If you’re going to embark on a business intelligence initiative, you ought to have these five items in place before you do.
Here’s a brief podcast (about three minutes) on my definition of business intelligence.
Back in May of last year, I wrote the following post, which in essence said “There’s a lot of talk about dashboards, but I’ve encountered very few myself.”
Well, while attending the Aptify Users Conference a few weeks ago, one of the speakers was demonstrating the dashboards available in the Aptify product (which are, quite frankly, pretty slick). But the speaker went on to say “This is one of the most underutilized portions of our system.”
So I’ll ask again, why aren’t more associations using dashboards? Do they not see the value? Or is it just to difficult to get them working correctly?
I’ll be speaking in Chicago next Thursday, October 2, for the Association Forum of Chicagoland. My presentation will be on “Data Mining and Business Intelligence.” You can get all the details here.
This is the last of four case studies on how associations are using business intelligence, about which I presented at a recent ASAE conference. There are a lot of details to these case studies, but since this is a blog, I’m going to keep this at a high level.
The mission of the National Defense Industrial Association is to bring together representatives from the government and the defense industry. NDIA accomplishes this by coordinating over 80 conferences per year. All of these events, as well as NDIA membership, are managed in a single association management system.
NDIA did some “inward looking” and determined that a significant portion of their key buyers audience (government and military personnel) were attending conferences but were not joining as members. NDIA identified over 20,000 individuals within their database with .mil or .gov email addresses that were not members, and developed a process for offering membership to these individuals during the conference registration process.
Ultimately what NDIA did was to develop an online conference registration process that used the registrant’s email address to identify them as a military or government employee. If the email address was .mil or .gov, during the registration process, the system would offer a free membership to the individual, simply by checking a box.
NDIA also developed “one-click” emails that they used in outbound marketing campaigns to sign up these individuals. The result is that in just over a year, NDIA has brought in 7,000 new individual members in this member class, representing a 100% increase.
This is the third of four case studies on how associations are using business intelligence, about which I presented at a recent ASAE conference. There are a lot of details to these case studies, but since this is a blog, I’m going to keep this at a high level.
The Professional Golfers Association includes a national association as well as 41 sections (think of them as chapters). PGA’s sections are the primary hosts of most of the golf events that occur in the US each year. Amazingly, both the PGA national headquarters and the 41 sections share a single membership database. (That story alone is worthy of an article or two.)
Because all of this information resides in one system, PGA had an opportunity to develop a simple yet very effective business intelligence system that would allow them to display key performance indicators (KPIs) to the 41 sections and headquarters. As Larry Green of PGA explained to me, “The sections always wanted to know how they were doing compared to the other sections. Now they can see for themselves.â€
PGA worked with their sections to identify 12 KPIs that would measure the effectiveness of each of the sections (things like course participation). For each KPI, the sections are ranked 1 through 41, and then the 12 KPIs are totaled to provide an overall ranking. All of this information is pulled from the primary membership database, but stored in a separate online business intelligence database that each of the 41 sections can access. The data is updated on a daily basis, and since all the sections share the same membership database, the data is complete and essentially real-time.
Now the sections can instantly view this information and see how they are doing relative to the other sections. And they can learn where they are doing well and where they need to improve, and adjust their programs and activities accordingly.
This is the second of four case studies on how associations are using business intelligence, about which I presented at a recent ASAE conference. There are a lot of details to these case studies, but since this is a blog, I’m going to keep this at a high level.
When Steve Doran arrived at NACUBO, he learned that NACUBO was investing about $19,000 per conference to sell 400 registrations. With a background in direct mail marketing, Steve felt confident he could lower that investment and still fill the 400 seats. Using data that Steve gathered from multiple sources within NACUBO, he was able to reduce his direct mail investment to around $3,000, while still filling all the seats. That was a direct reduction of costs of nearly 85%! So how did he do it?
In a nutshell, Steve developed a five-tiered system that classified his customer base (by organization) by their level of engagement. Using data points from 12 different areas within NACUBO (e.g., attendance at the NACUBO annual meeting, listservers, and benchmarking studies), Steve weighted each of these points of participation and created a total score for each organization. The results were pretty remarkable. Steve determined that of his 10,000+ universe of organizations, more than half were not participating at any appreciable level. Yet up to this point, NACUBO continued to market to them as if they were good buyers.
As a smart marketer, Steve knew that this was the first group to eliminate from future mailings. And while Steve met with some resistance (”What do you mean we’re not going to market to everyone?”), the results certainly speak for themselves. As noted above, NACUBO has cut their marketing expenses by more than 85%, a net savings of over $75,000 in the first year of this program.
I should note here that Steve did not use any fancy business intelligence software beyond an Access database that he built himself. And all of the data he used was pulled from sources within NACUBO.
What I’m most impressed with about this case study is that Steve did this without any fancy software, and he was able to convince the powers that be that marketing to everyone in the same manner was costly and ineffective. By determining who his best customers are, Steve was able to cut his costs and increase his marketing effectiveness.
This is the first of four case studies on how associations are using business intelligence, about which I presented at a recent ASAE conference. There are a lot of details to these case studies, but since this is a blog, I’m going to keep this at a high level.
The Texas Medical Association started using business intelligence several years ago. TMA invested heavily in true business intelligence software. They also hired a consultant to help them implement their BI initiative, using that consultant to help them develop a data warehouse, star schema, and decision cubes.
The results for TMA have been pretty spectacular. Shortly after implementing their BI program, TMA discovered that over 1000 doctors in the state of Texas (their primary membership) were taking advantage of a members-only program, even though they weren’t members of TMA. TMA was able to bring the majority of these doctors into TMA membership, the result being a nearly $500,000 increase to their top-line in dues revenue. That alone was probably worth the initial investment.
But TMA also saw additional benefits. With their decision cubes, they were able to see many different aspects of their membership and potential membership. As a result, TMA was able to alter their product offers and promotions. Since implementing this BI initiative, TMA has moved from an average of netting 300 new members per year to over 1,200 per year. Again, a result well worth the investment.
Of course, TMA had several things working in their favor:
Having said all that, this is a great example of an organization taking a calculated risk on business intelligence, and one that paid off quite well.
I was sitting in a consulting workshop a few weeks ago being led by Alan Weiss. Alan was making the point that the most valuable consulting is the kind that focuses on transformational behavior, rather than transactional behavior. That is, there is more value in helping an organization move to a higher level of performance (transformational) than there is helping an organization to better manage its current state (transactional).
The same applies to managing your association management system. For most organizations, the focus is on the transactions taking place in the database (e.g., sales being made, is the contact data correct, etc.). But to really get value from your database, you have to focus on the transformational. By that I mean leveraging the data within your system to really transform your organization. One way to do this is through business intelligence.
At the recent ASAE Marketing and Membership Conference, I spoke about business intelligence and how associations are using BI to leverage the data they have for improved marketing and membership activities. I presented four case studies on associations successfully using BI, and I’m going to use those four case studies as examples of moving from transactional to transformational. Stay tuned…
My favorite quote from Peter Drucker comes from The Effective Executive (written over 40 years ago!!). On page 5, Drucker writes: “The greatest wisdom not applied to action and behavior is meaningless data.”
Quite simply, what Drucker was saying is that any information that you have that doesn’t cause you to change how you behave is useless. So for example, if your organization knows that certain segments of the membership never attend the annual meeting, yet you continue to market to them as if they do, then your knowledge of that is meaningless. It’s useless.
All organizations must have a willingness to change their behavior based on the data they have. The best business intelligence initiative in the world will fail if the organization implementing it refuses to change its actions or behaviors based on what it learns.
So ask yourself: Is your organization willing to change its behavior based on new information?
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