MQL vs. SQL: What’s the Difference and Why are They Important?

October 29, 2021

Every salesperson knows time = money. The best way to maximize sales is to use time wisely and ensure the appropriate dedication of resources only to the leads that are most likely to convert. One of the most critical factors in understanding which leads are most likely to convert is the proper categorization of potential customers within the sales funnel.

A lead’s propensity to convert is indicated by sales and marketing teams deeming a lead “qualified” or “unqualified.” To better understand these classifications, there are two important concepts/acronyms you should know: MQLs and SQLs. Let’s explore MQL vs. SQL and each of their roles in the sales funnel.

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MQLs and SQLs: What are they?

MQL stands for marketing qualified lead, and SQL stands for a sales qualified lead. 

A lead is someone who has expressed an interest in your product or service. Classifying each lead as an MQL or SQL is an attempt to delineate leads further so sales teams know where to direct their efforts.

What is a marketing qualified lead? 

Within an MQL SQL funnel, the marketing qualified lead comes immediately before the sales qualified lead. Your marketing team has qualified them because they have expressed an interest in your product or service, but they haven’t revealed their pain points or provided any in-depth information.

So, how should MQLs be seen in the context of your funnel?

Leads that fall into this category should be seen as potential customers if nurtured correctly. On the other hand, they’re not far enough into your funnel to warrant personal attention from your sales team.

What is a sales qualified lead? 

The SQL meaning in marketing is the next stage in your sales funnel. Classifying a lead as an SQL means your sales team has already assessed these leads as warranting a direct follow-up.

When a lead graduates to the SQL sales stage of your funnel, oftentimes one-on-one consultations can turn these leads into opportunities. This means that when considering your customer lifecycle in an MQL vs. SQL context, SQLs should always be the priority.

 

Why do SQLs matter? 

Now that you have a basic idea of the difference between MQLs and SQLs, let’s explore the specific importance of SQLs. 

Many sales teams fail to make the distinction between each lead type; a mistake because proper categorization saves time for your salespeople. Successful sales teams are able to quickly identify high-value leads and spend their time selling to the right people at the right time.

When used correctly, the process of converting someone into an SQL gives your sales team better-qualified prospects. In practice, this will increase their conversion rates and enhance the likelihood of having meaningful consultations with a lead.

There’s also the aspect of tracking your leads. Maintaining broad data on your MQLs and SQLs allows your sales and marketing teams to better understand what’s working and what isn’t. It also enables managers to assess the performance of their sales teams, and even individual sellers.

Ultimately, the goal of marketing and sales is to take a lead and turn them into a customer. Without a lead qualification or prioritization system, you risk losing out on potential sales. Even the most enthusiastic lead isn’t going to stick around if your sales team is too busy talking to someone else.

 

MQL vs. SQL: Different pieces of the sales puzzle 

Converting an MQL into an SQL is a major jump. Studies show that 90% of MQL sales are never converted into SQLs because they were classified as an MQL much too early in the buyer’s journey.

A misunderstanding of the MQL definition and customer behavior is the main cause of this problem. Here’s what you need to know about how to differentiate between the two, including when it’s time to move a customer up to the next stage of your sales funnel.

Lead behavior

How someone behaves on your site and how they engage with your team is the simplest way to position someone in your sales funnel. This section will concentrate on decoding lead behavior to help you categorize leads appropriately.

How many times have they visited? 

With MQL and SQL definitions, a first-time visitor to your site would nearly always be categorized as a MQL. Someone who visits your site repeatedly and checks out your product pages is often considered very interested and often warrants the extra attention converting that lead into an SQL will provide.

To follow the customer path on your website, many sales and marketing teams leverage in-depth analytical tools to find:

  • The pages a lead visits;
  • How long leads spend on your site; and
  • When leads return (and which pages on your site they return to).

As a general rule, the more times someone visits, the greater the likelihood of them becoming an SQL.

Conversion count (or engagement count)

Chances are you have plenty of content and helpful resources on your various landing pages. Someone ready to be converted from MQL to SQL status has likely engaged with multiple landing pages or resources.

Of course, deciding what constitutes a good conversion count depends on how many different campaigns you’re running, and how much content you have on your site–we’re not talking about conversions into paying customers here, but “conversions” as defined by interactions with a landing page, filling out a form, etc. For example, it’s much harder to figure out if a lead is worth converting into an SQL if you have limited content or pages for them to engage with on your site. If you have many campaigns running and a ton of content, you can easily see which leads engaged in multiple places on your site and better understand how qualified they are.

Remember, SQL sales leads have expressed a strong interest in your product or service. These are not the people who are just looking for a free piece of content, so the more discerning you can be about which actions imply they are more likely to become a customer, the better.

Type of conversion 

The type of conversion–again, we’re referencing conversions from the lens of actions on your site–also influences where a lead lies in your funnel. Someone who downloaded a free eBook would be considered an MQL, whereas an SQL would be someone who requested a free demo of your latest product. In this case, the key differentiator is the intent associated with the action; you can safely assume someone who wants a demo of your product is likely interested in purchasing, whereas someone who downloaded an eBook might just be looking for more information on a particular subject.

In the same way leads can be scored and categorized, conversions can also be scored and categorized. Generally, the more effort a lead has put into interacting with a campaign and/or offer–i.e. filling out a form, submitting their email, etc.–the higher the chance of them being an SQL.

Referral channel

Any good business has multiple marketing channels. Most likely, the majority of your leads come from a select few, such as email or paid marketing.

Over time, you’ll be able to define which channels are most successful at turning leads into paying customers. This information can be used to categorize your leads.

For example, if your email marketing campaigns tend to deliver more leads that turn into paying customers than your Facebook presence, it’s natural that you’d bump up a lead that came from an email blast from an MQL to an SQL.

All engagements are a chance to make a sale, but you need to know as much about the effectiveness of your referral channels as possible when it comes to prioritization. The importance of tracking which channels your leads come from, and the general quality of leads coming from each channel, can help you better understand which leads are worth investing in.

Contact requests 

One of the easiest ways to designate someone as an MQL or SQL is if they request to be contacted. Most interactions involve the sales team asking to set up a call or a demo with the lead. If it’s the other way around, this is a sure-fire sign that a lead should be considered an SQL.

Leads who ask to be contacted show they’re determined to invest time into learning more about your product or service. This means they’re already seriously considering whether your product or service is right for them.

One caveat to this rule is who the lead is. Your sales team should still do due diligence to ensure each lead/contact actually has the authority to make a purchasing decision.

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Likelihood to buy 

The reason SQL marketing opportunities are more valuable is simple: SQLs are the leads that have a greater likelihood of purchasing. If a lead has shown a strong interest in your business’s offerings and they fit your ideal customer profile, they’re an SQL.

Someone who doesn’t fit your ideal customer profile, on the other hand, should be treated with more caution. The MQL vs. SQL designation is still relatively early in your sales funnel, and no sale is a guarantee.

But it’s not as simple as that. The BANT system is another concept employed by sales teams to help decide if a lead has a high likelihood of making a purchase.

  • Budget – Do they have the budget to afford your solution?
  • Authority – Does your contact have the proper authority to make a purchasing decision?
  • Needs – Is your solution appropriate for the contact’s business and pain points?
  • Timeline – How long is it likely to take to get a decision on a purchase? The shorter the timeline, the better.

BANT helps you assess each individual lead and prioritize them. It also ensures that they match your ideal customer profile. For example, there’s no point in trying to sell to someone who doesn’t have the authority to make a purchasing decision.

An SQL sales strategy must focus on the leads that have the highest likelihood of purchasing in the near future.

 

How can lead scoring help?

Lead scoring is a popular lead prioritization method. Prior to the implementation of lead scoring systems, sales teams determined whether someone was interested in purchasing simply by a gut feeling or paying attention to certain positive indicators.

Sales teams essentially used to guess whether a lead was ready to buy based on what they remembered about the person, or simply how they felt at the time. In modern sales, this isn’t an efficient or effective way to quantify the value of different sales opportunities.

Instead, lead scoring systems make it easier to use data to get the complete picture regarding a potential customer. But how do lead scoring systems work in practice?

Assigning numerical values to each lead 

Proper lead scoring requires sales and marketing teams to work together. The simplest way to collaborate is to determine the “weight” of each type of action with a specific score. Essentially, you need to figure out what sort of interaction or engagement is prioritized over others, and how you will quantify the importance of each action with a score.

For example, a lead who downloaded an eBook might earn 5 points for that action, while a lead that filled out a “talk to sales” form might receive 10 points because that action is generally tied with a higher propensity to convert. Then, your sales and marketing teams need to determine what lead score threshold will automatically move someone to the next stage of the sales funnel.

Likewise, a lead’s score may fall if they commit–or don’t commit–to certain actions. A lead that stopped opening your emails for two weeks would likely have their score reduced.

 

Find qualified leads, faster 

The days of lead qualification by gut feeling are long gone. Sales and marketing teams now have a wealth of data they can leverage to help them determine lead quality. Lead scoring, in particular, has become the new benchmark for data-driven sales teams. 

With the help of Crunchbase’s all-in-one prospecting solution, you can discover qualified accounts and connect with decision-makers all in one platform. Spend less time searching and more time selling with Crunchbase.

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