What data sources can provide intent signals?

Common intent sources include website analytics, content interactions, email engagement, product usage, sales and support conversations, and third-party or partner data that reveals off-site research on relevant topics or vendors.

Do we need a complex lead scoring model to use intent?

You can start with a simple tiering approach that groups a small set of high-value behaviors into low-, medium-, and high-intent bands. Over time, you can experiment with weights, additional signals, and predictive models as you learn which patterns best correlate with opportunities or churn.

How do intent signals support ABM programs?

In ABM, intent signals highlight which target accounts and buying group members are heating up so you can prioritize them for coordinated outreach. When you see multiple contacts at an account engaging on related topics, it often indicates a live buying initiative and justifies greater investment.

How should we handle privacy when using intent data?

Handle intent data within your existing privacy and compliance framework. Be transparent about tracking, honor consent and preference choices, avoid sensitive categories, and review new data sources and use cases with legal and privacy teams before deploying them at scale.

How do intent signals improve segmentation?

Intent signals improve segmentation by adding a “readiness” layer on top of who your buyers are. Traditional segments are built on firmographics (industry, size), demographics (role, seniority), or static attributes (tech stack, region). Intent signals—like topics researched, pages viewed, product usage patterns, and partner or third-party data—show who is actively exploring a problem or solution right now. When you feed these signals into your segmentation model, you can distinguish between lookalike accounts that are not looking and those that are in-market, then prioritize outreach, messaging, and offers accordingly. The result is fewer generic campaigns and more targeted plays that convert faster and more efficiently.

What Changes When You Add Intent to Segmentation?

From static ICP lists to live buying groups — Instead of “all target accounts,” you track which accounts and contacts are actively searching, comparing, or evaluating, and move them into in-market segments as behavior spikes.

From generic messaging to stage-specific plays — Early-stage topics (symptoms, best practices) signal education plays, whereas late-stage signals (pricing pages, competitor comparisons) trigger more direct offers and sales engagement.

From equal weighting to prioritized segments — Fit still matters, but high-intent accounts get priority routing, faster SLAs, and more personal touches than low- or no-intent segments at the same “tier.”

From siloed channels to coordinated outreach — The same intent tiers inform email, ads, SDR outreach, and web personalization, so every touch acknowledges what the buyer is researching instead of starting from zero in each channel.

From volume metrics to opportunity metrics — Because segmentation is tied to real buying activity, you can measure impact on pipeline, speed-to-opportunity, and win rate by intent segment instead of relying solely on lead volume and opens.

From subjective “hot leads” to shared definitions — Marketing, sales, and customer success agree on what “engaged,” “high intent,” or “at-risk” looks like, because those labels are driven by shared, visible signals.

The Intent-Driven Segmentation Playbook

Use this sequence to move from fit-only segments to intent-informed segments that drive better timing, prioritization, and conversion across your revenue engine.

Inventory → Normalize → Tier → Map Plays → Orchestrate → Optimize

Inventory your intent sources: List all the signals you can access today: website behavior, content downloads, email engagement, product usage, sales interactions, support activity, and any third-party or partner intent data.
Normalize and score signals: Standardize events into a common model (e.g., “topic X view,” “product Y usage spike,” “competitive comparison”) and assign relative weights based on how predictive they are of opportunities or churn.
Create intent tiers by segment: For each core segment or ICP tier, define what counts as low, medium, and high intent. Document thresholds so everyone understands when an account “moves up” a tier.
Map plays and SLAs to tiers: Decide which sequences, offers, and owners align to each intent tier. For example, high-intent tier might trigger SDR outreach plus targeted ads, while mid-intent stays in automated nurtures and remarketing.
Orchestrate across channels: Sync intent tiers to CRM, MAP, and ad platforms, and use them to drive targeting and personalization in email, web, sales engagement, and success motions.
Measure and refine: Track pipeline, win rate, and velocity by intent tier and segment. Use results to re-weight signals, adjust thresholds, and refine which plays you trigger at each level.

Intent-Driven Segmentation Capability Maturity Matrix

Capability	From (Ad Hoc)	To (Operationalized)	Owner	Primary KPI
Signal Collection	Basic web and email analytics only	Unified first-party, product, and third-party intent data mapped to accounts and buying groups	Data / RevOps	Signal Coverage, Match Rate
Fit vs. Intent Model	Single score trying to capture everything	Separate fit and intent dimensions with clear, documented bands	RevOps / Marketing Ops	High-Intent Opportunities Created
Segmentation & Tiers	Static ICP lists with no freshness	Dynamic segments that update as intent spikes or cools	Marketing Ops	Segment Velocity, Conversion Rate
Plays & Orchestration	One-size campaigns for all targets	Plays triggered by intent tier with aligned email, ads, and sales outreach	Demand Gen / Sales	Meetings Booked, Opportunity Rate
Sales & CS Visibility	Intent lives in separate tools	Signals and tiers surfaced in CRM views, alerts, and workflows	Sales Ops / CS Ops	Follow-Up Rate, Time-to-First Touch
Governance & Measurement	Occasional reports on “top intent accounts”	Regular reviews of pipeline, win rate, and NRR by intent tier and segment	RevOps / Leadership	Pipeline Efficiency, Win Rate, NRR

Client Snapshot: From Static Target List to Intent-Led Focus

A B2B SaaS company layered account-level topic intent and in-product signals onto its existing ICP tiers. They re-segmented accounts into fit+intent bands and focused outbound and ABM programs on high-fit, high-intent clusters. Within two quarters, SDR teams booked 30% more meetings from fewer total touches, and opportunity win rate improved because sales was spending more time with buyers already signaling active research and evaluation.

When intent signals shape your segments, you stop treating every “good-fit” account the same—and start matching timing, message, and investment to who is truly in-market.

Frequently Asked Questions about Intent-Driven Segmentation

What are intent signals?

Intent signals are observable behaviors that indicate a person or account is actively exploring a problem or solution. Examples include searches on relevant topics, repeat visits to key pages, downloading buying guides, comparing vendors, or showing specific in-product behavior that correlates with expansion or churn.

How are intent signals different from fit data?

Fit data tells you who might be a good customer based on characteristics like industry, size, or tech stack. Intent signals tell you who is acting like a buyer right now. Strong segmentation uses both: fit to define where you play, intent to decide where to focus attention today.

What types of intent signals should we track?

Start with first-party signals: website behavior, key content downloads, email engagement, product usage, and conversations with sales or support. If available, add third-party or partner intent that highlights research on industry topics, competitors, or related solutions outside your properties.

Do we need a complex scoring model to use intent?

No. Many teams start by defining a small set of high-value signals—like pricing page views, specific feature usage, or repeat topic engagement—and grouping them into simple intensity tiers (low, medium, high). You can refine and weight the model as you learn which combinations best predict opportunities or churn.

How do intent signals support ABM and buying groups?

In ABM, intent helps you see which accounts—and which roles within them—are heating up. When multiple contacts in a buying group show related intent, you can coordinate multi-threaded outreach and tailor messaging to each role’s concerns, while signaling to sales that an account is moving deeper into a buying process.

How should we handle privacy and data compliance?

Treat intent like any other customer data: be transparent about tracking, honor consent and preferences, and avoid using sensitive or personal categories where restrictions apply. Focus on aggregated, business-relevant behaviors and use your legal and privacy teams to review new data sources and use cases before rollout.

Make Intent-Led Segmentation the Backbone of Revenue Marketing

We’ll help you connect fit and intent, define segments and tiers, and orchestrate plays so your teams focus on the right buyers at the right time—with clear impact on pipeline and revenue.

Measure Your Revenue-Marketing Readiness Define Your Strategy

Explore Related Resources

Higher-Ed Growth Plan Revenue Marketing eGuide Revenue Marketing Maturity Assessment Account-Based Marketing

How Do Intent Signals Improve Segmentation?

What Changes When You Add Intent to Segmentation?

The Intent-Driven Segmentation Playbook

Inventory → Normalize → Tier → Map Plays → Orchestrate → Optimize

Intent-Driven Segmentation Capability Maturity Matrix

Client Snapshot: From Static Target List to Intent-Led Focus

Frequently Asked Questions about Intent-Driven Segmentation

Make Intent-Led Segmentation the Backbone of Revenue Marketing

Get in touch with a revenue marketing expert.

Send Us an Email

Schedule a Call

Solutions

Resources

About TPG