MQL vs. SQL: What’s the real difference?

Marketing and sales teams use a lot of acronyms, and we throw them around like everyone knows what they mean. Most people in the field quickly catch on and come to understand the words associated with the acronym, but often only at a superficial level. That’s exactly the case with MQL and SQL.

We know that MQL stands for marketing qualified lead, and SQL stands for sales qualified lead. But what do those terms actually mean? How are they different? How are they related?

This blog post breaks it down for you.


What is a marketing qualified lead?

A marketing qualified lead (MQL) is an individual that the marketing team has identified as a good fit for your product or service. The person has shown signs of interest (or need) in what you have to offer. These signs often include activities like downloading content from your website, signing up for a webinar, clicking an email link or filling out a contact form.


What is a sales qualified lead?

A sales qualified lead (SQL) is an individual that the sales team has identified as ready to move forward in the sales process. This means that the lead has expressed interest in purchasing your product or service and is ready to take action. 

Some SQLs were once MQLs. Others began as SQLs from the get-go. That’s because what makes an SQL an SQL is the sales-vetting process. You see, your sales team could get an MQL and qualify them as being ready to take the next step in the purchasing process and that’s how they’d become an SQL. Sales can also prospect and determine that the individual they spoke to is ready—bypassing marketing altogether and jumping straight into conversations with sales. 

How does a sales team know if a prospect or MQL is ready to move forward in the sales process? Often, the prospect or lead may request more information, ask for a quote, and respond to follow-up emails or calls from the sales team. 

While an SQL shows more potential to become a customer than an early-stage MQL, they still need to be nurtured until they are ready to purchase. 



MQLs and SQLs are both leads. But, as you can see, they’re not the same. To get really clear on what we touched on already, here’s what you need to know: 

  • The primary difference between an MQL and SQL is the intent to purchase
  • MQLs are often characterized by their interest in your product or service
  • SQLs have expressed interest in buying

But that’s not all. There are some other differences, and they include:


1. Behavior

MQLs have interacted with a company’s marketing efforts (such as viewing website content, engaging on social media and downloading content). SQLs are leads who have expressed an intent to buy by responding to a sales pitch or taking other action.


2. Timing 

Generally speaking, most MQLs take longer to become customers compared to SQLs. MQLs must go through marketing’s nurture process, while SQLs are ready to engage with your sales team. 


3. Conversion rate

MQLs typically have a lower conversion rate than SQLs, as they are still in the process of being nurtured and educated on the product or service. SQLs already know what they want and are more likely to convert into customers. 


4. Demographics

MQLs may or may not have demographics—things like age, gender, location and job title—associated with them. You don’t need to have all the details to have an MQL. You just need enough. SQLs, on the other hand, always do. Your sales team likely gathered the missing data while talking to the lead.


5. Lead score

Lead scoring is a process used by marketing and sales teams to rank leads in order of their potential value. This process is done by assigning a numerical score to each lead based on their behavior and interactions with a company’s marketing and sales efforts. 

MQLs typically have lower lead scores than SQLs. This is because MQLs are still in the early, nurturing stages of moving from “fit” to “ready” while MQLs are ready to make the move from “ready” to “tell me where to sign.”


6. Lead nurturing strategies

MQLs must be nurtured through the funnel with content tailored to their needs. This includes personalized emails, webinars, blogs or demo videos.

SQLs need to be nurtured, with content focused on closing the deal. This could include follow-up emails, special offers or discounts, product demos, and case studies, to name a few.


7. Request for contact

Studies show that SQLs are 2.5x more likely to request contact than MQLs. SQLs have already expressed an interest in the product or service and are ready to purchase. MQLs, on the other hand, are still in the process of being educated about the product or service. 

SQLs who request to be contacted are usually further down the funnel and more likely to become customers—faster.


What comes first: MQL or SQL

The MQL stage is the first step in the sales and marketing funnel. It typically involves getting potential buyers interested in what your product or service can do for them. MQLs are usually scored based on their engagement level, which helps determine if and when they move forward down the funnel. 

The SQL stage is the end of the funnel, where buyers decide whether to purchase your product or service. Here, buyers are looking into things such as 

  • Sales: The discounts and offers available
  • Pricing: What works best for their budget 
  • Financing: How they can best finance the purchase and whether they need to take advantage of payment plans or other options
  • Technology: Whether the product offers the best technology for their needs 

MQLs usually start at the awareness stage, but only sometimes. They often end up as an SQL in the conversion stage of the marketing funnel. Successful MQL-to-SQL conversions improve the overall efficiency of your marketing campaigns and increase the chances of making a sale.

For a  successful MQL to SQL conversion, sales and marketing teams need to be aligned  and understand what’s happening throughout the entire funnel. Here are a few things you can do to optimize for MQL-SQL conversion.


1.   Have a lead scoring system in place to properly assess MQLs

Lead scoring involves using MQL scoring parameters—such as website visits, form submissions and others—to determine the likelihood of an MQL converting into an SQL

For example, if an MQL has visited your website 10 times and opened your emails five times, they would receive more points than someone who has only been to your website twice and opened your emails once. 

This process helps ensure MQLs are being evaluated accurately and that the most promising MQLs move forward in the funnel. 


2. Share MQL data with the sales team

Once MQLs have been scored, it’s important to share this data with your sales team so they can focus their efforts on MQLs that have a higher chance of closing. By sharing MQL data, the sales team can more easily assess MQLs and determine which MQLs are most likely to become SQLs. 


3. Establish MQL-SQL goals

It’s also vital to establish MQL-SQL goals so you can track progress and measure success. Setting MQL-SQL goals sets up parameters around what you should be tracking and allows you to better understand how MQLs are converting and what areas to improve. It’s all about optimizing for the highest possible conversion rate.

Your goals should include the following:

  • Conversion rate: How many MQLs convert to SQLs
  • Conversion time: How quickly MQLs are converting 
  • Lead yield rate: How many MQLs are being generated
  • Engagement rate: How engaged MQLs are with your product or service (this is determined by calculating the weighted engagement score)


4. Maintain MQLs

Maintaining MQLs is also important to ensure MQL-SQL conversions. Maintaining MQLs involves staying in contact with them, providing them with content and resources, and responding to any questions or concerns they may have. 

This also helps build relationships and trust, which can ultimately lead to MQL-SQL conversion. 

It’s important to note that MQLs should not be pressured to purchase. This is because MQLs are still learning about your product or service and need time to decide. So, this process could take weeks or even months. 

Following these steps will put you on the right track for growing your MQL-SQL conversion rate, but it won’t guarantee that all will convert. Knowing this up front is important because it removes the friction that too often lives between the sales and marketing team—when sales blames marketing for sending poor leads and marketing blames sales for not correctly approaching the lead. In reality, some leads may not convert because of factors outside of your control: the lead is shifting strategies, had their budget reduced, etc.

To facilitate all of it—high conversion and deep learning, it’s important that sales and marketing teams stay in regular communication. 


Final thoughts

Whether you’re in marketing focused on MQLs or in sales focused on SQLs, the reality is that you need to focus on accounts that show the most promise. You don’t have the time or resources to be engaging with targets that will never close. And in order to bring in the right leads, you need to look deeper than the firmographics that make up the traditional ICP.

With Rev, you’ll be tapping into the pattern-matching capabilities of AI to evaluate your best customers, discover their deep characteristics that make them your best customers and find other accounts that share those same traits. It’s that simple. And just like that, you’ll be bringing in MQLs and SQLs that have a higher propensity to engage and move through the funnel faster.

We’d love to show you how it works. Contact us and we’ll conduct a free ICP audit and give you a list of target accounts that you should go after next.