The same basic information (event data, time, location, etc) is used for:
Higher education institutions rate 'obtaining accurate information for planning and decision-making' and 'reporting and analytics' among their top challenges to acting effectively as a department. The lack of ability to explore existing data sets to gain insight into trends remains a barrier to using technology effectively for the majority of institutions.
Source: 2012 Use of Technology for Development & Alumni Constituent Relations Among CASE Members. July 2012
Source: Third Annual Survey of Social Media in Advancement conducted by CASE, mStoner, and Slover Linett Strategies, August 2012
Rigorous analysis of alumni donor demographic data has the potential to improve donor targeting with the goal of measuring future donor contributions for a given motivation. Logistic regression modeling can be used to estimate the probability that a potential donor will make a donation based on his/her characteristics.
The breadth of available variables can also be interacted to get at specific relationships, e.g., male business majors vs female business majors. Once a base model is developed, the analysis could be extended to capture the increased probability of donation by alumni attending specific events (e.g. watch parties) or associated with a specific chapter. (This presents an opportunity to provide specialized data-driven applications) similar variables could be used in a multivariate linear regression model to estimate the donation amount among those who do choose to donate. In essence, one could use these tools and data to make a statement like the following:
Male business majors from St. Louis who have graduated within the last 20 years are X% more likely to make a donation than the comparison group. Those who regularly attend football watch parties are an additional Y% more likely to donate.
Again, similar statements could be made about the amount of donation when modeling is done on a group of those who have already donated.
The ideal model would extend these analyses across multiple alumni associations at different institutions. This would involve panel data econometric analysis which allows the use of across institution variation in data to strengthen modeling and institution-specific results. Panel data modeling would identify and control for effects unobservable, institution-specific factors, etc.
Mizzou alumni are Z% more likely to donate than the comparison group.
UConn Journalism alumni are T% more likely to donate than other Journalism alumni [from other institutions]
But even this explanation of all of this is tedious and doesn't provide much actionable use, so we want to create the platform that is smart enough to know all that but phrases it more like this: "Call Warren York; he has attended the last 4 watch parties and his donation history suggests he may be open to a renewable gift."
As of November 2014, average open rate is 52.99% (max: 83.33% min: 14.29%) and average click rate is 9.47% (max: 41.67% min: 0%)
There is no research about how the thousands of local groups engage local alumni or grow their effectiveness. So, we are starting the first large-scale survey of their activities which will provide indirect exposure for Alumni Spaces and topical conference lecture topics when the findings are finalized. Launching in December 2014, you can preview the survey here.
We offer all existing Ensemble clients a 50% renewal credit for each group they refer.
Alumni Spaces will be exhibiting at these upcoming conferences for alumni relations:
Starting in January 2015, Alumni Spaces will be hosting a monthly meetup series in New York with local alumni group leaders to facilitate knowledge sharing and co-planning.
We are currently exploring a number of sponsorship opportunities to support local alumni events such as weekly watch parties and annual sports teams.
The Mizzou Alumni Association has been instrumental in facilitating introductions and offering testimonials to other prospective clients. We are also in active conversations with an event management platform and a higher education CMS provider as possible integration/sales partners.
Our price point is a competitive advantage at this stage. The market is dominated by a single player with opaque pricing that is many times higher than ours for even the smallest implementation. This allows us to offer our platform at a significant discount and expand our revenue footprint as we grow our feature set.