An MQL (Marketing Qualified Lead) is a type of qualified lead that has demonstrated interest in the products or services offered by the company. Creating an MQL isn't an easy feat, but with the right strategy, obtaining high-quality leads that contribute to achieving business success is possible.
An MQL is a contact who has shown interest in a company's products or services through prior marketing activities. Qualifying as an MQL happens after surpassing a series of thresholds defined by the company. These thresholds help distinguish an MQL from a generic lead by satisfying specific qualification criteria.
The difference between an MQL and an SQL (Sales Qualified Lead) is closely related to the level of interest demonstrated by the lead and their position within the sales funnel.
An MQL is a contact who has shown interest in the company's products or services but might not be ready to make a purchase. MQLs are still in the consideration and evaluation phase of products/services, making them of interest to the company.
On the other hand, an SQL is a contact who has met the qualification criteria defined by the company and has exhibited strong interest in purchasing the company's products/services. SQLs are therefore ready to be contacted by the sales team to close the deal.
Various sources of information, such as browsing data, social data, e-commerce data, and CRM data, can be utilized to target MQLs. Specific analysis tools like Google Analytics, Webmaster Tools, and other behavior analysis tools on the web are available.
To analyze browsing data, specific tools like Google Analytics can be used. This provides detailed information about user behavior on website pages, identifying which content interests users the most, pages with the highest view counts, and other data related to the browsing experience.
Tools like Hootsuite and Mention can be used for social data analysis. These tools monitor the activity of followers and competitors on social media, identifying content generating interactions, trending topics, and new market trends.
E-commerce data analysis can be conducted through platforms like Shopify and Magento. They provide insights into customer purchasing habits, best-selling products, and website conversion rates.
CRM data analysis provides precise information about users interacting with the company, offering extensive visibility into people's behaviors related to the offered products and services.
To track and evaluate MQL engagement, a set of metrics providing information about user interactions with the website and content can be used. The most commonly used metrics include:
Time on site: Measures the duration of a user's website navigation, providing insights into how much time they dedicate to reading offered content.
Page views: Measures the number of pages visited by users.
Conversion rate: Measures the percentage of users who completed a specific action on the website (e.g., product purchase, form submission).
Lead interest signals are indicators that demonstrate a user's genuine interest in the company's products or services. Some common interest signals include:
Once MQLs are identified, the lead nurturing process begins. This involves providing useful and relevant information to prospects with the aim of persuading them to become acquired customers.
Successful lead nurturing starts with identifying prospect needs and offering them pertinent content. It's crucial to remember that lead nurturing isn't about direct sales but rather a process of providing information about the offered product or service. This builds prospect trust, increasing the likelihood of conversion.
For successful lead nurturing, having a structured marketing automation program is essential. This program can send personalized and timely emails, SMS, in-chat messages, or push notifications, guiding prospects through every stage of the purchase journey.
