ma-playbook

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?

Due Diligence Checklist

Domain	Key Questions	Red Flags
Financial	Revenue quality, customer concentration, burn rate	>30% revenue from 1 customer
Technical	Code quality, tech debt, architecture fit	Monolith with no tests
Legal	IP ownership, pending litigation, contracts	Key IP owned by individuals
People	Key person risk, culture fit, retention risk	Founders have no lockup/earnout
Market	Market position, competitive threats	Declining market share
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.

When You're Being Acquired

Readiness Signals

• Inbound interest from strategic buyers
• Market consolidation happening around you
• Fundraising becomes harder than operating
• Founder ready for a transition

Preparation (6-12 months before)

1. Clean up financials (audited if possible)
2. Document all IP and contracts
3. Reduce customer concentration
4. Lock up key employees
5. Build the data room
6. Engage an M&A advisor

Negotiation Points

Term	What to Watch	Your Leverage
Valuation	Earnout traps (unreachable targets)	Multiple competing offers
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.

Integration with C-Suite Roles

Role	Contribution to M&A
CEO	Strategic rationale, negotiation lead
CFO	Valuation, deal structure, financing
GC	LOI/term sheet review, contract risk scan, regulatory triggers
CDO	Data diligence: training-data rights, data-asset valuation
CTO	Technical due diligence, integration architecture
CHRO	People due diligence, retention planning
COO	Integration execution, process merge
CPO	Product roadmap impact, customer overlap

Resources

• references/integration-playbook.md — 100-day post-acquisition integration plan
• references/due-diligence-checklist.md — comprehensive DD checklist by domain
• ../general-counsel-advisor/SKILL.md — term sheet analyzer + contract risk scanner
• ../chief-data-officer-advisor/SKILL.md — data diligence + data-asset valuation