MQL v/s SQL

A Marketing Qualified Lead sits at the top of the funnel. MQLs are at an awareness and information gathering stage. These leads are exploring various solutions to solve their pain points by reading/ accessing your company’s content or responding to a cold email.

A Sales Qualified Lead is further into the funnel. SQLs have an intent towards buying your product and these leads may have already requested a demo, may already onboard a free trial you have been offering, and giving indications to be in the buying cycle.

To simplify:

MQL: Interested in your content SQL: Has intent to buy

How do you move a lead from MQL to SQL?

Your sales team may end up being frustrated if you are too quick to involve them as it ends up wasting their time. Also, you might risk losing the lead if you are not quick to respond as 78% of buyers go with the vendor who is the first to respond. But if you are lucky, the MQL is already requesting a demo and thus smoothly transitions to a SQL.

Time and qualification play an important role here. A well-qualified MQL saves time, improves ROI, and saves resources.

Handing off an MQL

Every sales and marketing team will agree that handing off an MQL as an SQL is the toughest part. Handing off well-qualified and good leads saves time, gives you an accurate forecast, and improves business performance, sales productivity, and growth.

Use the below qualifiers to help determine which leads should be prioritized.

Firmographics that qualify MQLs to become SQLs

You will have an ICP defined for your company. When an MQL is generated, the marketing team compares the lead firmographics to check if they match your ICP before assigning it to sales.

Key firmographics to consider:

• Industry
• Employee Size
• Revenue
• Current tech stack

Actions/Behavior of SQLs

Every visitor on your website, or prospect interacting with your content or responding to your email campaign may or may not be an ideal lead. Look for a particular action or their journey on your website to evaluate if they qualify as a prospect.

Actions could look like this:

• Page visited- customer stories, careers, pricing
• Number of visits- first time, multiple
• Type of conversion- signed up for a free trial, requested a demo
• Opened your email

So, a visitor going to the careers page might be looking for an opportunity, or someone opening your emails to unsubscribe should not be considered to pass along to the sales team.

MQL Qualification Criteria- Why BANT isn’t dead

Sales and marketing teams should consider the below attributes to check if the lead has a high likelihood to convert to an SQL.

Budget– do they have the budget to afford your solutions?

Authority– Check if they are an influencer, gatekeeper, or decision-maker in the buying process.

Need– Does your solution solve their pain points?

Time– Check urgency and ETA for purchase.

Using lead qualification methods like BANT allows sales teams to engage with meaningful leads.

Scoring MQLs

Assigning numerical values to specific actions helps you get a clearer picture, prioritize resources and generate a genuine pipeline. MQLs with a higher score can move further up in the sales funnel.

Score positive values for leads downloading gated content, signing up for a free trial, requesting a demo, and move those leads forward a SQL.

Score negative values if accessing knowledge base, product updates {as it is for customers}, browsing careers, etc., and qualify further.

Lead/Demand generation is a crucial aspect of generating ROI. Understanding MQL vs. SQL and effective ways of qualifying them eases the handing-off process and enables better selling.

Create an SLA(service level agreement) between your sales and marketing teams with pre-defined qualifiers. An SLA in place guarantees that both the teams are on the same page and thoroughly follow the steps to move an MQL to SQL.

Using data like firmographic data also helps sales reps personalize the conversation, deepen the connection, and ultimately increase the chances of winning the deal.

Start a conversation or request a demo to see how every contact in SMARTe’s 181M pool of database is profiled deeply to provide in-depth information on prospects.

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