MQL vs SQL – Different Types of Qualified Leads Explained

Imagine you are running a successful antique store. 

During busy hours, a few visitors enter your shop and all appear quite interested in your inventory. Yet swamped with work, you only have time to entertain one.

Terribly aware that by attending one you could very well lose the remaining prospects, who would you choose?

Or better, will the chosen one purchase the product, or would it all be for naught?

The above two queries are a constant struggle for entrepreneurs, regardless of their business size.

With time constraints and many leads to consider, businesses must prioritize who to approach and whom to leave out.

Marketing qualified lead (MQL) and Sales qualified lead (SQL) are two definite classifications marketers employ to handle such obstacles.

By successfully identifying MQL and SQL, you will be in an excellent position to not just plan your marketing strategy accordingly, but strengthen your customer base.

Here, I will share an overview of how to determine MQL and SQL, and the methods to help you increase their numbers.

But, before we dive in, let’s cover the basics first.

What are MQL and SQL?

Are you familiar with the buyer’s journey?

A potential customer goes through three major stages before they purchase any item.

First comes awareness of the product, followed by curiosity. If the item interests the customer, they take the time to ponder it. Once sure, they buy the product.

MQL (Marketing qualified lead) is the “ponderer” who is keenly interested in your product but doesn’t yet want to take a step further. SQL (Sales qualified lead), in contrast, definitely intends to buy.

 As the problem often arises in the “pondering” phase, MQL and SQL can make your challenging task easier.

How to Identify MQL and SQL?

To distinguish MQL from SQL, you must get an in-depth insight into customer behavior and their intentions toward your product.

1.Determine Customer’s Behavior to Discover MQL

 Small brick-and-mortar entrepreneurs, over time, develop certain instincts to weed out generic leads.

One look at the person and they can pin down a window shopper from afar.

Bah! He will not buy it. John, I tell you. Attend the lady in purple scarf instead.

This observation skill allows them to prioritize those they could positively convert into customers.

For a large enterprise, though, this approach is impractical. Instead, the marketing department uses lead magnets to gather data and evaluate visitors’ behavior.

Let me briefly explain how customer data can aid you.

If MQL makes frequent visits to your site shows a distinct interest in your brand.  If a lead subscribes to your newsletter, they want to keep up the connection in the event they may wish to buy your product.

If MQL adds a few items to the card merely for the sake of creating a wish list, they demonstrate their eagerness. They might even try a demo version, hinting at a potential sale in the future.

The marketing team observes the engagement factor to create a buyer’s persona, so they’d a benchmark to spot MQL.

I am not going into details on Buyer’s persona as the subject needs an entirely separate article for it. Suffice to say, just as you continuously tweak your attire until you find the right size, the marketing department follows similar procedures to create their template.

Should your potential lead meet the criteria, they are listed as MQL ready to move to SQL. If not, they are filtered out.

Once you’ve substantial data on hand, estimate a potential MQL through a scoring method.

2. Use Lead Score to Separate SQL from MQL

Ever noticed a gradient ombre shade? It begins with a dark tone, gradually moving to mid-shade until the tips fade into a lighter version.

Think of lead scoring like an ombre, only in numbers.

You assign points to your leads based on their behavior, common factors, and personal information. Once you recognize strong leads, you can pass the SQL on to the Sales department and filter out the low-chance leads.

Sounds easy, I know, but to create an accurate score every minute detail should be considered.

For instance; if you’ve heard about ETSY, the popular multinational marketplace caters to a wide array of clients but has limited demographic coverage.

To ETSY, people out of their range of delivery points would likely have a weak lead score.

Similarly, businesses with a target market over 35 would likely pursue their customers on Facebook instead of Instagram, as millennials mostly prefer the former—giving Facebook users a higher lead score.

Score your leads by their attributes and you’ll find qualified ones at the top of your list.

Is it Necessary to Separate Painstakingly MQL and SQL?

Yes, it absolutely does.

MQL and SQL may appear similar, no doubt, but a customer’s intention, and your approach towards that customer is the reason they must be separately listed.

Remember the antique shop example? Imagine you’re still running it. While habitually dusting the shelf, you noticed someone browsing Aladdin’s lamp and thought to hasten their decision.

If your quarry is not yet ready to buy, they would not appreciate your assistance and might make their escape. But SQL would require little convincing since they’d already decided to buy the lamp.

To cut the story short, sales strategies are direct. If you reach out to the MQL prematurely, you’ll likely scare them off.

Keep in mind, despite every precaution, not all MQL converts into SQL for countless reasons. In fact, 79% of MQL customers rarely make a purchase!

But since human behavior has a surprising tendency to be predictable, you’ll likely have more success by following standard procedures.

What is the Best Method to Convert MQL to SQL?

The sure way to convert leads into customers is through lead nurturing.

Companies with a strong nurturing strategy attract not just 50% of their SQL but could turn even generic leads to MQL.

Aqua is a classic example.

In 2016, a cloud security company named Aqua launched its brand in a highly saturated market. With a tight budget, Aqua successfully converted 25% of its leads to MQL through active marketing and lead nurturing.

The Aqua case study demonstrates that a passive approach could also bring prospective customers to your door.

To wraps up, use CRM software like HubSpot to customize your research and find MQL and SQL. Create awareness through blogs, videos, webinars, podcasts, and newsletters to attract leads.

And most of all, automate your tasks as much as possible to save the grunt work.

Good luck!