SQL vs. MQL:What They Are and How They Differ

Every organization today relies on leads materializing into customers to become successful. The tricky part is getting the leads, and this is where the sales and marketing teams come in.

Every company has a well-defined sales funnel that helps them understand how to approach customers. Many leads can be categorized under an MQL (marketing qualified lead) or an SQL (sales qualified lead).

Based on the organizational structure, some leads are individually generated through the sales or marketing team. Others are researched by the marketing team and sent to the sales team to increase the chances of success.

This article delves deeper into what MQLs and SQLs entail for organizations.

Let's get started.

A marketing qualified lead (MLQ) is any lead the marketing team considers the most likely to become a customer.

MQLs are determined based on how the prospective customers react to marketing efforts, such as:

• Visited web pages
• Calls to action (CTAs) they click on
• Average bounce rate on each page
• Social media post interactions
• Downloaded offers

For example, a prospective customer can be labeled as an MQL if they respond to an email marketing campaign successfully.

Marketing teams also determine MQLs through various key questions. Do they match the current buyer personas? If yes, where are they on the buyer's journey right now? Do they require a follow-up or lead nurturing?

Based on the answers and lead scoring, the marketing team decides whether to put in more effort or if it's time to qualify the lead as an MQL.

A sales qualified lead (SQL) is any potential customer prepared to talk to a sales team or SDR.

SQLs are high-ranking leads that the marketing team has researched and screened. Leads that express adequate interest in the product or service and are ready to start the sales process are sent to the sales team.

The difference between MQLs and SQLs is their intent to buy. A lead will typically begin as an MQL. Once it is clear the lead may become an active customer, they transform into an SQL.

An MQL is a lead that has already engaged with an organization in one way or another. These leads are viewed as ready to become customers if they are approached correctly.

This may be because they came across your social media ads, Google ads, blog, YouTube videos, or any other content or advertising. It may also be simply word-of-mouth or old-fashioned recommendations that lead them to your website.

This intent is relatively easy to track through your customer relationship management (CRM) software. Marketing teams use CRM to check how each potential lead is doing, what they're doing, and their location.

Based on this data, they can determine prospective customers' intent, where they are on the buyer's journey, and how likely they are to make a purchase. For example, a high bounce rate on product pages implies that the user is only browsing for ideas or getting a feel of the pricing.

If a user spends an unnecessarily high amount of time on one product page compared to others, it means they're interested. This would tell the marketing team that the user is a potential candidate for an MQL.

At this point, they start with the lead nurturing and follow-up process. This generally involves nudging the customer towards the company and solidifying them as an MQL.

If the team is successful, the user will become ready to talk to a sales team, especially if they click the contact page or a CTA that directs them toward a representative.

This is the exact point where a marketing qualified lead officially transforms into a sales qualified lead.

The intent to buy is the primary difference between MQLs and SQLs, which is why understanding intent is crucial.

Almost 90% of MQLs don't convert into SQLs because marketing teams classify the MQL too early in the buyer's journey. You must understand that the sales cycle is extremely delicate, and sales teams require as much actionable data as possible to convert leads.

If the marketing team has detected an intent to buy, the process becomes quite easy because the MQL can be passed on to the sales team. However, in most cases, the intent to buy is not clear. This requires the marketing team to provide an estimate based on the data available.

The following are a couple of ways marketing teams determine intent:

Website and CRM data can easily let you know whether a user is a first-time or returning visitor.

For the most part, a first-time visitor is the ideal example of a potential MQL. Unless they're on your site for a completely unrelated reason, they are starting off their buyer's journey and getting the information they need to move to the next step.

This is where the marketing team would start tracking the user to check their behavior. Difficulty determining why a user is browsing your pages is a primary pain point at this stage. Are they only checking it out for the sake of information, or are they actually looking to buy something?

A returning customer has been on your website several times. They have actively browsed key product pages and opted for bottom-funnel content offers. These customers are likely SQLs. That is because they are at the decision stage in their buyer's journey, where talking to a sales representative may give them the final push to make a purchase.

The fact that they keep coming back is evidence that they're interested in your products and intend to buy. Even more so if they keep returning to specific product pages and their crawl times increase.

In such cases, heat maps may come in handy. Does the user continuously click on the product description? Do they look at the images again and again? Are they trying to check if the price has changed?

This data ultimately helps build a case for the user, making it easier for the sales team to convert the lead.

Another way to check intent is to see what kind of content offers a lead is downloading.

MQLs will be downloading and using top-of-the-funnel content offers. This includes any information to educate users and help them understand what you sell.

For example, if a hospital director looks at healthcare data breach statistics, you can jot them down as an MQL. You know they are looking to either beef up their security, get a new CRM, or contain existing damage.

If the director clicks on any CTA within the content, they're interested in the product and can be a qualified lead. However, it doesn't mean that they're ready to buy yet. So, they are still considered a marketing-qualified lead.

This is because their intent isn't clear. You don't know whether their intent is preventative measures or reactionary measures.

Potential SQLs are more focused on bottom-of-the-funnel content offers. This is content that relates to a specific problem and its solution. In these cases, intent becomes extremely clear.

Looking at the same example above, the hospital director may be going through content about the best ways to secure themself against data breaches.

This tells you that a data breach hasn't happened yet, and the director is looking to deploy preventative measures to ensure it doesn't happen.

Now you have a clear intent. The user has done their research, knows what they want, and just needs help making a purchase. Hence, they have become a sales-qualified lead.

It's essential to know how to differentiate between SQLs and MQLs because both require varying levels and types of effort.

Developing the right content and offers for SQLs and MQLs provide a healthy nurturing experience. It's tricky to convince a customer to buy your product before you ask them to do it directly. This is why a qualified marketing lead is so important.

If you've already convinced a lead to buy your product, the sales team only needs to facilitate the process. This also adds to the overall customer experience.

Ensure you don't send the wrong kind of content to the wrong lead. For example, if you send a first-time site visitor to a sales representative, they're more likely to back off.

People usually like to research what they want by themselves first. Let them do that while assisting indirectly. This is where your marketing efforts come into play.

Similarly, if you send an email about your products to a returning visitor, the user will probably ignore it. That's because returning visitors would want emails about discounts, offers, and deals based on what they have been browsing.

This would push them towards opening their email and going through your offers.

The correct qualification of every lead improves your chances of success with leads and your marketing ROI, saving a lot of time and money.

The key to successful inbound marketing is to know how to convert an MQL into an SQL. For that, the marketing and sales teams need to be on the same page.

This shift happens over time with multiple interactions with your business. Timing is crucial in this case, and having as much information as possible is important.

Here are a few things to help you do it:

You should also pay attention to a lead's contact requests and your referral channels. If a lead asks to contact your sales team, you can designate them as an SQL. As for referral channels, check historical data to see which channels have the highest lead conversion success and use it to categorize your leads.

While cold calling can still be effective in certain situations, it is often not the most efficient or cost-effective method. Today, marketing and sales teams have adopted a more sophisticated approach to lead generation and conversion – identifying and categorizing leads into MQLs and SQLs.

By concentrating on MQLs and SQLs, sales teams can streamline their efforts to close deals. Instead of expending time and resources attempting to convert leads who are not yet ready to buy, they can concentrate on those who are most likely to make a purchase. This enables sales teams to be more productive and can result in higher conversion rates and increased revenue.

Overall, adopting an MQL and SQL approach is crucial for efficient sales in the long run. With the power of CRM software, data, and insights, sales teams can properly categorize leads, identify the most promising prospects, and drive the entire transition process from MQL to SQL.

This approach can turn a sales team into a true powerhouse, leading to increased revenue and greater success in the marketplace.