MQLs vs SQLs: What’s The Difference?
Attracting, qualifying and converting leads is essential to driving business growth, but understanding the difference between MQLs vs SQLs is commonly not well defined.
Attracting, qualifying and converting leads is essential to driving business growth, but understanding the difference between MQLs vs SQLs is commonly not well defined. Each lead will have their buyer’s journey or buying process. Aligning your sales funnel and lead lifecycle stages with the buyer’s journey enables you to streamline your lead nurturing process and drives conversions.
Common lead lifecycle stages consist of:
- Lead
- Marketing Qualified Lead (MQL)
- Sales Qualified Lead (SQL)
- Opportunity
- Customer
Prospects should undergo a rigorous qualification process to produce high-quality leads. This process usually starts with marketing team and transitions over to sales. Not defining this process can lead to confusion and frustration, and inhibits clear metrics.
This article focuses on MQLs and SQLs, clarifies their differences and includes several reasons why marketing and sales departments should be in agreement and working together.
MQLs vs SQLs: The Importance Of Defining Leads
Before discussing MQL vs. SQL, it’s paramount to understand the importance of defining leads and why sales and marketing should be on the same page.
Studies show that 73% of leads are not sales-ready. Establishing the definition between MQL vs. SQL will create a solid foundation for the handoff process of leads. In fact, 1 in 4 companies that aligned marketing and sales achieved faster growth rates.
It’s important to keep sight of the end goal for both marketing and sales is to increase revenue. There is obviously a broader mix of responsibilities, but put simply, the fundamental purpose of marketing and sales is to grow the business.
When marketing understands how SQLs deliver results for sales, and when sales understands how MQLs function for marketing, agreement on lead definitions is achievable.
Why Are MQLs and SQLs Different?
MQLs and SQLs tend to be considered part of a Consideration Stage in a buyer’s journey, but they are entirely different lead stages.
MQLs are those people who have shown interest in a business and are more likely to be converted to customers. MQLs are based on lead intelligence, backed by closed-loop reports and analytics. They are not yet in the buying stage, but their level of engagement with the brand, such as website visits, email sign-ups, or content offer downloads, are strong indicators that they have the potential to be.
SQLs on the other hand, are those leads who have either shown clear intent to buy or are seen to be have the right potential and the right fit for the target market profile of the business. SQLs are nurtured by the sales team to identify Opportunities and then convert those into Customers.
To summarise, the difference between MQL and SQL is in the lead stage; MQLs are hand-raisers, while SQLs are ready for or are already in the buying cycle.
How Does an MQL Become an SQL?
MQLs need to convert SQLs in order for sales to pursue, nurture and convert.
When MQLs and SQLs are both clearly defined, an MQL becomes an SQL when the lead meets the criteria set out in the definition.
This transition can either occur automatically where a CRM with lead scoring and marketing automation is in place, or the transition is done manually by marketing to transfer the SQL to sales.
How Many MQLs Are Needed To Find One SQL?
Every business is different and you will need to identify or reverse-engineer how many MQLs on average are needed in your sales funnel in order to produce an SQL.
A key metric that adds value is lead conversion rate.
The MQL to SQL conversion rate is about identifying the percentage of MQLs that transition to SQLs.
This metric is used to determine the marketing activities, investment, analytics and tools that the marketing team uses to find net new leads and qualify them. Achieving accurate numbers also help maintain the efficiency of the sales pipeline for the sales team.
A basic formula to get the conversation rate of MQL and SQL is:
MQL to SQL conversion rate = MQLs/SQLs x 100
Through historical data, getting the conversion rate can determine realistic targets to set for both lead types. It could also help give an accurate representation of the entirety of leads in the pipeline.
By following lead nurture best practices, you can produce 50% more sales-ready leads at a third of the usual cost.
It only makes sense that both teams should employ respective nurturing strategies because whether that’s an MQL or SQL, it’s still a lead. It has a long way to go before it becomes an actual buyer.
The Importance Of Tracking MQLs and SQLs With a CRM
Using marketing automation tools such as HubSpot CRM will ease the burden of tracking your MQLs. In fact, 80% of marketers believe that marketing automation will help them get more leads (and conversions).
With automation tools, there is less manual preparation for sales calls and personalised emails and give more opportunities to scale up practical strategies.
Tracking your MQLs and SQLs should come after setting qualification criteria. This way, you will be able to maximise channels that drive revenue more than just leads.
A CRM will unlock the ability to determine key metrics, including:
- Close rate- comparison of closed deals vs. total number of leads
- Upsell rate- for example, for every 1 out of every five customers to upgrade their purchase, the upsell rate is 20%.
- Length of each sales pipeline stage – helps gauge the average lead time in each stage
- Customer acquisition cost (CAC) – the total sales and marketing spend required to seal the deal
- Revenue generated by the campaign – the bottom line of the sales funnel
Want To Discuss How Tracking MQLs and SQLs Can Accelerate Your Sales?
Contact us to discuss how we can help you take a strategic approach.
