Choosing an MQL model for your HubSpot instance

One of the first things you'll encounter as a new HubSpot user is the need to create a lifecycle model. By default, HubSpot includes out-of-the-box properties and automation to help you get started. But you still have important decisions to make to reach marketing operations excellence. Much of it comes down to blending marketing operations with your business processes.

One of the most critical stages in your lifecycle is the Marketing Qualified Lead (MQL) stage. This is the hand-off point between sales and marketing. When a contact becomes an MQL, workflows kick off in HubSpot and signal to your sales team that a new contact is ready for outreach.

While some declare MQLs dead, the model remains foundational for many sales and marketing teams. Few teams succeed without a signal that a prospect is ready to engage with sales. Call it what you want—the spirit of the MQL is alive and well.

The modern approach doesn't disregard MQLs; it builds on them. Teams now incorporate conversational marketing, product-led initiatives, and other tools to grow revenue and strengthen the sales and marketing bond. MQL models are evolving to reflect how buyers actually engage with your business.

In this post, I'll walk you through the models used to define MQLs. Even if you don't adopt one of these frameworks directly, you'll gain insight into how marketing operations professionals approach the sales and marketing process.

What is a marketing qualified lead?

A marketing qualified lead (MQL) is a prospect who meets predefined criteria suggesting they are likely to convert. Once a prospect reaches the MQL stage, they are ready for sales outreach. Ideally, MQL criteria are backed by data and experience that signal a higher likelihood of conversion.

HubSpot defines an MQL as a "lead that the marketing team has deemed more likely to become a customer compared to others."

Too often, marketing operations teams get hung up on the "how" instead of the "what" and "why." Let's focus on the why: we define MQLs to streamline the sales and marketing process and become more effective at generating revenue.

If the best model for your team is to hand-pick MQLs, then do that. Be pragmatic but also experiment. I've worked with numerous models—from simple to sophisticated—and seen both succeed and fail. The key is understanding your business context.

How do you measure your MQL process effectiveness? Revenue is the obvious choice, but it's a lagging indicator. Instead, track your MQL to SQL conversion rate (also called MQL acceptance rate). Since MQLs are transitory—they either progress to the next stage by being "accepted" by sales or revert to a previous stage—this metric reveals whether your model is working. Aim for an MQL to SQL conversion rate above 80% to evaluate your model's effectiveness.

Types of MQL models

The most common MQL models are:

• Hand-picking MQLs
• Direct response to a campaign
• Lead scoring models
• Product-led MQLs

Each model has strengths, and you'll want to pick one that aligns with your current setup and resources. Let's explore each model in detail.

Hand-picking MQLs

A hand-picked MQL is one that marketing has specifically identified and passed to sales. In the age of marketing automation, it seems primitive and unscalable—but it happens more often than you'd think.

This is often the default starting position for two reasons: teams lack a marketing automation system like HubSpot, or they don't have in-house expertise to build an automated MQL process.

As a marketer, your mantra should be "know thy customer." If you live by this principle, you should be skilled at identifying qualified prospects. High quality and acceptance rates are possible with this approach.

However, this model doesn't scale as your inbound marketing engine grows. It's labor-intensive and creates bottlenecks as volume increases. Use this model as a starting point, then graduate to more scalable approaches as your team and processes mature.

Direct response to a marketing campaign

A direct response MQL is a contact who has engaged with a campaign, signaling high engagement and sales readiness. This includes filling out a demo request form, responding to a direct mail offer, or starting an online chat conversation on your website.

These are hand-raising prospects—ones directly or indirectly asking for sales engagement. Your job is to get these contacts in front of sales as quickly as possible. In HubSpot, implement this by:

• Setting up workflows to alert sales reps of a hot lead
• Creating automated emails to confirm receipt of the prospect's inquiry
• Giving prospects direct access to sales rep calendars on demo forms

Every inbound marketing team has some form of direct response campaign (unless your website has no forms, chat widgets, or contact information). As you build your MQL process, don't move away from this model—incorporate it into more advanced frameworks.

Lead scoring model

A lead scoring MQL model uses marketing automation to assign grades or scores to each contact in your system. This is the most common model for generating MQLs in an inbound marketing framework because it's reliable, scalable, and relatively accurate.

Within the lead scoring paradigm, you can use several sub-models:

• Numeric scoring
• Scoring matrix
• Algorithmic scoring programs

Let's explore each in detail.

Numeric scoring

A numeric lead scoring model assigns a score or grade to each contact in your database. This score can be a single property or a sum of multiple scoring properties, such as behavior and demographic scores.

This is the gateway model for lead scoring. It's straightforward to set up—a few workflows or modifications to the HubSpot Lead Score property, and you're ready. The most common implementation sums Behavior Score and Demographic Score to create an aggregate Lead Score.

One limitation is the potential for false positives. A highly engaged prospect may overcome a lower demographic score and still reach sales. Conversely, prospects matching all demographic attributes may be deemed sales-ready before engaging with your marketing or product.

I've seen strong results with this model over the years. However, continuously evaluate your scoring criteria and check in with sales on lead quality. On paper, the model is simple. As you discover exceptions to your rules (for example, highly engaged leads from a poor geographical location), you'll need to amend the model and reroute false positives.

Here's a basic example of this scoring model:

Behavior score

• +50 pts for form fill
• +5 pts for email clicks
• +15 pts for pricing page visit

Demographic score

• +15 pts for target role(s)
• +15 pts for target geography
• +5 pts for corporate email

Add these up, and you have your numeric lead score.

One drawback is that quality is represented purely as a score—the higher the score, the better the lead. This can be confusing, and the score often obscures which attributes determine MQL status. Transparency matters when sales teams need to understand why a lead was qualified.

Scoring matrix model

A scoring matrix model groups prospects on two axes: Fit and Engagement. Unlike numeric scoring, this model doesn't display an aggregate score; instead, it groups contacts into buckets.

The scoring matrix builds on the numeric scoring model using the same ingredients. Fit is a stand-in for demographic score, and Engagement is the equivalent of behavior score.

The approach is to take those two scores and apply a grade to each. Instead of showing a behavior score of 120 or 30, you assign a grade based on value bands. For example: 1–25 = Grade 4, 26–50 = Grade 3, 50–100 = Grade 2, and 100+ = Grade 1. Do the same for Fit Grade.

The key part is figuring out your buckets. Get into your data and determine where contacts actually land in the matrix. The best approach is to decide how many people you want in each stage, then work backward to set your thresholds.

What I like about this model is the predictability. You'll end up with a rough forecast for how many contacts land in each bucket monthly. This creates predictable MQL volume for sales and clear targets for marketing. The model also removes false positives and provides an intuitive value—an A1 lead is highly engaged and matches your best-fit profile; a D4 is unengaged and a poor fit.

Algorithmic scoring programs

The third model is using third-party software to set lead scores via algorithm. These models work well but require substantial data inputs and additional software costs.

Most work by entering a list of records representing your best-fit customers. This might include recent opportunities, opportunities at a certain stage, or existing customers.

The software compares those records to a global database and correlates attributes to predict if a new lead is more or less likely to convert. It's similar to how digital ad platforms like Facebook use lookalike audiences. You provide a list of your best customers, the platform analyzes shared attributes, and it predicts a contact's likelihood to purchase based on similarity to your best customers.

In my experience, these models require periodic tweaking. I've also seen teams run a lead scoring system in parallel to validate the algorithm's output. For example, run a scoring matrix and compare those results to the algorithm's predictions.

The hardest part of adopting this model—beyond budget—is trusting it. The qualification process is a black box for sales and marketing. You're giving control of your MQL criteria to software you can't see working in real time. This creates instinctive skepticism about accuracy and data sourcing. In an era of privacy and security concerns, many teams hesitate to hand control to third-party software.

Product-led MQLs

In software, product-led growth is gaining traction for good reason. MQLs in this model are product-qualified leads. What does that mean?

In a product-led organization, the product itself drives conversion and qualification. While demographic and behavioral attributes dominate traditional lead scoring, in product-led marketing, the product acts as the filter.

Users successful with your product and actively engaging with it are deemed best-fit. Success in the product, this model assumes, predicts future conversion. You don't discard demographic attributes entirely—they still help eliminate poor-converting geographies and roles—but they become secondary.

One advantage is the ability to raise your sales targets. If your product is primarily self-serve and most people purchase without a sales contract, you can focus your sales team on upsell. You might prioritize moving customers from monthly to annual plans or pitching premium features.

How do you action product-led MQLs in HubSpot?

This requires integration with your product, data warehouse, or a third-party tool like Mixpanel. The focal point is users engaging with specific features or hitting certain product milestones.

You'll talk more with product and data teams than sales to determine which product features correlate with conversion.

This model is strong and scalable. You're working with contacts who are already successful—sometimes, sales just needs to nudge them across the finish line or work on expansion revenue.

How to pick an MQL model

Unless your business model is completely novel, you'll likely incorporate elements from each MQL approach to create an optimized lifecycle. Hand-raising prospects filling out demo requests should have a direct line to sales. Highly qualified contacts matching your best-fit profile should be prioritized. Engaged product users deserve extra consideration.

HubSpot supports any of these models. That may not help you choose—and honestly, you may want to experiment with a few. My recommendation is to build a "shadow program" using temporary or test properties. Run your proposed model behind the scenes without impacting existing processes.

Use those results to inform stakeholders and determine which approach makes the most sense for your business. Start simple, measure results, and evolve as you learn.