Sales & Marketing Alignment
Most sales teams have an informal sense of what a good lead looks like. Almost none have it written down. Without a documented SQL definition, sales and marketing argue about lead quality, pipeline is inflated with junk, and close rates look worse than they actually are.
The definitions only matter if they're agreed upon by both sales and marketing — and built from your actual win data.
An MQL is a lead that marketing has determined shows enough interest or fits enough criteria to be worth sales attention — but hasn't been qualified by a sales conversation yet. MQL definitions vary wildly: it might mean 'downloaded a whitepaper,' 'attended a webinar,' or 'matches our ICP firmographics.' The core problem: without a specific, agreed-upon MQL definition, marketing sends everything and sales ignores everything.
An SQL is a lead that a sales rep has engaged with directly and confirmed meets the minimum bar for real pursuit. SQL qualification typically uses a framework like BANT (Budget, Authority, Need, Timeline) or MEDDIC. The key: it requires a human sales conversation, not just a data match. A lead that looks perfect on paper but has no budget or no urgency is not an SQL — it's a marketing hypothesis.
An SAL is the handoff stage between MQL and SQL — it's the moment sales formally accepts the lead from marketing and commits to engaging within a defined SLA (typically 24–48 hours). SAL is most relevant in companies with a formal SDR/AE structure where there's a clear handoff point. For most $1M–$10M companies, the SAL stage can be simplified — but the concept of a committed response SLA is valuable at any size.
The right SQL definition is built from your closed-won data — not from a framework template. Pull your last 30 closed deals and ask: what did they have in common at the point of first real engagement? Budget range confirmed? Decision-maker in the room? A specific trigger event (headcount growth, new funding, technology change)? The criteria that appear in 80%+ of your wins become your SQL definition. Update it quarterly as the win data evolves.
The absence of a documented SQL definition is one of the most common — and most costly — operational gaps in $1M–$10M companies. Without it, sales reps make individual judgment calls about which leads to pursue, marketing has no feedback signal to optimize against, and pipeline reviews become debates about individual deals rather than systematic analysis of conversion patterns. The cost isn't visible in any single quarter — it compounds silently over time.
Building an SQL definition is a half-day exercise. It requires a sales leader, a marketing leader (or founder), and access to the last 30 closed-won deals. The output is a one-page document that both teams commit to using — and review quarterly. That document, consistently applied, improves forecast accuracy, reduces wasted rep time, and gives marketing a target worth optimizing toward. Win/loss analysis is how you validate your SQL criteria →
One document. Two completely different ways sales and marketing operate.
Sales and marketing argue constantly about lead quality — each side blames the other
Clear SQL criteria mean both teams agree on what constitutes a real opportunity
Reps spend time on leads that will never buy — opportunity cost is invisible
Reps only pursue leads that meet the minimum qualification bar — time is protected
Pipeline is inflated with wishful opportunities that make the forecast look full
Pipeline reflects real opportunities — forecast accuracy improves significantly
Conversion rates are meaningless because the denominator includes unqualified garbage
Conversion rates are based on qualified leads — and become a meaningful performance signal
Marketing optimizes for MQL volume — sends leads sales will never work
Marketing optimizes for SQL conversion — generates fewer, better leads
Close rate appears low but the actual problem is poor lead quality at the top
Root cause of close rate problems is correctly identified — qualification vs. process vs. rep skill
If any of these are true, your pipeline data is unreliable and your close rate metrics are misleading you.
Sales and marketing have different definitions of what a good lead looks like
Reps complain that marketing leads aren't worth working
Marketing hits MQL targets but sales misses quota — and no one connects the two
Your pipeline looks full but conversion rates are low and deals are slow
You can't tell whether a close rate problem is a qualification problem or a process problem
There's no agreed SLA for how quickly sales should follow up on marketing leads
I'm Louie Bernstein — I have 50 years in business experience, including 22 as a bootstrapped founder. My Fractional Sales Leadership business has been helping founders since 2017.
In almost every engagement I start, there's no documented SQL definition. Sales and marketing are operating on separate assumptions about what a good lead looks like, and nobody has reconciled them. Creating that shared definition — built from closed-won data, not theory — is one of the first alignment exercises I run. It usually takes half a day and immediately improves both pipeline quality and the sales-marketing relationship.
BANT (Budget, Authority, Need, Timeline) is a useful starting framework — not a universal standard. For high-velocity, low-ACV sales, BANT is often too heavyweight and slows reps down unnecessarily. For complex enterprise deals, BANT may not be sufficient and frameworks like MEDDIC or SPICED provide more structure. For most $1M–$10M B2B companies, a simplified version works well: 'Does this person have the authority and budget to buy, and is there a specific problem they need to solve in the next 90 days?' Those three questions catch most unqualified leads.
In a functioning demand generation model, rejected leads go back to marketing with a specific rejection reason (not 'bad lead' — that's useless). Marketing tracks rejection reasons, adjusts targeting, and feeds the pattern back into campaign optimization. This feedback loop is what makes the MQL-to-SQL handoff valuable over time. Without it, sales rejects leads and marketing keeps sending the same low-quality volume, and the conversation never improves.
Your SQL definition should be a tighter version of your ICP. The ICP describes the universe of companies and buyers you want to sell to. The SQL definition adds the behavioral and situational signals that indicate a specific company is ready to buy now — not just potentially a good fit at some point. A company that perfectly matches your ICP but has no current urgency or budget is an ICP match, not an SQL. The SQL filter protects rep time from ICP matches that aren't ready.
At minimum, quarterly — and whenever the win data changes significantly. The most common trigger for an update is a shift in your average deal size or ICP segment. As companies grow from $1M to $5M ARR, the buyers they serve often shift: the SQL criteria that worked for SMB deals may not apply to the mid-market segment they're starting to win. Running a quarterly win/loss analysis and updating SQL criteria based on what closed is the discipline that keeps qualification current.
In 30 minutes I can walk through your current lead qualification process, identify where the definition is breaking down, and help you build an SQL framework from your actual closed-won data.