In last month’s column, I set a financial organization on the road to BI Oz with some tips that included monitoring scope, limiting sources, searching for savvy users and leveraging existing toolsets. This month, I will prove my case by looking at success stories in an industry facing budget and resource constraints. The industry is nonprofit, and the companies are membership associations. In addition to budget and resource issues, these organizations increasingly find themselves in a market where diminishing dues revenue and decreased volunteerism are commonplace, and where for-profit organizations increasingly compete for member time, talent and money. By selecting projects wisely and adhering to the constraints highlighted last month, many of these companies are finding that the road to BI nirvana is indeed paved with gold. Some of the successes these associations have experienced are highlighted below.1

The “Profitable” Member

One large membership association in the risk arena has taken an extended view of the term “member.” An analysis of the resulting membership yields interesting results. The original definition of member was pretty standard: any individual person or organization that paid membership dues. Preliminary analysis of product purchases sparked the association to expand that definition to include anyone purchasing services as well as anyone who was actually paying dues.

Now they track employees who work for member companies, actual dues-paying employees of those same companies and dues-paying members who do not belong to an affiliated company. Running BI trend analyses on the purchasing patterns across all of these member classes debunked the widely accepted feeling that dues-paying members were the most profitable.

They found that dues-paying members were senior enough in their careers that they rarely attended the seminars or purchased the lucrative training products. In fact, the majority of products were purchased by individuals who were affiliated with member companies but were not yet dues-paying members themselves. The analysis has caused the association to change several business practices, such as tuning training products to the nonmember segment, targeting a sales message to the dues-paying members and influencing more of their direct-report employees to attend classes.

Keeping Score for Successful Operation

One association serving the utility industry is implementing scorecards that monitor a variety of measures to help it keep pace with a dynamically changing environment and increasing competition for members. In addition to the fairly standard measures of membership, revenue and products sold, they are branching out into nonfinancial measures as well. To keep up with an industry that constantly branches into other market sectors, the organization analyzes its membership directory against those of other associations. It also factors in the demographics of those attending various professional meetings and seminars, including the degree of involvement from senior management of affiliated organizations. These help it gauge the potential for losing members to other associations in various market segments. It also repeatedly surveys the membership and uses the results to calculate member satisfaction, which is included on the scorecard.

Another nonprofit organization that supports a large medical facility is looking at several measures in combination to predict required fundraising amounts into the future. It analyzes historical donations and rate of new donor attraction to determine the net present value of expected pledge amounts for several years into the future. This analysis is then compared against expected collection amounts (also calculated through historical analysis) to determine the amount of funds it needs to raise for the year.

Gauging the Impacts of Change

An association that works with global financial institutions uses BI to perform what-if analysis to determine the impact of changes in the financial services industry, identify the costs of inaction and change sales messages based on predicted member actions. Early efforts were targeted at determining the length of time between when a member takes introductory training classes through when he or she progresses to the more advanced courses. Analysis found that many members advanced only partway through the full complement of courses because of job changes or departmental moves.

Applying that knowledge across the base of members currently in training yielded some interesting conclusions about the potential revenue impacts. Consequently, the organization changed its marketing frequencies for these courses and also reevaluated course content and sequencing. Future plans include the ability to predict impacts on membership, revenues and products sold as companies in the financial sector continue to merge and consolidate. Additionally, they want to look at potential impacts (both in terms of new members and new competition from other associations) as nontraditional organizations such as Wal-Mart start to offer financial services products.

As these success stories show, BI is not just for those with deep pockets. With the right focus, any organization can implement and benefit from analysis applications.

Reference:

  1. Lisa Loftis. “Business Intelligence for Non-Profits: Adding Value to Memberships-Lowering Operating Costs.” Intelligent Solutions, Inc., Fall 2007.

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