Hard to Kill: A Four-Part Framework for Evaluating Small Business Acquisitions

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I would make the world’s worst Venture Capitalist.

If you don’t believe me, keep reading.

As I’ve transitioned from operator to investor over the past 5 years or so, I’ve attempted to develop (and have attempted to articulate below) a general philosophy to guide my decision making, given that almost every investment decision seems to present me with a long list of reasons to be simultaneously hopeful and terrified.

The four-point framework that I’ve presented below is my attempt to add some structure and objectivity to these decisions. I’m not so vain as to think that my investment philosophy is one that ought to be emulated or studied by others (quite the opposite, in fact: I am a work-in-progress at best, and a complete novice at worst). Instead, I’ve decided to present the framework below in hopes that:

1. Acquisition entrepreneurs might benefit from it as they evaluate potential investment targets without the years of context and hundreds of repetitions that are typically required to reliably calibrate a sense of an attractive vs. less attractive opportunity
2. To keep myself honest, and to create a single place for me to go whenever I feel like I may be deviating from an investment philosophy that makes intuitive sense to me

Nothing below is likely to strike you as being particularly wise or insightful (and nothing will strike you as being particularly original, either: Precisely zero of these ideas originate directly from me). What follows is simply my attempt to “package” a lot of seemingly obvious observations into a simple framework that is (hopefully) easy to digest and easy to reference.

Lastly, note that this framework does not represent my view of the unambiguously “right” investment strategy. Such a strategy simply doesn’t exist. Don’t be surprised when plenty of reasonable, thoughtful, and experienced people disagree with some or all of what I’ve outlined below.

The Four-Part Investment Framework

When evaluating any given opportunity, I tend to look for the following four things:

(1) Asymmetry

The first concept contemplates the relationship between an investment’s possible upside and its possible downside. As much as possible, acquirors should be seeking out opportunities where the potential upside of an investment is asymmetrically larger than its downside. Famed value investor Monish Pabrai, author of The Dhando Investor, might have summarized this idea best when he explained it as “Heads I win, tails I don’t lose much”.

For an illustrative example of positive asymmetry, consider a company operating in a growing and resilient industry that has been growing steadily in the absence of a highly functioning sales & marketing operation: If the new owner successfully builds these internal capabilities, future growth is likely to far exceed historical growth. If, however, the new buyer is unable to do so, then historical growth – even if modest – is likely to persist. Heads I win, tails I don’t lose much.

A capital structure with too much leverage is a common example of negative asymmetry: In the best-case scenario, leverage amplifies equity returns by a known but ultimately capped amount. In the worst-case scenario, however, the value of the equity could be wiped out entirely. Heads I win a bit, tails I lose everything.

(2) Don’t Multiply by Zero

The mathematically inclined among us will know that any number, regardless of its size, will still equal zero when multiplied by zero. A similar concept in engineering deals with the importance of avoiding “single points of failure”, meaning any component whose failure would cause the entire system to stop operating. These closely related concepts essentially tell us to avoid existential risks at all costs, even if those existential risks feel unlikely to transpire, or may feel justified by commensurately large upside possibilities.

In our personal lives, we can do everything else right, but if we do something that ends up in addiction, serious illness, or in trouble with the law, then all of the other things that we did right simply won’t matter. This idea is similar in an investing context: Things like high revenue concentration with a single customer, or a highly leveraged capital structure, or serving highly cyclical end markets could prove to be existential problems, rendering all other points of progress essentially meaningless.

(3) Protecting the Downside Over Maximizing the Upside

Warren Buffett’s two rules for investing summarize this idea nicely: “Rule # 1: Never lose money. Rule # 2: Never forget Rule # 1”.  Buffett’s partner, Charlie Munger, summarized this idea in a slightly different way when he said “It is remarkable how much long–term advantage people like us have gotten by trying to be consistently not stupid, instead of trying to be very intelligent.”

In his thoroughly enjoyable book, What I Learned About Investing from Darwin, author Pulak Prasad differentiates between Type I errors ( “errors of commission”, when one makes a bad investment because they erroneously thought it was a good one) and Type II errors (“errors of omission”, when one rejects a good investment because they erroneously thought it was a bad one). Borrowing from evolutionary biology (of all things), he makes a compelling case that Type I errors are deadly in almost every domain of our lives (including investing), whereas Type II errors are eminently survivable.

Investor Jeremy Giffon put it more succinctly when he said “You die because of the bad deals that you do, not the good deals that you don’t do”.

As an investor, this means that one must learn to be OK with errors of omission (missing out on a home run), so long as they avoid errors of commission (a complete strike out) at all costs. It also means that I’d much rather have an 80% chance of a 3x return than a 25% chance of a 10x return, even though both options produce roughly the same expected value.

This “mistake minimization” concept is visible in countless domains outside of investing. In tennis, for instance, unforced errors decide far more points than winners. Across amateurs and pros alike, the player who commits fewer errors wins roughly 70–75% of the time. Counterintuitively, aggressive, high-risk shot-making almost always reduces win probability for all but the top 1% of players.

Upon reading this, venture capitalists would be forgiven for quickly seeking out a grave to roll over in (as their business is governed by power law dynamics), but as I suggested above: Mine isn’t a universally applicable investment framework that is appropriate across all situations or all asset classes.

(4) “Hard to Kill”

If you have performed any amount of research on the types of businesses and industries that search funds tend to target, you’ve likely encountered a remarkably consistent list of characteristics that investors tend to look for. If your first response to this list is that substantially every investor, across every asset class, is likely looking for companies and industries that demonstrate such structurally attractive characteristics, you’re not wrong. However, the reason why search funds tend to specifically target these types of industries and business models is because, as much as possible, they tend to lend themselves reasonably well to being run by a first-time CEO, a dynamic that is highly specific to the search fund asset class.

Though there are plenty of examples of successful businesses with project-based revenues, significant commodity price exposure, low percentage (but high dollar) margins, and a non-recurring base of customers between any two given years, these businesses are likely run by seasoned executives, each of whom has likely spent decades refining their craft as leaders and capital allocators.

Over the years, to help myself make decisions more than any other reason, I’ve tried to distill these ideas down into a single sentence, which can describe the ideal target for a Search Fund to acquire. At present, my best attempt at articulating this single sentence is as follows:

Search funds ought to target companies that are “hard to kill”.

All else being equal, here are some considerations that make a company “hard to kill”, most of which will be very familiar to those in the ETA ecosystem:

HARDER TO KILL	EASIER TO KILL
Recurring revenue	Project-based revenue
High customer switching costs	Commoditized: Low or no switching costs
Low customer concentration	High customer concentration
Low key person risk	High key person risk
Larger customer base (by simple count)	Smaller customer base (by simple count)
High customer retention	Low customer retention
Sufficiently large EBITDA (by dollar amount)	Small EBITDA (by dollar amount)
Sufficiently large EBITDA margins	Small EBITDA margins
Growing market	Flat or declining market
Cyclically defensible	Cyclically exposed/volatile
Simple day-to-day operations	Complex day-to-day operations

Of course, it’s unlikely that any investment opportunity will meet all of these criteria, but the ideal target should meet at least a majority of them.

Beyond company and industry characteristics, investment theses can also be placed on a spectrum of “harder to kill” versus “easier to kill”. Below are a few examples:

HARDER TO KILL	EASIER TO KILL
Low Purchase price	Higher purchase price that is largely growth dependent
Modest leverage (you can always add more later, but if you add too much at the outset, that’s a much harder problem to fix)	High leverage
Reasonable certainty around what the company will look like in 10 years	High unpredictability around what the target company may look like in 10 years
No single points of failure to the investment thesis	“As long as A, B, or C doesn’t happen, we’ll be fine”
“Even if the company pretty much just keeps doing what it’s been doing historically, we’ll do just fine”	“We’ll do well so long as the company can do X, Y or Z” (though the company has little to no history of actually doing X, Y or Z)

But Don’t “Tails Drive Everything”?

Like millions of other people around the world, I make it a habit to read everything that author Morgan Housel writes. In Morgan’s best-selling book, The Psychology of Money, he makes a persuasive argument that “tail events” (defined as a small handful of unlikely outcomes that don’t happen often – but when they do, they end up mattering way more than everything else combined) explain everything from investing, to politics to professional sports. Some examples provided by Housel include:

• Out of 21,000 venture financings from 2004 to 2014, 65% lost money. Only one half of one percent – about 100 companies – earned 50x or more, explaining the majority of the industry’s returns
• Public market investors are not exempt: Between 1980 to 2014, 40% of all Russell 3000 stocks lost at least two-thirds of their value and never recovered. Effectively all of the index’s overall returns came from 7% of its components

Closer to home, in October of 2025, the wonderful A.J. Wasserstein and his co-authors published a paper titled “How are Search Fund Investors Really Faring?”, and in it, he presents a case that the total returns of Search Fund investors are also highly dependent on “tail” events (referring to these 10x outcomes as “Griffins”). Though these 10x outcomes are extremely rare (at only ~2% of historical observations), they seem to explain a disproportionate share of investor MOIC outcomes.

So is the four-part investment framework above inconsistent with the reality that tails seem to drive everything? I don’t think that it is.

Mostly because achieving tail outcomes isn’t necessarily the result of taking unduly risky bets.

Consider the following “outlier” outcomes achieved within the Search Fund ecosystem. I would argue that each one of them is perfectly consistent with the framework that we’ve outlined above:

(1) FieldEdge (Steve Lau & Rameez Anzari): Acquired in 2015 for ~5x EBITDA, sold at ~8x revenue in 2019 for a gross IRR of ~85% and a ~8x multiple of capital.

• Entry multiple: 5.3x EBITDA
• Leverage at Entry: ~33% of enterprise value
• Percentage of total revenue that was recurring in nature: ~66%+
• Number of customers at entry: 2,000+
• Customer concentration at entry: None
• Retention rate of existing customers at entry: 100%+ net dollar retention
• EBITDA margins: ~40%
• Reliable and consistent historical profitability 

(2) Raptor Technologies (Jim Vesterman): 13.5x MOIC and a 53% IRR.

• Entry multiple: 6x EBITDA
• Senior Debt as a % of Enterprise Value: ~33%
• Recurring revenue as a % of Total: 60%+
• Number of existing customers at entry: 7,000+
• Customer concentration at Entry: None
• Retention rate of existing customers at Entry: Gross logo retention 95%
• EBITDA margin at Entry: 41%
• Track Record of Historical Profitability: “Profitable since day one”

(3) Asurion (Kevin Taweel & Jim Ellis): At entry, the best search fund outcome of all time featured the following characteristics:

• $5.9M of revenue and $1.5M of EBITDA (~25% EBITDA margins)
• Purchase price of 4.5x EBITDA despite the business having grown 90% in the prior year and 33% the year before that
• Recurring revenue, consistent historical profitability, low capital intensity, and simple day-to-day operations
• Base-case assumptions had revenue growing to $15M by 2000 (~17% CAGR) and $3.7M of EBITDA, with modest multiple expansion to ~5.8x EBITDA, underwriting a ~37% IRR.
• (The only place where they appeared to deviate from our framework was in their liberal use of leverage, but such a strategy can be a reasonable one when overlayed onto a business like the one that they purchased)

It would seem to me, then, that achieving tail outcomes isn’t necessarily the result of taking unduly risky bets that are “binary” in nature (i.e., “either a 0x or a 10x”). Instead, tail outcomes are probably driven by some combination of good luck, good timing, great leadership, a great market, and great execution, among other factors.

While risk and return do indeed tend to be inversely correlated, one shouldn’t assume that higher risk bets necessarily suggest the opportunity for higher rewards:

Sometimes, risky is just risky.

If luck at least partially explains tail outcomes, then perhaps the best reason why I’m likely to stick to my four-part framework is that, if nothing else, it’s at least likely to keep me in the game long enough to get lucky.

Realistically Putting this Framework into Practice

Finding a company to acquire is a brutally difficult undertaking: Not only does one have to find an attractive company in an attractive industry, but that company has to be for sale, for sale at the right price, for sale specifically to the buyer in question, and run by a high-integrity seller. Beyond that, Searchers have to successfully raise the equity, raise the debt, perform various streams of due diligence, manage the seller’s expectations and emotions, and avoid late-stage commercial surprises that can derail the transaction. Taken together, calling this a tall task feels like a dramatic understatement… to say nothing of doing all of this while remaining consistent with each of the four investment principles described above.  

Acquisition entrepreneurs and their investors aren’t in the business of taking no risk. And perfect deals don’t exist – every real-world transaction is likely to violate at least some part of this framework. Indeed, every single one of Mineola’s portfolio investments, without exception, has at least 2 or 3 easy-to-articulate reasons why a thoughtful investor might have reasonably chosen to pass.  

As a former credit investor, I was trained to think about all of the things that could go wrong with an investment (after all, lenders are less concerned with upside, and more concerned with getting their money back, plus interest). Interestingly though, now as an equity investor, my job is arguably to think about all of the things that could go right with an investment. This doesn’t mean applying blind optimism, but instead likely means calibrating a sense of calculated optimism while still managing the downside as much as possible.

Over the past several years, I’ve actually found this transition to be harder than I had originally expected. After all, pessimism almost always sounds smart. Optimism, on the other hand, can sound hopeful at best, and perhaps even naïve at worst.

However, maybe there is some truth to the old saying that “pessimists sound smart, but optimists make money” (though I might add a bit of nuance to that thought by suggesting that calculated optimists make money).

It’s reasonable to assume that your investment thesis will feature at least a few of the variables above that make aspects of your company or investment thesis “easier to kill”. And that’s OK – our goal here isn’t perfection or universal adherence to some sort of checklist. Instead, as author and investor Guy Speir once said (when discussing the use of checklists when making investment decisions) “As with all of these rules, the point is not to let them become a straightjacket, but to have them guide my behavior in a generally healthier direction”

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