revenue-operations

Run pipeline_analyzer.py with a JSON file containing deals, quota, stages, and average cycle days. It outputs coverage ratio against quota, stage conversion rates, sales velocity, aging deal flags, and concentration risk in text or JSON format.

Use forecast_accuracy_tracker.py with a JSON file of forecast periods containing forecast and actual values. It calculates MAPE, weighted MAPE, bias direction, accuracy trends, and category breakdowns by rep or segment, with ratings from Excellent (<10%) to Poor (>25%).

The calculator covers six core metrics: Magic Number, LTV:CAC ratio, CAC Payback months, Burn Multiple, Rule of 40, and Net Dollar Retention. Each metric is rated green, yellow, or red against industry benchmark thresholds with percentile placement.

It requires a JSON file with quota, an ordered stages array, average_cycle_days, and a deals array where each deal has id, name, stage, value, age_days, close_date, and owner fields. A sample input file is included in the assets directory.

No, all three scripts use only the Python standard library (argparse, json, sys, datetime, typing). No pip installs or external dependencies are needed to run the analysis.

MAPE becomes unreliable when actual values are zero or near zero, since the error formula divides by actuals. The tracker skips zero-actual periods, and small sample sizes limit trend analysis, which requires at least two periods.