Automate caseload variance calculations within Monarch to remove manual work, increase accuracy, and provide proactive insights for Account Managers.

Currently, Managers of Onboarding are required to manually calculate caseload variance by entering variables into the Average Calculator spreadsheet, transposing those numbers into the Caseload Calculator in the SSRs, and then drafting emails to Account Managers when variances are detected or when insufficient information prevents calculation. This manual process is time-consuming, error-prone, and delays resolution—we completed 670 caseload verifications last year using this method. Monarch could automate these calculations and generate notifications when a caseload does not match the contracted order. In addition to higher accuracy, Monarch could easily highlight variances at a school level across multiple practitioners and roll these into a customer-level variance view when several schools are involved. Over time, AI integrations could also provide Account Managers with likely causes of variances and recommend next steps based on available data, ensuring more proactive and informed customer conversations. This automation could also integrate with the related practitioner-facing idea (flagging caseload discrepancies), creating a holistic system where both practitioner and system-driven variance detection feed into Customer Success workflows.