Marketing Qualified Leads

Marketing qualified leads are sometimes confused with sales qualified leads (SQLs). The primary difference between the two is where a lead is at in the conversion process. An MQL is a user that has shown interest in your product or service and is considered qualified for follow-up with a salesperson. 

An SQL is an MQL who has already been in contact with the sales team. A salesperson has vetted the lead and determined they’re qualified enough to move into the buying cycle where the nurturing will continue.

Typically, it’s the marketing team that determines which users are MQLs and then the sales team deems whether an MQL is an SQL. Although MQLs are better prospects than general leads, many will not move on to become SQLs for a variety of reasons. 

Knowing the difference between MQLs and SQLs is important for understanding and optimizing marketing funnels. Successful transition from MQL to SQL isn’t always easy, but it’s crucial for maximizing revenue generation and minimizing cost per acquisition. Usually, this requires collaboration between the marketing and sales teams. You don’t want to send any and all leads to the sales team because many of them would take up valuable time with no payoff. Teams that don’t have a clear delineation between general leads, MQLs and SQLs often have bottlenecks in their sales funnels. 

Leads that make the transition to SQLs may sound more important, but all MQLs are vital for the sales funnel. You need a healthy number of MQLs in the pipeline because they won’t all reach the end of the buyer’s journey at the same time. And just because an MQL isn’t ready to become an SQL right now that doesn’t mean they won’t get there with time, more messaging or a bit more research.  

MQL is one metric that you want to continuously improve since these visitors tend to be more engaged and convert at higher rates. But how do you know which visitors are MQLs? 

Data tracking helps you spot marketing qualified leads because it gives you lead intelligence. You’ll be able to determine interest level, which is needed to move a user into the MQL category and pass them along to the sales team. 

Tracking MQL metrics can tell you where a lead is at in the buyer journey and give you a better idea of how to move them down the funnel. You may even discover the optimal point in the MQL journey to initiate contact with the sales team and improve the chances of converting them to an SQL.

Tracking MQL metrics serves another purpose. It helps you better understand how many qualified leads the marketing team needs to generate. Setting goals for marketing qualified lead generation is a practice that successful companies like ServiceNow follow. It can quickly tell you whether your marketing campaigns are resonating with highly qualified leads and if adjustments need to be made. 

MQLs can also reveal the ROI of your marketing efforts. Generally speaking, the more MQLs a campaign delivers the higher the ROI should be.

Now that you know what an MQL is and why it’s an important metric to track there’s one question remaining. How do you track MQLs? 

First, Define Your Marketing Qualified Lead – You must define what a marketing qualified lead looks like for your business. User personas and cohort analytics can be used to help create an MQL definition. Feedback from the sales team is another way to determine the characteristics of an MQL. Once the MQL is defined you’ll want to repeat the process at least once every few quarters to ensure it’s still relevant. 

Analyze Demographic Data – Tracking demographic data will often reveal that users who fall into the MQL category share some common characteristics. These shared characteristics can be used as MQL qualification factors. 

Analyze Behavioral Data – Behavioral data will help you identify MQLs and provide insight into whether they are ready to become an SQL. Using the data you can figure out what actions were taken by MQLs that went on to become customers. The behavioral analytics can even help you identify specific actions that signal the sales team should step in because the MQL is motivated.

Analyze Engagement Factors – Engagement is another key indicator of whether a user fits the MQL mold and if they are ready for sales team interaction. Look for things like how much time is spent on offer or pricing pages, whether they revisit a web page shortly after the initial visit, if they clicked a sales-ready CTA, etc. It’s important to also factor in email and social engagement as well.

Create a Lead Scoring System – It may be helpful to create a scoring system based on demographic, engagement and behavioral data. This requires associating a certain score to each desirable attribute or action. For example, visiting the website three times in two days could have a score of three whereas filling out a demo request form has a score of six. Once the user score reaches a certain number you know it’s time for the sales team to step in and nurture the lead.

By tracking MQLs you’ll be able to build more effective marketing campaigns and funnels, improve products, gauge when they are ready for sales team interaction and ultimately increase the number of MQLs in your pipeline. Over time you’ll even be able to make MQL projections and determine if you have enough MQLs to meet your goals. As long as you have a robust, intuitive data analytics platform like Mixpanel, tracking this type of information should be a task that the marketing team can handle.