clari-core-workflow-b

name	clari-core-workflow-b
description	Build Clari revenue analytics: pipeline coverage, forecast accuracy, and rep performance dashboards from exported data. Use when analyzing forecast accuracy, building attainment reports, or creating executive revenue dashboards. Trigger with phrases like "clari analytics", "clari dashboard", "clari forecast accuracy", "clari pipeline coverage".
allowed-tools	Read, Write, Edit, Bash(python3:*), Grep
version	1.0.0
license	MIT
author	Jeremy Longshore <[e-mail]>
tags	["saas","revenue-intelligence","forecasting","clari"]
compatible-with	claude-code

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: []) -> :
    df = pd.DataFrame(entries)

     {
        : df[].(),
        : df[].(),
        : df[].(),
        : df[].(),
        : df[].() / df[].()
             df[].() >   ,
        : df[].() / df[].()
             df[].() >   ,
        : df[].() / df[].() * 
             df[].() >   ,
        : (df[df[] < df[] * ]),
        : (df[df[] >= df[] * ]),
    }