At the most recent Global Leaders Summit, I had the privilege of hearing four world-class speakers present break-through ideas on Alumni engagement. Each idea was driven by technology and social media, and impacted alumni relations, career services, or the advancement world in general.
You can watch the four talks in full below but this week I am focusing on Charlie Cumbaa, who is the EVP Corporate and Product Strategy at Blackbaud and spoke about ‘Predictive Modelling and Alumni Engagement’ (minutes 14-27)
For some time now we have seen the increasing sophistication in the methodology, analytics and use of big data generally to help professional fundraisers do a better job in targeting prospects.
In addition, we have seen case studies in the world of alumni relations of correlations between alumni engagement and financial giving. (See the recent case study I wrote about of Tulane University who have seen over 50% of the 10,000 engaged alums using their career platform being donors.)
In Charlie’s ‘TED-Style’ talk, he raises a new intriguing big data question for the advancement world: what if we could predict which alumni will be engaged?
For 84% of alumni relations professionals, as Charlie cites from a recent CASE survey, their number one goal is to drive alumni engagement. What would happen if we could apply technology and predictive modelling to improve alumni engagement?
What would happen if we could predict which of the alumni in your database had the highest propensity to both be engaged, and critically also the social influence to engage their wider network?
The consequences of smarter targeting of alumni could be to see a better utilization of limited resources and hence maximizing the level of alumni engagement.
Do you agree that it is possible to target alumni based upon their propensity to engage and ability to influence?
How does your education institution prioritize which alumni to focus on in terms of alumni engagement?
Do you agree that big data can play a significant role in your engagement strategies?
I would welcome your thoughts.