SQL vs. MQL: What They Are and How They Differ
In the world of B2B sales and marketing, not all leads are created equal. While some may have recently engaged with your marketing campaigns, they might not yet be ready for a direct sales follow-up. Others may have requested a product demo and are eager to speak with a sales rep ASAP.
Distinguishing between these two types of leads – often referred to as marketing qualified leads (MQLs) and sales qualified leads (SQLs) – is crucial to ensuring that each lead receives the appropriate nurturing and follow-up. By doing so, marketing can deliver higher-quality leads to sales, and sales can focus their time and energy on prospects most likely to convert.
Consider these statistics:
- 61% of B2B marketers send all leads directly to sales; however, only 27% of those leads will be qualified. (Source: MarketingSherpa)
- Sales reps ignore 50% of marketing leads. (Source: HubSpot)
- Organizations that use lead scoring increase their close rates by 30% and revenue by 18%. (Source: Salesforce)
Clearly, implementing a formal process to qualify and hand off leads between marketing and sales is no longer a nice-to-have – it‘s critical to hitting your revenue goals.
In this comprehensive guide, we‘ll dive deep into what defines an MQL vs. SQL, why distinguishing between the two matters, and best practices for implementing your own lead qualification system. Let‘s get started!
What Is a Marketing Qualified Lead (MQL)?
A marketing qualified lead (MQL) is a lead that your marketing team has deemed more likely to eventually become a sale based on their demographic information and interaction with your brand across multiple channels.
The defining characteristic of an MQL is that while they have engaged with your marketing efforts, they are not yet ready for a direct sales follow-up. In terms of their buyer‘s journey, they may be in the awareness or consideration stage, but are not yet ready to make a purchase decision.
Common criteria used to determine if a lead is an MQL include:
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Demographic/firmographic fit: Does the lead match your ideal customer profile in terms of job title, company size, industry, etc.? Typically this information is captured via lead capture forms.
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Engagement level: Has the lead been consistently engaging with your content and marketing efforts across channels (email, website, social, etc.)?
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Behavioral signals: Has the lead taken actions that signal interest in your product/service, such as visiting key pages on your website, downloading educational content, or attending webinars?
For example, HubSpot‘s definition of an MQL is: "a lead who has been deemed more likely to become a customer compared to other leads based on lead intelligence. This may be due to the lead‘s demographic information and interaction with your website and brand."
What Is a Sales Qualified Lead (SQL)?
A sales qualified lead (SQL) is a lead that has been vetted and deemed ready to enter into a formal sales process. This means they match your target customer profile, have a defined need for your product/service, and have taken actions that indicate a readiness for sales conversations.
The key difference between an MQL and SQL is that an SQL has been identified as having a high likelihood of making a purchase in the near term. They are further along in their buyer‘s journey, and have met criteria agreed upon by both the marketing and sales teams.
Common SQL criteria include:
- Matching ideal customer profile (company size, industry, job title, etc.)
- Expressing an active need or pain point relevant to your product/service
- Having an established budget and purchasing process
- Requesting a product demo or sales consultation
- Starting a free trial
- Visiting a pricing page multiple times
Many companies also set a lead score threshold that, when reached, automatically qualifies a lead as sales-ready. This allows the handoff from MQL to SQL to happen automatically behind-the-scenes.
For example, Marketo‘s definition of an SQL is: "a prospective customer that has been researched and vetted – first by the marketing department and then by the sales team – and is deemed ready for a direct sales follow-up."
Why Distinguishing Between MQLs and SQLs Matters
It can be tempting to have your marketing team send any and all new leads directly to sales. However, this is a recipe for misalignment, wasted resources, and subpar conversion rates.
Consider these statistics:
- 79% of marketing leads never convert into sales. (Source: MarketingSherpa)
- Sales reps spend 50% of their time on unproductive prospecting. (Source: Markempa)
- A 10% increase in lead quality can result in a 40% increase in sales productivity. (Source: Marketo)
By taking the time to define separate MQL and SQL stages, you can ensure that:
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Your marketing team is passing only high-quality, sales-ready leads to your reps. This prevents your sales team from wasting time chasing leads that aren‘t ready or qualified to buy. According to a Lattice Engines study, 42.5% of sales reps feel they don‘t have enough information about leads before making contact.
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Your sales reps can prioritize and tailor their outreach based on a lead‘s status. SQLs should take priority and receive more personalized, bottom-of-funnel messaging compared to MQLs.
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Your marketing team can better measure the ROI of their lead generation efforts. Tracking key metrics like MQL-to-SQL conversion rates provides valuable insight into which channels and content are driving the highest quality leads.
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Both teams are aligned around a common definition of a qualified lead. This promotes greater trust and collaboration between marketing and sales. According to Forrester Research, companies that align lead generation with sales goals see 27% faster revenue growth.
Best Practices for Defining Your MQL and SQL Criteria
So, how do you go about defining your own MQL and SQL stages? Here are some best practices to keep in mind:
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Get input and buy-in from both marketing and sales. Both teams must be involved in defining and documenting your lead qualification criteria. Consider holding a collaborative workshop to get on the same page.
"The most successful lead qualification systems are created through close collaboration between marketing and sales from the very beginning." – Ardath Albee, Marketing & Sales Strategist
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Use data and past customer information to inform your criteria. Analyze your existing customers and closed-won deals to identify common attributes. This will help ensure your MQL and SQL definitions reflect your real-life ideal customers.
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Start with a simple definition and iterate over time. Your MQL and SQL criteria don‘t need to be perfect right out of the gate. Start with a few key attributes, and commit to regularly reviewing and refining based on feedback from both teams.
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Implement a lead scoring model. A lead scoring system allows you to automatically qualify leads based on a pre-defined rubric. This ensures an objective, consistent approach and allows for easier handoff between teams.
According to Hubspot, lead scoring is used by 68% of highly effective marketers versus only 28% of the least effective marketers.
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Leverage automation where possible. Use your marketing automation and CRM tools to automatically track lead behavior, assign lead scores, and alert sales when a new SQL is identified. This increases efficiency and prevents leads from slipping through the cracks.
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Make lead status visible to both teams. Ensure that both marketing and sales can easily view a lead‘s current status (MQL vs. SQL) as well as key lead intelligence. Many CRM tools allow you to create custom properties to track this.
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Establish Service Level Agreements (SLAs). Put SLAs in place between marketing and sales that specify time frames and processes for MQL and SQL follow-up. This ensures no qualified lead goes untouched and increases accountability on both sides.
The MQL to SQL Journey in Action
What does the actual journey from initial lead to SQL look like in practice? While the specifics will vary by company, here is a typical flow:
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A new lead engages with your marketing by filling out a form, attending a webinar, downloading a whitepaper, etc.
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Based on the information gathered and their level of engagement, marketing qualifies them as an MQL. This may happen automatically via your marketing automation tool.
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Marketing initiates targeted lead nurturing campaigns to educate the MQL on relevant topics and move them closer to a purchase decision. This could include:
- Encouraging them to download more product-focused content
- Inviting them to attend a product demo or join a free trial
- Serving them relevant testimonials/case studies
- Addressing common objections via targeted content
According to Invespcro, nurtured leads produce a 20% increase in sales opportunities compared to non-nurtured leads.
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Based on the MQL‘s continued engagement and any additional bottom-of-funnel signals (such as viewing a pricing page or requesting a consultation), your system deems them ready for sales follow-up. They are officially an SQL!
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Your sales team receives an automated notification with key details on the newly qualified SQL. Using the context provided by marketing, a sales rep initiates personalized outreach to further qualify the opportunity and, hopefully, convert them to a paying customer.
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Your sales and marketing teams meet regularly to review the quality and quantity of MQLs and SQLs being generated, and to refine the process as needed based on feedback from the front lines.
Lead Qualification Success Story: HubSpot
HubSpot, a leading inbound marketing and sales platform, is a prime example of a company that‘s seen major results from implementing a formal MQL and SQL system.
In the early days of the company, HubSpot had no formal definition of an MQL or SQL. As a result, sales reps were often wasting time working leads that weren‘t ready or fit to buy. This led to frustration on both the sales and marketing sides.
To address this issue, HubSpot‘s marketing and sales leaders worked together to define their MQL and SQL stages based on two key factors:
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Fit: How well a lead matches HubSpot‘s ideal customer profile in terms of company size, industry, etc.
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Interest: The level of interaction a lead has had with HubSpot‘s website and content.
By taking into account both fit and interest level, HubSpot is able to automatically qualify and route leads to the appropriate team. MQLs enter into one of several lead nurturing workflows designed to educate and bring them closer to a buying decision. Once a lead has reached a certain lead score threshold, they are passed to the sales team as an SQL for more direct follow-up.
The results speak for themselves. Since implementing their MQL and SQL definitions:
- HubSpot has increased their leads-to-customer conversion rate from 0.8% to 2.6%.
- The number of marketing-generated SQLs has grown by 10X.
- The close rate on SQLs is 5X that of MQLs.
By putting in the work to define and implement an MQL and SQL system, HubSpot has been able to scale lead generation efforts while ensuring high-quality, sales-ready leads are being passed from marketing to sales. It‘s a win-win.
The Bottom Line
In today‘s B2B sales landscape, implementing a formal process to define and qualify marketing and sales leads is no longer optional – it‘s a must-have for driving efficient growth. By taking the time to document your MQL and SQL criteria and putting systems in place to automate lead qualification, you‘ll be able to:
- Increase the quality and quantity of leads passed from marketing to sales
- Improve sales productivity and conversion rates
- Scale your lead generation efforts without sacrificing quality
- Align your marketing and sales teams around a common goal
Of course, implementing an effective MQL and SQL system is not a one-and-done undertaking. It requires close collaboration between marketing and sales, as well as a commitment to ongoing optimization based on lead data and feedback.
But by following the best practices and strategies outlined in this guide, you‘ll be well on your way to building a well-oiled lead qualification machine that delivers real results for your business.
The bar has been raised – now it‘s time to put these insights into action!
