M&A strategy for acquiring companies or being acquired. Due diligence, valuation, integration, and deal structure. Use when evaluating acquisitions, preparing for acquisition, M&A due diligence, integration planning, or deal negotiation.
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M&A strategy for acquiring companies or being acquired. Due diligence, valuation, integration, and deal structure. Use when evaluating acquisitions, preparing for acquisition, M&A due diligence, integration planning, or deal negotiation.
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</> SKILL.md
ma-playbook.SKILL.md
---name: "ma-playbook"
description: "M&A strategy for acquiring companies or being acquired. Due diligence, valuation, integration, and deal structure. Use when evaluating acquisitions, preparing for acquisition, M&A due diligence, integration planning, or deal negotiation."
license: MIT
metadata:
version: 1.0.0
author: Alireza Rezvani
category: c-level
domain: ma-strategy
updated: 2026-03-05
---# M&A Playbook
Frameworks for both sides of M&A: acquiring companies and being acquired.
## Keywords
M&A, mergers and acquisitions, due diligence, acquisition, acqui-hire, integration, deal structure, valuation, LOI, term sheet, earnout
## Quick Start
**Acquiring:** Start with strategic rationale → target screening → due diligence → valuation → negotiation → integration.
**Being Acquired:** Start with readiness assessment → data room prep → advisor selection → negotiation → transition.
## When You're Acquiring
### Strategic Rationale (answer before anything else)
- **Buy vs Build:** Can you build this faster/cheaper? If yes, don't acquire.
- **Acqui-hire vs Product vs Market:** What are you really buying? Talent? Technology? Customers?
- **Integration complexity:** How hard is it to merge this into your company?
| Customers | Churn rate, NPS, contract terms | High churn, short contracts |
### Valuation Approaches
The ranges below are **illustrative, not current market data** — always verify against current market comps before using them in a model or negotiation.
- **Revenue multiple:** Industry-dependent (illustrative range: 2-15x ARR for SaaS, varying with growth rate, NRR, and rate environment)
- **Comparable transactions:** What similar companies sold for — the most defensible anchor
- **DCF:** For profitable companies only (most startups: use multiples)
- **Acqui-hire:** Illustrative range: $1-3M per engineer in hot talent markets
**Sources to verify against (check the latest edition):** the SaaS Capital Index (private SaaS revenue multiples, updated monthly), Software Equity Group (SEG) Annual/Quarterly SaaS M&A Reports (transaction multiples), and Aventis Advisors' SaaS valuation multiples reports. Cross-check at least two before anchoring a price.
### Integration Frameworks
See `references/integration-playbook.md` for the 100-day integration plan.
| Earnout | Milestone definitions, measurement period | Cash-heavy vs earnout-heavy split |
| Lockup | Duration, conditions | Your replaceability |
| Rep & warranties | Scope of liability | Escrow vs indemnification cap |
| Employee retention | Who gets offers, at what terms | Key person dependencies |
## Red Flags (Both Sides)
- No clear strategic rationale beyond "it's a good deal"
- Culture clash visible during due diligence and ignored
- Key people not locked in before close
- Integration plan doesn't exist or is "we'll figure it out"
- Valuation based on projections, not actuals
## Verification Loop (before any LOI or signature)
This skill frames the deal; two sibling skills verify it. Hand off — don't duplicate:
1. **Legal terms** → `general-counsel-advisor`: run the LOI/term sheet through `../general-counsel-advisor/scripts/term_sheet_analyzer.py` (12-dimension 0-100 score) and the definitive docs through `../general-counsel-advisor/scripts/contract_risk_scanner.py` (12 founder-killer patterns: earnout traps, uncapped indemnity, vague IP, etc.). Any 🔴 finding goes to outside counsel before signing.
2. **Data diligence** → `chief-data-officer-advisor`: run `../chief-data-officer-advisor/scripts/ai_training_data_audit.py` (training-data rights, GDPR Art. 6 basis) and `../chief-data-officer-advisor/scripts/data_asset_valuator.py` (data-asset value, M&A multiplier with carve-out penalties) on the target's data estate. Undocumented consent provenance is a price-reduction or walk-away item.
3. **Valuation math** → `cfo-advisor` tools for the quantitative model; this playbook stays qualitative.
Loop the findings back into the negotiation-points table above before the next counter.
