An MQL (Marketing Qualified Lead) is a prospect the marketing team has judged "worth handing off to sales," and an SQL (Sales Qualified Lead) is a lead the sales team has further judged "can be converted into a meeting." The two are "stages" where the same lead's label changes as it advances through the buying process, and they function as a shared definition for articulating the collaboration criteria between marketing and sales.
An MQL (Marketing Qualified Lead) refers to a prospect that meets the scoring criteria set by the marketing team. Typical judgment materials are a combination of behavior data—viewing specific pages, downloading content, attending webinars, email open rates—plus attribute information such as industry, company size, and job title. Many companies adopt a staged funnel where a lead starting as a MAL (Marketing Accepted Lead) is promoted to MQL through nurturing. What matters is that the definition of MQL is something each company designs to fit its own product, sales cycle, and organizational structure—there is no industry-wide fixed value.
An SQL (Sales Qualified Lead) refers to a lead that the sales team (or inside sales) receiving the MQL has scrutinized with criteria such as BANT (Budget / Authority / Need / Timeline) and judged "can advance to an appointment / meeting." In Japanese B2B organizations, there are cases that set four stages—MAL → MQL → SAL (Sales Accepted Lead) → SQL—and cases that omit SAL and manage in two stages, MQL → SQL. Setting an SAL lets you separate the "marketing send-off criteria" from the "inside-sales acceptance criteria," preventing the back-and-forth pushing of low-quality leads and making cross-team consensus easier.
The biggest value the MQL/SQL distinction brings to an organization is the "clarification of handoff criteria" that lets marketing and sales speak the same language. If this definition is operated while ambiguous, sales become dissatisfied that "the leads marketing sends are low quality" and marketing feels that "sales don't follow up properly"—the typical siloing occurs. Also, according to studies such as Forrester, 57–70% of B2B buyers have already finished gathering information before contacting a sales rep, so incorporating lead signals at that stage as MQL criteria has become indispensable in modern inbound marketing.
The MQL-to-SQL conversion rate varies greatly by industry and channel, but around 12–21% is widely cited as a reference value for B2B SaaS (First Page Sage, 2026). The speed of first contact with a lead is also directly tied to the conversion rate. According to multiple studies such as the MIT Lead Response Management Study, there is a large difference in contact success rate between responding within 5 minutes of an inquiry and responding 30 minutes or later. Meeton ai's AI SDR modules (Meeton Chat / Calendar) are designed as one response to this "Speed to Lead" challenge, and are used to automate conversation, content recommendation, and meeting booking the moment an MQL is detected on the website.
MQL is judged by the marketing team based on the scoring and behavior criteria it sets, and SQL is judged by the sales team (or inside sales) from the perspective of BANT and meeting feasibility. What matters is documenting a definition both teams have agreed on and reviewing it periodically, which prevents misalignment between organizations.
The starting point is to analyze your own past deal data and extract the behavior patterns and attributes common to leads that closed. In a scoring model, it is common to assign points to page views, form submissions, content downloads, etc., and define leads exceeding a threshold as MQLs. The initial values can be provisional; an agile operation that adjusts quarterly based on actual data is recommended.
A combination of lead nurturing (continuous information provision via email and content) and outreach by inside sales at the right timing is effective. In particular, the speed of first contact right after an inquiry or behavior signal occurs is said to affect the contact rate and meeting-conversion rate, so building a system to approach "while the lead's heat is high" is the key to improving the conversion rate.
What an AI SDR is and how to raise the meeting-conversion rate—the category's definition and practical know-how.
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