Most comp programs that break don't break loudly. They drift. A data transformation shifts upstream. A plan rule that worked in Q1 stops fitting by Q3. A CS comp structure designed for one motion quietly accumulates edge cases. The payout runs, no error messages appear, and the team moves on.

This session is not about catastrophic comp failure. It's about the harder problem: building a program resilient enough to catch what accurate-looking outputs can hide. Kimberly Fairchild from Sprout Social shares how she built and maintained a comp program spanning 600 quota-carrying reps across AE and CS teams in multiple global markets — and what it took to move from a program that functioned to one that was genuinely reliable. The session covers institutional knowledge risk, CS comp as the new frontier for comp teams, and the operational discipline of proactive accuracy over reactive correction.

Key Takeaways

• Why "no errors" and "correct" are not the same thing — and how to close that gap operationally before the next payout cycle
• How CS comp (NDR, expansion MRR, floor guarantees) creates accuracy challenges that traditional AE plan structures don't expose
• What institutional knowledge risk looks like in a scaling global comp program — and the decisions that reduce it
• The discipline of pre-payout accuracy checks vs. post-payout escalation management
• How a complex data stack creates comp risk at every handoff — and what owning those handoffs actually requires