A historical semantic analysis of #caseDRIVE
We took a moment today to run every @DRIVExchange tweet through our Natural Language Processor and here's a glimpse of the internally facing tool's results:
This tool helps us gauge how well our natural language engine that powers QuadWrangle is doing. And, scouring through the several dozen tags from nearly 500 tweets by @DRIVExchange, it looks to have done a solid job.
But what have we learned? The top results here are those with the highest weight across the analysis - those that not only appear often, but for which contextual language indicates importance and higher value. So what?
So what? Well, look. We're all technologists and data analysts. And the thing that is MOST important to all of us is people. Matt Gullatta is a leading thinker inside of higher ed. Theresa from CASE. Lori and David Lawson. Our world is about people pushing technology and thusly our institutions to do more.
The same can be said about prospect research. Is it enough to know someone has the financial capacity to give? No. It never is. That's just a way to prioritize time, but not change output. Change comes by knowing what truly matters to someone. In the case of the QuadWrangle platform, we take rich semantic insights into a donor and use it to predict the exact designation(s) they are most likely to give to. Imagine a major gift officer sitting down with a highly qualified donor and knowing what that donor is most likely to want to hear about and give to. That's what we do here and we're excited to share that story with DRIVE/.
See you all in a few short weeks, DRIVERS. It's all of you who truly matter. The tech is just a platform to your awesome human innovation.