For years, the Marketing Qualified Lead (MQL) has been the cornerstone of B2B marketing strategies. It has served as a signal to sales teams that a prospect is ready for further engagement. But with changing buyer behaviors, the rise of complex buying committees, and the evolving sales cycle, the MQL is facing serious scrutiny. Some even claim that the MQL is dead. But is it really?
The MQL concept is not new. In fact, it’s more than 25 years old, with various definitions across industries. Traditionally, an MQL was a lead that showed enough engagement—like downloading an eBook or attending a webinar—to be passed from marketing to sales. However, this lead-based approach focused on individual interactions, often ignoring the broader context of B2B buying decisions.
Today’s buyers are more informed than ever, with most of the decision-making process happening before they even speak to a sales rep. Additionally, the rise of buying committees—often composed of 6-10 people or more—means that no single action from one individual tells the whole story of an account’s readiness to buy.
This shift has led some experts to declare the MQL dead, but the truth is more nuanced.
An MQL (Marketing Qualified Lead) is a lead that has shown interest in a company’s products or services through various marketing efforts but is not yet ready for direct sales contact. Typically, MQLs are identified based on their engagement with marketing content—such as downloading whitepapers, attending webinars, or subscribing to a newsletter. While these leads have demonstrated curiosity or potential interest, they require further nurturing to become fully sales-ready. The marketing team works to guide MQLs through the funnel by providing relevant content, building brand awareness, and encouraging deeper engagement.
An SQL (Sales Qualified Lead), on the other hand, is a lead that has progressed beyond initial interest and has been deemed ready for direct sales engagement. SQLs have taken more definitive actions that indicate a stronger intent to purchase, such as requesting a demo, engaging in a conversation with a sales representative, or showing clear buying signals. These leads have been vetted by both the marketing and sales teams and are more likely to convert into actual customers.
The key difference between MQLs and SQLs lies in their stage of readiness for a sales conversation: while MQLs still require nurturing, SQLs are primed for direct outreach and are closer to making a purchasing decision.
Contrary to the “death” narrative, MQLs still play an important role in lead generation. According to some reports, as much as 90% of revenue in certain organizations comes from MQLs. So, it’s premature to write their obituary.
What is changing, however, is how businesses define and use MQLs. Rather than relying solely on individual leads, marketers are increasingly adopting an account-based approach, focusing on the collective behaviors of the entire buying committee. Tools from solutions providers are designed to target these broader buying groups, not just individuals. This shift doesn’t eliminate the MQL but reframes it within a larger context of account-based engagement.
The MQL model is no longer the be-all and end-all of lead generation. Businesses are moving toward more comprehensive metrics focusing on driving opportunities rather than generating leads. Account-Based Marketing (ABM) and intent-based strategies are at the forefront of this shift, emphasizing the importance of nurturing the entire buying committee rather than a single contact.
The key is no longer whether an individual checked off engagement boxes, but whether the entire account shows buying intent. This requires close alignment between marketing and sales to identify and act on behaviors that signal readiness, such as product usage, cross-channel engagement, or intent signals captured through AI-driven tools.
The death of the traditional MQL doesn’t mean the death of lead generation—far from it. Today’s lead generation relies on richer, data-driven insights powered by AI and predictive analytics. By analyzing a wider array of behaviors and intent signals, marketers can prioritize the right accounts before direct engagement even happens.
For example, by leveraging intent data, marketers can identify accounts that are researching relevant topics or interacting with competitor content. This allows sales teams to have more meaningful conversations when prospects are actively in-market, improving conversion rates and shortening sales cycles.
It’s not about abandoning the MQL but evolving it. Some companies are experimenting with concepts like the “group MQL,” which tracks engagement across multiple stakeholders in a target account. This reframing helps capture the complexity of modern B2B sales processes, where multiple people within an organization must sign off before a deal is made.
At the same time, marketers must continue nurturing MQLs, driving them through the funnel to become Sales Qualified Leads (SQLs) and eventually opportunities. The key question isn’t just, “Is this lead qualified?” but also, “What is being done to move this lead toward a buying decision?”
As B2B marketing evolves, here’s how organizations can transition from a strict MQL framework to a more comprehensive, intent-based approach:
While the MQL as we knew it may be fading, its evolution signals the rise of more sophisticated, intent-based lead generation strategies. It’s not about chasing individual leads but engaging the entire buying group, creating opportunities, and nurturing them to the point of conversion.
The MQL may be evolving, but lead generation is more alive than ever—if you’re willing to adapt. So, while we bid farewell to the traditional MQL, let’s welcome the future of B2B marketing where the buying group reigns supreme.
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As Content Marketing Manager, Natasia is responsible for helping strategize, produce and execute Data Axle's content. With a passion for writing and an enthusiasm for data management and technology, Natasia creates content that is designed to deliver nuggets of wisdom to help brands and individuals elevate their data governance policies. A native New Yorker, when Natasia is not at work she can be found enjoying New York’s food scene, at one of NYC’s many museums, or at one of the city’s many parks with her two teacup yorkies.