clari-core-workflow-b

# Clari Core Workflow: Revenue Analytics ## Overview Build revenue analytics from Clari export data: forecast accuracy tracking, pipeline coverage analysis, rep performance dashboards, and forecast call change detection. ## Prerequisites - Completed `clari-core-workflow-a` (export pipeline) - Historical forecast exports for accuracy tracking - Pandas/SQL for data analysis ## Instructions ### Step 1: Forecast Accuracy Analysis ```python import pandas as pd def calculate_forecast_accuracy( forecasts: list[dict], actuals: list[dict] ) -> pd.DataFrame: df_forecast = pd.DataFrame(forecasts) df_actual = pd.DataFrame(actuals) merged = df_forecast.merge( df_actual[["ownerEmail", "crmClosed"]], on="ownerEmail", suffixes=("_forecast", "_actual"), ) merged["accuracy_pct"] = ( 1 - abs(merged["forecastAmount"] - merged["crmClosed_actual"]) / merged["forecastAmount"] ) * 100 merged["variance"] = merged["crmClosed_actual"] - merged["forecastAmount"] return merged[["ownerName", "forecastAmount", "crmClosed_actual", "accuracy_pct", "variance"]].sort_values("accuracy_pct") ``` ### Step 2: Pipeline Coverage Report ```python def pipeline_coverage_report(entries: list[dict]) -> dict: df = pd.DataFrame(entries) return { "total_pipeline": df["crmTotal"].sum(), "total_closed": df["crmClosed"].sum(), "total_quota": df["quotaAmount"].sum(), "total_forec...