The Shift from Vague Maps to Precision Navigation: Why Contact-Level Intent is the Future of B2B Data

Imagine two explorers setting out on a quest for treasure. One has a meticulously detailed map, showing every tree, mountain, and river—guiding them directly to their prize with confidence. The other is holding a vague, ambiguous sketch with no specifics, leaving them to wander in circles, hoping they stumble upon the treasure.

For years, marketers and sales teams have been the second explorer—relying on broad, account-level intent and technographic data, much like an imprecise map that hints at a general direction but lacks the details to get you to the treasure. This is changing.

With advancements in contact-level intelligence, we now have a highly detailed roadmap, pinpointing exactly who within an organization is engaging, what they care about, and how deep their interest runs. This shift is transforming the way go-to-market (GTM) teams operate, enabling precise messaging, improved account scoring, and more effective prioritization.

From Account-Level Intent to Contact-Level Intent: The Difference in Action

For years, intent data providers like Bombora functioned like rough maps, marking the general vicinity of treasure but leaving explorers to figure out the specifics. If Bombora detected interest in "cloud security solutions" from ACME Co., it simply meant that someone within the company was researching that topic. But who? A procurement manager? An intern? The methodology often relied on reverse IP lookups, tying activity to a corporate network rather than individuals.

Now, with contact-level intent from tools like RB2B, GTM teams have a precise map:

• Exactly who at ACME Co. is engaging with security content.
• What content they are consuming—pricing pages, whitepapers, or competitive comparisons.
• Their job titles and department, indicating how much influence they have in the buying process.

Instead of knowing that "ACME Co. is researching cloud security," sales teams can now see:
"John Smith (Director of IT) and Sarah Lee (Security Engineer) have visited our pricing page and downloaded a case study in the last 48 hours."

This level of specificity turns blind prospecting into targeted, data-driven outreach.

How Contact-Level Intent Rolls Up to Account-Level Insights

A common argument for account-level data has been that buying decisions are made by committees, not individuals. But that doesn't mean we should navigate with a blurry map. Contact-level intent can be aggregated to provide an even clearer picture of an account’s buying journey:

• If multiple decision-makers and end-users are engaging, it signals strong buying intent.
• If procurement, finance, and technical stakeholders start interacting, it suggests a deeper evaluation stage.
• If only one lower-level employee is engaging, it may indicate early-stage research.

By layering contact-level data on top of account-level signals, sales teams can identify when an account is merely exploring versus actively moving toward a purchase.

Contact-Level Technographics: A More Accurate Map of Technology Usage

Just as intent data has evolved, technographic insights have become far more precise. Traditionally, technographic providers like HG Data offered the equivalent of an old, hand-drawn map. They could tell you that ACME Co. was using Apache Kafka and Google Cloud—but they couldn’t tell you who was actually using those tools or how deeply they were integrated.

Now, with contact-level technographics, we can differentiate between:

• A company with 60 engineers actively working on Kafka repositories in GitHub (indicating deep investment).
• A company with just two engineers using Kafka, suggesting minimal adoption.

If you're selling a competitive streaming solution, which company would you prioritize? The one with 60 engineers actively using Kafka (who might be experiencing scalability issues and looking for alternatives) or the one with two engineers experimenting with it?

How This Improves Technology Spend Estimation

Traditionally, estimating SaaS and infrastructure spend was like trying to find treasure without a map. Companies relied on:

• Headcount-based models (more employees might mean bigger tech budgets).
• Industry benchmarks (highly generic and imprecise).
• Third-party reports (often outdated and unreliable).

Contact-level technographics change this. If Company A has 50 active Salesforce users and Company B has 5, it’s obvious which one is spending more. If thousands of developers are using AWS Lambda, it's a strong signal of high cloud spending.

This data allows GTM teams to prioritize high-value accounts with accurate spend predictions, rather than relying on rough estimations.

Key Takeaways: Why This Shift Matters

Moving from vague, account-level data to precise, contact-level insights is like upgrading from a crude, unreliable map to a GPS-guided treasure hunt. Here’s why:

1. More Precise Outreach: Knowing who is researching your solution (and what they’re consuming) enables highly targeted messaging and better conversion rates.
2. Improved Account Scoring: Aggregating contact-level signals paints a clearer picture of which accounts are in-market and which are just browsing.
3. Better Technology Spend Estimation: Understanding how many people are actively using a tool is a far better predictor of budget than company size or industry benchmarks.
4. Smarter Prioritization: Sales teams can focus on high-propensity buyers with clear pain points rather than wasting time on accounts that may not be a fit.

GTM teams that embrace this shift will gain a massive competitive advantage—driving higher engagement, better conversion rates, and ultimately, more revenue.

The future of B2B data is no longer about wandering aimlessly with vague maps. It’s about precision navigation, where every tree, mountain, and river is marked, guiding sellers directly to the gold.