Using Product Intent for Lead Scoring: Enhancing Lead Qualification

For years, marketing teams have relied on demographic-heavy lead scoring models — job title, company size, industry, geography. While these attributes provide context, they do not indicate buying intent.

A VP title does not mean readiness.
A Fortune 500 account does not mean interest.
And a perfectly filled form does not mean urgency.

What actually separates high-quality leads from noise is intent — specifically, product intent.

Product intent allows us to answer the most important question in lead qualification:

“Is this person actively showing interest in what we sell — right now?”

In my experience leading global marketing automation and analytics initiatives, I’ve seen a clear pattern:

The moment we shift lead scoring from “who they are” to “what they are doing,” MQL quality improves dramatically.

What Is Product Intent — and Why It Matters

Product intent refers to observable behaviors that indicate interest in a specific product, solution, or capability, not just general brand awareness.

Examples include:

• Repeated visits to product or pricing pages

• Engagement with feature-specific content

• Trial sign-ups or sandbox usage

• Documentation or integration page visits

• High-frequency interactions over a short time window

Unlike generic engagement (email opens or blog views), product intent is directional. It tells you what the buyer cares about, not just that they exist.

This is the missing layer in most lead scoring frameworks.

The Problem with Legacy Lead Scoring Models

Most legacy models fail for three reasons:

1. Overweighting Static Attributes

Titles and firmographics do not change — but buyer intent does. Scoring models that treat static data as primary signals become stale the moment they’re built.

2. Treating All Engagement as Equal

A webinar attendee and a pricing-page visitor are not the same — yet many models score them similarly.

3. No Signal Recency or Velocity

Intent decays fast. A lead active last week is more valuable than one active three months ago — but many models ignore this entirely.

This leads to:

• Inflated MQL volumes

• Poor AE trust in marketing leads

• High follow-up latency

• Low conversion from MQL to pipeline

How Product Intent Transforms Lead Scoring

When product intent is integrated correctly, lead scoring becomes:

• Behavior-driven, not assumption-driven

• Dynamic, not static

• Aligned with sales reality

Here’s what changes:

The outcome is simple: fewer MQLs, higher conversion, faster pipeline velocity.

Practical Framework: Product Intent–Driven Lead Scoring

Based on what I’ve implemented across multiple organizations, here’s a proven framework:

1. Define Product-Specific Intent Signals

Not all behaviors are equal. Identify actions that correlate with real buying conversations:

• Pricing page visits (high weight)

• Product comparison views

• Integration documentation access

• Trial activation or feature usage

• Repeat visits within 7–14 days

2. Apply Recency and Frequency Logic

A single visit is curiosity.
Repeated visits in a short timeframe signal intent.

Use:

• Time decay (e.g., last 14 or 30 days)

• Frequency thresholds (e.g., 3+ product views)

3. Separate Awareness from Consideration

Top-of-funnel activity should not inflate lead scores meant for sales. Product intent belongs in mid-to-late funnel scoring.

4. Align Scores with Sales Motion

Your scoring model must reflect how sales actually sells:

• Inbound vs outbound motion

• Product-led vs sales-led

• Enterprise vs SMB cycles

There is no universal scoring model — only contextually correct ones.

Case Study: Improving MQL Quality Using Product Intent

In one of my recent engagements, we faced a familiar challenge:

• High MQL volume

• Low MQL-to-SQL conversion

• Sales feedback: “These leads aren’t ready”

What We Changed

We rebuilt the scoring model around product intent, not just engagement.

Key changes:

• Reduced demographic score weight by ~40%

• Introduced product-page and feature-level scoring

• Added recency-based multipliers

• Required at least one strong product intent signal for MQL

The Results

Within two quarters:

• MQL volume dropped by ~25%

• MQL-to-SQL conversion increased significantly

• AE trust in marketing leads improved

• Sales follow-up time decreased

• Pipeline influenced by marketing became more predictable

Most importantly, marketing and sales started speaking the same language — intent, not volume.

Product Intent and Revenue Cycle Analytics

Product intent becomes exponentially more powerful when paired with Revenue Cycle Analytics.

It allows you to:

• Attribute pipeline to specific product interest

• Identify intent trends by segment or persona

• Predict pipeline earlier in the funnel

• Optimize content and campaigns around actual buyer behavior

This is where marketing transitions from a cost center to a revenue intelligence function.

Final Thoughts: Intent Is the New Currency

Lead scoring is no longer about collecting points — it’s about interpreting behavior.

If your scoring model does not answer:

• What product are they interested in?

• How recently did they show intent?

• How strong is that signal?

…then it’s not a lead scoring model — it’s a contact ranking exercise.

Product intent is not a “nice to have.”
It is foundational to modern lead qualification.

About Me

I’m Raghav Chugh, a seasoned digital marketing and technology professional with close to two decades of experience building and scaling marketing automation, analytics, and lead lifecycle frameworks.

With four Marketo Certified Expert (MCE) certifications and deep hands-on expertise across lead scoring, Revenue Cycle Analytics, and database architecture, I focus on turning raw behavioral data into actionable revenue intelligence.

I’ve worked closely with global marketing, sales, and data teams to redesign lead qualification models that sales actually trusts — and uses.

Connect with me on LinkedIn:
https://www.linkedin.com/in/raghavchugh/

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