Let's Get Specific

As a Researcher for a university with multiple campuses, we grapple every day with how to maintain a unified identity between two very different campuses in two very different socio-economic areas. Each has its own constituency, strengths, weaknesses, challenges, and threats. While I am a fan of "big-picture" thinking, I do not find it to be as useful in analytics. In cases like this, sometimes data mining is more effective on a local level.

Variables that indicate affinity for one campus will not carry over to the other campus.
For example:
                                          Campus A                                     Campus B
1.  Population served       Immigrant/ blue collar pop.         Upper-middle class pop.
2.  Location                      Urban                                             Suburban
3.  Academic focus          Sciences                                         Liberal Arts & Business

Because the foci of these two campuses and the student populations are so different, so will be those factors that create a higher alumni affinity. If sports participation is an indicator of affinity at Campus B, it may not indicate affinity at Campus A.

This is not to say that a wholistic perspective is useless--quite the contrary. Creating an overall predictive model can help to inform your analytics efforts on the local levels and speed those processes along. But it is often easier to start with a smaller scale and build outwards, refining and redefining your models as you go. The process is longer and more drawn-out; however, with patience the results are worth it.