Pharma incentive compensation analytics adds an intelligence layer on top of existing IC engines (Varicent, Xactly, Excel) so they don’t just calculate payouts, but actually explain them. The post dives into why reps build shadow spreadsheets—geographic inequity, data gaps, opaque plans, and risky Excel processes—and how AI + a semantic layer + conversational access + agentic workflows can investigate payout variance, monitor fairness, simulate plan changes, and catch data issues before statements go out. It also outlines practical use cases (automated variance investigation, fairness monitoring, scenario planning, data validation, plan simulation), a phased 9–13 month implementation approach, and the ROI metrics that show reduced disputes, faster resolution times, and higher rep trust.
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