have simply been a marketer and association exec who brought his toolkit of aptitudes with him to each new position, just as you do. I often meet and work with staff who quickly identify themselves as "not numbers people" and I totally understand.

However, today there is almost no excuse to NOT have this capability, somewhere inside or outside your organization. We already invest so much in expensive AMS and in the maintenance of our data. In case studies such as TMA, ASHP or CRS there is an incredible return on investment from analyzing and leveraging this data, even if it only occurs occasionally, through data exports and using outside analysts to do the queries, reports, and analysis. Your stored knowledge of your customer base is such an incredible asset waiting to be harvested, it seems a shame that so many of the success stories reflect the contributions of odd staff with "extra skills" in their background rather than a conscious, purposeful effort to harvest the past and thereby predict future behavior.

In future posts I will do much more to explain our methods, philosophies, and illustrate key examples to help others do their own in-house data mining, but for now I wanted to begin with a history lesson...

  -Kevin