1. Introduction

The MQL is broken. But it’s here to stay.

The martech boom of the 2000s: When everything changed

The martech boom of the 2000s transformed how marketing teams qualify and route their leads. What was previously a manual process became streamlined, with companies and platforms such as Marketo, Pardot, and Hubspot growing to become synonymous with marketing automation. Marketers gained the power to score their prospects systematically, focusing on key indicators across engagement metrics and prospect characteristics.

During that time, SiriusDecisions (since acquired by Forrester) also proposed the demand waterfall model, defining a funnel structure that’s become ubiquitous across B2B marketing functions.

This waterfall visualized how prospects pass through multiple stages throughout the journey to purchase, ranging from awareness to consideration.

Adopters found themselves segmenting their pipeline based on this model, and marketers began to focus on the MQL. However, this shift hasn’t been without problems, with the biggest pain point being establishing a consistent definition of what makes a ‘good’ MQL.

Marketing automation tools and the demand generation waterfall model transformed how marketers qualify leads. But when it came to defining what actually makes a lead “qualified”, many teams didn’t see eye to eye.

The great MQL debate

The marketing qualified lead has become a hotly debated topic, causing misalignment between thousands of marketing and sales functions.

With many marketing teams historically evaluated on the volume of MQLs they deliver, hitting that target becomes the most critical focus for senior marketers and leaders. This has often pushed marketing teams to overinflate their scoring models or resort to manual qualification to hit their quota.

By comparison—and frequently as a result—sales teams find it difficult to sell to MQLs, often receiving no response from prospects or outright rejecting contacts based on poor alignment with ideal customer profiles.

This chasm between the functions has caused inefficiency within demand waterfall models, skyrocketing customer acquisition costs, and ultimately missed revenue targets despite MQL targets being hit.

So, one big question remains: How can marketing and sales align to hit their revenue targets while considering each other’s needs?

rejecting contacts based on poor alignment with ideal customer profiles.

This chasm between the functions has caused inefficiency within demand waterfall models, skyrocketing customer acquisition costs, and ultimately missed revenue targets despite MQL targets being hit.

So, one big question remains: How can marketing and sales align to hit their revenue targets while considering each other’s needs?

Think the MQL is dead?
Think again.

Get a closer look at why many companies struggle to score leads accurately, and discover how Turtl increased its MQLs by 64% while doubling sales qualified leads quarter-on-quarter in this webinar.

Watch now

The next major shift:
Reader engagement

There’s a simple way to close the gap between marketing and sales, but marketers must challenge the status quo.

If we strip back the jargon, programs, and targets, marketing is centered on reader engagement. Stronger engagement drives education, improves qualification, and strengthens lead scoring models. But not all engagement is equal.

Short-form content—whether it’s videos, emails, or social posts—only provides a superficial level of engagement.

If you want deeper insight into reader engagement, you need to start by creating content that drives deeper reader engagement.

Online documents can help marketers meet that need. By telling detailed stories in engaging ways, they help marketers capture and hold reader attention for longer. Plus, they’re built to capture reader data, so marketers can see exactly what each individual prospect engages with in a document—providing insight into their intent, and where their journey should go next.

Which metric do you wish you could track more accurately across your content?

Total read time
The time readers spend looking at specific pages or sections
Engagement with interactive content elements