You’ve probably already heard about sales-qualified leads, but have you heard about marketing-qualified leads or MQLs?
Implementing MQLs into your business requires direct collaboration and alignment between sales and marketing teams. This may sound daunting, but it will only improve your ability to turn prospects into loyal customers through the buyer journey!
I’m here with what you need to know, from the basics to how to implement this lead management system into your ABM best practices. Let’s get into it!
What is a MQL?
Great question! A Marketing Qualified Lead (MQL) is a potential customer who has shown interest in your company’s products, but isn’t quite ready to be passed on to the sales team. An MQL could have downloaded content, filled out a form, or engaged with marketing emails.
MQLs bridge the gap between marketing and sales in your ABM strategy; however, they must be nurtured properly before you can graduate them into the direct sales funnel.
MQL vs. SQL: Similarities and Differences
So, what’s the difference between a marketing-qualified lead and a sales-qualified lead? Simply, an MQL becomes an SQL once they’re ready to speak with someone on sales!
MQL | SQL |
Shows initial interest through company marketing campaigns | Leads who are ready for direct sales engagement |
Engages with content, webinars, likes a post, white papers, opens an email | Requests a demo, contacts sales, fills out an interest form with contact information |
Based on behavioral data and fit (Firmographic, demographic) | Based on deeper engagement and readiness to buy |
Primarily managed by marketing | Transitioned to the sales team for further action |
Goal: Nurture and educate | Goal: Convert to paying customer |
How to Identify and Qualify an MQL?
Qualifying an MQL accurately is crucial for ABM management. You don’t want your team wasting time on a target account list of individuals that don’t align with your goals. Here’s how you do it:
Step 1: Align with Marketing and Sales on the Definition of an MQL
First, you need to align your marketing department and sales teams on the definition of your MQL. Does marketing and sales working together sound intimidating? Having a clear definition and explicit buyer persona helps both teams understand what you want in an MQL.
Regularly review this alignment to stay agile and responsive to team feedback.
Step 2: Develop Firmographic and Demographic Qualification Factors
Next, take a deep dive into firmographic and demographic data. Factors such as company size, industry, and job title will help you fine-tune the ideal customer profile.
Use these factors to prioritize leads so your marketing efforts target the right audience.
Step 3: Determine Behavioural Qualification Data
Behavioural data is the final key. It shows the lead intent and readiness to purchase. Tracking actions like downloads, email engagement, and website visits will define their interest level and clarify their intent to purchase.
Use marketing automation tools to monitor and analyze behavioral data! Be sure to constantly refine your MQL criteria based on what you find.
Examples of MQL Actions
What kinds of things can you expect an MQL to do?
5 Best Practices for Managing Your MQLs
So you have your MQLs and you know what ABM mistakes to avoid, so you’re done, right? Not so fast! Here are five best practices for keeping your MQLs in top shape.
Get Feedback on the Historical Performance of MQLs from Sales
For this to be a success, you need to consistently check in with your sales team and get feedback on the quality and conversion rates of past MQLs.
Use periodic review meetings to discuss which MQLs converted successfully and which did not! What do those who successfully converted have in common? Understanding what works helps refine your MQL definition, ensuring higher lead quality.
Tools: Use Customer Relationship Management (CRM) systems like Salesforce or HubSpot to track and analyze MQL performance data.
Forecast if Your ABM Team Can Deliver Enough MQLs to Sales
How many MQLs do you need for success? Don’t pull this number out of thin air; talk to your ABM team to forecast the volume needed to meet their sales targets.
You can also use historical data and predictive analytics tools to estimate the number of MQLs required. When you can accurately forecast, your teams will get a steady flow of qualified leads.
Tools: Use tools like Marketo or Pardot to forecast and track lead generation efforts.
Revisit the MQL Definition Quarterly
As I mentioned before, you should constantly review your MQL definition. Schedule quarterly reviews into your calendar to ensure your criteria remain relevant and effective.
Your marketing and sales teams must participate in these reviews to provide input and alignment. Regular updates to MQL definitions ensure your team is adapting to changing markets or business goals.
Tools: Data visualization tools like Tableau or Power BI are used to present performance data during review meetings.
Revisit Past MQL Behavior in the Sales Funnel
Use the behavior of past MQLs throughout the sales funnel to find patterns and insights. Focus on high-converting behaviors and shape your MQL criteria around them. Historical lead behavior data will help refine marketing and B2B prospecting criteria and attract more high-quality leads.
Tools: Use the funnel analysis tools within your CRM or marketing automation platforms.
Monitor Your Lead to Conversion Metrics
Last but certainly not least, track and analyze your “lead to conversion” data to measure the success of your MQL strategy. Set up dashboards to see metrics in real time and make data-driven adjustments. Regular monitoring will identify bottlenecks and optimize the sales funnel over time for better results.
Tools: Use analytics tools like Google Analytics, HubSpot, or Mixpanel for detailed tracking.
Now It’s Your Turn!
Bring your sales and marketing teams together and put MQL strategy to work. When you align those teams, leverage firmographic and behavioral data, and continuously refine criteria, your business will see a consistent stream of high-quality leads.
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