How Machine Learning Helped a Non-Profit Use Less Money to Create More Jobs
Data is an extremely powerful tool for businesses, especially if harnessed in a smart, efficient and predictive way. Organizations like JobsOhio, a non-profit that helps companies identify and pursue growth opportunities in Ohio, must parse hundreds of thousands of data points daily to be successful – a process that can prove time-consuming, complicated and a hindrance to business processes and objectives if using outdated data collection methods.
But modern solutions like machine learning, which leverages pattern recognition tools to identify trends in immense amounts of data, could help JobsOhio make valuable, informed decisions in less time and with less money.
While JobsOhio was no stranger to the power of data, they were unsure how they could best use it to prioritize projects, maximize resources, improve close rates and have a larger impact on the Ohio economy. And as a non-profit, they also wanted to do all this with limited funds.
JobsOhio’s funding model is unlike the funding model for any economic development organization in the country in that absolutely no tax dollars or other public dollars are used to support it. Despite being wholly self-funded, JobsOhio has been very effective at generating new jobs in Ohio. For example, in 2019, JobsOhio executed 306 projects generating $1.2B in new payroll, creating 22,770 new jobs.
As effective as they are with their limited funds, they still wanted to do more for the state of Ohio. That meant prioritizing the employers with whom they would negotiate to create, retain or expand operations in Ohio – a very complex challenge given over 32.5 million employers in the United States (250,000 of those in Ohio) spread across over 1,000 industry specialties. JobsOhio realized that the complexity of this problem was a good match for machine learning, but they needed the help of a partner.
Aware of our deep data expertise and proven success with machine learning, JobsOhio engaged Centric Consulting to explore predictive models’ development to help with this prioritization.