Every RevOps leader has lived through the planning season disconnect. You build a total addressable market (TAM) analysis to align with the board, but it's based on a third-party Gartner or Forrester report with numbers you can't validate at the account level. Then you create territories from a different data source. Then you close deals that roll up into segments that don't match your original plan. By the time you're trying to report on how you performed against your addressable market, you're reconciling two or three completely different datasets — and the answer is always "it's complicated."
That's the problem Spencer Hardey has lived firsthand, as VP of Operations at HG Insights. In this episode of RevOps Demos That Don't Suck, we sat down with Spencer Hardey to walk through how HG Insights solves the problem most RevOps teams don't even frame correctly: the gap between market planning and go-to-market execution isn't a process problem. It's a data continuity problem.
HG Insights is a B2B intelligence platform built on one of the largest technographic datasets in the market — covering over 71 million B2B tech-related companies. It tracks firmographic data, IT spend by category, technographic signals (what tools companies use and how that's trending over time), intent signals from sources including TrustRadius technology reviews, and LLM research activity. The company has been collecting this data for over 15 years — and actually coined the term "technographics."
The platform is not purely a data vendor. It spans market analysis, account scoring, and rep-facing sales execution through a native Sales Copilot — positioning itself as a unified data layer from TAM to territory to outbound.
The classic RevOps problem Hardey describes isn't a lack of data — it's a lack of data continuity. You're using one source to tell your board what your market is worth, a different source to build territories, and yet another to enrich contacts for campaigns. When you try to close the loop on performance, the seams show.
"Your whole financial plan in some ways has to align to this addressable market. And so that was always a challenge, especially when once you do that, you create your segmentation, what segment of the market you're gonna go after and what accounts are in there, but you don't have any of the actual underlying accounts to validate with your sales team and create territories and then go execute." — Spencer Hardey
This is the kind of data management challenge that RevOps teams often defer until it becomes a reporting crisis. HG Insights is built to prevent that crisis from forming in the first place, by grounding every layer of planning — TAM, serviceable addressable market (SAM), and serviceable obtainable market (SOM) — in the same underlying dataset.
The Market Analyzer is where planning starts. Rather than referencing a static market report, you build your addressable market directly inside the platform using HG Insights' own data. Spencer walked through a live example: filtering to companies that spend on sales and marketing software, applying a 20% market share assumption, and arriving at a dollar-figure TAM — all backed by actual account-level records that can be audited, drilled into, and exported.
From there, the workflow layers down through SAM (limiting by region, industry, or company description to reflect where you actually have sales capacity) to SOM (the accounts you're realistically going after in the next 12 months, filtered by ICP fit signals like employee count and CRM presence). The result is a TAM/SAM/SOM model that is not a slide deck estimate — it's a live list of accounts you can hand to sales.
This matters especially for companies that ever go through board presentations or investor diligence. As Hardey put it, when your addressable market estimate is built on the same data as your territory assignments, you can actually defend the numbers. That's a capability most RevOps teams don't have today, and it directly connects to building a credible financial plan that stakeholders will trust.
The platform also tracks over 10 years of historical technology adoption trends, so you can see how competitor presence within your addressable market is changing over time — useful context for both planning conversations and competitive strategy.
Once a SOM is defined, the next problem is prioritization. With 20,000 accounts in a segment, no sales team can work all of them equally. HG Insights' AI-assisted scoring automatically generates a scoring profile for each of your products or use cases by crawling your website and product pages, then scoring accounts across three dimensions:
You can build scoring profiles for each product line or use case separately, then view how every account in your SOM scores across each one. This makes it possible to give different instructions to different parts of your sales org: enterprise reps get the highest-fit, highest-intent accounts; marketing gets accounts with strong fit and need but low intent (to warm them up before they enter a sales rep's territory); and SDRs get high-intent accounts outside the core SOM as a feedback mechanism to pressure-test the ICP.
That last use case is particularly useful: when high-intent accounts outside your modeled SOM start converting, it tells you something is missing from your fit assumptions. The scoring model becomes a living system, not a static artifact.
This kind of structured account prioritization is what makes territory design equitable — reps get balanced distributions of tier-A and tier-B accounts rather than territories based on whoever grabbed the biggest logos first.
The Sales Copilot is where the market analysis becomes actionable for individual reps and SDRs. It lives directly inside HubSpot or Salesforce via CRM integration, surfacing the same scoring and signals that RevOps used to build territories — without requiring reps to ever leave their CRM.
For each account in a rep's territory, the Sales Copilot generates:
Spencer walked through a live example using Stripe: the copilot flagged it as a high-scoring account for a specific use case, identified that a competitor was present, and generated a battle card in real time — including proof points, displacement angles, and likely objections. This is the kind of pre-call preparation that most enablement programs aspire to but rarely deliver consistently at scale.
HG Insights also automates account brief generation on meeting book — so when a rep books a first call, a brief is generated automatically before the meeting happens. This is a use case the HG Insights team runs internally.
HG Insights integrates natively with HubSpot and Salesforce for CRM sync. For sequencing and execution, it connects with tools like Outreach, Salesloft, and Gong Engage. For product usage signals, it integrates with Amplitude and Pendo. And for marketing automation, Marketo is supported.
Beyond point integrations, HG Insights offers a Model Context Protocol (MCP) connector — available in OpenAI and Anthropic environments — called Phenix. This allows teams to query HG Insights data directly through Claude or ChatGPT-style interfaces: identifying which accounts to prospect this week, generating account briefs on demand, running competitive takeout analysis. For smaller teams where one person is running the entire RevOps function, the MCP layer removes the need to build internal tooling to activate the data.
HG Insights is currently offering 100 free credits for MCP access — searchable as "HG Insights Phenix" — which is enough to build account briefs, score a territory segment, or run a competitive analysis. For teams that want to test the data quality before committing to the full platform, that's a low-friction entry point.
For enterprise teams that need full flexibility, HG Insights also offers a direct data feed API — which is how customers like Microsoft, Amazon, and Google have historically consumed the data, building internal intelligence systems on top of it.
HG Insights has been moving down market over its 15-year history. The current platform is best suited for:
The platform is less relevant for companies that don't sell into the B2B tech space — HG Insights' dataset is purpose-built around technology adoption and IT spend signals.
Pricing was not disclosed in the demo. HG Insights offers three primary access models:
For teams wanting to evaluate the data before committing, the free MCP credits are the most accessible starting point.
The problem Hardey describes — planning with one dataset, executing with another, and reporting on a third — is not a workflow problem that gets fixed with better project management. It's a structural data continuity problem. And it compounds every year as AI gets layered on top of these same fragmented inputs. As we've noted elsewhere, most revenue stacks aren't ready for AI precisely because the underlying data infrastructure can't support it.
HG Insights addresses that at the foundation: one dataset spanning market sizing, firmographic context, technographic signals, intent data, and contact intelligence. The TAM you show your board is built from the same records that become your territories, which are the same records that populate your reps' Sales Copilot. That continuity is rare, and it's worth a serious look for any B2B tech company that's tired of reconciling planning slides with execution reality.
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