The Complete Guide to
Micro PE Deal Scoring

What Is Micro PE?

Micro private equity is the acquisition and operation of small, cash-flowing businesses — typically with enterprise values between $200K and $5M. These are real companies: service businesses, software products, e-commerce brands, content sites, and B2B tools that have paying customers and meaningful revenue. They're just too small to attract institutional PE attention.

The operators who buy these businesses aren't hedge funds. They're individual operators, search funders, and small holding companies who use SBA loans, seller financing, or personal capital to buy 1–5 businesses over a career. The thesis is simple: acquire a business generating $150K–$800K in annual free cash flow, operate it efficiently, and either hold it for income or sell at a multiple in 3–7 years.

What distinguishes serious micro PE operators from casual business buyers is systematization. They source deals at volume, filter ruthlessly, and only spend significant diligence time on deals that pass initial screening. At the core of that screening process is a deal scoring framework — a repeatable method for comparing a $500K e-commerce brand to a $2M SaaS business against the same objective criteria.

Micro PE has grown sharply over the past decade as the "buy then build" model gained visibility. Platforms like Acquire.com, BizBuySell, and Empire Flippers list thousands of deals at any given time. The constraint is no longer deal sourcing — it's deal evaluation. The operators who win are the ones who can evaluate 40 deals per month without burning out, and score accurately enough to skip the bad ones fast.

Why Deal Scoring Matters

Most failed micro acquisitions weren't failures of capital — they were failures of evaluation. The buyer paid the right price for a business they understood incorrectly. Deal scoring exists to close that gap between "this looks good" and "this actually is good."

Without a scoring framework, deal evaluation is driven by narrative. The seller tells a compelling story about growth potential, the P&L looks clean, and the buyer's optimism does the rest. The problems that surface 6 months post-close — customer concentration, hidden owner dependency, a revenue channel about to disappear — were discoverable before close. They just weren't systematically checked.

The business case for structured scoring is compounding. If you evaluate 30 deals per month and spend 5 hours on each one, you've consumed 150 hours before you've even started due diligence on a single deal. A scoring system compresses initial screening to 20–30 minutes per deal. You spend real time only on deals that score above your threshold. Operators who do this close 2–3x more deals per year at higher quality than those who don't.

There's also a psychological benefit. A good scoring framework removes the emotional attachment that makes deal evaluation unreliable. When you've spent 10 hours researching a business, confirmation bias kicks in hard — every new data point gets interpreted charitably. Scoring disciplines you to weigh each dimension independently before synthesizing a view. It's the difference between a medical diagnosis and a hunch.

The final argument for scoring is post-acquisition performance. Operators who score deals rigorously before acquiring develop a clear picture of where the business is strong and where it needs work. That picture becomes the 100-day plan. Operators who skip scoring spend their first six months just figuring out what they bought.

The 5-Dimension Scoring Framework

PocketFundOS scores every deal across five dimensions, each weighted by its correlation with post-acquisition outcomes. The composite score (0–100) provides a consistent benchmark across deal types. Here's what each dimension measures and why it matters.

These five dimensions are scored from 0–20, then weighted to produce a composite 0–100 score. Deals scoring below 55 are filtered out automatically. Deals scoring 70+ go directly to active diligence. The scoring thresholds and weightings are configurable — operators in specific verticals (SaaS-only, services-only) often adjust weights based on what matters most in their deal type.

See the full scoring methodology →

Common Scoring Mistakes

Even experienced operators make predictable mistakes when evaluating deals. Most of these aren't analytical errors — they're behavioral ones. Knowing them in advance is the only reliable way to avoid them.

1. Anchoring on the asking price

   When a seller lists at 3x EBITDA, buyers unconsciously treat that as the correct valuation frame. Score the business first, derive a fair value from the score, then compare to asking price. Never let the listed multiple shape your assessment before you've done independent work.

2. Ignoring revenue concentration until LOI

   Customer concentration is the single most common post-close surprise in micro PE. Ask for a revenue-by-customer breakdown in your first data request — not after you've signed an LOI. If a seller won't provide it pre-LOI, that's already a red flag worth scoring.

3. Conflating owner income with business profit

   Many small business owners run significant personal expenses through the P&L. Legitimate add-backs (owner salary above market, one-time expenses) are valid. Discretionary personal charges, family salaries for no-show jobs, and inflated vehicle allowances are not. SDE calculations require forensic attention, not seller representations.

4. Overweighting growth potential

   Growth leverage should be scored on evidence, not possibility. "They haven't tried paid acquisition" is not a growth lever unless you can demonstrate the unit economics support it and you have the competence to execute it. Buying a business because of what you'll do with it rather than what it is today is how you overpay for optionality you'll never capture.

5. Skipping the transition risk dimension

   Some businesses survive a change of ownership easily. Others collapse when the seller stops answering the phone. Seller dependency isn't just about relationships — it's about institutional knowledge, customer relationships, supplier terms negotiated on personal trust, and employee loyalty to the founder. A transition plan that requires 6 months of seller involvement at 20 hours/week is a risk, not a feature.

6. Treating "no red flags" as a positive signal

   A clean deal with no red flags is table stakes, not an achievement. Score affirmatively: the business should have positive evidence of financial health, documented processes, diversified customers, and clear growth levers. "Nothing bad happened yet" is a description of luck, not quality.

7. Scoring once and treating it as final

   Deal scores change as you learn more. A score based on the CIM should update after the seller call, after you receive the data room, and again after reference calls. Operators who finalize a score early and don't revise it miss critical information gathered later in the process. Treat scoring as a living document through close.

How to Build Your Own Scoring System

If you're building a scoring framework from scratch, start with the dimensions that cause the most post-acquisition regret — not the ones that are easiest to measure. For most operators, that means starting with financial health and operational complexity, since those are the categories where surprises derail integrations most often.

Step 1: Define your threshold dimensions

Some dimensions should be threshold-based rather than scored. Customer concentration above 50%, revenue declining more than 20% YoY, or a pending lawsuit — these aren't low scores, they're disqualifiers. Separate your knockout criteria from your scoring criteria before you build anything. A business that scores 92 on everything but has a knock-out deficiency shouldn't make it to the next round.

Step 2: Weight by your deal type

A services business weights operational complexity more heavily than a SaaS business does. An e-commerce business weights platform dependency (part of risk profile) more heavily than a local services business. Weight your scoring dimensions based on what actually drives outcomes in your target segment — not generic frameworks built for a different deal size or type.

Step 3: Calibrate with historical data

If you've already acquired businesses, retroactively score them on your new framework and compare the scores to how each acquisition actually performed. This calibration step is what separates a principled scoring system from an arbitrary checklist. You're looking for dimensions where your scoring correctly predicted outcomes, and dimensions where high scores correlated with poor outcomes — those weights need adjustment.

Step 4: Make it fast enough to use

A scoring system you skip because it's too time-consuming is worse than no system. Your initial screen should take under 30 minutes per deal using publicly available information (CIM, broker summary, Wayback Machine). Reserve detailed data requests for deals that pass the initial screen. The goal is to eliminate 80% of deals quickly, not to fully diligence everything.