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By Itamar GeroJuly 19, 2022In MarketingSales

MQL vs SQL, What’s the Difference and how to Qualify?

There are no correct definitions for MQL and SQL. Everyone has their own understanding of the terms. What’s important, though, is to adopt the vision that works for you and ensure that you and your team are on the same page.

I often see teams confused about what MQL and SQL really mean, and part of the reason is that different articles have different definitions for each term. With so many views on defining marketing and sales-qualified leads, getting industry voices to agree on one seems impossible.

There are no correct definitions for MQL and SQL. Everyone has their own understanding of the terms

This is why, from my experience, the best approach is to find the definition which works for you and stick to it. To help you do that, I will share my perspective on what MQL and SQL are and hopefully bring clarity to the two terms.

What is an MQL?

MQL, also known as a marketing-qualified lead, is a lead that, according to the marketing team, has increased chances of becoming a customer. Since marketing requires significant effort and resources, pinpointing what goes into the MQL category is crucial. Good accurate targeting ensures brands expand their marketing communications and spend additional budgets on consumers who have business potential. To put it more visually, this is a laser-focused approach compared to scattershot.

Tracking whether a lead qualifies as an MQL is done by analyzing the engagement level and specific consumer behaviors, to check whether they indicate an interest in the brand’s products or services. This may be done by voluntarily submitting contact information, opting into a newsletter, visiting the site several times a week, or downloading brand-related materials, such as e-books, research presentations, and white papers.

Indicators that a lead is a marketing-qualified lead:

  • Repeat website visits within a timeframe.
  • Submitting contact information.
  • Signing in for newsletters.
  • Opening emails from your company.
  • Downloading brand-owned content.
  • Adding objects to carts.
  • Asking questions about products.
  • Landing on specific pages.
  • Coming from specific high intent campaigns.
  • Coming from a particular geo-location.
  • Using certain devices.

Even behaviors that might look minute, like scrolling all the way to the bottom of a landing page, could be considered a lead that showed higher than average interest and should be marketed to!

What is an SQL?

Some would assume that an SQL (sales-qualified lead) is a lead that’s eager to buy and ready for a sale, but, in most cases, things aren’t that simple. From my experience, an SQL has a higher than average intent to make a purchase. They might be at the end of their consideration process, meaning they qualify for a sales conversation.

Once a potential lead is categorized as an SQL, they will be assigned to a sales account or executive that will get in touch to determine the actual buying potential and, if applicable, walk them through the specific product or solution, emphasizing how they respond to their needs.

Suppose the intent of discussing with a sales representative has been confirmed. In that case, the SQL may enter a more carefully designed touch point system that includes getting an offer, negotiating, and closing a deal. 

Possible indicators that a lead is a sales-qualified lead:

  • Opened a marketing email more than a few times (it might show that they forwarded it to other colleagues for their review)
  • Requesting to be contacted by a representative.
  • Requesting a free demo.
  • Requesting a presentation.
  • Requesting an offer.
  • Left certain combinations of answers upon onboarding.

MQL vs. SQL: Differentiating between the two

While differentiation between marketing and sales qualified leads is crucial, the right strategy goes beyond MQL vs. SQL. Understanding that these are both sales pipeline stages increases your team’s chances of blending marketing and sales into a successful strategy that delivers.

Differentiating MQL from SQL is essential for businesses because it optimizes their marketing and sales efforts, generating more ROI.

Still, while distinguishing between the two is extremely valuable, understanding that they represent different stages of the sales pipeline is even more so. When nurtured well, an MQL becomes an SQL, which, in return, becomes a customer and, if the sales and post-sales experience are excellent, a brand promoter.
In this context, when discussing MQL vs. SQL, Hubspot identifies three key factors that need to be taken into account:

Lead Score

Making a conversion between MQL and SQL starts with lead scoring. During this process, leads are assessed based on several attributes, receiving points for how they engage with the brand’s website and platforms.

Lead Behavior

Analyzing lead behaviors is done by focusing on some critical aspects agreed upon by the marketing and sales teams. Through collaboration, the two teams define positive and negative actions. 
For example, attending webinars, responding to an email, or booking a meeting could all be considered positive actions that support the fact that an MQL may be converted into an SQL and passed over to sales.
Conversely, not opening emails or unsubscribing from a newsletter will bring down someone’s lead score.

Likelihood to Buy

Marketing and sales professionals use the BANT system (Budget, Authority, Needs, and Timeline) to identify whether making a conversion between MQL and SQL is possible. When analyzing potential customers, you must understand whether they have the need, infrastructure, and budgets to buy your solution.

Turning MQL vs. SQL into MQL and SQL

The MQL vs. SQL approach is quite popular in today’s business ecosystem, but, from my perspective, marketing qualified leads and sales qualified leads are not the job of independent departments. Since they are codependent, you need marketing and sales efforts to align and focus on the same goals – developing strategies that turn MQLs into SQLs and SQLs into sales.
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