MQL vs SQL: SaaS Metrics for Leads

MQL vs SQL: SaaS Metrics for Leads

Nurturing leads from the first touchpoint to marketing qualified leads (MQL), sales qualified leads (SQL), up to paying and loyal customers are essential for the success of any business. SaaS companies, in particular, need to pay attention to their MQL-to-SQL rate – the number of MQLs that turn into SQLs – as it’s a key indicator of success.

But what are MQLs and SQLs, exactly?

What’s the difference between an MQL and an SQL, and how can your sales team leverage MQLs to close more deals?

Here’s a closer look at MQL vs. SQL, and why they’re one of the most important SaaS metrics to monitor for any SaaS company.

What is a sales-qualified lead (SQL)?

Sales-qualified leads are leads that have been vetted by the sales team and are determined to be ready for a sales call or demo.

SQLs are at an entirely different stage of the buyer’s journey than fresh leads, which are leads that have just made initial contact with a SaaS provider.

It’s up to your sales team to determine whether a lead is already “sales qualified.” In practice, a lead becomes qualified when they move from the consideration stage to the decision-making stage, a.k.a the time when they’re ready to buy.

In order to be considered an SQL, a lead must usually meet certain criteria, such as the following.

Training your sales and marketing team to recognize the moment when a lead becomes an SQL is critical. The sooner they can identify and qualify an SQL, the better chance they have of closing the deal.

What is a marketing qualified lead (MQL)?

Unlike SQLs, MQLs are not quite ready to buy yet – but they’re getting there.

MQLs have shown enough interest in your SaaS product that the marketing team feels confident they can be passed along to sales.

MQLs also need to meet certain criteria, and this is usually based on their web behaviour. For example, MQLs are those who:

  • Visit your website regularly
  • Download gated content (e.g., e-books, guides)
  • Attend a webinar or live event
  • Engage with your brand on social media
  • Downloaded demo software
  • Created an account for a free trial of your SaaS solution

The MQL criteria will vary from company to company, but the common thread is that MQLs are leads who have shown more interest than the average website visitor.

MQLs vary in quality as well. As an example, a person who voluntarily signed up for a demo would be considered a higher-quality MQL than someone who simply attended a webinar.

Why? Because the former is clearly interested in your product, while the latter may just be curious.

This is another reason why your SaaS sales and marketing team need to be on the same page when it comes to MQLs. It’s important that they have a shared understanding of what qualifies as an MQL so they can prioritise the best leads and develop those relationships accordingly.

Key differences between SQLs and MQLs

In terms of converting leads, the ultimate goal is to nudge those MQLs into becoming SQLs, and, eventually, paying customers.

Doing that typically involves the use of a lead qualification framework. These frameworks help salespeople identify MQLs and SQLs based on your organisation’s criteria of what makes a good lead.

For instance, a popular lead qualification framework is BANT, which stands for Budget, Authority, Needs, and Timeline. Under this framework, salespeople assess each lead to see if they have the budget to buy your SaaS product, the authority to make decisions within their organisation, the need for your product, and a timeline for making a purchase.

If a lead meets all four criteria, they’re considered an SQL.
Another common tool is lead scoring systems. These systems assign numerical values (“scores”) to each lead based on their individual characteristics. The higher the score, the more qualified the lead is.

For example, a lead who’s been to your website 10 times in the past month would be given a higher score than someone who’s been to your website just once.

Using a lead scoring system is a great way to objectively compare leads and prioritise the ones that are most likely to convert.

Again, since MQLs and SQLs are at different stages of the customer journey, they require different approaches.

Here are key differences to keep in mind when working with MQLs and SQLs:

1. MQLs are at the top of the funnel, while SQLs are at the bottom

MQLs are at the top of the funnel, which is where leads are first introduced to your SaaS product.

This is the stage where you’re focused on building brand awareness and generating interest in your solution. MQLs are still getting to know your product and company, so your goal is to provide them with helpful information and resources that will educate them about your solution and how it can help them solve their problem.

On the other hand, SQLs are already at the bottom of the funnel. This is the stage where leads are actively considering your SaaS product as a solution to their problem. They’re already familiar with your brand, and they understand how your product works. Now, it’s up to you to seal the deal and convince them to buy.

2. MQLs need educational content, SQLs need sales collateral

Since MQLs are just getting to know your brand, they need educational content that will help them understand your product and how it can solve their problem.

This could include blog posts, e-books, guides, infographics, case studies, and so on. MQLs need this content to help them move down the funnel towards becoming an SQL.

Since SQLs are already actively considering your SaaS product, they need sales collateral like pricing pages, product demonstrations, and proposal templates. This type of content will help them make a purchasing decision.

3. MQLs need to be nurtured, SQLs need to be sold to

MQLs are not ready to hear your sales pitch yet – in fact, that’s likely to scare them off. Instead, you’ll want to take a more subtle approach with MQLs and focus on connecting, answering their questions, and providing helpful resources. This is known as lead nurturing, and it’s an essential part of the MQL to SQL conversion process.

SQLs are already receptive to sales pitches, so they can be passed on to your sales team already. However, hard-sell tactics may still scare them off, so it’s important to focus on building trust and demonstrating how your SaaS product can solve their specific problem.

Understanding these differences isn’t just crucial for refining your sales and marketing strategy.

Using the wrong tactic with MQLs or SQLs – like sending a generic blog post to an SQL or trying to set up a sales call with an MQL – can actually damage your relationship with the lead and could prevent them from doing business with you down the line.

Why SaaS lead metrics matter

You might be asking, “why should I even bother tracking MQLs and SQLs? Isn’t it enough to just track my overall sales numbers?”

The answer is no. Your sales numbers are a direct result of how well you’re scoring, nurturing, and moving leads from the MQL stage to the SQL stage.

Tracking these metrics reveals valuable insights about your strategy, such as the following:

  • How effective is your lead generation?
  • How well are you nurturing MQLs?
  • Are you moving MQLs to SQLs efficiently?
  • How many MQLs are converting to SQLs?
  • What went wrong with MQLs that didn’t convert to SQLs?
  • What’s the quality of your SQLs?
  • Are your SQLs converting to customers?
  • Are you using the correct criteria to score MQLs?
  • Are your MQL criteria too strict or too lenient?

If you’re not tracking MQLs and SQLs, you won’t be able to answer these essential questions. And without the answers to these questions, you won’t be able to optimise your sales and marketing strategy.

On top of that, you should also be tracking other essential SaaS lead metrics. Below are some examples:

MQL to SQL conversion rates: This is a straightforward metric that measures how many MQLs turn into SQLs.

A high MQL to SQL conversion rate indicates that your lead generation and nurturing efforts are effective. A low conversion rate, on the other hand, suggests that there’s room for improvement.

Lead-to-customer ratio: This metric measures how many leads turn into customers. This is different from the MQL to SQL conversion rate because it tracks the entire journey from fresh leads to paying customers.

A low lead-to-customer ratio tells you that you’re losing leads somewhere along the way – whether it’s during the MQL stage, the SQL stage, or even after the sale.

By tracking MQLs, SQLs, and other essential SaaS lead metrics, you’ll be able to get a clear picture of how effective your sales and marketing strategies are. This, in turn, will help you make informed decisions about how to improve your MQL to SQL conversion rate – and your overall sales numbers.

Generate & nurture leads the data-driven way

At Common Ground, we can help you identify and track the SaaS lead metrics that matter most to your business. We’ll also help you develop and implement a strategy for converting MQLs to SQLs. This can include everything from setting up lead scoring to creating MQL-specific content lead nurturing workflows, and more.

To learn more about our SaaS marketing strategies, book a call today.

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