Even if an MQL is defined as having filled out a product-related form, if that form is lacking key qualifying criteria (like company size, revenue range, etc.) that’s important to sales, that could also cause sales to feel like their time is being wasted on bad leads. These are perfect examples of the tensions between MQL and SQL. That’s why it’s important to identify the criteria you’ll use to measure MQLs by working together with sales to determine when they’re ripe for the next stage.
Here are some of the common criteria than can help with lead scoring and identifying sales-ready MQLs: Industry Number of Employees Revenue Job Title Level of Engagement with’ll also need to identify the lead qualifications that make sense for your business. For example, a PPC agency will likely want to know about a lead’s goals for PPC and where they advertise: egypt consumer email address mql vs sql 1 And an agency that works on conversion rate optimization for eCommerce needs to know how much traffic a lead’s website gets and which eCommerce platform they use: mql vs sql 2 That’s why MQL criteria needs to be based on real data about your existing customers and the buyer’s journey they took to become customers.
But in most companies, the task of defining MQLs falls to one person who comes up with relatively arbitrary characteristics—and those go on to constrain the whole organization. Instead, MQL criteria should be set by sales and marketing, who work together to identify proven indicators that a lead is likely to buy from you. Working with Sales to Reverse Engineer Lead Qualification So how should you define an MQL to avoid these problems between marketing and sales and MQL vs.