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Healthcare philanthropy predictive modelling tech gets second patent from US Government

Melanie May | 21 July 2023 | News

close up of a doctor's white coat, stethoscope and pens in their pocket. By Darkostojanovic

GOBEL, a consulting and technology firm working exclusively for healthcare philanthropy, has been awarded a second patent from the US Government for its Gratitude to Give™ (G2G) predictive modelling technology.

G2G uses AI and machine learning, and according to GOBEL, is 4-5 times more likely to forecast who will give compared to traditional wealth screening models. 

The technology helps healthcare clients like hospitals, hospices and academic research institutes predict and segment patient populations, identifying the best prospects for annual, planned and major gift prospects. It looks into a patient’s unique experience and uses big data sets to determine their likelihood to give. Results are presented in one platform that offers multiple views so users can identify the best prospects for high wealth and high gratitude. 

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Chad Gobel, Founder and CEO commented:

“We have long believed that gratitude drives the decision to give and wealth drives how much someone gives. Last year’s patent awarded by the U.S. Government was a milestone for our company, but also for all our clients who have long pursued the next level of fundraising, the kind of revenue generation that G2G realises. It was the first step on this journey to formalise our breakthrough methodology and technology. This second patent is an enormous next step.”

The original award last July was a patent on generating gratitude scores, using a machine-learning model. The new patent covers more products using machine learning models to identify gratitude. 

Gobel added:

“This next step in predictive modelling involves the use of more and current data in real time. As people’s information changes, our models change to stay as current as possible. Effective fundraising in the healthcare – or any – industry relies on keeping pace with change. These models will better and more thoroughly refine the information we provide for clients about their current and potential donors.”

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