Updated Dec 7, 2023: We’ve added a new section on industry benchmarks and expanded other sections with more up-to-date information.
If you’ve been working with B2B sales processes in the past few years, you know that they’ve been getting more and more complex. It used to be that your sales team could achieve acceptable conversion rates with cold-calling. Today, they need to manage a whole array of metrics coming from MQL and SQL funnels.
The lead qualification and conversion processes themselves are becoming longer. Potential customers take time to evaluate multiple products on the market and are not ready to be sold to at the moment they discover a new product or service.
Trying to actively sell to leads like that can be a waste of your sales efforts. Even worse, being pushy might detract visitors who are early in their buyer’s journey.
At the same time, marketing teams shouldn’t keep eager prospects at the nurturing stage for a long time, since they might leave if they don’t get enough attention.
The question is — where’s the right balance between MQLs (marketing qualified leads) and SQLs (sales qualified leads)? How do you accurately qualify leads as they proceed through your marketing and sales funnel?
Start by identifying the strategic roles MQL and SQL play in your conversions.
Tip: Qualify all website visitors (even anonymous ones) automatically with the power of machine learning using Lift AI.
Understanding MQLs and SQLs
Marketing qualified leads (MQLs) are potential customers who fit your ideal customer profile but haven’t shown clear willingness to buy your product or service yet.
MQLs need to know more information about your offering. That’s why it’s important to make it easy for them to follow you on social media, sign up for your marketing emails, read your educational content, and download your reports or white papers.
Not all MQLs will do business with you, but giving them access to information early on increases the likelihood of conversion to the next stage of the funnel.
How do you qualify and prioritize MQLs? While your business experience is the ultimate decision-making tool, here are a few extra ideas to get you started:
- Ideal customer profile (ICP). Since you define your ICP in advance, you can be more objective when comparing your marketing leads against it.
- Previous customers. You can see what type of businesses purchased from you before by analyzing closed deals in your CRM.
- Engagement. If you find certain leads consuming a lot of your marketing content, you can prioritize them over those who don’t engage at all.
- Technology. Breakthroughs in AI and machine learning now allow marketing teams to decipher MQL quality in much better ways. Read more on this below.
As more leads go through your marketing cycle, you get more data to refine your qualification criteria even further and adjust your marketing content accordingly.
After the nurturing process, a significant number of your MQLs should become SQLs and be delegated to your sales team.
Sales qualified leads (SQLs) are prospects that are ready to move through the sales funnel. They are interested in buying your product but need guidance and answers to questions related to their specific use case.
The exact moment when MQLs turn into SQLs varies for every company, but there are general guidelines about the conversion rates you should expect.
Industry Benchmarks for MQL to SQL Conversion Rates
The more people convert from MQLs to SQLs the more effective your sales process is. But how do you know whether your conversion rate is good enough?
FirstPageSage published a report tracking data from over 50 B2B SaaS businesses across various industries. They found that for the majority of businesses the average MQL to SQL conversion rate is somewhere between 35 and 45%.
This means that if your conversion rate routinely falls below 35%, you need to change the way your sales pipeline works. Those with over 45%, however, can think of implementing a similar process to the other stages of the funnel.
Calculating Your MQL to SQL Conversion Rate
If you’re running two separate funnels — MQL and SQL — it could be tricky to find the right point where you transfer a lead from one to the other.
The problem is that if you assign a lead as a sales qualified lead too early, it will increase the MQL to SQL conversion rate, but it won’t produce better results in the end and overwhelm your sales team.
A more balanced approach is to tie the MQL to SQL conversion rate to the total funnel conversion rate.
To calculate the MQL to SQL conversion rate, divide the number of SQLs by the number of MQLs. Then adjust the MQL to SQL conversion point by moving it either closer or further away so that the final number is not far off your overall conversion rate.
Why MQL to SQL Conversion Rate Matters
The MQL to SQL conversion rate tells you how effective your marketing is at steering potential customers toward purchasing your product or service.
You can measure the conversion process by assigning a score and ranking every positive action, whether it’s coming from MQLs or SQLs. This is called lead scoring and is foundational to any efficient sales team today.
If you have a lead scoring system in place, you can use it to move prospects through the different stages of your conversion funnel, including classifying them as MQLs and SQLs.
You can design a manual lead scoring process that involves as many custom criteria as you need. But making it effective will take a lot of time, and even then it will be subject to a large margin of error due to human bias and assumptions. This makes off-the-shelf lead scoring solutions that integrate with first- and third-party data a more compelling proposition.
Strategies to Boost Your MQL to SQL Conversion Rate
Businesses worldwide spend hundreds of thousands of dollars perfecting their funnels and making sure their MQL to SQL calculations are on the right trajectory.
Every business is different. For example, long B2B sales cycles negatively affect conversion rates. Where your leads are coming from is equally important. Finally, who your BDRs choose to engage will make or break your sales results.
Lots of sales teams use various lead scoring tools. But most of them don’t go far enough to predict which leads want to buy your product or service. The best solution for that is Lift AI.
Lift AI is a buyer intent solution powered by a unique machine-learning model. This tool accurately predicts the likelihood of every visitor on your website to convert to a paying customer, even if that visitor is not known to you or is anonymous (as more and more visitors are).
The machine learning model that runs Lift AI has been trained on billions of data points and more than 14 million live sales interactions. Lift AI uses your own first-party data (e.g. the number of pages visited, time on page, and countless others) in real time. As a result, Lift AI successfully identifies visitors with the highest buyer intent with over 85% accuracy every time/
On average, high intent, ready-to-convert visitors on your website represent around 9% of your traffic — and you could be missing them.
With Lift AI, you won’t have to worry about getting the MQL to SQL conversions right — it will automatically surface the best leads for your sales team to engage. This is the most effortless way to streamline your sales process and improve your conversions.
The Rise of Product Qualified Leads (PQLs)
Product qualified leads (PQLs) are leads that have tried a demo or engaged with the free tier of your product enough to understand its true value. PQLs have become popular in the past few years, coinciding with the widespread use of freemium pricing models.
Funnel-wise, PQLs are past the MQL stage and could exist in parallel or right before SQLs. PQLs might have consumed your marketing content and self-selected into the PQL stage by signing up for the free version of your product without waiting to be contacted by your sales reps.
As a general rule, PQLs tend to exhibit higher buyer intent than MQLs, since they are being proactive, and should be prioritized by marketing and sales teams.
A sense of urgency by PQLs is a good sign and could lead to a deal closing quickly. Try to accommodate urgent requests as efficiently as possible (e.g. through online chat).
What you have to be mindful about when moving MQLs to SQLs or PQLs to SQLs is whether they have the authority to buy from you. It’s easy to mistake an engaged professional with no say in procurement for a decision-maker and waste your sales resources as a result.
Lift AI: Revolutionizing Lead Qualification
When you use Lift AI, it spots high-value visitors to your website instantly, regardless of whether they are MQLs, PQLs, or SQLs.
You no longer need to invest in finding out the firmographics and demographics of your visitors, instead you can focus on their behavior.
Lift AI sees high buyer intent and allows you to engage those visitors in various ways. For example, it can connect those visitors directly to your BDRs (through any chat platform you have installed). Visitors with lower scores get directed to a nurturing bot or a self-help guide instead to avoid overwhelming your sales reps.
The magic of Lift AI starts working immediately by automatically integrating with your marketing stack. During the first 90 days, customers have reported increasing their chat conversions anywhere from two to 10 times.
For example, PointClickCare grew its chat conversions by 400%, attributing over $1M of extra revenue to its Lift AI integration. Formstack, another customer increased its conversions by 420%.
When you’re thinking about how a marketing qualified lead or a sales qualified lead affects your business growth, it’s critical to keep the idea of buyer intent in mind. Get started with Lift AI and find out what the buyer intent is for everyone who visits your website, even before the initial contact.
FAQ
What is the difference between MQL and SQL?
While a marketing qualified lead (MQL) fits your ideal buyer persona, they don’t yet exhibit high buyer intent that defines a sales qualified lead.
How to improve MQL to SQL conversion rate?
The best way to improve the MQL to SQL conversion rate is to provide your MQLs with engaging marketing materials (e.g. landing pages, ebooks, nurturing processes, social media ads) that highlight the benefits of your product or service.
What is the conversion rate for MQL to SQL?
The MQL to SQL conversion rate is the number of SQLs divided by the number of MQLs. It shows the efficacy of your content marketing.
What is the average conversion rate for MQL to SQL?
The average conversion rate across industries varies between 35 and 45%. However, this rate could fluctuate depending on your buyer journey, sales cycle, and what kind of a potential customer you’re looking for.
