Novi Association Management Software (AMS) identifies at-risk members before they lapse with Member Health, the Predict pillar of Amplify, which learns from real renewal outcomes. Member Health blends three models: one trained across associations, one trained on the association's own members and one for each member type. It backtests its predictions against prior years, labels each current member On Track, At Risk or New Member, and shows the healthy and warning signals behind each label. Member data stays on Novi's own infrastructure and is never sent to third-party AI models. Predictions show likely outcomes rather than certainties, so they work best as a guide to where staff should start.

Predictive analytics for member retention uses past member behavior and actual renewal outcomes to estimate how likely each current member is to renew, so staff can address churn before it happens. In Novi Association Management Software (AMS), Member Health applies predictive analytics to every current member, calculating a score between 0 and 1 that represents the probability the member will renew and turning it into a simple status: On Track, At Risk or New Member. Scores are recalculated once a month, so staff focus on lasting trends rather than day-to-day changes.

Warning signs that a member won't renew usually appear as declining activity over time, such as fewer event registrations or purchases, and sometimes in details staff rarely track, such as missing contact information. In Novi Association Management Software (AMS), Member Health looks at two years of quarterly trends along with profile, renewal, event, purchase, survey, committee and continuing education data, and it learns which signals matter for each association. The same signal can point toward renewal for one member and away from it for another, depending on the rest of their record. Warning signs are correlations, not causes, so they work best as conversation starters.

Predictive analytics needs enough real examples of members who renewed and members who lapsed, because a model trained on too few examples can look confident while being wrong. Novi Association Management Software (AMS) builds an association-specific Member Health model once an association has at least 20 expired members and 20 current members who have renewed at least once, so even small associations can qualify, as long as the two groups are reasonably balanced. Rather than show unreliable predictions, Novi keeps Member Health off until then, although an association can turn it on using models trained across all Novi associations. Novi checks eligibility monthly and turns Member Health on automatically when an association qualifies.

Associations should reach out to at-risk members before the renewal invoice goes out, because outreach before the invoice feels like member service while outreach after it feels like a collection call. In Novi Association Management Software (AMS), Member Health flags At Risk members months before renewal, giving staff time to strengthen the relationship. Staff can filter the member list by status and build dynamic groups that sync with Mailchimp or Constant Contact for targeted campaigns. Some teams spend 15 to 20 minutes a day calling At Risk members, while others divide the At Risk list among staff and volunteers for personal check-ins before invoices are sent. Leaders can also use status totals to support renewal forecasts, budgets and board reports.