Automated Churn Risk Prediction in Professional Services

Revenue Institute's system ingests engagement economics from Maconomy, Workday PSA, and Salesforce - utilization rates, project margins, resource allocation, and account activity - then applies prediction models trained on your historical churn patterns, built to flag at-risk clients 60-90 days before renewal. Unlike generic churn tools, it learns which specific combinations of margin compression, consultant turnover, or scope creep predict loss in your firm's business model. The AI surfaces not just risk scores but causation: "This account's utilization dropped 18% and margin is below 20%," enabling Marketing to intervene with precision rather than gut feel.

Yes. The system we deploy runs inside your own environment under your existing permissions, and implements zero-retention policies for AI processing - your engagement and account data never trains external models. Data remains encrypted at rest and in transit, with access controls aligned to your SOX and SEC compliance requirements. For accounting and tax advisory firms, we align with your firm's professional confidentiality and due-diligence standards and support NDA-compliant data governance. All processing occurs within your authorized infrastructure, with audit logs available for compliance review.

Plan for a working system inside the first 100 days. Weeks 1-2 focus on data integration: connecting to Maconomy, Workday PSA, Salesforce, and HubSpot, validating data quality, and aligning schemas. Weeks 3-6 involve model training on your historical engagement and churn data. Weeks 7-10 cover pilot testing with a subset of accounts, validating accuracy against your firm's specific churn patterns. Weeks 11-14 include full rollout, team training, and integration into Marketing workflows. A rollout like this is scoped to show measurable results - validated churn predictions and successful interventions - within 60 days of go-live.

It trains on your firm's own history: which engagements renewed, which churned, and what the project economics looked like in the months before each outcome. Over time it learns your specific churn signatures - maybe margin below a certain threshold predicts loss in your audit practice but not in advisory. That is also why outcome logging matters: every renewal or loss your account teams record sharpens the next quarter's scores.

The pilot runs on a subset of accounts after data integration and model training are complete. It matters most because the model's predictions get validated against renewals and losses your partners already know about - which is what earns the team's trust before the scores start driving real outreach. A rollout like this is scoped to show measurable results within 60 days of go-live.

Firms under $10M in revenue, or teams where the volume is still low enough for one person to handle comfortably - at that scale the math rarely clears, and we will say so. This is built for Professional Services firms of 50-500 people where the work is real enough that the default fix would be another process hire. If you are not sure which side of that line you are on, the free AI Opportunity Assessment will tell you.