How to Write a Buyer’s Deal Memo for Marketplaces and SaaS

Create concise, repeatable deal memos that speed decisions and reduce risk — templates, data sources, automation tips, and a ready checklist to implement today.

Buy-side deal memos summarize a target’s value, risks, and investment thesis so stakeholders can decide quickly. This guide shows what to include, how to extract and automate data, run sensitivity scenarios, and embed memos into your process.

• TL;DR: essential memo elements, data sources, automated comps, risk scenarios, templates, and workflow integration.
• Practical examples and compact tables for valuation inputs and metrics.
• Common pitfalls, remedies, and an implementation checklist to operationalize memos.

Quick answer — one-paragraph summary

Use a one-paragraph “quick answer” at the top that states the deal type, ask/offer, headline valuation range, three primary value drivers, and the top 1–2 risks and mitigations — this gives executives the short conclusion they need to act.

What a buyer’s deal memo must include

Keep memos lean and structured so readers can scan and then dive where needed. Aim for a single page summary + 2–4 annexes (financials, cap table, diligence checklist, model).

• Header: Target name, date, author, deal stage, recommendation (pass/hold/buy +/-).
• Quick answer: One-paragraph verdict (see earlier section).
• Investment thesis & value drivers: 3 bullets describing why this business is attractive.
• Key financials & KPIs: ARR, revenue run-rate, growth %, LTV:CAC, gross margin, churn, CAC payback, EBITDA (or adjusted).
• Valuation range & comps: multiple-based and DCF ranges, top comps and rationale.
• Risks & mitigations: 3–5 material risks and specific mitigations or contingency triggers.
• Deal terms & structure: proposed price, earnouts, escrow, key covenants, closing conditions.
• Action items & timeline: next steps, owners, deadlines.
• Annexes: supporting data extracts, model, diligence checklist, legal exceptions.

Extract and structure listing data (sources & tools)

Accurate, structured data is the backbone of a deal memo. Collect raw inputs, normalize them, and store them in a central place for reuse.

• Primary sources: seller-provided data room (revenue by month, MRR, churn cohorts), accounting exports (CSV/QuickBooks/Xero), analytics (GA/Amplitude/Segment), payment processors (Stripe/Paddle), and marketing platforms.
• Public sources for comps: Crunchbase, PitchBook, BuiltWith, SimilarWeb, AppAnnie/Store metrics, and marketplace listing history (Flippa, MicroAcquire copies).
• Verification tools: Plausibility checks via bank statements, Stripe/PayPal reconciliations, and basic forensic checks (login timestamps, user lists sampled with NDAs).
• Data tools: ETL into Google Sheets or a small warehouse (BigQuery/Snowflake) and use data connectors (Stitch/Fivetran) for repeated pulls.

Automate valuation and comps (methods & metrics)

Automating valuation calculations reduces human error and speeds iteration. Build templates that accept normalized inputs and output ranges, sensitivities, and rationale notes.

• Valuation methods to automate: revenue multiple approach (ARR × multiple range), precedent comps, and a simple DCF with 3–5 year forecast plus terminal value.
• Key metrics to feed models: ARR, YoY growth, gross margin, churn, CAC, LTV, NRR, and EBITDA adjustments.
• Multiples: derive a multiple distribution from 5–10 nearest comps, then show median, 25th, 75th percentiles rather than a single number.
• Model outputs: implied purchase price ranges, implied IRR (if modeling returns), payback months, and sensitivity tables.

Assess risks and run scenarios (sensitivity analysis)

Quantify how sensitive valuation and returns are to changes in core inputs. Present scenario tables that executives can scan quickly.

• Define base, downside, and upside scenarios (inputs for growth, churn, margin, multiple).
• Run tornado charts or simple sensitivity tables showing valuation vs. +/- 10–30% changes in each input.
• Model triggers tied to deal structure: e.g., if churn rises above X% or revenue declines Y% then reduce earnout or extend escrow.

Craft repeatable prompts & memo templates

Templates and prompts make memos consistent and allow junior analysts to produce usable drafts fast. Combine a fillable memo plus a set of automation prompts.

• Memo template fields: header, quick answer, thesis bullets, key metrics table, valuation range, risks, terms, action items, annex list.
• Automated prompt examples: “Summarize top 3 revenue drivers from this CSV and list months with >10% YoY growth.” Use tools that accept CSV or JSON inputs.
• Use prompt snippets for generative summarization of user reviews, churn reasons, or feature requests to surface qualitative risks.

Example prompt:
"Given monthly MRR CSV, output ARR, last-12-month growth %, average churn, and 3-month trend summary in bullets."

Integrate into your workflow (tools, automation, roles)

Decide ownership, cadence, and tools: who prepares the memo, who signs off, and which systems host the canonical memo and model.

• Roles: Analyst (data extraction & draft), Associate (modeling & scenario), Partner/Head (final recommendation & signatures), Legal (terms & diligence exceptions).
• Tools: Google Sheets or a BI tool for live models, Notion/Confluence for memo hosting, Slack/email automation for notifications, and Git-like versioning for models where possible.
• Automation: scheduled pulls for active listings, Git-style PR review for memo updates, and templates in your CRM for attaching memos to opportunities.

Common pitfalls and how to avoid them

• Pitfall: Overloading memos with raw data. Remedy: Keep one-page summary and put raw extracts in annexes or data store.
• Pitfall: Relying on single comp or multiple. Remedy: Use a comp distribution and show percentile ranges.
• Pitfall: Unverified revenue. Remedy: Reconcile payments, bank statements, and run sampled user verification under NDA.
• Pitfall: Missing operational risks (concentration, single engineer). Remedy: Add operational checklist and mitigation terms (escrow/earnout).
• Pitfall: No process ownership. Remedy: Assign roles and enforce cadence with templates and deadlines.

Implementation checklist

• Create a one-page memo template and annex structure.
• Build a normalized data ingestion (Sheets or warehouse) for repeated pulls.
• Automate valuation template with multiple and DCF tabs.
• Define roles & SLAs for memo production and sign-off.
• Establish verification steps for revenue and key customers.
• Set up scenario/sensitivity outputs and tie them to deal terms (escrow/earnout triggers).

FAQ

• Q: How long should a memo take to prepare?
  
  A: With templates and verified inputs, initial drafts should take 1–2 days; full diligence may take weeks.
• Q: Which valuation method is best?
  
  A: Use both multiples (market signal) and a simple DCF (cashflow logic) and present a combined range.
• Q: How many comps are enough?
  
  A: Aim for 5–10 nearest comps; fewer if no close matches, but always show distribution, not just a single comparable.
• Q: Should memos include raw customer data?
  
  A: No — include summarized metrics and provide raw data under NDA in annexes or the data room.
• Q: How to handle conflicting seller data?
  
  A: Flag discrepancies, prioritize verified payment/accounting sources, and list unresolved items as diligence contingencies.