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

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

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_forecast": df["forecastAmount"].sum(),
        "coverage_ratio": df["crmTotal"].sum() / df["quotaAmount"].sum()
            if df["quotaAmount"].sum() > 0 else 0,
        "close_rate": df["crmClosed"].sum() / df["crmTotal"].sum()
            if df["crmTotal"].sum() > 0 else 0,
        "attainment_pct": df["crmClosed"].sum() / df["quotaAmount"].sum() * 100
            if df["quotaAmount"].sum() > 0 else 0,
        "at_risk_reps": len(df[df["forecastAmount"] < df["quotaAmount"] * 0.7]),
        "on_track_reps": len(df[df["forecastAmount"] >= df["quotaAmount"] * 0.9]),
    }

Step 3: Forecast Change Detection

def detect_forecast_changes(
    current: list[dict], previous: list[dict], threshold_pct: float = 10.0
) -> list[dict]:
    curr = {e["ownerEmail"]: e for e in current}
    prev = {e["ownerEmail"]: e for e in previous}

    changes = []
    for email, curr_entry in curr.items():
        prev_entry = prev.get(email)
        if not prev_entry:
            continue

        prev_amount = prev_entry["forecastAmount"]
        curr_amount = curr_entry["forecastAmount"]

        if prev_amount == 0:
            continue

        change_pct = ((curr_amount - prev_amount) / prev_amount) * 100

        if abs(change_pct) >= threshold_pct:
            changes.append({
                "rep": curr_entry["ownerName"],
                "previous_forecast": prev_amount,
                "current_forecast": curr_amount,
                "change_pct": round(change_pct, 1),
                "direction": "up" if change_pct > 0 else "down",
            })

    return sorted(changes, key=lambda x: abs(x["change_pct"]), reverse=True)

Step 4: SQL Analytics Queries

-- Forecast accuracy by quarter
SELECT
    time_period,
    owner_name,
    forecast_amount,
    crm_closed AS actual_closed,
    ROUND((1 - ABS(forecast_amount - crm_closed) / NULLIF(forecast_amount, 0)) * 100, 1) AS accuracy_pct
FROM clari_forecasts
WHERE time_period = '2025_Q4'
ORDER BY accuracy_pct DESC;

-- Pipeline coverage trend
SELECT
    time_period,
    SUM(crm_total) / NULLIF(SUM(quota_amount), 0) AS coverage_ratio,
    SUM(crm_closed) / NULLIF(SUM(quota_amount), 0) AS attainment
FROM clari_forecasts
GROUP BY time_period
ORDER BY time_period;

Error Handling

Resources

• Clari API Reference
• Pandas Documentation

Next Steps

For error troubleshooting, see clari-common-errors.