A large, public research institution foundation in the Midwest
In 2020, the foundation’s campaign leadership team wanted to identify foundation trustee candidates and prospective campaign volunteers. As an additional benefit, they wanted to update employment histories and records to identify known relationships within their existing trustee network and expand their prospect pipeline.
The project kickoff began with a series of data and ranking discussions to articulate what success would look like in the ALUMinate deliverable files and customize our engagement scoring algorithms to their data and goals.
First, we deployed the employment enrichment data and shared with this client which prospects had new employers and/or job titles.
Second, through our MATCH’d post-processing we evaluated the quality of the data received from the vendors and parsed out the high confidence data for this client to import into their database. This ensures that when this client’s advancement team engages with these prospects, they know the information they have on that prospect is accurate and up-to-date. This high confidence data was also appended into the data set run through SCORE’d analytics, so the prospects scores we delivered were as accurate as possible.
Through SCORE’d, we analyzed the client and vendor data to provide both wealth and inclination scores, alongside a series of flags, generated by our proprietary algorithms, to filter and find top trustee prospects. Through on-going collaborative discussions with this client, we uncovered another opportunity of how we could help them meet their goals: by identifying and flagging additional prospects for planned giving. This case study is a strong example of how we build customized flags and data enrichment packages to meet the goals of unique projects.