- The buyer intent data market is estimated at 3.8 to 4.5 billion dollars in 2026 and growing near 17 percent a year, but these are market-research estimates and the value in energy depends on fit, not spend.
- Forrester's Q1 2025 evaluation named Intentsify, 6sense, Bombora, Informa TechTarget and Demandbase as leaders, and flagged the shift from raw signals to activation-ready intelligence.
- Around 70 to 75 percent of the B2B buying journey now happens anonymously before a vendor is contacted, which is the gap intent data is built to close.
- In energy the target account list is small and stable, so third-party topic intent works best as a timing layer over a named account list, not as a discovery engine.
- First-party signals, your own site, webinars, email and LinkedIn, blended with third-party intent beat either source alone.
- Signal quality is the most cited complaint among intent users, so a defined topic taxonomy and a clear score-to-action playbook matter more than the provider logo.
Signal, not noise
Buyer intent data is any observed behaviour that suggests an account is researching a purchase. It splits into two families. First-party intent is what you observe on your own properties: pricing-page visits, repeat article reads, webinar attendance, email engagement, and demo requests. Third-party intent is what a provider observes across a wider web, most commonly topic and keyword consumption on a co-op of publisher sites, plus review-site activity on platforms such as G2 and TrustRadius.
The distinction matters because the two answer different questions. First-party intent tells you an account is interested in you. Third-party intent tells you an account is interested in your category, whether or not it has heard of you yet. In energy B2B, where a seller may already know every account by name, the second question is the more valuable one, because it surfaces timing you would otherwise miss.
What does not count as intent is a single visit, a generic newsletter open, or a job title that fits your persona. Fit is not intent. An account can be a perfect fit for a decade and only be in market for a few months of it. Treating static fit data as a buying signal is the most common way intent programmes waste sales time.
Project 54An energy operator monitors signals at a control console, the physical analogue of reading buyer intent.A small, known universe changes the job
Most intent-data playbooks were written for high-volume software sales, where the addressable market runs to tens of thousands of accounts and the core problem is discovery: finding the needle accounts that are in market this quarter. Energy B2B inverts that. The universe of national oil companies, integrated majors, EPC contractors, oilfield-services firms, utilities and grid operators is a few hundred organisations, and a serious seller already knows them by name.
When the account list is fixed, intent data stops being a discovery engine and becomes a timing layer. You are not asking which accounts exist. You are asking which of your known accounts is quietly researching a capability right now, so you can reach the buying committee before a shortlist forms. That reframing is the single most important point for an energy marketing or sales leader evaluating intent tools.
The energy buying context sharpens the case. Cycles are long, often 12 to 24 months, committees are large, and procurement is a top-three influencer in most enterprise purchases. By the time a tender appears, the vendor set is usually decided. Intent data is one of the few instruments that can register movement inside an account during the anonymous research phase, which is where the real decision is made.
Discovery becomes timing
The account list is known, so intent answers when an account is active, not which accounts exist.
Committee, not contact
A surge is an account-level signal. Route it to the whole buying committee, not a single lead.
Long cycle, early window
The decision forms in the anonymous phase. Intent is one of the few reads on that window.
The 2025 leader set
In its Q1 2025 evaluation of intent data providers for B2B, Forrester named Intentsify, 6sense, Bombora, Informa TechTarget and Demandbase as leaders, assessing fifteen providers across twenty-one criteria. The report's central theme was a shift from raw signal delivery to activation-ready intelligence: buyers increasingly want providers that operationalise signals, not just surface them. For energy teams with small account lists, that activation question is decisive, because a raw topic feed you cannot route is worthless.
The providers differ mainly in where their signal comes from and how much modelling sits on top. The table below is a working guide, not an endorsement, and the best-fit column reflects the energy timing use case rather than a general ranking.
| Provider | Signal source | Core product | Best fit for energy timing |
|---|---|---|---|
| 6sense | First-party de-anonymisation plus own third-party and technographics | Predictive account staging (6QA) inside an ABM platform | Teams that want signals modelled into buying stages and activated in one place |
| Bombora | Co-op of 5,000 plus publisher sites, 20,100 plus topic taxonomy | Company Surge topic and account scoring | Teams that want a clean topic feed to overlay on a named account list |
| Demandbase | First and third-party intent | Account-based GTM and advertising activation | Teams running account-based advertising alongside sales outreach |
| Informa TechTarget | Publisher and buyer-level research intent | Buyer-level intent in technical and industrial categories | Teams selling technical products who value named buyer signals |
| Intentsify | Aggregated multi-source intent | Signal aggregation plus activation | Teams that want to blend several sources without stitching feeds themselves |
Five steps from signal to pipeline
Intent data fails far more often from weak process than from weak data. The following sequence is what separates a programme that compresses cycles from one that floods sales with noise.
Define the account list
Start from your named universe of NOCs, majors, EPCs, services firms and utilities. Intent scores mean something only against a list you have chosen.
Define a topic taxonomy
Map the specific research topics that precede your sale. Vague topics create false positives. Precise ones create timing.
Blend first and third-party
Overlay your own web, webinar and email signals on third-party topic surges. The blend is more precise than either source alone.
Build a score-to-action playbook
Decide in advance what a surge triggers: an alert, an ad audience, a sales touch, or nothing. A signal with no defined action is noise.
Feed the buying committee
A surge is account-level. Route it to marketing and sales together so the whole committee is reached, not a single contact.
The five failure modes
Signal quality is the most cited problem among intent users, and it is usually a symptom of the failures below rather than a fault in the data itself. Acting on a topic without tying it to a named account produces a lead list no energy seller can use. Chasing a single contact when the signal is account-level misses the committee that actually decides. Running intent without a written service level between marketing and sales means alerts are ignored within a fortnight.
Two further failures are specific to how intent is bought. The first is buying a provider for its logo rather than its topic taxonomy and activation fit, then discovering the feed cannot be routed. The second is neglecting privacy and compliance: third-party intent must be handled under GDPR and equivalent regimes, and an energy brand selling into regulated markets cannot afford a shortcut here. Governance is not overhead, it is the condition of using the data at all.
From signals to activation, and now to AI research
Two shifts are reshaping intent. The first is on the buyer side. Buyers now complete most of their research alone, and a growing share use generative AI to synthesise vendor information and build shortlists before contacting anyone. Gartner's 2025 research indicates buyers using generative AI in evaluation are markedly more likely to finalise a shortlist before speaking to a vendor. When the shortlist forms inside an AI tool, the anonymous window gets shorter and harder to read, which raises the premium on any signal that registers early movement.
The second shift is on the provider side. A large and rising share of intent investment is going into AI-driven signal detection and activation rather than raw collection, which is the same direction the Forrester evaluation flagged. For energy sellers the implication is practical. The advantage is moving to teams that can detect a research theme early, tie it to a named account, and act on it across the committee within days. That is a process capability as much as a data purchase, and it is the capability worth building now.
The through-line is consistent with how Project 54 frames the wider discipline: revenue is engineered, not assumed. Intent data is not a lead machine bolted onto a funnel. It is a timing instrument that, used with a defined account list and a disciplined playbook, tells an energy seller when to move. In a market this concentrated, knowing when is often worth more than knowing who.
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What is the biggest barrier to using intent data in your energy B2B team?
Frequently asked
Yes, but for a different reason than in high-volume software. With a small, known account list, intent data is a timing layer rather than a discovery engine. It tells you which of your named accounts is researching now, so you can reach the buying committee before a shortlist forms. Value comes from precision on a defined list, not from feed volume.
Both, blended. First-party intent shows an account is interested in you. Third-party intent shows it is interested in your category, whether or not it knows you yet. Overlaying your own web, webinar and email signals on third-party topic surges is more precise than either source alone.
Accuracy varies by provider and by how tightly you define your topics. Signal quality is the single most cited complaint among users, and it usually reflects a loose topic taxonomy rather than a fault in the data. A precise taxonomy tied to a named account list produces far cleaner timing than a broad topic feed.
No. It powers the timing inside account-based marketing. ABM defines who you pursue and how you engage the committee. Intent data tells you when a given account moves from passive to active research, so your ABM effort lands in the window that matters. See our guide to account-based marketing for energy B2B for the wider framework.
It can be, but compliance is a condition you must manage, not a given. Reputable providers operate on anonymised, consent-based co-op data, and you remain responsible for how signals are stored and used. For an energy brand selling into regulated markets, governance around intent data is not optional, it is the basis for using the data at all.
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