There are several types of qualified leads, i.e. leads whose behavior makes them qualified to be sold to. Marketing Qualified Leads (MQLs) are one of these lead types.
What is an MQL?
A Marketing Qualified Lead has engaged with your company in some way but has not given you their business yet. For example, they may have downloaded a white paper, attended a webinar or visited your website before.
An MQL is assumed to be in the consideration stage of their purchase. They are evaluating different options but have not picked one yet. As a result, they’re more likely to become a paying customer than someone who has never encountered your business before.
Differences Between an MQL and SQL
The main distinction between an MQL and a Sales Qualified Lead (SQL) is their readiness to buy. While an MQL has shown some interest, they are not yet ready to convert. You can tell them more about your business or product in order to keep your product top-of-mind.
Meanwhile, an SQL is further down the funnel. She has shown a clear intent to purchase and is ready for a direct sales pitch. You can sell them more - or showcase your product with a free trial, a sales demo, or a direct offer.
By identifying and nurturing MQLs around the time they first engage, your business can turn them into SQLs. Whether this falls to marketing or sales depends on a specific business, its strategy, and its goals.
How to Value an MQL
To find the effect an MQL has on your bottom line:
- Calculate how often MQLs convert to SQLs or paying customers.
- Compare the cost of acquiring an MQL to the average revenue they ultimately bring in as a customer.
The potential value of an MQL multiplied by their likelihood of converting is their total value to your organization. For example, if an MQL is worth $1,000,000 once a customer, and 1 in 10 (10%) convert…
$1,000,000 x 10% = $100,000
In other words, a single MQL is worth $100,000 to your business.
Just remember: the value of an MQL isn't just in their potential to boost revenue. It’s also in what their actions teach you about consumers. Always collect data and analyze it to help refine and improve business performance over time.
Looking to the Future: Predictive Analytics for MQL Scoring
With advancements in AI and data analytics, businesses can now use predictive analytics to score MQLs. This involves using AI to analyze historical data and predict which leads are most likely to proceed down the sales funnel.
Data points can include demographic information, behavioral signals, and past interactions. By employing predictive analytics, businesses can proactively identify high-value leads, optimize their marketing efforts, and drive higher conversions.
Following Up with MQLs
Because MQLs can so easily go unnoticed, it's important to be clear with your sales and marketing teams about how to classify and follow up with them. Recommended steps include:
- Defining what qualifies someone as an MQL.
- Introducing feedback loops between sales and marketing to refine MQL criteria as lead behavior evolves.
- Establishing a clear hand-off process for your sales team when transitioning an MQL to an SQL.
- Integrating CRM systems to ensure seamless communication and lead tracking.
