Guide: What is Marketing Qualified Lead (MQL)?

The marketing world is buzzing with claims that marketing qualified leads (MQLs) are losing their edge. As the picture becomes more complex, these claims are triggering necessary debates and reassessments. Yet MQLs continue to evolve with new insights and methods.

What is a marketing qualified lead (MQL)?

A marketing qualified lead (MQL) is a lead that has caught the attention of your marketing team due to their higher likelihood of becoming a customer compared to others. MQLs have demonstrated interest in your product or service by engaging in specific actions that align with the criteria set by your marketing and sales teams. Additionally, they should match your Ideal Customer Profile (ICP).

To elaborate, an MQL is a lead that has shown a level of interest in your product or service that is above average. This interest can be exhibited in various ways. For example;

• downloading a whitepaper or free ebook
• signing up for a free trial
• signing up for a newsletter
• visiting your help center
• booking a meeting
• joining online sessions
• spending a significant amount of time on your website
• submitting an email address for a mailing list
• filling out your forms or surveys
• reaching out to you with you via contact information
• clicking on your ads
• engaging with your chatbot or live support

What is a marketing qualified lead not?

An MQL is not a guarantee of payment or a sale. The low-weight criteria that define a marketing qualified lead are intended to segment a group of potential buyers who have shown awareness of your solution, product, or brand. While MQLs indicate interest, they do not necessarily reflect a readiness to purchase. MQLs are an early stage in the buyer’s journey, representing leads that are more engaged than others but still require nurturing before they can be moved further down the sales funnel.

Marketing qualified lead vs sales qualified lead

In contrast to marketing qualified lead, a sales qualified lead means an individual who has passed the initial engagement stage. They are now prepared to engage directly with the sales process. They’ve shown a clear intent to make a purchase of the offered product or service and are ready for a more direct, personalized sales approach.

To illustrate the difference between MQLs and SQLs, consider the following table:

	Marketing qualified lead	Sales qualified lead
Stage in Sales Process	Intermediate stage, after initial engagement but before direct sales engagement.	Advanced stage, ready for direct sales engagement.
Sales Approach	Not yet prepared for personalized engagement from the sales team.	Primed for a direct, personalized sales approach.
Example	A lead that has downloaded a white paper or subscribed to a newsletter.	A lead that has requested a demo or a sales call.

Marketing qualified lead vs product qualified lead

A Product qualified lead, is a lead who has derived significant value from your product through a free trial or freemium model. These leads have not only shown interest, but have also experienced your product first-hand and found it valuable. This makes them highly likely to convert into paying customers.

To help illustrate the differences between MQLs and PQLs, consider the following table:

	Marketing qualified lead	Product qualified lead
Qualification Criteria	Marketing materials and content	Product usage data
Source of Leads	Marketing campaigns	User product interactions
User Experience Phases	Marketing channels	Hands-on experience
Customer Journey Stages	Early-to-mid stages	Mid-to-late stages
States of Buyer Readiness	Need further nurturing	Ready to further engagement
Conversion Probability	Surface-level interest	Higher probability

How to identify marketing qualified leads?

Identifying marketing qualified leads is an important step in ensuring that your marketing and sales efforts are focused on the most promising prospects.

1. Identifying intent signals

The first step in identifying MQLs is to monitor and recognize the intent signals that indicate a lead’s interest in your product or service. These signals can vary depending on your business and industry but typically include:

• Monitoring how often and how long a lead visits your website, such as pricing or product details.
• Tracking leads who download resources like whitepapers, eBooks, or case studies.
• Identifying leads who fill out forms, such as contact forms, surveys, or signups for webinars.
• Identifying interactions with your email campaigns, such as opens, clicks, and replies.
• Tracking how leads engage with your brand on social media platforms.

2. Building a lead scoring model

Once you’ve identified the key intent signals, the next step is to build a lead scoring model. You can prioritize leads most likely to convert by assigning point values to different MQL qualifications and behaviors.

Lead scoring rules use a predetermined MQL formula to allocate points to leads based on their marketing activities.

• Assing points: Each action a lead takes should be assigned a point value according to its significance. For example, downloading a whitepaper might be worth 10 points, while attending a webinar could be 20 points.
• Weight the criteria: Not all actions are equal in importance. Prioritize high-intent actions like requesting a demo or spending significant time on a pricing page by assigning them higher scores.
• Include negative scoring: Deduct points for actions that indicate a lack of interest or poor fit, such as unsubscribing from emails or inactivity over a certain period.
• Set a threshold: Determine a score threshold that a lead must reach to be classified as an MQL. This threshold should be based on historical data and refined over time.

Building a lead scoring model can be time-consuming, especially when it requires fine-tuning criteria, assigning meaningful point values, and constantly adjusting based on performance data. However, with UserMotion, you don’t have to spend your valuable time constructing this model from scratch.

UserMotion helps you build a predictive lead scoring model with predefined criteria, weights, and point assignments tailored to your specific needs. By leveraging historical insights and data-driven algorithms, Usermotion automatically adjusts scores and thresholds, allowing you to focus on engaging your MQLs and driving conversions. This powerful tool ensures that your scoring model is not only accurate but also evolves with your business, enabling you to prioritize the leads that matter most.

3. Engage with your leads

After identifying and scoring your MQLs, the next step is to engage with them through targeted marketing efforts. The goal is to nurture these leads by providing relevant content and interactions that guide them further down the sales funnel.

• Send custom emails based on the lead’s previous interactions with your brand, offering content that aligns with their interests.
• Use retargeting campaigns to keep your brand top-of-mind for leads who have interacted with your website or content.
• Provide a steady stream of valuable content, such as blogs, webinars, case studies, or product updates, that addresses the specific needs and pain points of your MQLs.
• Use tools like chatbots, live chat, or personalized video messages to offer real-time support and answer questions that may arise.

4. Convert MQLs to SQLs

The final step is to transition your marketing qualified leads to sales qualified leads. This is where the lead is handed off to the sales team for more direct, personalized engagement. 

1. Continuously track the lead’s engagement and behavior to determine the right moment for a sales handoff. When a lead meets specific criteria, such as reaching a certain lead score or requesting a demo, it’s time to transition them.
2. Ensure your sales team is fully informed about the lead’s journey, including their interactions and preferences. This allows for a seamless and informed handover.
3. The sales team should reach out with personalized communication, addressing the lead’s specific needs and concerns, and guiding them towards a purchase decision.
4. Even after the handoff, continue to support the sales team by providing relevant content and insights that can help close the deal.

Tools to track marketing qualified leads

In the era of digital technology, utilizing its power is a necessity, not a choice. Tools such as CRM systems and automation can significantly improve the management and progression of MQLs.

Customer relationship management

Customer Relationship Management (CRM) systems are not just a lifesaver but a time-saver when it comes to managing MQLs. They play a crucial role in organizing lead data, tracking engagement, and ensuring timely follow-up on leads. Some popular CRMs include:

1. Salesforce: Known for its comprehensive features, Salesforce offers robust tools for managing leads, tracking customer interactions, and automating marketing tasks.
2. HubSpot: HubSpot’s CRM is user-friendly and comes with a suite of integrated inbound marketing tools. It allows tracking of every interaction a lead has with your content, making it easier to identify MQLs.
3. Pipedrive: Pipedrive is a sales-focused CRM that helps you plan your activities based on your sales pipeline. It offers great visualization of sales processes and is easy to use.

For instance, if a lead downloads an eBook from your website, the CRM system will automatically record this action. It will mark the lead as an MQL and could help you to schedule a follow-up email to nurture the lead further.

Similarly, if a lead attends one of your webinars, the CRM system will track this engagement and schedule a follow-up call from the sales team. Thus, CRM systems automate various tasks, making the process of managing MQLs much more efficient and effective.

Predictive lead scoring tools

Predictive lead scoring tools are a vital component of the MQL identification process. These tools use a combination of data points and predictive analytics. They determine which leads are most likely to convert into customers. The predictive lead scoring tools analyze various factors. These include the lead’s demographic information, behavioral patterns, and engagement with the company’s content.

The advantage of using predictive lead scoring tools is their ability to analyze vast amounts of data quickly and accurately. They can sift through hundreds or even thousands of data points to identify patterns and trends that might be missed by the human eye.

These tools are not just about assigning scores to leads. They are about predicting the future actions of leads based on their past behaviour. This can provide valuable insights that can help guide your marketing strategies and sales efforts.

Here are some examples:

1. 6sense: 6sense’s Account Engagement Platform employs predictive modeling to identify the most promising leads and accounts. It monitors engagement and intent signals to deliver a more accurate lead score.
2. HubSpot: HubSpot is not just a user-friendly CRM with a suite of integrated inbound marketing tools, but it also provides a robust feature for lead scoring, enabling businesses to assign value to their leads based on their interactions and engagement levels, thus helping in the effective identification of MQLs.
3. Outfunnel: Outfunnel is a powerful marketing automation tool that seamlessly integrates with your CRM, providing marketers with the ability to automate and align their sales and marketing efforts, thereby making it easier to identify and nurture MQLs.

Analytics tools

In the digital age, businesses rely on analytics tools. They use them to track and manage their Marketing Qualified Leads (MQLs). These tools provide a comprehensive view of your leads’ behaviors. They also show interactions and engagement levels. They help you identify potential MQLs and guide them through the sales funnel.

1. Google Analytics: This tool allows you to track website traffic and user behavior, providing insights into which pages or content are attracting potential MQLs. You can also set up goals to track conversions and monitor how leads are interacting with your site.
2. Marketo: Marketo provides a robust marketing automation platform with advanced analytics capabilities. It helps in tracking the digital footprints of leads, scoring them based on their actions, and nurturing them with personalized content.
3. Pardot: As part of the Salesforce family, Pardot offers B2B marketing automation features with a focus on analytics. It allows for tracking and scoring of leads based on their activities, helping you identify and prioritize MQLs.
4. Kissmetrics: This tool is designed to understand, segment and engage your audience based on their behavior. It provides insights into how leads are interacting with your website and content. This helps you identify potential MQLs.

4 MQL Conversion Metrics

Understanding your MQL conversion metrics provides a clear direction and valuable insights into your leads’ journey through the sales funnel. They are instrumental in assessing how well your strategies are working. Here are some important metrics to consider:

1. MQL to SQL Conversion Rate: This metric measures how many of your MQLs are progressing to become SQLs. A high MQL to SQL conversion rate indicates that your marketing efforts are successfully nurturing leads and moving them further along in the sales funnel.
2. Lead-to-Customer Conversion Rate: This metric shows the percentage of leads that eventually become customers. This is crucial for understanding the overall effectiveness of your marketing and sales strategies.
3. Cost per MQL: This metric helps you understand how much you’re spending to acquire each MQL. By keeping an eye on this, you can ensure you’re investing your resources wisely and getting a good return on your marketing investment.
4. Time to Conversion: This measures the average time it takes for an MQL to become an SQL and then a customer. This can help you identify any bottlenecks in your sales process and make necessary adjustments.

Conclusion

In conclusion, MQLs are the seeds that, with the right nurturing, can blossom into SQLs and eventually into paying customers. By defining MQLs and creating a lead scoring model, you can streamline the management and progression of MQLs. Utilizing tools like CRM systems and automation helps too. Analyze data insights continuously and adjust tactics based on these insights. This way, you can optimize MQL conversion rates and overall sales success.