MQL vs SQL

Top digital marketing channels help businesses generate leads regularly. Marketing and sales teams evaluate, prioritize, and categorize these leads or potential customers using the lead scoring model. While assigning scores to leads, they consider several factors, including demographic characteristics, buying behavior, engagement level, and response to marketing initiatives.

While evaluating and categorizing leads, marketing and sales teams divide potential buyers into two broad categories – marketing-qualified leads (MQL) and sales-qualified leads (SQL). Marketers detect and vet MQL based on particular factors. Likewise, salespeople identify SQL using specific requirements and criteria. These requirements and criteria make MQL and SQL different from each other.

We can understand these differences by discussing important questions – what MQL is, what SQL is, and what makes MQL different from SQL.

Marketing Qualified Leads (MQL)

Marketing qualified leads (MQL) refer to the potential customers who have engaged with a brand by taking specific actions. MQL engages with a brand in several ways – visiting its website regularly, downloading materials, sharing contact information, or adding products to shopping carts. These engagement levels indicate that the potential buyer is interested in specific products/services offered by the business.

However, he is not yet ready to purchase the product or service despite showing his interest. While scanning and evaluating leads, marketers identify MQL using various criteria – demographic features, engagement level, interaction history, interaction frequency, and behavioral signals. Data analytics solutions make it easier for marketers to identify MQL based on information collected from various communication and marketing channels.

Sales-Qualified Leads (SQL)

Sales-qualified leads (SQL) refer to potential customers who have shown their intention to buy the product or service. They show their readiness to buy by taking specific actions like responding to the marketing materials. The engagement level indicates that the lead has moved from showing interest in a product/service to becoming a paying customer. The sales team usually picks MQL from the sales accepted leads (SAL) passed on by the marketing team.

However, they identify SQL by evaluating the SAL using specific criteria. For instance, many marketing teams detect SQL by evaluating SAL using the lead scoring model. At the same time, many marketing teams identify SQL using frameworks like BANT qualification. As a widely used sales qualification framework, BANT helps salespeople check if the sales accepted lead meets important criteria like budget, authority, needs, and timeline (BANT). Sales teams convert SQL into paying customers by building strong relationships.

MQL vs SQL

Sales Funnel Position

While making purchase decisions, leads pass through three important stages in the sales funnel – awareness, consideration, and decision. MQL remains at the top of the sales funnel in the awareness stage. They engage with a brand intending to collect information and evaluate options.

Marketers increase their interest in the product/service by delivering educational content indirectly. On the other hand, SQL remains at the bottom of the sales funnel. These leads are in a position to make purchase decisions. Salespeople convert SQL into paying customers by adopting a direct approach.

Lead Behavior

Potential customers these days engage with a brand using various digital marketing channels. Marketers usually detect MQL based on how they interact with a brand. Also, they evaluate the lead’s behavior using buyer personas.

Marketing automation tools help marketers find MQL based on the time spent on the website, pages accessed, and forms filled in. On the other hand, salespeople consider budget, authority, needs, and timeline while scanning SQL. Most sales teams analyze the behavior of SQL using the BANT framework.

Intention

MQL and SQL differ from each other primarily in the category of intention. A marketing-qualified lead shows his interest in a product/service. However, he is not ready to buy the product or service. He is simply comparing options and gathering additional information.

However, a sales-ready lead is ready to buy the product or service. Hence, salespeople can convert him into a paying customer by addressing his concerns and answering his questions through direct communication.

Criteria/Requirements

MQL and SQL are detected using different criteria. While detecting MQL, marketers usually consider demographic and industry information. They check if a potential customer is interested in the product/service based on soft indicators like website activity and social media activities.

Hence, they often do not use criteria to measure the lead’s intention to purchase. On the other hand, the salespeople assess the potential customer’s buying intent based on hard indicators using the BANT framework and lead scoring models.

Engagement

Both MQL and SQL engage with brands by downloading various types of content. MQL usually downloads top-of-the-funnel content that marketers distribute to increase brand awareness. Marketers influence and nurture MQL by sharing educational content like blogs, guides, and reports.

In addition to creating brand awareness, the content increases the lead’s interest in the product/service. However, SQL usually accesses bottom-of-the-funnel content like product demos, free trials, discounts, and complimentary services. Salespeople convert them into paying customers by offering whitepapers, comparison sheets, datasheets, and tailored demos.

Acquisition Costs

Companies spend money on acquiring MQL and SQL. However, the acquisition costs of MQL and SQL vary across industries. As highlighted by researchers, tech companies spend around $200 to $300 on each marketing-qualified lead. At the same time, they spend around $500 to $750 on each sales-qualified lead. However, most companies increase the number of MQL and SQL by investing in omni-channel digital marketing.

Top digital marketing channels help them receive inquiries that convert into MQL or SQL. However, several research studies suggest that most inquiries convert into MQL. Marketing teams convert MQL into SQL using various lead nurturing tactics. Likewise, sales teams convert SQL into paying customers through paying customers by winning their trust. Hence, companies often find it challenging to determine the cost of MQL and SQL accurately.

MQL vs SQL: Conclusion

Marketing and sales teams detect and vet leads based on different requirements and criteria. However, marketers focus on converting MQL into SQL. They pass on the SQL to salespeople when the potential customers are ready to talk to the sales team. While discussing MQL vs SQL, we must remember that there are a slew of factors that depict the transition. Marketing and sales teams determine the transition by considering important factors like lead score, lead behavior, and chances of buying.