What Is HG Insights Intent Data?

HG Insights intent data is an aggregated, account-level score that estimates how likely a company is to be researching a given technology category, delivered as part of HG’s broader Revenue Growth Intelligence platform. The signal is generated primarily from bidstream web activity - ad-exchange level browsing mapped to companies - plus verified second-party review behaviour from TrustRadius, which HG acquired in June 2025, interpreted against HG’s technographic and spend datasets. It is designed for enterprise ABM and market-intelligence teams working at the account-planning altitude, not for reps who need to know which specific accounts to work this week. If timing signals and rep-level actionability matter more than dataset breadth, an event-based signal source is a better fit; see MarketSizer vs HG Insights.

What is HG Insights intent data?

HG Insights positions its platform as AI-powered Revenue Growth Intelligence, built on its Revenue Growth Intelligence Fabric data layer. Inside that platform, intent data is one signal type layered on top of a much larger technographic and spend dataset. Where technographics tell you which technologies a company already owns, HG’s intent product estimates which technologies the same company might be researching now.

The output is an account-level score. For a given topic or product category, a target account is assigned a value that reflects how much recent research behaviour HG has observed for that topic from people at the account. Sales, marketing and RevOps teams typically pull the score into their CRM or account-planning tools to surface accounts showing higher-than-baseline research activity.

Two properties of the score matter more than most buyers realise:

• It is aggregated. The score reflects rolled-up research behaviour across many people at the account. You do not see the specific person, session or content that produced the reading.
• It is inferential. The score is a probability that research is happening, not a record that a specific buying event occurred.

Both properties are deliberate. Aggregating protects privacy and produces smoother signals for account-planning; inference makes the score usable across many categories where direct evidence is impossible. The trade-offs matter when the signal is used for prioritisation instead of planning, which is where most execution teams get stuck.

How the score is built

HG Insights describes its intent data as predominantly derived from bidstream data, contextualised against its technographic and spend datasets, and - since its June 2025 acquisition of TrustRadius - complemented by verified second-party review intent. The exact inputs vary by product tier and category, but the general pattern looks like this:

• Bidstream web-behaviour signals. Ad-exchange level browsing activity, mapped to companies and office locations using firmographic data. HG’s scoring model measures the frequency of defined topics on pages viewed, the number of locations showing activity, and the trajectory over time.
• Second-party review signals. Verified research behaviour on TrustRadius - category, product and comparison-page activity from identified buyers - folded in since HG acquired TrustRadius.
• Technographic context. The company’s existing tech stack, spend estimates and firmographics, used to interpret raw behaviour (a company that already owns your competitor reading about you means something different from one that does not).
• AI models and topic taxonomies. HG’s topic library groups the raw signals into research categories that map to how buyers describe their evaluations.

The score is refreshed weekly - HG describes taking in nearly 2 billion intent records each week - and delivered via CRM sync, native connectors for Salesforce and Snowflake, data snapshots or an API for downstream tooling. The exact cadence and delivery depend on the customer package.

Who it is built for

HG Insights is enterprise-heavy by design. The dataset shape, product surface and go-to-market posture all point at strategy, marketing and RevOps teams working at the account-planning altitude - people who build territories, size markets, structure ABM programmes and set annual GTM strategy.

HG does not publish pricing publicly. Industry pricing aggregators consistently place HG’s enterprise packages in the tens of thousands per year, reflecting the depth of the technographic and spend dataset alongside the intent product. That price point makes sense for a strategy-facing tool where the return is measured in years of programme design, not in weekly rep prioritisation.

The typical customer is a large B2B technology company running structured ABM. Roughly, if your GTM function has a dedicated ABM team, a market-intelligence function or a formal territory-planning process, HG will land inside your existing motions cleanly.

Where it works well

HG intent shines in three specific jobs.

Territory design and TAM sizing. Combining tech-install breadth with an intent overlay lets an enterprise strategy team estimate which segments of the addressable market are actively researching adjacent categories, and size territories accordingly.

Long-cycle ABM programmes. When a marketing team runs a six-month ABM programme against a fixed set of a few hundred accounts, an intent overlay helps decide sequencing and content pacing across the programme.

Analyst-facing reporting. The dataset breadth and structured taxonomy make it easy to build the kind of reports market-intelligence teams present internally: adoption curves, category maturity, competitive share.

Where it falls short for sales execution

The same properties that make HG intent good for planning make it awkward for the rep-facing execution surface where most GTM teams actually spend their week.

The score is aggregated, which means a rep cannot see what evidence produced it. There is no underlying event to reference in outreach, no timing anchor, no specific behaviour to build a message around. The signal helps you rank accounts but not open a conversation.

The score is inferred, so it does not tell you that anything happened. It tells you the account’s recent behaviour looks like accounts that have historically bought. That is a useful prior, but it is not a buying event.

The score is refreshed slowly. Weekly cadence works for a quarterly programme; it does not survive a two-week trial window on a specific competitor.

And the tool is enterprise-shaped. The setup, procurement and price make it a heavy lift for a mid-market team where the buyer is a Head of Sales and the deployment target is a working SDR.

Worked example: how the score composes for one account

To make the mechanics concrete, take a hypothetical target account - a 500-employee B2B SaaS company selling into finance. The GTM team has that account on a watchlist for the topic "customer support automation." Here is an illustrative sequence, consistent with the scoring dimensions HG describes (topic frequency, number of locations, trajectory over time). The specific numbers, thresholds and score scale below are hypothetical.

• Week 1. Two employees at the account read three articles on customer-support automation across HG’s content-partner network. The IP addresses map cleanly to the account’s known office ranges. No score change yet - in this illustration, the volume is below the level that moves the score.
• Week 2. A researcher at the account downloads a buyer’s guide on the topic from a review platform. Another employee visits three vendor comparison pages. Signal volume crosses the threshold. The account’s topic-intent score for "customer support automation" ticks up from a baseline of, say, 20 to around 45.
• Week 3. The signal continues but does not accelerate. Score holds around 45. HG’s topic taxonomy also cross-references adjacent topics ("live chat" and "helpdesk software") and lifts those scores modestly.
• Week 4. The customer’s CRM sync runs. The rep who owns the account sees the score in HubSpot: “Customer support automation: 45 (baseline 20)”. What the rep does not see: which two people read what, which vendors they compared, or whether the buying committee has actually convened.

Two things worth noticing. The lag between the first web-behaviour signal and the score being visible to a rep is roughly 10 to 14 days in this example. And the rep gets a topic bucket plus a delta - not the underlying content, not the specific people, not the specific vendors under evaluation.

Signal latency, in weeks

Latency stacks up across four stages between a real buying behaviour and a score change that a rep can act on. The ranges below are our own editorial estimates based on the publicly described mechanics of aggregated third-party intent - signal scale, accuracy and overlap are the dimensions Forrester recommends assessing when evaluating providers:

• Behaviour to partner-network capture. 0 to 2 days. IP-level web activity is captured close to real time on well-covered publisher networks, longer where partner coverage is thin.
• Capture to aggregation. 2 to 5 days. Raw sessions are rolled up into account-level signal counts before mapping to topics.
• Aggregation to topic-mapping and modelling. 3 to 7 days. Signals are matched to HG’s topic taxonomy and pushed through modelling to produce a score.
• Score to rep-visible surface. Up to 7 days. Weekly-cadence refresh from HG to the customer’s CRM or account-planning tool.

End-to-end that is roughly 7 to 21 days between a buying behaviour and a rep-visible signal. Fine for a quarterly programme; often too slow for the fourteen-to-thirty-day trial window that decides a competitive-displacement outreach.

How to interpret an HG score before acting

Three things to check before a rep touches an account on the back of an HG intent score.

Look at the trend, not the level. A single high score is less informative than a rising delta from baseline. A jump from 20 to 45 says something new started happening; a flat 55 could be background noise for a large account whose employees always browse the topic.

Cross-reference the technographic layer. HG’s underlying strength is technographic and spend data. An intent uplift on "customer support automation" from an account that already runs the incumbent for years is a stronger buying signal than the same uplift from an account with no relevant install-base context.

Rank against ICP fit, not against each other. Topic intent alone tells you an account is researching. It does not tell you whether they can afford your product or whether they fit your target segment. Combine with an ICP score and a win-likelihood layer before a rep gets the account. See Qualified Opportunity: the three-part definition.

How MarketSizer signals compare

MarketSizer sits at a different altitude. Rather than infer research intent from browsing patterns, MarketSizer detects observable subscription events - trials, switches, renewals, churn - and attaches each signal to the specific event that produced it. A signal on an account is a record that something happened, not a probability that something might be happening.

Three practical implications:

• Explainability. Every signal is traceable to the underlying event. A rep can see what triggered it and reference the specific behaviour in outreach.
• Timing. Signals fire when the event occurs, not on a weekly aggregate. That matters when the account’s buying window is fourteen to thirty days.
• Coverage shape. MarketSizer indexes deeper history per vendor (200+ vendors, with deep subscription-event history) rather than the wide surface HG covers.

The comparison is not a rip-and-replace argument. Enterprise teams often keep a broad intent overlay for planning and add an event-based signal source for execution. The full breakdown lives at MarketSizer vs HG Insights.

Frequently asked questions

What is HG Insights intent data? HG Insights intent data is an aggregated account-level score that estimates whether a company is researching a given technology category. It sits inside HG’s Revenue Growth Intelligence Fabric alongside their technographic, spend and firmographic datasets. It is generated primarily from bidstream web activity plus verified second-party review signals from TrustRadius, and refreshed on a weekly cadence.

How is HG intent different from other intent data providers? Most third-party intent providers - HG, Bombora, 6sense, ZoomInfo intent - work with similar mechanics: aggregate account-level web-behaviour signals delivered as a score, though the source mixes differ (bidstream, publisher co-ops, review platforms). HG’s difference is the depth of technographic and spend context sitting alongside the score. This makes HG particularly useful for enterprise account-planning where the intent overlay needs to be interpreted against install-base data.

Who uses HG Insights intent data? Predominantly enterprise strategy, marketing and RevOps teams. HG’s pricing (roughly $24,000 to $150,000+ per year with a median around $52,000, per Vendr contract data) and product surface are built for structured ABM, territory design and market-intelligence functions rather than day-to-day rep prioritisation.

Is HG Insights intent data event-based? No. HG intent is inferential and aggregated. The score reflects probability that research is happening at the account, not a record of a specific buying event. Event-based signals - a trial started, a vendor switched, a subscription approaching renewal - come from a different category of data source. See What Replaces Intent Data in 2026 for the shift towards event-based signals.

How fast is HG intent data refreshed? Typically weekly, depending on the customer package. That refresh cadence supports quarterly and annual planning motions but is often slower than the fourteen-to-thirty-day evaluation windows that decide competitive-displacement outreach outcomes.

Can HG Insights and MarketSizer be used together? Yes. HG’s aggregated intent and technographic-plus-spend overlay work at the planning altitude; MarketSizer’s event-based signals work at the execution altitude. Enterprise teams commonly keep both. Full breakdown at MarketSizer vs HG Insights.

How is HG Insights different from Bombora? Bombora is a specialised topic-level intent provider built on a co-op of B2B publishers. HG partners with Bombora on advertising audiences, but Bombora does not feed HG’s intent score - HG generates its own intent from bidstream and TrustRadius review activity, bundled with its wider technographic, spend and contract dataset. If a team already uses Bombora natively, HG is often adding the context around the intent signal (what does the account already own, what would they spend) rather than a replacement for it.

How does HG intent compare to 6sense? Both target enterprise ABM teams and both deliver aggregated account-level intent. 6sense leans further into predictive scoring across the full funnel with its own AI models; HG leans further into the underlying technographic and spend depth. See HG Insights vs 6sense for the full comparison.

Does HG Insights include contact data? Historically HG was company-level intelligence - technographics, spend, contracts and intent - with contact enrichment left to an integrated ZoomInfo, Cognism or LinkedIn Sales Navigator layer. Since acquiring TrustRadius in June 2025, HG also markets verified contact and buying-centre intelligence and intent-driven leads, though a deep contact database is still not its core product.

How is HG intent priced? HG does not publish pricing publicly. Enterprise packages consistently sit in the tens of thousands per year, reflecting the depth of the technographic and spend dataset and the enterprise procurement path. Individual product modules (intent, spend, contracts) are typically bundled rather than sold standalone.

Is HG Insights intent data usable for outbound SDRs? Partly, and HG would argue yes: since acquiring TrustRadius it markets intent-driven leads and competitive-displacement plays. Our view is that the core score remains aggregated and refreshed weekly, which struggles with the fourteen-to-thirty-day trial windows that decide most outbound competitive-displacement outreach. It is most useful as a territory-planning input that decides which accounts SDRs should have on their list at all.

Sources

All qualitative and quantitative claims in this piece are traceable to public sources. Where a specific number is cited, we link to the source or note that we are describing the enterprise-pricing bracket rather than a specific quote.

• HG Insights homepage - positioning line, product description.
• HG Insights Buyer Intent product page - how the intent product describes itself.
• HG Revenue Growth Intelligence Platform and RGI Fabric - HG’s own platform-versus-data-layer positioning.
• HG Insights Contextual Intent launch announcement - the bidstream-based methodology, weekly refresh and the nearly-2-billion-records-per-week figure.
• HG Insights acquires TrustRadius (June 2025) - the second-party review intent, intent-driven leads and contact intelligence additions. Disclosure: HG Insights has owned TrustRadius since June 2025, so TrustRadius content about HG is not an independent source.
• HG Insights integrations page - delivery via native connectors, API and data snapshots.
• How HG Insights partners with Bombora - the advertising-audience partnership described in the Bombora FAQ.
• HG Insights resource library - published product briefs and dataset documentation.
• GZ Consulting on Contextual Intent - independent analyst writeup of the scoring mechanics.
• Forrester - How To Evaluate Intent Data Providers - the evaluation framework (signal scale, accuracy, overlap) referenced in the latency section; the day-range estimates there are our own.
• The Forrester Wave: Intent Data Providers For B2B, Q1 2025 - the category’s current analyst benchmark.
• Vendr - HG Insights marketplace data - source of the contract-price range and median cited above. HG publishes no list pricing; brackets are third-party contract data and review-site aggregators (TrustRadius, G2).
• MarketSizer platform capabilities and data-coverage claims (70M+ subscription records, 17M+ active subscriptions, 2.5M+ active trials, 200+ vendors indexed, 140+ countries) - internal Claim Registry, sourced from live production tables and refreshed quarterly. Available on request via a demo.

Recommended reading

• MarketSizer vs HG Insights - side-by-side breakdown of the two tools.
• Best purchase-intent data platforms for 2026 - the full intent-tool landscape, categorised.
• What replaces intent data in 2026? - why event-based signals are displacing aggregated intent for execution teams.
• Purchase intent vs intent data - the definitional split between behavioural aggregation and buying events.
• How to detect competitor trials - the four detection methods and the fourteen-to-thirty-day window that decides outreach outcomes.