Olo Deal Screening

提供并购目标筛选框架：从战略契合、财务状况、估值吸引力、风险敞口和执行可行性五个维度按权重打分，设置一票否决项检查与PE专项标准，并输出结构化评分、推荐结论、优势、风险与后续步骤。

aniebyl

热度 52.60

最近更新 2026-05-11 17:28:04

分类：数据依赖：内部调用大模型应用场景：财务 / 市场分析 / 报告撰写 / 研究

安装技能

复制下面的话术发给你的 Agent，它会通过 secure-skills 检索并安装该技能。

执行命令安装此 skill： npx secure-skills-cli add olo-deal-screening

Metadata

name: olo-deal-screening
version: 1.0.0
description: Target company evaluation and deal qualification for PE and strategic buyers
author: ololand.ai
author_url: https://ololand.ai
license: MIT
triggers: ["deal screening","target evaluation","investment criteria","acquisition criteria","deal qualification","deal fit","buyer criteria","deal scoring"]
tags: ["finance","screening","m-and-a","due-diligence","private-equity"]

Deal Screening for M&A

Score and qualify acquisition targets against buyer investment criteria.

Screening Framework

Evaluate targets across five dimensions, each scored 0-100:

1. Strategic Fit (25% weight)

Industry/sector alignment with buyer portfolio
Geographic fit (markets, operations, customer base)
Product/service complementarity
Technology or capability gap fill
Brand and market position value

2. Financial Profile (25% weight)

Revenue scale (minimum threshold check)
Revenue growth trajectory (3-year trend)
EBITDA margin vs. industry benchmark
Revenue quality (recurring vs. one-time, customer concentration)
Working capital efficiency

3. Valuation Attractiveness (20% weight)

EV/EBITDA vs. comparable transactions
EV/Revenue vs. sector median
Implied IRR at estimated purchase price
Multiple arbitrage potential (buy low, exit higher)

4. Risk Profile (15% weight)

Customer concentration (top 10 customers as % of revenue)
Key-person dependency
Regulatory exposure
Technology obsolescence risk
Litigation or compliance issues

5. Execution Feasibility (15% weight)

Management team quality and retention likelihood
Integration complexity estimate
Competitive auction dynamics
Seller motivation and timeline
Financing availability

Scoring Output

Overall Fit Score: 78/100 — PROCEED TO DD

Strategic Fit:     85/100 ████████░░
Financial Profile: 72/100 ███████░░░
Valuation:         80/100 ████████░░
Risk Profile:      68/100 ██████░░░░
Execution:         82/100 ████████░░

Recommendation: PROCEED TO DD
Key Strengths: [top 3]
Key Concerns: [top 3]
Suggested Next Steps: [prioritized actions]

Thresholds

Score Range	Recommendation
80-100	Strong fit — prioritize for DD
65-79	Good fit — proceed with caution
50-64	Marginal — requires strategic justification
Below 50	Poor fit — pass unless compelling thesis

Deal-Breaker Checks (Auto-Fail)

Before scoring, check for absolute disqualifiers:

Revenue below buyer's minimum threshold
Negative EBITDA (unless growth-stage thesis)
Active material litigation exceeding 20% of EV
Sanctioned entities in ownership chain
Industry explicitly excluded by buyer mandate

PE-Specific Criteria

For financial sponsor buyers, additionally evaluate:

LBO feasibility: Can the deal be levered 3-5x EBITDA?
Value creation levers: Revenue growth, margin expansion, add-ons, multiple expansion
Exit path: IPO viability, strategic buyer universe, sponsor-to-sponsor
Hold period returns: Target 20-25% gross IRR over 3-5 years
Fund fit: Check size, vintage, sector focus, geographic mandate

Output Format

Provide structured JSON-compatible output with:

overall_score: 0-100
recommendation: proceed_to_dd | proceed_with_caution | pass
dimension_scores: object with each dimension
deal_breakers: list of any auto-fail conditions triggered
strengths: top 3 positive factors
concerns: top 3 risk factors
next_steps: prioritized action items