What makes a lead “qualified”? It depends. Different teams and functions define lead qualification differently, which is how we ended up with MQL vs. SQL vs. SAL.
The problem is these terms were defined for linear, waterfall approaches. In modern B2B GTM and RevOps, however, the distinctions are less important. Customer journeys are non-linear, handoffs happen more than once, and cross-functional collaboration is critical.
Yet marketing teams need to measure their effectiveness independent of sales performance. So how do you do that in today’s modern B2B landscape? Read on to find out.
What is an MQL vs. SQL vs. SAL?
Marketing-sales alignment requires common ground around which to align. Revenue, while the ultimate goal, depends too heavily on intervening factors to solidly measure marketing success. Not to mention the fact that many of the benefits marketing provides—brand elevation, audience growth, increased conversation among prospects, etc.—aren’t always measurable.
As such, marketing and sales teams have developed their own approaches for defining lead qualification, resulting in the MQL vs. SQL vs. SAL divide.
Marketing Qualified Lead (MQL)
Since measuring revenue and lead value directly depends on salespeople’s ability to perform, marketing teams have established their own KPI: the marketing qualified lead (MQL).
Simply put, MQLs are leads the marketing team has determined are likely to become a customer. Generally, they determine this by looking at three primary criteria:
- Does the lead have a problem your product solves?
- Are they aware enough of that problem to actively seek solutions?
- Do they have the budget and internal authority to buy your product?
If you’re interested in a more robust lead qualification checklist <-- check out our resource.
Marketers will use criteria like the following to determine whether a lead is qualified:
- Web pages visited
- Content offers downloaded
- CTAs clicked
- Emails opened
- Social post engagement
Here is one of our most popular Workflow temples that qualifies, routes, and schedules inbound demos. Learn more to find out.
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MQLs can defined by looking at these criteria quantitatively (how many interactions), qualitatively (which types of content), or a combination of both. High engagement doesn’t necessarily mean the lead is ready to buy, but it means the lead is more likely to buy than others.
Sales Accepted Lead (SAL)
In many GTM organizations, marketing and sales teams align their efforts through service-level agreements (SLAs) that establish expectations around how many leads the marketing team should generate, and the steps salespeople should take upon receipt.
A sales accepted lead (SAL), then, is an MQL that has been reviewed by the sales team and meets the agreed upon criteria for sales readiness, but not necessarily determined to be sales-ready.
Sales Qualified Lead (SQL)
Once the sales team accepts the lead, they must determine whether they’re actually ready to buy. This involves engaging that SAL one-on-one, until they accept a meeting and demonstrate a concrete intent to buy.
This means that not all MQLs will end up as SQLs. While SQLs are partly indicative of Marketing’s effectiveness in qualifying the lead, it’s also indicative of salespeople’s effectiveness in engaging it. So marketers prefer to measure MQLs separately from SQLs.
What are the problems with this framework?
If you’ve been reading the Default blog for a while now, you’ve probably already noticed a problem with the MQL-SAL-SQL framework: it’s based on a linear, waterfall approach. Here are some of its drawbacks.
Linear approach to inbound
Inbound journeys are far from linear. Not only are the majority of leads 57-70% finished with their buying journey when they first engage with sales, often leads will double back, engage in omnichannel touchpoints, or pass the decision along to another member of their buying unit.
The linear approach mentioned above really only works with a waterfall RevOps strategy. A more agile or modern approach, however, requires some adaptation.
No cross-functional operations
The MQL-SAL-SQL framework assumes distinct operational silos among marketing and sales teams. Not only does this approach foster division and competition among internal teams, but it limits each team’s ability to maximize their performance—by as much as 200% in some cases.
Siloed performance metrics
As revenue functions become more integrated and cross-functional, the metrics used to measure them must do the same. RevOps KPIs and metrics enable a more comprehensive, accurate view of how each team is contributing to the other’s success. Unfortunately, the traditional MQL-SAL-SQL approach doesn’t allow for this. You can see the differences between the teams in our guide on sales operations vs revenue operations vs marketing operations.
Process-driven vs. outcomes driven
Because the MQL-SAL-SQL approach centers around a service-level agreement, the focus of each team becomes on adhering to internal processes. Instead, the focus should be on driving outcomes; namely, accelerating revenue.
What’s a better way for modern GTMs to approach lead qualification?
Given these challenges, there’s a better way to think about MQLs and SQLs. For starters, as marketing and sales teams become more integrated, formal SLAs become less relevant. Which means SALs are quickly becoming an outdated metric.
As with many RevOps KPIs, compound metrics often tell you more than just looking at a single stat. Let’s look at three examples that can be helpful to integrated, cross-functional RevOps and GTM teams.
MQL to SQL conversion rate & benchmark
The first is to look at your MQL to SQL conversion rate. If this number is too low, it means either that your marketing team is sending poor quality leads to sales, or that sales isn’t taking enough action on them. Both are easy to verify if you’re using a centralized inbound platform.
If your MQL to SQL conversion rate is too high, that means that marketing has too strict qualification standards, and you need to widen the circle.
A typical MQL to SQL conversion rate is 13%.
MQL to opportunity conversion rate & benchmark
Another approach is to skip the SQL stage entirely and look at MQL to opportunity conversion rate. This KPI tells you the percentage of MQLs that end up in the sales pipeline, actively engaged in buying conversations.
This compound KPI gives you a good idea of the health of the overall organization, but can’t effectivley measure marketing and sales success individually.
A typical MQL to opportunity conversion benchmark is around 5-7%.
Lead to SQL conversion rate & benchmark
Then there’s another approach that removes the MQL metric entirely, and focuses just on how many SQLs marketing drives. This approach allows for more flexibility in marketing activity, allowing for lead conversion scenarios that may not fit the linear approach mentioned above.
A typical lead to SQL conversion rate benchmark is 3%.
See our guide on b2b conversion rate optimization.
Final thoughts on MQL vs. SQL vs. SAL
If you’re using the linear, waterfall approach to measuring MQLs vs. SQLs vs. SALs, you’re going to struggle with GTM success. Instead, it’s time to look at a more integrated, cross-functional approach that accounts for the dynamic nature of B2B sales cycles and customer journeys.
The level of integration and alignment required is difficult to accomplish with a distributed tech stack. For this reason, it’s important to have an integrated inbound platform that’s built for the modern GTM and RevOps use case, like a complete sales workflow software.
To learn more about how Default handles these integrated use cases with our revenue operations software <-- click here.
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