Intent Data Vendor Landscape 2026: Where Delivr.ai Fits

The B2B intent data market has grown to $3.5 billion and now includes more than 36 vendors spanning four distinct resolution tiers. For buyers, the sheer number of options obscures a fundamental question: does this vendor tell me which company is interested, or which person is interested? The answer determines everything downstream, from activation speed to conversion rate.

This report maps the entire vendor landscape across four tiers, scores key capabilities, and explains where Delivr.ai fits, delivering person-level, PII-resolved intent.

The Four Resolution Tiers

The intent data market is stratified by resolution depth: how far down the funnel a vendor can identify the buyer. Understanding these tiers is essential for any evaluation.

• Tier 1, Anonymous / IP-Only (Declining): Vendors that map IP addresses to companies but cannot resolve to individuals. This tier is shrinking as buyers demand more precision. Examples: older reverse-IP tools, basic website analytics overlays.
• Tier 2, Account-Level (Dominant): The largest tier by revenue. Vendors like Bombora, TechTarget, and G2 aggregate behavioral signals and report at the company level. You know "Acme Corp is researching cloud security" but not who at Acme is doing the research.
• Tier 3, Contact / Buying Group (Growing): Platforms like 6sense, Demandbase, and ZoomInfo layer contact databases on top of account-level intent. They infer which personas or titles are likely involved, but the intent signal itself remains account-level. The person is modeled, not observed.
• Tier 4, Person-Level / PII-Resolved (Emerging): Delivr.ai operates here. Every intent signal resolves to a known individual with full PII: name, email, title, company, phone. The intent is tied to the person's actual behavior, not a probabilistic overlay.

Why Resolution Tier Matters

The gap between account-level and person-level intent is not incremental. It is structural. Account-level intent tells your sales team to "call Acme Corp." Person-level intent tells them to call Sarah Chen, VP of IT, who has been researching zero-trust architecture for the past 72 hours and whose interest is accelerating.

That specificity eliminates the research phase that consumes 40–60% of SDR time. It transforms outreach from cold to warm. And it gives marketing the signal quality needed to build precision audiences that actually convert.

The Competitive Scoring Landscape

Across 36 vendors evaluated on 12 capability dimensions, signal methodology, resolution depth, topic taxonomy, activation breadth, data ownership, compliance posture, and more, a clear pattern emerges: no single vendor excels at everything, but vendors that resolve to the person level consistently outperform on downstream metrics.

Delivr.ai rests on three capabilities together: 100% deterministic person-level resolution, a 52,000+ topic taxonomy scored daily, and a built-in DSP. The combination of knowing who, what they care about, and the ability to reach them creates a closed loop from signal to activation.

Signal Methodology Breakdown

Intent vendors collect signals through fundamentally different methods, each with trade-offs:

• Publisher Co-op (Bombora model): Publishers share anonymized content consumption data. Broad coverage but contextual. The signal says "someone at this IP read this article," not "this person read this article."
• Editorial / Opted-In (TechTarget model): Gated content on owned publications. High-quality signal but narrow. Limited to the vendor's own properties.
• Bid-Stream (programmatic model): Behavioral data from ad exchanges. Massive scale but noisy. Requires heavy filtering and often mixes B2B with B2C signals.
• Multi-Source / AI-Resolved (Delivr.ai model): Combines licensed supply-side data plus first-party pixel events. Signals are resolved through Delivr.ai's identity graph to a known person, then classified by AI into 52,000+ topics. This approach captures a broad behavioral surface while maintaining person-level precision.

The V2 Intent Model: Precision Beyond Surge

Most intent vendors deliver a binary signal: this account is "surging" or it isn't. Delivr.ai's V2 Intent Model introduces two dimensions that change how teams operationalize intent:

• A 1-99 strength score per person per topic, with high, medium and low tiers: derived from this person's own recent activity on the topic over a 14-day window. It is not a ranking against the crowd, and it is not a percentile.
• Audience momentum (Predictive Signals): each in-market member of an audience is placed in one tier, new, rising, steady or cooling, by comparing this week's activity on the audience's topics with the prior week's. This lets teams catch buyers early (new or rising), act before the window closes (cooling), and identify steady researchers who need nurture, not a hard sell.

These two dimensions support cold topic activation (detect when someone is new to a topic), momentum-based audiences, threshold tuning on the 1-99 score, and win-back alerts (notify when a prospect's momentum is cooling).

Where Delivr.ai Fits

Delivr.ai combines person-level PII resolution, licensed supply-side and first-party signal collection, a per-person scoring model, and a built-in DSP in a single platform. This eliminates the vendor stack that most enterprises assemble: one vendor for intent signals, another for identity resolution, a third for activation, and a fourth for analytics.

For teams evaluating the landscape, the question is straightforward: do you need to know which company is interested, or which person is interested? If the answer is "person," the vendor list narrows considerably.