b2b-revenue-forecasting 0.2.2

A Python framework for hierarchical B2B sales quota cascading and pipeline reconciliation.

B2B Revenue Forecasting (b2b_revenue_forecasting)

An open-source Python framework designed mathematically for Enterprise RevOps and Data Strategy teams.

Unlike traditional bottom-up time-series libraries (which are strictly built for B2C retail/inventory forecasting and rely on mathematical averages), this package is explicitly architected to handle the realities of B2B enterprise sales: Hierarchical Quotas, Managerial Cascading, Pipeline Health Analysis, and "Sandbagging" Biases.

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🚀 Features

Module	Purpose
SalesHierarchy	Build flexible org charts as DAGs from flat CRM data — supports 3-level startups to 10-level enterprises
QuotaCascader	Distribute macro-targets top-down using rolling N-quarter capacity models with configurable managerial hedges
CommitReconciler	Detect sandbagging and "happy ears" bias via historical Bias Quotients, then auto-correct forecasts
PipelineAdjuster	Diagnose pipeline health with per-region thresholds and redistribute IC quotas using zero-sum logic

What's New in v0.2.0

• PipelineAdjuster: Post-cascade pipeline health analyzer with diagnose() and adjust() modes
• Flexible quarter support: QuotaCascader now auto-discovers any number of _Attainment columns (4, 8, 12 quarters)
• New IC handling: Partial-history imputation and equal-share allocation for brand-new hires
• CRO overrides: Lock specific IC quotas via new_ic_overrides to bypass the algorithm
• Per-node hedging: Apply different hedge multipliers to different regions/managers
• GitHub Actions CI/CD: Automated testing on Python 3.9–3.12

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📦 Installation

pip install b2b-revenue-forecasting

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💻 Quickstart

1. Build the Org Hierarchy

import pandas as pd
from b2b_revenue_forecasting.hierarchy import SalesHierarchy

# ⚠️ Use keep_default_na=False if your data has 'NA' as a region name
df = pd.read_csv('your_crm_data.csv', keep_default_na=False)

# Works with any depth: 3 levels or 10 levels
hierarchy = SalesHierarchy()
hierarchy.from_dataframe(
    df, 
    path_cols=['Global', 'Region', 'RVP', 'Director', 'Manager', 'IC'], 
    metrics_cols=['Q1_Attainment', 'Q2_Attainment', 'Q3_Attainment', 'Q4_Attainment',
                  'Current_Pipeline']
)

print(f"Nodes: {len(hierarchy.graph.nodes)}")
print(f"ICs:   {len(hierarchy.get_leaves('Global_Corp'))}")

2. Cascade Quotas Top-Down

from b2b_revenue_forecasting.quota_cascader import QuotaCascader

cascader = QuotaCascader(hierarchy)

# Basic: distribute $100M evenly by historical capacity
quotas = cascader.cascade_quota('Global_Corp', 100_000_000.0)

# With 5% hedge at every management level (compounds: 1.05^5 ≈ 27.6% overassignment)
quotas = cascader.cascade_quota('Global_Corp', 100_000_000.0, hedge_multiplier=1.05)

# Per-node hedge: NA gets aggressive 10%, others standard 5%
quotas = cascader.cascade_quota('Global_Corp', 100_000_000.0, hedge_multiplier={
    'Global_Corp': 1.05, 'NA': 1.10, 'EMEA': 1.05, 'APAC': 1.05
})

# CRO override: strategic hire gets exactly $500K regardless of history
quotas = cascader.cascade_quota('Global_Corp', 100_000_000.0,
    hedge_multiplier=1.05,
    new_ic_overrides={'IC_Strategic_Hire': 500_000.0}
)

3. Detect & Fix Forecasting Bias

from b2b_revenue_forecasting.commit_reconciler import CommitReconciler

historical = pd.DataFrame({
    'Manager_ID':              ['Mgr_A', 'Mgr_A', 'Mgr_B', 'Mgr_B'],
    'Historical_Commit':       [200_000,  250_000, 300_000,  350_000],
    'Historical_Actual_Closed': [300_000,  375_000, 270_000,  280_000],
})

reconciler = CommitReconciler(historical)

# Mgr_A is a sandbagger (bias = 1.5x) — commit inflated automatically
adjusted = reconciler.reconcile_forecast('Mgr_A', current_commit=100_000)
# → $150,000

# Blend with ML baseline (50/50 average)
blended = reconciler.reconcile_forecast('Mgr_A', 100_000, machine_forecast=120_000)
# → $135,000

4. Pipeline Health Diagnosis & Redistribution

from b2b_revenue_forecasting.pipeline_adjuster import PipelineAdjuster

adjuster = PipelineAdjuster(hierarchy, quotas, pipeline_attr='Current_Pipeline')

# Configure per-region coverage thresholds (ICs inherit from ancestors)
thresholds = {
    'NA':       {'healthy': 1.5, 'at_risk': 0.8},
    'EMEA':     {'healthy': 2.5, 'at_risk': 1.2},
    'APAC':     {'healthy': 3.0, 'at_risk': 1.5},
    '_default': {'healthy': 2.0, 'at_risk': 1.0}
}

# Diagnose — returns a DataFrame with risk status for every node
diagnosis = adjuster.diagnose(thresholds)
print(diagnosis.groupby('Risk_Status')['Node'].count())

# Flag-only mode — returns original quotas unchanged (for pre-approval review)
flagged = adjuster.adjust(mode='flag_only', coverage_thresholds=thresholds)

# Redistribute mode — zero-sum IC adjustment within each manager's team
adjusted = adjuster.adjust(
    mode='redistribute',
    coverage_thresholds=thresholds,
    max_adjustment_pct=0.20,                          # ±20% cap per IC
    locked_nodes={'IC_Protected': 500_000.0}           # CRO-locked ICs excluded
)
# ✅ Manager totals preserved | ✅ Donors give, receivers get | ✅ 20% cap enforced

---

🧠 Key Concepts

Managerial Hedge (Overassignment Buffer)

A multiplier applied at each management level to create mathematical safety. A 5% hedge across 5 layers compounds to ~27.6% total overassignment (1.05⁵), ensuring the enterprise hits its number even if some ICs miss.

Bias Quotient

Bias Quotient = Σ(Actual Closed) / Σ(Committed)

• > 1.0 = Sandbagger (closes more than committed → inflate their forecast)
• = 1.0 = Neutral
• < 1.0 = Happy Ears (over-promises → deflate their forecast)

Pipeline Coverage Ratio

Coverage = Current Pipeline / Cascaded Quota

Coverage	Status	Action
≥ healthy threshold	🟢 Healthy	May donate quota
≥ at_risk threshold	🟡 Moderate	No action
≥ 1.0	🟠 At Risk	May receive quota
< 1.0	🔴 Critical	Urgent — pipeline below target

New IC Handling

Scenario	Behavior
Full history	Proportional by total capacity
Partial history (e.g., 1 of 4 quarters)	Zero quarters imputed with own non-zero average
Brand new (all zeros)	Equal share of team target
CRO override	Fixed amount, excluded from pool

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🧪 Testing

# Run all tests
cd hierarchical_sales_forecasting
pip install -e .
python -m pytest tests/ -v

# Run the full demo
python demo_full_pipeline.py

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📄 Publications

This framework is the subject of peer-reviewed research and technical publications:

Publication	Venue	Status
Hierarchical Sales Target Cascading using DAGs in Python	Towards AI	✅ Published
Graph-Theoretic Approaches to Hierarchical Revenue Target Allocation in B2B Enterprises	SSRN (Preprint)	⏳ Under Review
Graph-Theoretic Approaches to Hierarchical Revenue Target Allocation in B2B Enterprises	Journal of Revenue and Pricing Management (Springer)	⏳ Under Review

If you use this package in your research, please cite:

Karwa, S. (2026). Graph-Theoretic Approaches to Hierarchical Revenue Target Allocation
in B2B Enterprises: A Methodological Framework. SSRN Working Paper.

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📋 Requirements

• Python ≥ 3.8
• pandas ≥ 1.0.0
• networkx ≥ 2.5
• numpy ≥ 1.19.0

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🤝 Contributing

Built explicitly for RevOps analysts, Data Scientists, and VP Revenue Operations executing scaling go-to-market strategies. Contributions, issues, and pull requests are warmly welcomed!

• Report bugs: GitHub Issues
• Source code: GitHub

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📄 License

MIT License — see LICENSE for details.