Updated Nov 7, 2023: We’ve refreshed the article to focus more on the MQL to SQL conversion, with more actionable tips.
All companies interested in selling more should establish a conversion funnel and clear qualification criteria for assessing potential customers.
How do you separate blog readers from email subscribers? How does MQLs and SQLs marketing differ? At what point should your sales representative step in to assist in the MQL to SQL conversion process?
Tracking the conversion funnel progress between different stages correctly is critical to getting as much of your audience from the top of the funnel (TOFU) to becoming paying customers. Approaching your leads too early might push them away, while reacting too late might lose them to a competitor.
To see how an effective transition from MQL to SQL works, you should start tracking the process even before website visitors become MQLs.
Tip: Identify website visitors’ probability to buy immediately with Lift AI, the only buyer-intent solution that works on anonymous visitors in real time.
What Are MQL and SQL Definitions?
Marketing qualified leads (MQLs) express significant interest in your offering but aren’t ready to buy yet. They are looking for a solution to a problem and are aware that your offering could solve it, but they either need more time or more information to commit.
The exact criteria for MQLs differ between companies, but here are a few common indicators:
- Watching product demos on your website
- Downloading free in-depth content (e.g. a white paper)
- Leaving items in the shopping cart to calculate shipping costs
- Sending a message to your chatbot
- Participating in free coupon giveaways (e.g. by signing up for a newsletter)
- Answering quiz questions to get a product recommendation
MQLs take more action and are more engaged with your marketing content than occasional visitors or passive content consumers (e.g. blog readers or newsletter subscribers). But how do they differ from SQLs?
A sales qualified lead (SQL) not only knows about your solution and engages with your marketing content — they are ready to buy but need more information for their specific use case.
For example, when MQLs realize they need a solution for qualifying B2B leads, SQLs are busy comparing a shortlist of buyer intent platforms.
In lifecycle marketing, MQLs are somewhere between awareness and consideration stages, whereas SQLs are between consideration and making a decision (the perfect time for a sales team to step in).
But how do we know that an MQL should become an SQL?
The Role of Content in Nurturing Leads
Website visitors don’t become MQLs and then SQLs in a matter of minutes. For certain buyers the process might take weeks.
The key ingredient guiding the MQL to SQL conversions — your marketing content.
A lot of people find out about your company because it ranks high in their search results on Google. When your marketing team structures your website in the right way and regularly produces high-quality content — your SEO improves.
Another way you can become prominent in the market is through social media. People like following interesting industry accounts and might eventually transition from casual readers to MQLs.
Ads, affiliate deals, and sponsorships play an important role in attracting your target audience and serve as an introduction to your solution.
As a tool, content marketing spans multiple stages and can attract and nurture a lead from the first interaction up to becoming an SQL, where they are passed to the sales team.
Collaboration Between Sales and Marketing Teams
You can identify five steps that visitors go through to turn into buyers:
- Visitors become contacts
- Contacts become MQLs
- MQLs are nurtured with high-quality content
- MQLs turn into SQLs when they are ready to buy
- SQLs are handed off to sales teams
1. Visitors Become Contacts
Tracking the amount of visitors on your website doesn’t tell you anything about their engagement. A million bot hits might spike your analytics but have no effect on your sales. Besides, visitors could end up on your websites by accident or quickly become uninterested.
However, when visitors volunteer information about themselves they become contacts. Now your marketing or sales team has permission to reach out to them and develop the relationship further.
The most frequent information visitors give businesses is their email address, phone number, or social media username (by becoming followers).
2. Contacts Become MQLs
In terms of engagement, contacts stay at the top of the funnel. They might consume the content provided to them by the marketing team but rarely take any actions that would lead to a sale.
It’s important to assess contacts based on your ideal customer profile (ICP) criteria. That way, when they move closer to the middle of the funnel (MOFU), they can become a marketing qualified lead or even a sales qualified lead much faster.
3. MQLs Are Nurtured With High-Quality Content
Contacts turn into MQLs when they realize that their problem can be solved by your product or service. Even though there might be significant barriers — from costs to training, to technological compatibility — the process is moving along the conversion funnel.
Marketing qualified leads are interested in evaluating your offering more closely. They want comparison tables, testimonials, FAQs for specific questions, pricing structures, webinars, and more. In other words, anything that helps them overcome friction.
4. MQLs Turn Into SQLs When They Are Ready to Buy
Different sales teams might define sales qualified leads differently. In general, an SQL is a lead that is ready to buy but is not yet certain of all the details. For example, they might be trying to find out whether your product fits their specific use case or how the implementation is going to look like.
SQLs are leads that move to the bottom of the funnel (BOFU). Once an MQL turns into an SQL, they should be handed over to your sales team for conversion.
5. SQLs Are Handed Off to Sales Teams
When your sales team contacts a new SQL, they should resolve any doubts and answer any questions that the prospective customer might have regarding how your product would help them alleviate their pain point and achieve their goals. Plus, this is the right time to discuss price quotes and terms of sale.
Contacting SQLs at the right time is key to the success of your sales strategy. But in the time of artificial intelligence, trying to guess the state of your SQLs manually is inefficient. Finding the right tool that can accelerate the process is essential.
Practical Examples of MQL to SQL Transition
Effective sales and marketing teams have well-defined objectives that help determine which actions would transform MQLs to SQLs. Asking for a live demo or hopping on a sales call would fall into that category, for example.
Other qualifying actions depend on multiple factors. If there are not enough SQLs for your sales team to work with at the moment, they might decide to contact more MQLs earlier in the funnel. But if your sales reps are too busy, they delay contacting recently converted SQLs to tackle the backlog first.
Various lead-scoring systems promise to automate the MQL to SQL conversion process by assigning quantifiable values and highlighting SQLs to the sales team once they pass a certain threshold. These solutions could work off either first-party or third-party data.
For manual lead scoring, segmentation becomes imperative:
- Demographic. Job title, income, industry, country.
- Firmographic. Company revenue, number of employees, growth trajectory.
- Behavioral. Downloading reports, spending time reading website content, engaging with customer support.
- Technographic. Using technologies that either integrate with or are alternatives for your current solution.
Even though experienced sales reps can identify qualified prospects to work with, high-quality lead scoring software powered by AI can do that more efficiently.
The Role of AI in Lead Scoring and Nurturing
Automatic lead-scoring tools rely on first- or third-party data to classify MQLs and SQLs. But they tend to stop short of integrating different types of data together and leveraging machine-learning capabilities to better predict the ideal time to contact new SQLs.
A buyer-intent solution like Lift AI is different. It’s powered by a machine-learning model trained on billions of data points and over 14 million live sales conversations. Its advanced model integrates with your website’s first-party data to make accurate real-time predictions about which visitors are more likely to turn into buyers (around 9% of your traffic).
What’s unique about Lift AI is that it even works for every anonymous repeat visitor (can be up to 98% of the traffic) that’s not recorded in your CRM.
Tools like Lift AI accelerate lead scoring (handling as many leads as possible), improve conversion accuracy, and remove inefficiencies in your buying cycle — instantly.
Key Steps to Convert MQLs to SQLs with AI
The way Lift AI works is by connecting high-value visitors that land on your website with BDRs. This can be done via online chat (any chat platform you use) or another platform your marketing department is using.
In the case of chat, imagine high-value visitors being connected to BDRs immediately, while mid-scoring and low-scoring visitors would be greeted by a chatbot or a self-help guide instead. This one change would free up a significant chunk of your sales team’s time.
Even better, you can see the results almost immediately. Within 90 days, Lift AI customers have reported increasing their chat conversions by anywhere from two to 10 times.
PointClickCare, for example, improved its conversions by 400%, attributing over a million dollars in additional revenue to its Lift AI integration. Another customer, Formstack, grew its sales pipeline by 420%.
Lift AI is powerful because it doesn’t require any complex integrations or training. The process leverages the automated nature of AI and starts to bring measurable results fast.
Leave worrying about the minute details of MQLs and SQLs to other companies and enjoy a steady flow of qualified leads without any manual filtering. As a result, you can direct more sales efforts to the bottom of the funnel, where they tend to matter a lot.
What are SQLs in marketing?
An SQL is a sales qualified lead — someone who is past the stage of being an MQL and wants to reach out to sales to buy your product or service.
What do SQLs mean in sales?
SQLs are the primary targets of BDRs and account executives who can guide them through the sales process and explain any details to finalize the deal.
How do MQLs become SQLs?
MQLs can become SQLs by being exposed to a high-quality content marketing strategy. Once an MQL goes through a lead nurturing process and gets enough information about how your solution solves their problem they might be interested in buying it.
What is an SQL in lead generation?
In lead generation, an SQL is a lead that reaches the bottom of the funnel, passing through less serious stages of being a visitor, a contact, and an MQL. Sometimes, your inbound sales process might even find leads that show significant interest and become SQLs instantly.