Two “Equally Qualified” Leads Can Still Be Worth 20x Different Amounts
A B2B SaaS company selling add-on software to e-commerce merchants once ran the numbers on its own customer base, split by which platform each merchant sold on. The gap was staggering: accounts running on Amazon FBA generated ten to twenty times more lifetime value than accounts on Etsy or eBay — higher order values, longer retention, all of it. The sales cycle to close either one? Statistically identical.
Same sales effort. Wildly different payoff. If your lead qualification process can't tell the difference between those two prospects before a rep ever picks up the phone, what exactly is it qualifying them for?
That's the throughline of Episode 2 of B2B Effectiveness: Evidence-Based Marketing Ideas for B2B Practitioners, where Liam Moroney and Dale W. Harrison move from last episode's argument — the MQL is a structural necessity, not a fad — into the harder practical question: what would an MQL actually need to include to be useful?
A good MQL is a bet, not a certainty
Sales is expensive. A single rep's time is worth vastly more per prospect than a single marketing impression, and it doesn't scale the same way — you can put an ad in front of a hundred thousand people at once, but a rep can only work one conversation at a time. That asymmetry is exactly why a qualification step has to exist: someone has to decide which names are worth the cost of that one-to-one investment.
The mistake almost the entire industry has made is treating that decision as something you can know for certain. You can't. The information that would tell you with certainty whether someone is ready to buy right now — their private conversations, their internal debates, what's actually happening inside their head — is information marketing will never have access to. What's left is a tiny, fractional signal, and the honest move is to stop pretending otherwise and start thinking in probabilities instead.
The five things a useful qualification actually needs to answer
Rather than a single score, Dale lays out five distinct questions — each one answerable to a different degree of confidence, and each one necessary before a lead is genuinely worth a sales investment:
- Broad ICP fit — is this the kind of person, at the kind of company, that ever buys something like what you sell? This is the one question the available data can answer with reasonable confidence.
- In-market fit — are they actually in a buying cycle right now? This is the one the industry has spent two decades and hundreds of billions of dollars trying to solve with “intent data,” with essentially nothing to show for it.
- Consideration set eligibility — even if they're ready to buy, are they willing to buy from a company that looks like yours? A perfectly good CRM that isn't HIPAA-compliant will never make it into a hospital's shortlist, no matter how good the product is.
- Conditional win probability — if they're genuinely willing to consider you, is your sales team actually equipped to beat the competition for that specific type of buyer? Two identical companies selling identical products can have completely different close rates with the same prospect, purely based on which accounts their sales team has gotten good at closing.
- Lifetime value fit — the Amazon FBA versus Etsy problem. Even among prospects who are equally likely to buy, the payoff on that investment can differ by an order of magnitude.
None of these are things you can know for certain. Some — broad ICP fit — you can estimate with real confidence. Others, especially in-market fit, you mostly can't, and pretending otherwise is where lead scoring keeps failing.
Why checklists like MEDDIC don't fix this
A tempting workaround is to force prospects through a structured checklist — does this account have budget, authority, need, timing? Frameworks like MEDDIC formalise exactly this. The problem is that people will truthfully answer yes to every one of those questions without having the slightest intention of buying from you.
Dale's example: enterprise procurement teams routinely already know which vendor they're going to buy from before a formal process even starts — but procurement policy requires five to seven competing bids. So teams go find other vendors, run them through calls, let them believe there's a real opportunity, and never intend to buy from any of them. Every box on the checklist gets ticked. The deal was never real.
Hand raisers aren't a golden ticket either
Someone filling out a form and asking to talk to sales is a better bet than a cold outbound name pulled at random — but “better than random” is a long way from “likely to close.” Especially for smaller brands, the majority of hand raisers still won't convert. The honest way to think about it: a hand raiser is a somewhat better poker hand, not a winning one. It's worth playing. It's not worth promising sales it's a sure thing.
Thinking in bets, not certainties
The mental model Dale keeps returning to — borrowed from the book of the same name, written by a psychology researcher who was a professional poker player before her PhD — is that good decisions and good outcomes aren't the same thing. A good decision can still lose. A bad decision can still win. Salespeople tend to grasp this intuitively, because they're already making exactly this kind of decision every time they choose whether to keep working a deal or move on. Marketing, by contrast, keeps reaching for false certainty — a 100-point threshold, a checklist, a score that implies a yes-or-no answer the data was never capable of giving.
The fix isn't a better checklist. It's replacing the question “is this lead ready?” with “given everything we can actually know, is this worth the bet?” — and building a qualification process honest enough to admit what it can and can't tell you.
Next up: Dale and Liam get into the details of why most lead scoring systems are guaranteed not to work — and what to do instead.
