Defining success is a challenge for many large nonprofit organizations whose missions are broad. Under the Peer Yardstick™ approach, we spend a significant amount of time up front designing logic models to clearly identify what is a successful outcome and how to measure it. To qualify as an outcome measure of organizational success, a measure must be something for which the organization can take credit and can be managed in real time.

Strictly as an example, one might submit that success for an organization could be to strengthen the self-esteem and identity of individuals who participate in programs. However, the organization would have a very tough time assuming credit for an individual's increased identity given that the same individual might be involved or have been involved with other organizations from their synagogue to youth groups to camps. Furthermore, increases in identity can take a long time to build and may require a longitudinal study to capture. For a measure to be manageable, it must be able to be captured in close-to-real-time. It would not greatly help an organization from a management perspective to learn in 2015 that its work in the year 2005 was successful. We will need to find intermediary measures to capture what is happening today: for example, participation of that individual in key programs over the course of the year. We will have to make certain assumptions that if an organization can increase family participation in certain programs, the long-term outcome of strengthened identity will result. These assumptions are necessary to generate manageable measures for which an organization can take credit.

The second key challenge in implementing any performance measurement system is identifying which key activities and factors actually contribute to organizational success (that is, are associated with our outcome measures identified previously). Undoubtedly any conversation on the topic will generate a multitude of hypotheses about what are the key drivers of success for your organization. The Peer Yardstick™ takes advantage of collecting the same data from many affiliates running the same organizational model throughout your network and uses empirical analysis to identify which factors are associated with our measures of overall success, and which are not. We will learn just as much from those factors which turn out not to be associated with success as those which do, because the evidence may debunk some long-held assumptions. While proving causality is nearly impossible in business modeling (though over time, the data can point strongly in a given direction), the more affiliates submit data, the greater our confidence in the associations between activity drivers and outcomes.

Collecting the same data from many affiliates is also leveraged by the Peer Yardstick™ to control for environmental factors which are outside of the organization's hands. Factors such as market size, density of population, and region may all play roles in the success of an affiliate. The Peer Yardstick™ will again employ empirical analysis (provided sufficient data points) to capture the impact of these factors outside of the affiliate's control. For the tool to be useful to affiliates in setting goals and managing more effectively, the tool must not only identify and capture the effect of those activities which an affiliate can control, but also account for the effect of those factors outside of the affiliate's control. Returning to the previous example, we may find that market size and other environmental factors account for 30% of the participation levels an affiliate can achieve; still, that means that 70% of participation is within the affiliate's control as opposed to their blaming all lack of success on its demographics.