What Would a New MQL Have to Look Like?

A B2B SaaS company selling add-on software to eCommerce merchants once analyzed its customer base by the platform each merchant sold on. The difference was striking. Accounts running on Amazon FBA generated ten to twenty times more lifetime value than accounts on Etsy or eBay, driven by higher order values, longer retention and stronger economics overall.

Yet the sales cycle was statistically identical.

In other words, the same sales effort can produce dramatically different returns depending on an account's characteristics. If a qualification process can't distinguish between those accounts before a salesperson ever picks up the phone, what exactly is it qualifying them for?

That's the question at the heart of Episode 2 of B2B Effectiveness: Evidence-Based Marketing Ideas for B2B Practitioners, where Liam Moroney and Dale W. Harrison build on the previous episode's argument: the MQL remains a necessary part of the marketing and sales process, but the way most organizations define and score one is not particularly useful. The more practical question is what a qualification process would actually need to tell sales.

A good MQL is a bet, not a certainty

Sales is an expensive resource. Marketing can put an ad in front of hundreds of thousands of people simultaneously, whereas a salesperson can only work through one conversation at a time. That difference in scale is why some form of qualification is necessary. Someone has to decide which prospects are worth the cost of that one-to-one attention.

The problem begins when that decision is treated as if it can be made with certainty.

The information that would provide absolute confidence about whether someone is ready to buy today largely sits outside a marketer's view. It includes private conversations, internal discussions, competing priorities, budget decisions and all the other factors that shape a buying decision before they ever produce a trackable digital signal.

What marketing has instead is partial evidence. The sensible response is to work with that uncertainty rather than disguise it. Qualification is ultimately an exercise in estimating probabilities: given what we know, where is sales effort most likely to pay off?

The five questions a useful qualification process needs to answer

Harrison argues that a useful qualification process needs to address five different questions. They cannot all be answered with the same level of confidence, but each contributes something important to the decision about whether a prospect deserves sales attention.

• Broad ICP fit – Is this the kind of person, at the kind of company, that could realistically buy what you sell? Of the five questions, this is one the available data can often answer with reasonable confidence.
• In-market fit – Is there a genuine buying process underway right now? This is the question the industry has spent years trying to answer through intent data, despite the difficulty of observing an internal decision before it becomes visible.
• Consideration set eligibility – Even if the prospect is ready to buy, would a company like yours make it onto their shortlist? A CRM platform that doesn't meet a hospital's compliance requirements, for example, is unlikely to be considered regardless of how strong its other features are.
• Conditional win probability – If the prospect is willing to consider you, how well positioned is your sales organization to win that particular deal? Two companies selling similar products can have very different close rates with the same type of buyer because their sales teams have developed different areas of experience and strength.
• Lifetime value fit – Even when two prospects are similarly likely to buy, how valuable would the resulting customer be? This is the issue illustrated by the Amazon FBA and Etsy example. The probability of winning can be similar while the commercial return is radically different.

These questions are estimates, not facts. Some can be estimated relatively well; others, particularly the question of whether someone is genuinely in-market, are much harder to observe. A qualification process becomes more useful when it reflects that difference instead of reducing every answer to a single score.

Why checklists like MEDDIC don't solve the problem

A tempting workaround is to force prospects through a structured checklist – does this account have budget, authority, need, timing? Frameworks like MEDDIC formalize 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.

Harrison gives the example of enterprise procurement teams that already know which vendor they intend to select before a formal process begins. Their procurement rules may require five or seven competing bids, so they bring other vendors into the process, hold meetings and request proposals even though the decision has effectively already been made.

From the outside, those opportunities can look extremely well qualified. The prospect has a need, there is a defined process, senior stakeholders may be involved and the vendor has been invited to participate. The checklist can look excellent right up until the moment the deal is lost.

The problem isn't that the checklist was completed incorrectly. It is that the checklist cannot reveal information the buyer has no reason to disclose.

Hand raisers aren't a golden ticket either

A prospect who fills out a form and asks to speak with sales is generally a better bet than a name selected at random for an outbound campaign. That distinction matters, but it shouldn't be confused with a high probability of closing.

Especially for smaller or less established brands, most inbound hand raisers will still fail to become customers. The useful interpretation is that the prospect has given you a stronger starting position. They may be worth pursuing, but the signal doesn't turn the opportunity into a certainty.

Thinking about it as a poker hand is useful here. A hand raiser may give you better cards than a random prospect, but it doesn't tell you how the game will end.

Thinking in bets, not certainties

This is the broader mental model Harrison keeps returning to, drawing on the ideas explored in Thinking in Bets. Good decisions and good outcomes are not the same thing. A well-informed decision can still produce a loss, just as a poor decision can occasionally produce a win.

Salespeople tend to understand this because they make these judgments constantly. They decide which opportunities deserve another meeting, where to invest more time and when the probability of winning has fallen far enough that their effort is better spent elsewhere.

Marketing has historically tried to turn that uncertainty into something more definitive: a 100-point threshold, a qualification checklist or a score that appears to provide a simple yes-or-no answer.

The more useful approach is to ask a different question: given everything we can actually know about this prospect, how attractive is the bet?

That changes the role of qualification. The goal isn't to identify leads that are guaranteed to buy. It is to give sales a better basis for deciding where its limited time and effort are most likely to generate a return, while recognizing that uncertainty is part of the decision.

Next up: Dale and Liam examine why most lead scoring systems are fundamentally incapable of doing that job – and what a different approach could look like.