Using Intent Data to Identify In-Market Buyers

Your best leads aren't on your website yet. They're researching solutions on review sites, reading third-party content, and evaluating competitors. You have no visibility into any of it.

That's where intent data comes in. It reveals which accounts are actively researching your category, what topics they're interested in, and how serious their buying intent is—before they ever fill out a form on your site.

But most teams use intent data wrong. They treat it like a list to spam instead of a signal to prioritize. Here's how to actually use intent data to drive pipeline.

What Intent Data Actually Is

Intent data tracks digital behaviors that indicate buying interest. There are three types:

First-party intent: Actions on your own properties. Website visits, content downloads, pricing page views, demo requests. You own this data. It's accurate but limited to people already engaging with you.

Third-party intent: Research behavior on external sites. Reading reviews on G2, consuming content on industry publications, searching for competitor comparisons. Providers like Bombora, TechTarget, and 6sense aggregate this data across publisher networks.

Contact-level vs. account-level: Some intent data is tied to specific people (contact-level). Most is tied to companies (account-level). Account-level tells you a company is researching. Contact-level tells you who specifically is researching.

The highest-value signals come from combining all three types: account showing third-party intent + first-party website engagement + contact-level actions.

Why Most Teams Fail With Intent Data

Common failure patterns:

Treating intent as a lead list. You get a list of 500 accounts showing intent and blast them all with generic outreach. That's not strategic—that's spam with better targeting.

No prioritization framework. Not all intent is created equal. An account researching "marketing automation comparison" is further along than one reading "what is marketing automation." Treating them the same wastes resources.

Marketing and sales misalignment. Marketing sees intent data and launches campaigns. Sales sees intent data and starts cold calling. Neither knows what the other is doing. Double-touching accounts with conflicting messages burns trust.

No measurement framework. You're paying for intent data but don't know if it's actually generating pipeline. Without proper attribution, you can't prove ROI.

The teams that win with intent data do something fundamentally different: they use it as a prioritization signal, not an action trigger.

The Intent Data Prioritization Framework

Start by scoring accounts based on multiple intent signals, not just one.

Dimension 1: Topic relevance. Are they researching topics that directly relate to your solution, or adjacent topics? Research on "sales enablement platforms" (direct) is stronger than "sales productivity tips" (adjacent).

Scoring:

• Direct solution research = High
• Adjacent problem research = Medium
• General industry research = Low

Dimension 2: Intent strength. How intense is their research activity? Multiple topics, multiple visits, sustained over weeks = high intent. Single topic, single visit = low intent.

Scoring:

• 10+ intent signals in 30 days = Surge (very high)
• 5-9 signals = Growing (high)
• 2-4 signals = Emerging (medium)
• 1 signal = Baseline (low)

Dimension 3: Recency. Intent decays quickly. Signals from this week matter more than signals from last month.

Scoring:

• Signals in last 7 days = Hot
• Signals in last 30 days = Warm
• Signals 31-60 days ago = Cooling
• Signals 60+ days ago = Cold

Dimension 4: Account fit. Does the account match your ICP? High intent from a poor-fit account is still a poor-fit account.

Scoring:

• Matches ICP = Yes
• Partial match = Maybe
• No match = No

The prioritization matrix: Combine these dimensions to create priority tiers.

Tier 1 (Act now): High fit + High/Surge intent + Hot recency + Direct topics. These are actively evaluating solutions. Route to sales immediately for personalized outreach.

Tier 2 (Nurture strategically): High fit + Medium intent + Warm recency + Direct/adjacent topics. Launch targeted ABM campaigns. They're interested but not ready to buy yet.

Tier 3 (Monitor): Medium fit OR Low/emerging intent. Add to general nurture. Watch for intent increases that move them to Tier 1 or 2.

Tier 4 (Ignore): Low fit regardless of intent. Don't waste resources on poor-fit accounts just because they're researching.

This framework prevents the "everyone showing intent gets the same treatment" mistake.

Building Intent-Triggered Workflows

Once you've prioritized accounts, automate responses based on intent tier.

Tier 1 workflow (high-priority accounts):

1. Alert sales: Real-time notification to account owner with intent topics and recent activity
2. Personalized outreach: Sales sends research-based email: "Noticed your team is researching [topic]. We work with companies like yours on exactly this. Worth a conversation?"
3. Multi-channel engagement: Add to LinkedIn ad campaign, show personalized website content, send industry-specific case study
4. Executive outreach: If no response in 5 days, your VP reaches out to their VP with executive briefing offer
5. Track and iterate: Monitor response rates and adjust approach based on what works

Tier 2 workflow (strategic nurture):

1. Add to ABM campaign: Targeted content series on topics they're researching
2. Personalized landing pages: Show custom content based on their intent topics when they visit your site
3. Retargeting ads: LinkedIn and display ads featuring relevant use cases
4. Event invitations: Invite to webinars or events related to their research topics
5. Watch for engagement: When they engage (open email, visit pricing, attend webinar), escalate to Tier 1

Tier 3 workflow (monitoring):

1. Add to standard nurture: General newsletter and content distribution
2. Set intent alerts: Notify if intent increases to Tier 2 levels
3. Quarterly check-ins: Sales reviews list quarterly to see if any are worth manual outreach

The key is matching effort to intent strength. Don't waste high-touch resources on low-intent accounts.

Combining First-Party and Third-Party Intent

The magic happens when you layer first-party and third-party signals together.

Example scenario: Third-party intent shows Account X researching "sales enablement tools" (high intent, direct topic). First-party data shows they haven't visited your website yet. This is an early-stage opportunity to get on their consideration list before competitors.

Action: Personalized outreach from sales + targeted LinkedIn ads + personalized website experience when they do visit. You're meeting them early in their journey.

Example scenario 2: Third-party intent shows Account Y researching your category (medium intent). First-party data shows they visited your pricing page 3 times in the past week (high intent). This is a hot opportunity.

Action: Immediate sales outreach + send pricing guide + offer to walk through pricing options on a quick call. They're actively evaluating—speed matters.

Example scenario 3: Third-party intent showed Account Z researching 60 days ago (cooling). First-party data shows no recent activity (cold). This opportunity likely stalled.

Action: Re-engagement campaign: "Saw you were researching [topic] a couple months ago. Did you solve it or still evaluating? Happy to help if timing is better now."

Layering intent types gives you complete visibility into buyer journey.

Measuring Intent Data ROI

Intent data costs money. Prove it's worth it.

Metrics to track:

Account coverage: What percentage of your TAM is showing intent signals? If only 2%, you're missing most of your market. If 80%, your targeting is too broad.

Intent → engagement rate: What percentage of accounts showing intent engage with your campaigns (email opens, content downloads, website visits)? Target 20-30% for Tier 1 accounts.

Intent → opportunity rate: What percentage of intent accounts create sales opportunities? Track by intent tier. Tier 1 should convert at 10-15%, Tier 2 at 3-5%.

Intent → revenue: How much pipeline and revenue is sourced from intent accounts vs. non-intent accounts? Intent accounts should have higher conversion rates and faster sales cycles.

Payback period: How long until the pipeline generated from intent data exceeds the cost of the data + campaign costs? Target 90-180 days.

Comparative analysis: Do intent-sourced deals close at higher rates and larger deal sizes than non-intent deals? They should, because you're reaching them at the right time.

If intent data isn't improving these metrics, you're either using the wrong data or executing poorly.

Intent Data Vendor Selection

Not all intent data is created equal. Here's how to evaluate vendors:

Coverage: How many accounts in your TAM does the vendor track? Bombora has broad coverage across B2B. Smaller vendors may have deeper coverage in specific verticals but miss others.

Data quality: Request a sample dataset for 50 accounts you know are in-market. How many did the vendor identify? How accurate were the topics? False positives are expensive.

Topic taxonomy: How granular are the topics? "Marketing" is too broad. "Marketing automation for B2B SaaS" is useful. More granular taxonomies give better signals.

Freshness: How frequently is data updated? Weekly updates are standard. Daily is better for high-velocity sales cycles.

Integration: Does it integrate with your CRM, marketing automation platform, and sales tools? Manual uploads don't scale.

Pricing model: Per account, per user, or flat fee? Calculate cost per qualified opportunity to understand true ROI.

Test vendors with pilots before committing to annual contracts.

Common Intent Data Mistakes

Mistake 1: Buying data without a clear use case. Intent data is useless if you don't have a plan for what to do with it. Define workflows before you buy.

Mistake 2: Set-and-forget workflows. Intent signals change. Review and optimize monthly. Topics that mattered in Q1 may not matter in Q3.

Mistake 3: Overwhelming sales with alerts. Sending sales 100 intent alerts per day trains them to ignore all of them. Prioritize ruthlessly and send only high-quality signals.

Mistake 4: Ignoring fit in favor of intent. High intent from a poor-fit account still produces poor results. Always filter for fit first, intent second.

Mistake 5: No feedback loop with sales. Ask sales: "Are the intent leads we're sending you valuable?" If not, adjust your prioritization criteria.

Privacy and Compliance Considerations

Intent data is powerful but raises privacy concerns.

Account-level data: Generally safer because it's aggregated company behavior, not individual tracking. GDPR and privacy regulations are less restrictive for account-level data.

Contact-level data: More regulated. Ensure vendors are compliant with GDPR, CCPA, and other privacy laws. Understand how data is collected and get consent where required.

Transparency: Be upfront with prospects about how you found them. "Saw your team was researching [topic]" is honest and builds trust. Pretending you connected randomly feels deceptive.

Opt-out respect: If someone requests to be removed from your marketing, honor it even if intent data says they're still researching. Trust > short-term conversion.

The Reality

Intent data won't fix broken fundamentals. If your product isn't differentiated, your messaging doesn't resonate, or your sales team can't close, intent data just helps you reach unqualified buyers faster.

But for teams with solid product-market fit and strong go-to-market execution, intent data is a game-changer. It helps you identify in-market buyers before competitors, prioritize limited resources on high-probability opportunities, and engage prospects at exactly the right time.

Use it as a prioritization signal, not a blunt instrument. Layer it with first-party data. Measure rigorously. And always filter for fit before acting on intent.

That's how you turn research signals into revenue.