The lead that doesn't just look right - it's worth winning.
Next-Gen MQL scores how financially valuable a lead is for your business, built on buyer-seller fit, not borrowed intent.
Every account in a CRM was "ICP" once. Intent data proves a company is consuming content, but every competitor working that account sees the same score. We measure something else: the fit between this buyer and this seller, learned from your own history of wins and losses.
Apply for the Next-Gen MQL.
Re-watch the LinkedIn Webinar
Join Dale on-demand to watch him break down the Next-Gen MQL from start to finish or jump straight into the PDF doc.
Intent data isn't the problem. Treating it as the whole answer is.
AI-driven content consumption is exploding. Digital engagement alone no longer signals real buyer intent. Volume-based models reward activity, not revenue. Ten e-books downloaded, three points each. Easy to understand, completely wrong.
We replace generic, guessed-intent scores with client-specific commercial-value prediction, built on your data and where you actually win. The question stops being "is this account in-market?" and becomes "how financially valuable is this lead, for us?"
What an MQL should mean
Broad ICP Fit
Is this account likely to buy something that looks like what you're selling?
In-Market Probability
Who is researching now? Topic-exploration signals weighted against your category, never mistaken for the whole answer.
Buyer-Seller Fit
Is this account likely to buy from someone who looks like YOU and are you well-suited to sell to an account this looks like THEM?
Customer Value Fit
Is this account likely to become a high-value customer? (CLTV – high purchase volume and/or lowchurn risk)
Rising markets
Where is demand actually rising — and where can you afford to compete?
Regional market growth steers budget by geography. The true competitive cohort penetration vs. displacement comes into view, and brand-vs-performance impact is proven through real-time Share of Search.
The outcome: guidance on where to prioritise budget, how much is needed to compete by region, and whether your brand-and-demand mix is genuinely effective.
Intent, Read Properly
Who is actively researching now — and is that signal real, or just noise?
Topic-exploration signals identify accounts moving into an active buying window. Surges are weighted against your category, not generic content consumption and intent is treated as one input, never the whole answer.
The outcome: a live read on which accounts are genuinely in-market, so spend follows demand instead of chasing every click that looks like interest.
Vector Modelling
Which account types do you, specifically, close faster and at higher value?
Built on your real deal data, not borrowed category-wide assumptions. Normalised AOV × deal-velocity scoring ranks the vectors you actually win, producing one target list sales and marketing both trust.
The outcome: a weighted account list that concentrates spend where win rate and value are proven and deprioritises the segments quietly draining budget.
CLTV from Invoicing
Two accounts can look identical at the point of sale — and be worth wildly different amounts.
Contract and invoicing metadata reveal who stays, expands, and pays. Same fit, same ACV — but one churns in a year while the other compounds for five. The Next-Gen MQL favours leads with the highest lifetime-value expectation.
The outcome: lead value reframed around durable revenue, so acquisition effort flows to the accounts that keep paying.
Same 10,000 accounts. Three ways to choose.
Each list is the same size and equally "in-market". Only the targeting logic changes and the close-won rate swings 50×.
Broad ICP
0.35%
35 closed-won from 10,000
Likely to buy from the category.
Big-Logo Bias
0.02%
2 closed-won from 10,000
Chasing the Fortune 1000 by name.
Buyer-seller fit
1.0%
100 closed-won from 10,000
Consideration-set × win probability.
Questions we hear about Next-Gen MQL
We use human expertise to build the model, deliberately, with judgement about how your data should be read, and use automation only to execute it. You're choosing between a system with very little transparency and one that's designed by people who can show you exactly why it works. When you're about to commit budget on the output, being able to interrogate the method isn't a nice-to-have, it's the whole point.
Intent data tells you an account is consuming content on a topic, and so does every competitor's intent tool, reading the same signal about the same account. It's shared information you're all bidding against. We measure something only you have: the fit between an account and your business, learned from where you actually win. Intent tells you who's looking. Fit tells you who's worth your sales team's time.
Two things most teams aren't doing. First, if you're confident in your ICP, we'll either confirm it with hard evidence, or show you where it's quietly costing you. Either outcome is worth knowing. Second, even when your account list is right, we add what it's usually missing: prioritisation and timing. Which of your good accounts is worth the most over its lifetime, and which markets are heating up right now. We don't replace your list, we sequence it. (And the usual point-based lead scoring, three points for an e-book, five for a pricing page, is easy to understand and almost always wrong, because adding up engagement isn't the same as measuring fit.)
A lookalike list is a black box: you hand a platform some names, it hands back more names, and the machinery in between is invisible. Worse, ad platforms are incentivised to pad those lists with high-traffic companies that burn your budget, which is why lookalikes deliver reach but no engagement. We do the matching openly: not "find companies like Adobe," but companies in these industries, these revenue ranges, these geographies, then we score everyone against your model and cut everything below a quality threshold. Reach you can explain, not reach you have to trust on faith.
Often, no names at all. The model learns from the shape of your deals, industry, size, geography, how they closed, not the identities behind them. We can work from a summarised, anonymised extract where every account is just a profile. We'll meet your data wherever it sits: the more you can enrich and roll up in-house, the faster you get an answer and the fewer governance questions anyone has to field. If you don't have the enriched data then we can help you get this done.
Your data trains a model that's yours, built on your history. We're happy to sign NDAs, DPAs, whatever your team needs. And because much of the work can run on anonymised, rolled-up data, for many engagements the sensitive detail never leaves your side at all.
Every CRM is messy, it's the universal starting condition, not a disqualifier. A lot of the mess washes out, because duplicate and mistyped accounts roll up into the same profile anyway. Where data is thinner or more anonymised, the model gets less precise, wider error bars, but not less valid. And we can show you those error bars: this is a method that reports its own confidence, not one that hands you a falsely precise number and hopes you don't ask.
Yes, the model is built on your data, so that's thefirst step; there's no generic version to hand you. But "first step" can be fast: if you can give us a clean, rolled-up extract, we can be close to an answer quickly. The best way to see it is small and concrete, a proof-of-concept on a slice of your accounts, where we compare what the model surfaces against the list you already trust.
Prove Next-Gen MQLs against your current baseline.
We'll run the model on your numbers and show you where the return actually is. Bespoke, evidence-backed and fully transparent.