Asb Problem

Problem Score — from "The Problem" to "Viable Business Model"

Here is how companies fail: a founder has a flash of insight — the world has a Problem. Potential customers agree the Problem is real (they're right!). The founder builds a product that truly solves it (it does!). And then sales never materialize, and the company shuts down within a couple of years. Solving a real problem is — perhaps surprisingly — not nearly enough to build a successful company. Between "real problem" and "viable business" sit seven conditions, and founders systematically over-rate themselves on all of them. This skill scores those conditions honestly, one specific target market at a time; the user walks away with a scorecard, a directional verdict, and either a niche that rescues the idea or the clear-eyed conclusion — far cheaper now than in two years — that it isn't viable.

The mental model

A series of "ands" — why the scores multiply

A sale requires that enough people have the problem AND they know and care AND they have budget AND they can buy now AND they'd buy from you AND they'll stick around. The best case is that all seven hold. Realistically, some will be strong and some weak; the real question is whether the big strengths overcome the few weaknesses. Multiplication answers that question mechanically: compounding "ands" means one near-zero factor drags down the whole product no matter how impressive the rest are — which matches reality. Gut feel is no guide here: every idea feels true to its founder, including the majority that turn out wrong. The multiplication is how the weak link gets seen instead of glossed over.

Fermi estimation: powers of ten by default

Every score is a power of ten (or a fixed coarse value like 0.1 / 0.5 / 1.0) — no in-between numbers, no false precision — with one exception: hard data is used as-is (8 billion humans is 8B, not rounded to 10B; measured 4%/mo churn is 4%). Precision comes from data, never from feel, and since it multiplies with rough estimates, the final score is still only good to a power of ten. The coarseness is what makes the exercise fast and honest: most values are easy to pick because the adjacent choices are absurd ("100k or 1M? — certainly not 10k, certainly not 10M"), and when a value IS controversial, that order-of-magnitude disagreement is genuine strategic uncertainty worth its own conversation, not a rounding argument. Real evidence — the user's own data, or numbers found by quick research — settles a value immediately. Without it, the honest move is the power of ten whose neighbors are clearly wrong, not the flattering one.

The score is guidance, not analysis

This is dangerously close to a silly quiz, and it must be used as guidance, never as precise analysis: a 0.8 is not doom, a 1.2 is not salvation. What the score reliably does is expose the weak links and show whether a different target market changes the answer by a lot — and having to think through the answers and trade-offs is most of the value, more than the final number. The score also deliberately measures only the path from problem to business model. It says nothing about reach, marketing cost, team, skills, or execution — a great score can still fail.

Optimism is the default failure mode

People are almost always too generous with what they can do and what customers will do, think, and pay. Every score therefore gets challenged before it is recorded — a scorecard filled in by unchallenged optimism is worthless; it will say "viable" about anything.

The seven criteria

Score each with respect to ONE specific target market: a specific type of buyer, solving a specific problem, with a product that has made specific trade-offs, at a specific price. The rubric applies to any kind of company — software, services, restaurants, hardware, content — not just tech startups.

1. Plausible — do enough people have the problem?

Scale (power of ten only): 1k, 10k, 100k, 1M, 10M, 100M, 1B — the number of consumers or businesses that actually have the problem.

Why the bar is high: marketing math. Ads convert roughly 1% of impressions to visitors, and a good product site converts roughly 1% of visitors to paying — about 10,000 impressions per customer. A sustainable small company needs on the order of 1,000 customers (at $30–$100/mo; cheaper means more needed), so ~10,000,000 impressions and about two years — a timeline even eventual giants needed for their first 1,000. Consumers: ~10M must have the problem. Businesses pay orders of magnitude more and convert better, so ~100k suffices.

Press on: counting everyone who could theoretically use the product instead of the specific buyer defined above; confusing "has the problem" with "matches my product description." Exception-with-conditions: a high-price product in a small niche can be a fine company, and so can a deliberately-small business replacing a salary — but then the other scores must be strong, and the user must genuinely want that path.

2. Self-Aware — do they know and care that they have the problem?

Scale: 0.01 few agree or care · 0.1 thought-leaders care and evangelize · 0.5 industry standard practice · 1.0 almost impossible to find someone who doesn't care.

Someone who doesn't believe they have a problem isn't searching for a solution, and won't spend money on one even if they stumble across it. Failure comes in two flavors: ignorance (millions of website owners truly are targets of hackers, yet think "no one would attack little old me," so they never shop for security), and knowing-but-not-caring (nearly everyone agrees an inaccessible website is a problem — and it still never cracks their top three priorities, so nothing happens). Market timing is a version of this: the same idea can fail years before the market is ready and succeed after.

Press on: "they just don't realize it yet — once we explain, they'll get it." That is a market you must CREATE: difficult, expensive, and slow. Exception-with-conditions: a founder who is a natural evangelist on a genuine mission can educate a market into existence — but must truly want years of that work, not merely tolerate the idea of it.

3. Lucrative — do they have substantial allocated budget?

Scale (power of ten only, of net revenue): $1, $10, $100, $1k, $10k, $100k, $1M — annual budget actually allocated to this problem. "Net revenue" means your revenue after pass-through costs (an eCommerce platform processing $100 and keeping $10 counts $10) — but do NOT subtract your marketing, support, or infrastructure costs; this measures top-line, not efficiency.

Agreeing the problem exists is not the same as having money assigned to solving it. Consumers mostly refuse to pay for software at all — people publicly agonize over $15/year for an app they use daily. Whole customer categories are structurally broke (college students — and therefore also the businesses that sell to them). In large companies, budget only exists for the top few problems of the year, and internal teams already tasked with the problem often fight outside solutions; target the companies that outsource this problem, not the ones that staff it.

Press on: "they'd definitely pay for this" without a story for whose budget line it comes from and who approves it. Score the budget the market demonstrably allocates — what these buyers pay anyone today, with your realistic annual revenue per customer as evidence; when your sticker price and the demonstrated allocation diverge, score the allocation and note the gap. Exception-with- conditions: a huge market at a low price can work IF the cost basis is extremely low (self-service, near-zero support, cheap acquisition, a product simple enough to scale unattended) — and then the Plausible number must rise accordingly.

4. Liquid — are they willing and able to buy right now?