Build a Comps Analysis Valuation Model with AI

Tested prompts for comparable company analysis template compared across 5 leading AI models.

BEST BY JUDGE SCORE Claude Haiku 4.5 9/10

You need a comparable company analysis template because you're valuing a business and the DCF alone isn't going to cut it. Maybe you're an analyst building a pitch deck, a founder preparing for a fundraise, or an MBA student working through a case. Either way, you need a clean, defensible framework that pulls peer multiples (EV/Revenue, EV/EBITDA, P/E) and applies them to your target's financials.

The problem with most templates you find online is that they're either empty Excel shells with no guidance on peer selection, or they're bloated with 40 tabs you'll never use. What you actually want is a working structure: a peer set, the right multiples for your industry, trading data pulled correctly, and a valuation range at the end.

This page shows you how to generate that template using AI. You'll see the exact prompt, four model outputs compared side by side, and guidance on which output to use for which situation. By the end you'll have a comps model you can drop into Excel or Google Sheets and defend in a meeting.

When to use this

Comparable company analysis works best when you have a reasonable peer set of publicly traded companies operating in the same industry, with similar size, growth, and margin profiles. Use it for market-based valuations where you need a defensible range grounded in how investors are actually pricing similar businesses today, not in theoretical future cash flows.

Valuing a private company for a fundraise or internal strategic review
Building a football field valuation alongside DCF and precedent transactions
Pricing an IPO where public market comps set investor expectations
Sell-side pitch books benchmarking a client against listed peers
Equity research initiations where relative valuation anchors the price target

When this format breaks down

The target operates in a niche with no truly comparable public companies (deep tech, unique business models, early-stage biotech with no revenue)
The company is pre-revenue or has negative EBITDA, which breaks most standard multiples
Peer multiples are distorted by a market bubble or crash, making the range meaningless without adjustment
You're valuing a control transaction where a change-of-control premium matters more than trading multiples

The prompt we tested

You are a senior investment banking analyst specializing in equity valuation and comparable company analysis (CCA). Build a complete, professional comparable company analysis template for the target company described below.

Target company and context:
I'm valuing Rivian Automotive (RIVN), a US-based electric vehicle manufacturer with ~$4.4B LTM revenue, negative EBITDA, and a ~$12B market cap. I need a comps analysis using other EV and auto OEM peers to derive an implied valuation range for a pitch deck to our investment committee next week.

Formatting and content requirements:
Output as clean Markdown with labeled tables for every comps section — do not use prose paragraphs where a table belongs. Keep all monetary values in $ millions (except per-share data), show multiples to one decimal place, and end with a concise 3-5 bullet 'Valuation Conclusion' summarizing the implied equity value range and recommended central estimate.

Deliver the full template in a structured format with the following sections:
1. Target Company Overview (business description, sector, geography, size metrics)
2. Peer Selection Criteria & Selected Comparable Companies (5-8 peers with justification)
3. Comps Table — Market Data (ticker, share price, market cap, net debt, enterprise value, shares outstanding)
4. Comps Table — Financial Metrics (Revenue, EBITDA, EBIT, Net Income, EPS for LTM, FY+1, FY+2) with growth rates and margins
5. Trading Multiples Table (EV/Revenue, EV/EBITDA, EV/EBIT, P/E, PEG — LTM and forward), plus Min/Max/Mean/Median/25th/75th percentile rows
6. Applied Multiples & Implied Valuation Range for the target (low, median, high — per share and equity value)
7. Football Field Summary (valuation range visualization described in text/table)
8. Key Assumptions, Adjustments (non-recurring items, stock-based comp treatment), and Caveats

Populate every cell with either realistic illustrative figures based on the input or clearly labeled [placeholder] tags so the user can drop in their own data. Include formulas where relevant (e.g., EV = Market Cap + Debt − Cash).

Model comparison

Model	Quality	Cost	Latency	Tokens
Claude Opus 4.7	9	$0.1650	28.1s	760 / 2048
Claude Haiku 4.5	9	$0.0108	18.7s	539 / 2048
GPT-5.4	Not run yet
Gemini 2.5 Pro	Not run yet
Grok 4.1 Fast Reasoning	9	$0.0011	43.7s	601 / 2048

Outputs

Claude Opus 4.7 9/10

# Comparable Company Analysis: Rivian Automotive (NASDAQ: RIVN)

*Prepared for Investment Committee Review | All figures in $ millions unless otherwise noted*

---

## 1. Target Company Overview

| Attribute | Detail |
|---|---|
| Company | Rivian Automotive, Inc. |
| Ticker | NASDAQ: RIVN |
| Sector / Sub-sector | Consumer Discretionary / Automotive OEM — Electric Vehicles |
| Geography | United States (HQ: Irvine, CA); manufacturing in Normal, IL |
| Business Description | Designs, develops, and manufactures category-defining electric vehicles and accessories. Core products: R1T (electric pickup), R1S (electric SUV), and EDV (Electric Delivery Van, commercial partnership with Amazon). R2 platform in development. |
| LTM Revenue | ~$4,400 |
| LTM EBITDA | ~($5,400) (negative) |
| LTM Gross Margin | ~(40%) |
| Market Capitalization | ~$12,000 |
| Cash & Equivalents | ~$7,900 |
| Total Debt | ~$4,400 |
| Net Debt | ~($3,500) (net cash position) |
| Enterprise Value | ~$8,500 |
| Shares Outstanding (diluted) | ~1,020 million |
| Current Share Price | ~$11.75 |

---

## 2. Peer Selection Criteria & Selected Comparable Companies

**Selection Criteria:**
- Primary business in EV design/manufacture OR traditional OEM with material EV exposure
- Publicly traded with sufficient float and analyst coverage
- Revenue scale between $1B and $100B (excluding mega-cap Toyota/VW for relevance)
- Similar growth profile OR strategic relevance as valuation benchmark

| # | Company | Ticker | Category | Rationale for Inclusion |
|---|---|---|---|---|
| 1 | Tesla, Inc. | TSLA | Pure-play EV | Market leader; sets sector multiples; scale benchmark |
| 2 | Lucid Group | LCID | Pure-play EV (early-stage) | Closest US pure-play EV peer; similar pre-profitability profile |
| 3 | Li Auto | LI | Pure-play EV | Scaled EV peer; profitable benchmark |
| 4 | NIO Inc. | NIO | Pure-play EV | Pre-profit growth EV; relevant scale comparison |
| 5 | XPeng Inc. | XPEV | Pure-play EV | Growth-stage EV peer with similar margin profile |
| 6 | Ford Motor Co. | F | Legacy OEM + EV | Direct R1T / F-150 Lightning competitor |
| 7 | General Motors | GM | Legacy OEM + EV | Ultium platform EV exposure; US OEM benchmark |
| 8 | Polestar Automotive | PSNY | Pure-play EV | Premium EV; similar sub-scale profile |

---

## 3. Comps Table — Market Data

*Pricing as of [valuation date]; Enterprise Value = Market Cap + Total Debt − Cash & Equivalents + Minority Interest + Preferred Equity*

| Company | Ticker | Share Price ($) | Shares Out. (M) | Market Cap | Total Debt | Cash | Net Debt | Enterprise Value |
|---|---|---:|---:|---:|---:|---:|---:|---:|
| Tesla | TSLA | 242.50 | 3,180 | 771,150 | 9,570 | 29,100 | (19,530) | 751,620 |
| Lucid Group | LCID | 2.95 | 2,300 | 6,785 | 2,050 | 4,280 | (2,230) | 4,555 |
| Li Auto | LI | 22.40 | 1,060 | 23,744 | 2,500 | 14,800 | (12,300) | 11,444 |
| NIO Inc. | NIO | 4.85 | 2,010 | 9,749 | 3,900 | 5,700 | (1,800) | 7,949 |
| XPeng | XPEV | 10.20 | 945 | 9,639 | 1,850 | 5,100 | (3,250) | 6,389 |
| Ford Motor | F | 10.75 | 3,975 | 42,731 | 157,500 | 40,700 | 116,800 | 159,531 |
| General Motors | GM | 48.20 | 1,125 | 54,225 | 117,000 | 27,300 | 89,700 | 143,925 |
| Polestar | PSNY | 1.15 | 2,120 | 2,438 | 4,800 | 770 | 4,030 | 6,468 |
| **Rivian (Target)** | **RIVN** | **11.75** | **1,020** | **11,985** | **4,400** | **7,900** | **(3,500)** | **8,485** |

---

## 4. Comps Table — Financial Metrics

### 4a. Revenue ($M) & Growth

| Company | LTM Rev. | FY+1 Rev. | FY+2 Rev. | LTM→FY+1 Growth | FY+1→FY+2 Growth |
|---|---:|---:|---:|---:|---:|
| Tesla | 95,300 | 102,400 | 118,500 | 7.4% | 15.7% |
| Lucid | 620 | 810 | 1,550 | 30.6% | 91.4% |
| Li Auto | 19,800 | 25,200 | 32,600 | 27.3% | 29.4% |
| NIO | 8,200 | 10,500 | 14,300 | 28.0% | 36.2% |
| XPeng | 5,100 | 7,600 | 11,200 | 49.0% | 47.4% |
| Ford | 176,200 | 180,500 | 186,400 | 2.4% | 3.3%

Claude Haiku 4.5 9/10

# COMPARABLE COMPANY ANALYSIS — RIVIAN AUTOMOTIVE (RIVN)
## Equity Valuation & Implied Price Target

---

## 1. TARGET COMPANY OVERVIEW

| Metric | Value |
|--------|-------|
| **Company Name** | Rivian Automotive, Inc. |
| **Ticker** | RIVN |
| **Headquarters** | Irvine, California, USA |
| **Sector** | Automotive Manufacturing |
| **Sub-Sector** | Electric Vehicle (EV) Manufacturers |
| **Primary Products** | R1T (Electric Adventure Truck), R1S (Electric SUV), vans (Commercial) |
| **Geographic Focus** | North America (primarily US) |
| **LTM Revenue** | $4,400 |
| **LTM Gross Margin** | (12.0%) |
| **LTM EBITDA** | ($1,100) |
| **LTM Net Income** | ($2,550) |
| **Current Market Cap** | $12,000 |
| **Enterprise Value** | $11,200 |
| **Stage** | Growth-stage, Pre-profitability (Ramp-up Phase) |
| **Key Catalysts** | R1T/R1S volume scaling; Commercial van revenue ramp; Path to EBITDA breakeven by 2025–2026 |

---

## 2. PEER SELECTION CRITERIA & SELECTED COMPARABLE COMPANIES

### Selection Criteria

| Criterion | Rationale |
|-----------|-----------|
| **Business Model** | Pure-play EV OEM or significant EV revenue exposure; vertically integrated manufacturing |
| **Geography** | Global reach or North American presence; similar regulatory/market exposure |
| **Scale** | Revenue between $1B–$200B LTM to capture mix of scaled peers and growth-stage comparables |
| **Profitability Profile** | Mix of profitable (legacy OEMs) and unprofitable/early-stage (pure-play EV makers) to bracket valuation |
| **Stage of Maturity** | Blend of mature legacy OEMs and growth-stage EV startups for relevant trading multiple dispersion |
| **Data Availability** | Publicly traded with reliable quarterly/annual financials and analyst forecasts (FY+1, FY+2) |

---

### Selected Comparable Companies

| # | Company | Ticker | Business Description | Justification |
|---|---------|--------|----------------------|----------------|
| 1 | Tesla, Inc. | TSLA | Vertically integrated EV OEM; sedans, SUVs, trucks, energy storage | Pure-play EV leader; highest profitability/scale in peer set; establishes valuation ceiling |
| 2 | Lucid Group | LCID | Luxury EV manufacturer; sedans, SUVs; pre-volume/early ramp | Pure-play EV startup; similar pre-profitability stage to Rivian; establishes valuation floor |
| 3 | General Motors | GM | Legacy auto OEM; ~25% revenue from EV/battery operations; Ultium platform | Scaled legacy OEM; diversified revenue; significant EV exposure; lower EV multiple |
| 4 | Ford Motor | F | Legacy auto OEM; ~20% EV revenue; Mustang Mach-E, F-150 Lightning ramp | Scaled legacy OEM; similar EV truck exposure to Rivian; Ford EV division tracking |
| 5 | Volkswagen AG | VOWG | European OEM; ~30% EV revenue (ID series); ID.4, ID.5 ramp in US | Major global EV push; largest EV investment; lower EV revenue concentration than Rivian |
| 6 | Li Auto Inc. | LI | China-based EV/EREV manufacturer; Ext-range EV focus; profitable | Profitable growth-stage EV OEM; higher multiples than legacy; Chinese market exposure |
| 7 | NIO Inc. | NIO | China-based premium EV manufacturer; sedan, SUV, MPV portfolio | Pure-play premium EV startup; pre-profitability; global ambitions; Chinese exposure |
| 8 | BMW Group | BMW | German luxury OEM; ~25% EV revenue; iX, i4 ramp; premium positioning | Luxury positioning similar to Rivian brand; high EV investment; stable profitability |

---

## 3. COMPS TABLE — MARKET DATA (As of Valuation Date: [INSERT DATE])

| Company | Ticker | Stock Price ($) | Shares Out. (M) | Market Cap ($M) | Total Debt ($M) | Cash & Equiv. ($M) | Net Debt ($M) | Enterprise Value ($M) |
|---------|--------|-----------------|-----------------|-----------------|-----------------|-------------------|---------------|----------------------|
| **RIVIAN** | **RIVN** | **24.50** | **490** | **12,005** | **800** | **(605)** | **1,405** | **11,200** |
| Tesla | TSLA | 238.00 | 3,210 | 763,980 | 8,000 | 28,500 | (20,500) | 743,480 |
| Lucid Group | LCID | 3.20 | 640 | 2,048 | 1,600 | 2,400 | (800) | 1,248 |
| General Motors | GM | 42.50 | 720 | 30,600 | 18,000 | 4,800 | 13,200 | 43,800 |
| Ford Motor | F | 11.80 | 1,890 | 22,282 | 24,000 | 3,200 | 20,800 | 43,082 |
| Volkswagen AG | VOWG | 108.50 | 680 | 73,780 | 45,000 | 18,500 | 26,500 | 100,280 |
| Li Auto Inc. | LI | 18.50 | 530 | 9,805 | 500 | 8,200 | (7,700) | 2,105 |
| NIO Inc. | NIO | 6.40 | 850 | 5,440 | 2,200 | 3,100 | (900) | 4,540 |
| BMW Group | BMW | 87.00 | 640 | 55,680 | 32,000 | 12,500 | 19,500 | 75,180 |

**Notes:**
- **Net Debt Formula:** Total Debt + Preferred Stock − Cash & Equivalents
- **Enterprise Value (EV) Formula:** Market Cap + Net Debt
- Stock prices and share counts are illustrative as of [INSERT VALUATION DATE]; update with real-time data before IC presentation
- RIVN net debt excludes operating leases (treated separately in DCF, if applicable)

---

## 4. COMPS TABLE — FINANCIAL METRICS

### LTM & Forward Financials ($M, except EPS)

| Company | Ticker | **LTM Rev.** | **LTM Rev. Growth %** | **LTM EBITDA** | **LTM EBITDA Margin %** | **LTM EBIT** | **LTM EBIT Margin %** | **LTM Net Inc.** | **LTM EPS** |
|---------|--------|--------------|----------------------|----------------|------------------------|--------------|----------------------|------------------|-------------|
| **RIVIAN** | **RIVN** | **4,400** | **[+180%]** | **(1,100)** | **(25.0%)** | **(1,450)** | **(33.0%)** | **(2,550)** | **(5.20)** |
| Tesla | TSLA | 81,462 | +25.2% | 21,462 | 26.3% | 18,624 | 22.9% | 12,586 | 3.92 |
| Lucid Group | LCID | 652 | +450% | (2,104) | (322.8%) | (2,265) | (347.2%) | (2,428) | (3.79) |
| General Motors | GM | 122,485 | +6.1% | 12,847 | 10.5% | 8,250 |

Grok 4.1 Fast Reasoning 9/10

# 1. Target Company Overview

| Metric              | Value                          |
|---------------------|--------------------------------|
| Business Description | US-based electric vehicle (EV) manufacturer focused on premium adventure vehicles, commercial vans, and SUVs; produces R1T truck, R1S SUV, and EDV for Amazon. |
| Sector              | Automotive / Electric Vehicles |
| Geography           | Primarily US (production in Illinois and Georgia); sales global. |
| LTM Revenue ($M)    | 4,400                          |
| LTM EBITDA ($M)     | -5,000                         |
| Market Cap ($M)     | 12,000                         |
| Shares Outstanding (M) | 992                          |
| Enterprise Value ($M) | 15,000 (EV = Market Cap + Net Debt; illustrative Net Debt = 3,000) |

# 2. Peer Selection Criteria & Selected Comparable Companies

**Criteria**: Selected 6 peers based on (1) EV exposure (>20% of revenue or primary focus), (2) public US-listed auto/EV OEMs, (3) similar growth stage or business model (pure-play EV vs. legacy OEM with EV transition), (4) comparable revenue scale where possible. Excludes private firms, SPACs without revenue, and non-OEM suppliers.

| #  | Ticker | Company Name     | Justification |
|----|--------|------------------|---------------|
| 1  | TSLA  | Tesla            | Leading pure-play EV OEM; highest growth benchmark. |
| 2  | LCID  | Lucid Group      | Premium EV startup; similar loss-making growth profile to RIVN. |
| 3  | NIO   | NIO Inc.         | Chinese premium EV OEM; comparable scale and negative margins. |
| 4  | LI    | Li Auto          | Chinese EV SUV maker; scaling production with deliveries growth. |
| 5  | XPEV  | XPeng            | Chinese smart EV OEM; tech-focused like RIVN. |
| 6  | GM    | General Motors   | Legacy US OEM with major EV push (e.g., Ultium platform); size/scale peer. |

# 3. Comps Table — Market Data

*As of illustrative date (e.g., latest close). EV ($M) = Market Cap ($M) + Net Debt ($M), where Net Debt = Total Debt - Cash & Equivalents.*

| Ticker | Price ($) | Shares Out. (M) | Market Cap ($M) | Net Debt ($M) | EV ($M)  |
|--------|-----------|-----------------|-----------------|---------------|----------|
| TSLA  | 220.0    | 3,189           | 701,580         | -25,000       | 676,580 |
| LCID  | 3.42     | 2,286           | 7,820           | 1,200         | 9,020   |
| NIO   | 4.85     | 2,071           | 10,045          | 6,500         | 16,545  |
| LI    | 20.15    | 1,058           | 21,319          | 1,800         | 23,119  |
| XPEV  | 9.80     | 1,845           | 18,081          | 4,200         | 22,281  |
| GM    | 47.22    | 1,152           | 54,442          | 85,000        | 139,442 |
| **Min** | 3.42   | 1,058           | 7,820           | -25,000       | 9,020   |
| **Max** | 220.0  | 3,189           | 701,580         | 85,000        | 676,580 |
| **Mean** | 50.9  | 1,934           | 135,548         | 12,117        | 147,665 |
| **Median** | 15.0 | 1,919         | 19,703          | 3,000         | 22,700  |

# 4. Comps Table — Financial Metrics

*LTM = Last Twelve Months. FY+1/FY+2 = Consensus estimates. Growth = YoY % change. Margins = % of Revenue. [Placeholder] for non-public data; illustrative based on recent reports/estimates.*

| Ticker | LTM Rev ($M) | LTM EBITDA ($M) | EBITDA Margin LTM (%) | LTM Rev Growth (%) | FY+1 Rev ($M) | FY+1 EBITDA ($M) | FY+2 Rev ($M) | FY+2 EBITDA ($M) | FY+1 Rev Growth (%) | FY+1-2 Rev CAGR (%) |
|--------|--------------|-----------------|-----------------------|--------------------|---------------|------------------|---------------|------------------|---------------------|---------------------|
| TSLA  | 95,000      | 15,200          | 16.0                  | 15.0               | 98,000        | 17,500           | 110,000       | 20,000           | 3.2                 | 11.7                |
| LCID  | 595         | -2,800          | -470.6                | [200.0]            | 900           | -2,200           | 2,500         | -1,000           | 51.3                | 67.0                |
| NIO   | 7,930       | -2,900          | -36.6                 | 10.0               | 9,500         | -2,000           | 13,000        | -500             | 19.8                | 17.0                |
| LI    | 17,300      | 1,400           | 8.1                   | 80.0               | 22,000        | 2,200            | 28,000        | 3,500            | 27.2                | 26.2                |
| XPEV  | 4,570       | -1,600          | -35.0                 | 5.0                | 6,000         | -800             | 9,000         | 200              | 31.3                | 22.3                |
| GM    | 172,000     | 13,900          | 8.1                   | 2.0                | 175,000       | 15,000           | 182,000       | 16,500           | 1.7                 | 2.0                 |
| **Mean** | 46,233   | 3,900           | -85.7                 | 52.0               | 51,900        | 4,283            | 74,083        | 6,450            | 22.4                | 24.4                |
| **Median** | 13,650 | -1,250         | -35.8                 | 12.5               | 16,250        | -100             | 20,750        | 1,250            | 23.5                | 20.0                |

| Ticker | LTM EBIT ($M) | LTM Net Income ($M) | LTM EPS ($) | FY+1 EBIT ($M) | FY+1 Net Income ($M) | FY+1 EPS ($) | FY+2 EPS ($) |
|--------|---------------|---------------------|-------------|----------------|----------------------|--------------|--------------|
| TSLA  | 13,000       | 13,000              | 4.10        | 15,000         | 14,500               | 4.60         | 5.50         |
| LCID  | -3,200       | -2,900              | -1.45       | -2,500         | -2,300               | -0.95        | -0.30        |
| NIO   | -3,300       | -2,800              | -1.45       | -2,300         | -1,900               | -0.90        | -0.40        |
| LI    | 1,100        | 1,000               | 0.95        | 1,800          | 1,600                | 1.50         | 2.30         |
| XPEV  | -1,900       | -1,700              | -0.95       | -1,000         | -800                 | -0.40        | 0.10         |
| GM    | 11,500       | 11,000              | 9.50        | 12,500         | 12,000               | 10.50        | 11.50        |
| **Mean** | 3,867     | 3,267               | 1.78        | 3,917          | 3,850                | 2.46         | 2.62         |
| **Median** | -2,250  | -1,950              | -0.70       | -1,150         | -1,100               | 0.55         | 0.60         |

# 5. Trading Multiples Table

*N/A for negative denominators. PEG = Forward P/E / FY+1 EPS Growth % (illustrative growth from consensus).*

**LTM Multiples**

| Ticker | EV/Rev (x) | EV/EBITDA

What makes these work

01

Anchor on business model fit
Peer selection is the single biggest driver of output quality. Tell the AI exactly what makes a company comparable (revenue model, customer type, geography) rather than just naming an industry. Reject peers that look similar on the surface but monetize differently.
02

Specify which multiples matter
EV/EBITDA works for mature industrials. EV/Revenue works for high-growth software. P/E works for banks and insurers. Name the multiples upfront so the AI doesn't dump every ratio in the book. This keeps the template focused and defensible.
03

Request outlier flagging
Ask the model to identify peers that should be excluded or footnoted (companies with one-time items, different scale, or recent M&A distortions). A template that shows its work on exclusions is far more credible than one that just takes a blind median.
04

Build in a sensitivity layer
Single-point valuations get torn apart in diligence. Ask for a range driven by the 25th to 75th percentile of peer multiples, plus a sensitivity table on the target's own metrics. This mirrors how investment banks actually present comps.

More example scenarios

#01 · SaaS company fundraise valuation

Input

Build a comps template for a vertical SaaS company with $45M ARR growing 35% YoY, 80% gross margin, burning $8M/year. Peers: HubSpot, Monday.com, Asana, Smartsheet, Freshworks. Focus on EV/Revenue and EV/ARR multiples with growth adjustment.

Expected output

A structured template with peer trading data (market cap, EV, LTM revenue, NTM revenue, growth rate), median and mean EV/NTM Revenue of 7.2x, a growth-adjusted scatter regression, and an implied valuation range of $280M to $380M for the target. Includes sensitivity table on growth assumptions.

#02 · Industrial manufacturer benchmarking

Input

Create a comps analysis for a mid-cap industrial pump manufacturer, $800M revenue, 18% EBITDA margin. Peers: Flowserve, Xylem, Watts Water, Franklin Electric, Roper. Use EV/EBITDA and P/E.

Expected output

Output includes peer trading multiples, LTM and forward EV/EBITDA of 11.5x median, P/E of 18x median, and cross-checks against the target's $144M EBITDA. Delivers an implied EV range of $1.55B to $1.75B with footnotes on why Roper trades at a premium and should potentially be excluded.

#03 · Consumer retail IPO pricing

Input

Build a comps template for a DTC apparel brand preparing for IPO: $320M revenue, 12% EBITDA margin, 22% growth. Peers: Lululemon, On Holding, Allbirds, Warby Parker, Figs.

Expected output

Template with EV/Revenue, EV/EBITDA, and P/E multiples, separating profitable peers from unprofitable ones. Notes Allbirds should be excluded as an outlier. Implied IPO valuation range of $1.9B to $2.6B based on 6x to 8x EV/Revenue, with a discount recommendation for IPO pricing.

#04 · Healthcare services acquisition target

Input

Need a comps model for a multi-site dental services organization, $180M revenue, 22% EBITDA margin. Peers: Smile Direct Club, Henry Schein, Pediatrix, US Physical Therapy.

Expected output

Template flags that dental DSOs have limited pure-play public comps and recommends supplementing with precedent transactions. Delivers EV/EBITDA range of 9x to 13x, implied EV of $360M to $515M, with commentary on scale and geographic concentration discounts.

Common mistakes to avoid

Using too broad a peer set
Throwing 15 'sort of similar' companies into a template produces a wide, useless range. Five to eight truly comparable peers beats a large bucket of loose matches every time.
Ignoring calendarization
Peers have different fiscal year ends, so raw LTM numbers aren't apples to apples. Failing to calendarize means your multiples are technically wrong, and a good analyst will catch it immediately.
Applying the wrong multiple to the business stage
Using EV/EBITDA on an unprofitable SaaS target gives you nothing. Using EV/Revenue on a mature utility overstates value. Match the multiple to the target's financial profile, not just the industry.
Forgetting the control premium question
Trading comps reflect minority public market prices. If you're valuing a full acquisition, you need to either add a control premium or supplement with precedent transactions. Skipping this step understates takeover value.
Treating the median as the answer
The median is a starting point, not a conclusion. You still need to argue why your target deserves a premium or discount based on growth, margins, or competitive position.

Related queries

Frequently asked questions

What should a comparable company analysis template include?

At minimum: a peer list with selection rationale, trading data (market cap, enterprise value, share price), financial metrics (revenue, EBITDA, earnings for LTM and NTM), calculated multiples, summary statistics (mean, median, quartiles), and an implied valuation range applied to your target. Good templates also include outlier flags and a sensitivity table.

How many comparable companies should I use?

Five to eight is the sweet spot. Fewer than five and your statistics aren't meaningful. More than ten and you're usually including companies that aren't truly comparable, which widens the range and weakens the analysis. Quality beats quantity.

Can AI actually pull real trading data for the template?

AI can structure the template and populate it with recent training-data estimates, but for live trading multiples you'll need to verify against Bloomberg, CapIQ, or FactSet. Use AI to build the framework and do peer selection, then drop in current data before presenting.

What's the difference between trading comps and transaction comps?

Trading comps use current public market multiples of similar companies, reflecting minority shareholder prices. Transaction comps use multiples paid in recent M&A deals, which include control premiums. For a full valuation you usually show both.

Which multiple is best for valuing a SaaS company?

EV/Revenue or EV/ARR with a growth adjustment (sometimes shown as EV/Revenue per unit of growth). EBITDA multiples are less useful for SaaS companies that reinvest aggressively and report low or negative EBITDA.

How do I handle peers with very different growth rates?

Either exclude peers whose growth profile is meaningfully different, or use a regression approach (plot EV/Revenue against growth rate and read off the implied multiple for your target's growth). The regression method is more defensible when peer growth ranges widely.