| 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 <jeremy@intentsolutions.io> |
| 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[] * ]),
}
