MQL vs. SQL — how they’re different and how to use them together to drive revenue growth
Optimizing the funnel and closing more business means nurturing every lead appropriately. But digital transformation and omnichannel strategies are creating more opportunities than ever and making it difficult for sales and marketing teams to manage a greater variety of leads.
The first big step is to separate marketing qualified leads from sales qualified leads.
But the two terms are easily confused. In this guide, you'll discover the main differences between MQLs and SQLs so you can optimize your marketing and sales efforts, deter leads from going to competitors, and ultimately build a base of happy, loyal customers.
- The difference between an MQL and an SQL
- Why the difference between an MQL and an SQL matters
- How to map MQLs and SQLs to the sales funnel
- How to transition an MQL to an SQL
What is the difference between an MQL and an SQL?
An MQL is a marketing qualified lead, or someone who is interested in your products or solution. An SQL is a sales qualified lead, or someone who is interested and intends to buy. The difference between an MQL and an SQL is intent, so each type of lead requires different ads, outreach, and other brand messaging.
What is an MQL?
A marketing qualified lead (MQL) is an individual or organization that has engaged with your marketing efforts and could become a customer with proper nurturing.
An MQL might be someone who visits your site, clicks on a programmatic ad, or downloads an ebook about a high-level topic in your industry. Their behavior doesn’t indicate direct purchase intent — often because they don't have enough information about solving their problem or about your solution. But their behavior does put them at the top of the marketing funnel, and tells you that they might be open to hearing more from your brand.
What is an SQL?
A sales qualified lead (SQL) is a contact or account with buying intent that appears interested in your company as a candidate for their purchase. They have probably engaged with the brand several times and have shown interest in more advanced content like case studies, product comparisons, and pricing charts.
A lead becomes sales-qualified when they have:
- The information they need to make a decision
- The budget and resources to make the purchase
- Executive buy-in
After several engagements, the marketing team determines when it’s time to pass that buyer to the sales team to nurture through the bottom of the funnel. When an MQL transitions to an SQL, a one-on-one consultation with a sales representative can turn these leads into revenue opportunities.
Why the difference between an MQL and SQL matters
Understanding the MQL and SQL classifications is essential for your marketing and sales teams because it helps you operate efficiently. MQLs and SQLs are both leads in different stages of their buyer journeys and knowing how to qualify them helps determine which marketing or sales messaging each one should receive.
An MQL and SQL system also helps your sales and marketing teams get more closely aligned. Teams have to work together to determine a scoring system or a benchmark that indicates a lead is ready to pass to sales. They will also need to communicate regularly to hand off leads and evaluate the metrics that they’re using.
Lead behavior is all the actions a prospective customer takes while engaging with your brand. Seeing how a prospect engages with your website, social media channels, and other platforms can tell you a lot about their location in the buyer journey.
You can pull behavior analytics from your website tracking platform and examine specific actions like:
- What pages the lead has visited and in what order
- How much time they've spent on your website
- What forms they’ve filled out
Once you know a lead's site habits, you'll be able to identify whether they fit one of your buyer personas and if they have the potential to become an SQL.
One way to make this determination is to use the BANT evaluation system.
- Budget. Does the lead have the budget to make a purchase?
- Authority. Does the lead have purchasing or decision-making authority?
- Need. Does your solution address the lead’s pain points and fill a need?
- Timeline. How long will the lead’s organization take to make a purchase decision?
Some marketing tools can even help you automate these steps so you don't have to do them manually.
Lead scoring is the process of ranking a lead's sales readiness by assigning points based on a list of qualifications and actions they take. Once a contact reaches a certain threshold, they're ready to hand off to sales.
In addition to lead behavior, assign points based on:
- Demographic information
- Company information
- Other online behavior
- Email engagement and subscription status
- Social engagement level
For example, a decision maker in one of your target companies would start with more points than a lead from a smaller company or with a more entry-level position. A website visit would add a few points to the lead’s score and downloading an ebook would add even more.
Lead scoring automation tools can also help you simplify this process. You assign points to different criteria and behaviors, and the software will track scores for every lead in your system.
How to transition an MQL to an SQL
A common mistake when trying to transition an MQL to an SQL is to send leads too soon. Sometimes marketing teams see a lot of engagement from a lead and send it to sales based on the number of interactions — but if most of those touchpoints are answering questions early in the buyer’s journey, that lead isn’t ready for sales yet. On the other hand, a purchase intent download might send a lead to the sales team — but if that lead downloaded pricing as one of their first engagements, they’re not really for sales either.
Look at a lead's total behavior with your brand when deciding whether they're ready to hand off.
Once a prospect has reached the ideal lead score, customer relationship management (CRM) automation can deliver the new SQL to sales via an email notification or task. Even with automation, marketing and sales teams should still collaborate regularly to discuss SQLs and whether to adjust the handoff process or lead score threshold.
Of course, most leads take time to nurture and manage before they're ready to talk to sales. Offering helpful content throughout the sales funnel is the most practical approach to operating efficiently, hitting your lead targets, and creating a loyal customer base that looks to you as a trusted partner.
Get the right data to develop leads into customers
A clear understanding of the differences between MQLs and SQLs is a significant first step in optimizing your marketing and sales funnel. A streamlined process for nurturing and handing off leads will also help your sales and marketing teams build alignment.
When you’re ready to get started, get sales and marketing team members together and start outlining a profile of a sales-ready lead. Decide what criteria and qualifications need to be met to indicate a true purchase intent.
Then make sure you have the software you need to optimize all of your lead nurturing efforts. Adobe Marketo Engage offers sales and marketing teams advanced lead management to create custom scoring models that update in real time, automate nurturing based on persona and buying stage, and lots more.