Nimble AMS Launches Association-Focused AI Technology for Predictive Analytics
Community Brands, the leading provider of software and payment solutions for associations, nonprofits, and schools announces the launch of a predictive analytics tool, Nimble AMS Predictions, to bring the power of AI (Artificial Intelligence) technology to associations on the Nimble AMS platform through the power of Salesforce Einstein.
Nimble AMS Predictions is hyper-targeted toward innovative trend-setting associations who want to leverage advanced tech to become much more proactive in utilizing member behavior and trends to make faster, smarter decisions. Associations who previously struggled with how to implement an AI solution will find that Nimble AMS Predictions is designed to make AI much more approachable and easier to implement.
Associations’ biggest business challenges include recruiting and retaining members, member engagement, and funding, according to the 2021 Associations Trends report, Building the Next Normal. By creating custom AI models to predict member/non-member behavior and integrating additional data sources with Nimble AMS, associations can finally build a more intelligent solution aimed at their specific strategic initiatives.
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Even better, Nimble AMS takes all the great things that Salesforce develops and puts an association spin on it, so teams don’t have to tackle big technology alone. Insights from predictive analytics help prioritize leads, strengthen relationships, and drive revenue, and they can be utilized to predict many member behaviors – including the potential for member lapse, recruitment and donation possibilities, and ideal targeted education courses.
“The predictive analytics in Nimble AMS helps IACP identify members who are at a high risk of lapsing,” said Chuck Everhart, Business Systems Software Manager, International Association of Chiefs of Police (IACP). “Utilizing the predictive analytics makes it possible to target this group with specific marketing efforts that decrease lapse and increase retention rates.”
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