Google officially launched Gemini Skills on October 1, and the rollout is already underway: Skills started landing in Google Workspace on October 5 and hit the Gemini app on October 13, with both rollouts completing by mid-November. Gems — the custom chatbot feature your team may have spent the last year building on — are being retired.

If your sales team runs on Google Workspace, this is worth fifteen minutes of your attention. Not because Skills are revolutionary on their own, but because of two things: they finally work inside Gmail, Docs, and Sheets instead of a separate chat window, and Google built them on the same open SKILL.md standard that Claude, Codex, and 30+ other AI tools already use. Write a skill once, run it everywhere your reps work.

Here's what shipped, what dies, and what a sales team should actually do with it.

HubSpot dropped its Fall 2026 Spotlight at UNBOUND in Boston on September 16 — 134 updates, and most of them are rolling into public beta or general availability through October. The company is calling it its "most foundational product release," and for once the marketing label is close to accurate: this release restructures how the platform works, not just what it does.

The headlines: a rebuilt Breeze Assistant that executes work instead of suggesting it, a self-updating Smart CRM that logs calls, emails, and meetings without rep input, Agent Hub as the single control plane for every AI agent, and a new Revenue Hub that takes over quoting, contracts, and collections.

On September 30, 2026 — the same 48-hour window in which Gong, Apollo, and ZoomInfo all shipped agent builders — Google quietly dropped the most important model release of the quarter: Gemini 4 Argon.

Here's the twist: you can't use it. Not on the API, not in the Gemini app, not even with a Google AI Ultra subscription. Argon launched exclusively to vetted cybersecurity teams through Google's Fairwind Program, with everyone else waiting on an unannounced date.

So why should a sales leader care about a model they can't touch? Two numbers: Argon leads the benchmark built from real sales, marketing, and ops workflows by roughly 10 points — and it hallucinates at about a third the rate of OpenAI's frontier models. Both matter enormously for revenue teams. Here's the full picture.

Between September 30 and October 1, 2026, three of the biggest names in sales tech — Gong, Apollo, and ZoomInfo — each announced an AI agent platform. Not a copilot. Not a chat sidebar. Agents that are supposed to watch signals, make decisions, and execute multi-step GTM plays on their own.

Three near-identical announcements in 48 hours is not a coincidence. It's the whole category pivoting at once — and it tells you exactly where the sales stack is going in 2027. Here's what each vendor actually shipped, what's real today versus waitlisted, and how to evaluate any of them without getting burned by a keynote demo.

OpenAI's DevDay 2026 (September 29) packed more than 20 announcements, but one matters far more than the rest for revenue teams: Dots — always-on ChatGPT agents that run on their own cloud computers, connect to 4,000+ apps, and keep working after you close the tab.

If the last two years were about chatting with AI, Dots are OpenAI's bet that the next two are about delegating to it. Here's what actually shipped, what it costs, where it breaks for sales use cases, and five workflows worth testing this month.

On September 15, 2026, the Salesforce in Claude plugin hit open beta on all paid Claude plans. It's the first shipping product from Claudeforce — the expanded Salesforce–Anthropic partnership announced on August 26 — and it puts 37 prebuilt sales skills inside Claude: meeting prep, account research, deal scoring, pipeline reviews, forecast narratives, and CRM updates that write back to Salesforce after you approve them.

Most coverage treated this as partnership theater between two giants. The practical story is simpler: the hours your AEs spend assembling context before calls and typing updates after them just became a chat message. If your team runs on Salesforce and anyone on it already uses Claude, this beta is worth a structured two-week pilot — not a shrug.

Here's what actually shipped, how to roll it out without scaring your admin, and the one thing Claudeforce fundamentally cannot do for your pipeline.

What Shipped, Exactly​

The facts, stripped of press-release gloss:

• Announced August 26, 2026 as "Claudeforce," with select pilot customers first. Open beta arrived September 15 on all paid Claude plans — no Enterprise-only gate.
• 37 prebuilt sales skills built jointly by Salesforce and Anthropic, covering the daily work of account executives: briefings, account research, call prep, deal-health scoring, post-call opportunity updates, pipeline dashboards with risk flags, and forecast drafts.
• Built on AIforce, Salesforce's enterprise harness that exposes CRM data and workflows through MCP servers and APIs. Translation: no custom integration project. Your admin authenticates once centrally, assigns access to groups, and every action routes through Salesforce's existing permission model.
• Approval-first writes. By default, Claude proposes a change (say, updating an opportunity from your call notes) and the seller approves it before anything is written to the CRM.
• Optional Slack integration pulls deal-channel summaries and account-team threads into the same context.
• More skills land in late 2026, with service, marketing, and commerce skills slated for the following quarter.

Two details matter more than the rest. First, permission inheritance: users can't see records through Claude that they can't see in Salesforce. That kills the most common security objection before it starts. Second, there's no self-service for individual reps — distribution runs through AgentExchange and your Salesforce admin. If you're a rep reading this, your first move is a Slack message to whoever owns your org.

The 37 Skills, Organized by When You'd Use Them​

Salesforce hasn't published the full list, but the skills cluster cleanly around the rhythm of a selling week:

When	What Claude does	The prompt that triggers it
Every morning	Daily briefing across accounts and tasks	"What do I need to handle today?"
Before a call	Account research and call prep from CRM, email, and Slack	"Prep me for the 2 pm with Acme"
Deal reviews	Scores deals against your sales methodology	"Score this deal against our process"
After a call	Drafts opportunity updates from your notes, writes after approval	"Update the opportunity from my notes"
Weekly	Pipeline dashboard with stage-by-stage risk flags	"Show pipeline by stage with risk"
Monthly	Forecast narrative for the leadership readout	"Draft my forecast narrative"

If you've been building this manually with prompts and exports, the table above probably looks familiar — it's roughly the workflow we documented in our complete guide to Claude for SDRs and the AI meeting prep guide. The difference is that the copy-paste tax is gone. Claude reads the live opportunity, the email thread, and the Slack channel itself.

The deal-scoring skill deserves special attention. Scoring "against our process" means MEDDIC, MEDDPICC, or whatever methodology your org runs — applied consistently, on every deal, without a manager spending Sunday night doing it. Forecast narratives similarly compress what is, for most sales leaders, a half-day of spreadsheet archaeology. (If you want to see how far AI forecasting can go beyond narratives, we broke that down in AI sales forecasting with Claude Code.)

How to Roll It Out Without a Committee​

The beta's admin-gated distribution is actually a feature for sales leaders: you can pilot with one pod instead of boiling the ocean.

Setup is five steps:

1. Request access through AgentExchange (Salesforce's marketplace — not the Claude app).
2. Your admin creates one organizational connection. No per-user OAuth dance.
3. Assign access to a pilot group — one team, not the whole org.
4. Optionally connect Slack for deal-channel context.
5. Reps sign in with their own Salesforce credentials, inheriting their existing permissions.

Then run a one-week ramp:

• Days 1–3: read-only. Briefings, account research, call prep. Zero risk, immediate time savings, and it builds trust in the output.
• Day 3: test deal scoring on a handful of live opportunities. Compare against your manager's read.
• Days 4–5: enable approved writes for one rep. Post-call updates are the highest-value, lowest-risk write path because the rep reviews every field change.
• Day 5+: add Slack. Context quality jumps when Claude can see the account team's actual conversation.

Measure one thing: minutes per rep per day spent on prep and CRM updates, before versus after. Sellers routinely lose 60–90 minutes daily to this. If the pilot claws back even half of that across a 10-rep team, that's roughly a full head of selling capacity you didn't have to hire.

One caution: billing is still separate. Your Salesforce contract and your Claude contract don't know about each other yet, and combined pricing hasn't been announced. Budget both lines.

What This Means for the AI-CRM Race​

Benioff's line for the launch — "UI is the AI" — is the tell. Salesforce spent two decades getting people into its interface, and it's now conceding that sellers would rather work where the reasoning happens. That's the same pattern we've tracked all year: Claude Docs and Slides moved deliverables into the conversation; Claudeforce moves the CRM into it too.

For buyers, the practical takeaway is that the "which AI does my stack support" question is collapsing. Salesforce built AIforce to serve any agent through MCP — Claude is simply first. If you're mid-evaluation between assistants, our Claude vs ChatGPT for sales teams comparison covers the reasoning-quality gap that likely drove Salesforce to lead with Anthropic, and the Opus 5.5 breakdown covers the current model ceiling.

It also raises the bar for every point tool charging for "AI meeting prep" or "AI pipeline summaries" as a standalone product. When the CRM vendor and the model vendor ship that natively for the cost of a Claude seat, the standalone version of those features is on a clock.

The One Thing Claudeforce Can't Do​

Read the skill list again. Briefings, prep, scoring, updates, dashboards, forecasts. Every single one operates on deals that already exist in your CRM.

Claudeforce makes your team dramatically faster at working pipeline. It does nothing to create pipeline. It can't tell you that a VP of Sales at a target account visited your pricing page twice yesterday, that a champion just changed jobs, or that an account that ghosted you in Q2 is suddenly researching your category. Salesforce only knows what someone put into Salesforce — and the buyers you most need to reach aren't in there yet.

That's the layer MarketBetter owns: identifying in-market accounts and the anonymous visitors already on your site, then telling your reps who to contact and exactly what to do next — not just summarizing what's already logged. Claude can then research those leads and draft the outreach, but the signal has to come from somewhere. (And no, Claude can't pull this from LinkedIn on its own either.)

The teams winning in 2026 run both layers: signal intelligence to fill the top of the funnel — because outbound isn't dead, it's just evolved — and AI-native execution like Claudeforce to work it without administrative drag.

Want the pipeline-creation layer to pair with your Claudeforce pilot? Book a MarketBetter demo →

FAQ​

What is Claudeforce? Claudeforce is the expanded Salesforce–Anthropic partnership announced August 26, 2026. Its first product, the Salesforce in Claude plugin, entered open beta September 15, 2026 with 37 prebuilt sales skills.

What Claude plans support Salesforce in Claude? All paid Claude plans. There's no free-tier access, and individual reps can't self-serve — a Salesforce admin must enable the plugin through AgentExchange.

Can Claude change my Salesforce data? Only with approval. By default, Claude proposes each write (like an opportunity update) and the seller confirms before it lands in the CRM. Users also inherit their existing Salesforce permissions — Claude can't touch records they couldn't already access.

Does Anthropic train on our CRM data? Anthropic states it does not train on Team and Enterprise data by default.

How much does it cost? There's no combined pricing yet. You pay for Salesforce and Claude separately under existing contracts.

Does this replace an SDR tool or signal platform? No. All 37 skills operate on existing CRM records. Identifying new in-market accounts, website visitors, and buying signals — the pipeline-creation layer — still requires a platform like MarketBetter.

In mid-September 2026, Anthropic quietly killed Claude Cowork as a separate mode, folded it into the main Claude app, and shipped two new surfaces in beta: Claude Docs and Claude Slides. Most of the coverage framed this as an office-suite play against Google and Microsoft.

That's the wrong frame for anyone who carries a quota. The right frame: the most expensive non-selling activity in sales — building documents and decks — just moved inside the AI conversation. Claude's output is no longer text you paste somewhere. The deliverable itself is the output.

We've been running Claude in production GTM workflows since early 2025 (start with the complete guide to Claude for SDRs if you're new). Here's what actually shipped, why it matters more for sales teams than for the "AI office suite" crowd, and five workflows worth stealing this week.

Contextual targeting places your ad based on what the page is about, not who the reader is. A CFO reading an article on revenue forecasting sees your FP&A tool's ad because of the article — no cookie, no device ID, no identity graph involved.

It's the oldest idea in advertising (trade magazines did this for a century) reborn as the default answer to a cookieless web. In 2026 it's also quietly one of the cheapest ways to put a B2B message in a relevant environment.

Here's how it works, what the current numbers say, and the one structural limit that decides where it belongs in your program.

How Contextual Targeting Works​

Modern contextual engines do four things in the milliseconds before an ad auction:

1. Crawl and classify the page. NLP models read the article — topic, entities, sentiment, even buying-stage cues ("comparison," "pricing," "implementation").
2. Match against your targeting profile. You buy categories ("enterprise software," "supply chain management"), keywords, or custom segments built from example URLs.
3. Apply brand-safety filters. Negative categories and sentiment thresholds keep you off pages you don't want to fund.
4. Bid. If page context matches your profile, your bid enters the auction like any other programmatic impression.

The critical distinction from behavioral targeting: nothing about the person is known or needed. Which is exactly why it survived cookie deprecation untouched.

Why It's Having a Moment in B2B​

Three 2026 realities pushed contextual from fallback to first-class:

• Reach. In cookieless environments, behavioral targeting loses 30–60% of addressable inventory. Contextual keeps 100% — it never needed the cookie.
• Cost. Advertisers report CPCs up to 48% lower and CPMs 20–30% lower than behavioral equivalents, and cookieless CPMs overall run ~22% below cookie-based ones.
• Performance parity. Recent studies put contextual within 5–8% of cookie-based behavioral targeting on CTR and conversion quality — a gap most B2B budgets happily trade for the cost savings.

And B2B is structurally a good fit: long, content-driven research cycles mean your buyers spend months reading exactly the pages a contextual engine can classify. Targeting the content of the research is a decent proxy for targeting the researcher.

Contextual vs. Behavioral vs. Account-Based​

	Contextual	Behavioral	Account-based (IP/identity)
Targets	Page content	Individual's history	Named companies
Data needed	None (page-level)	Cookies/device IDs	IP-to-company, identity graph
Privacy exposure	None	High	Moderate
Reach in 2026	Full inventory	Shrinking	Depends on match rates
Precision	Environment-level	Person-level (decaying)	Account-level
Typical B2B use	Category air cover	Retargeting	ABM programs

The three aren't rivals — mature programs layer them. Contextual finds relevant environments, account-based ad platforms (see our ABM tools roundup) find target companies, and first-party signals find in-market people.

Where Contextual Fits in an ABM Program​

Platforms like Propensity made contextual a standard channel in omnichannel ABM campaigns — and used well, it plays three roles:

1. Top-of-funnel air cover on a budget. Run category contextual against your solution space so that when your SDR calls, the brand isn't cold. At $3–10 CPMs this is the cheapest awareness you can buy. (For what the full program costs, see our omnichannel ABM cost breakdown.)

2. Intent amplification. When accounts on your in-market list surge on a topic, shift contextual budget onto that topic's content categories. You're betting the surging accounts are reading that content — often a good bet.

3. Competitive adjacency. Custom segments built from competitor-review and comparison URLs put your ad next to the exact moment of evaluation.

The Structural Limit Nobody Puts on the Slide​

Contextual targeting can never tell you who saw the ad or which account is now in-market. It's anonymous by design — that's the privacy feature. Which means:

• No account-level reporting beyond click-throughs
• No signal you can route to an SDR
• No way to know if the CFO who read the forecasting article works at a target account or a student newspaper

That's fine for awareness. It's fatal if awareness is your whole program, because nothing downstream converts impressions into conversations. The teams getting real pipeline from contextual pair it with a capture layer: person-level visitor identification on their own site, so that when the air cover works and the buyer shows up, someone actually knows — and routes the signal to a rep while it's warm.

Ads rent attention. Signals book meetings. Budget accordingly.

FAQ​

Is contextual targeting GDPR-compliant? Yes — it processes no personal data, which is why European budgets adopted it fastest. Standard consent rules still apply to any measurement pixels you add.

What does B2B contextual inventory cost in 2026? Display CPMs typically run $3–15 depending on category competitiveness; premium B2B publications and custom segments price higher. Still consistently 20–30% below comparable behavioral buys.

Can contextual targeting reach specific accounts? No. It targets content, not companies. If you need named-account delivery, that's account-based advertising — compare approaches in our MarketBetter vs Propensity breakdown.

Does contextual work for niche B2B categories? It's actually strongest there. Niche categories have well-defined content universes (trade pubs, analyst blogs, subreddits with display inventory), so classification is precise and waste is low.

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Air cover only pays off when you can see who showed up. MarketBetter identifies the visitors your ads send and turns their signals into booked meetings. Book a demo →

Geofencing draws a virtual boundary around a physical place — a convention center, a competitor's headquarters, an airport terminal during a conference week — and serves ads to the mobile devices inside it. The devices get captured into an audience you can keep retargeting for 30 days after they leave.

In consumer marketing it's a coupon cannon. In B2B it's something more interesting: one of the few ways to build an audience out of verified physical behavior — this device attended that industry event; this device works in that building.

Here's how B2B teams actually use it in 2026, what it costs, and the step most programs skip that decides whether it produces pipeline or just impressions.

How It Works​

1. Draw the fence. GPS/Wi-Fi/Bluetooth-based polygons around target locations — typically 100–300 meters for an office building (tight enough to avoid the coffee shop next door), or the exact footprint of a convention hall.
2. Capture device IDs. Devices that dwell inside the fence during your window get added to an audience segment.
3. Serve and retarget. Ads run on apps and mobile web while devices are in the fence — and, more valuably, for up to ~30 days afterward across their other devices via cross-device graphs.
4. Layer filters. Daypart to business hours (Mon–Fri, 8am–6pm) to bias toward employees over visitors; some platforms layer firmographic data on top.

Accuracy realities: outdoor precision is roughly 5–50 meters depending on device and environment. Good enough for a convention center; do not expect it to separate floor 12 from floor 14 of a shared tower.

The Four B2B Plays​

1. Trade show and conference capture. Fence the venue (and the headline hotels) during show week. Everyone captured is a verified industry attendee — a prequalified audience you retarget for the month after, precisely when follow-up matters. This pairs with the pre-event motion in our conference prospecting playbook: outreach books the meetings, geofenced air cover keeps the brand warm between touches.

2. Competitor's event or booth. Fence a competitor's user conference and spend the next 30 days putting your comparison content in front of their most engaged customers. Aggressive, legal, and common.

3. Competitor and partner offices. Fence competitor HQs to reach their employees (recruiting, or seeding doubt before a renewal cycle) or fence your target accounts' offices as an account-based display channel that doesn't depend on IP resolution.

4. Your own funnel's physical layer. Fence your own event, your roadshow stops, even the steakhouse where you ran the exec dinner — then retarget attendees with the follow-up asset instead of hoping they open the recap email.

What It Costs in 2026​

Line item	Typical range
Mobile/desktop display CPM	$3.50–$15
CTV geofencing CPM	$20–$50
Advanced (behavioral triggers, CRM sync)	up to $20–$25 CPM
Platform minimums	Often $1K–$5K/mo managed

A single-event fence with 30-day retargeting is typically a $2K–$10K line item — cheap enough to test, which is why it shows up as a standard channel in omnichannel ABM platforms like Propensity. (Full program math in our omnichannel ABM cost breakdown.)

The Honest Limits​

• Device ≠ decision-maker. Your convention-center fence captures the AV crew, the baristas, and 400 vendors along with the buyers.
• No account resolution. You know a device was at SaaStr; you don't know it belongs to the VP of Sales at a target account — most platforms can't close that gap reliably.
• It's still just impressions. Nobody ever geofenced their way directly to a booked meeting. It warms; it doesn't convert.
• Privacy drift. Location data sits in regulators' crosshairs; platform capabilities have narrowed each year and will keep narrowing.

The Step That Turns Location Into Pipeline​

Geofencing's output is an anonymous warm audience. Pipeline starts when a member of that audience does something identifiable — and for B2B, that moment is almost always a visit to your website.

That's the handoff most programs fumble. The event attendee you spent $8 CPM warming clicks through Tuesday morning, browses your pricing page, and leaves — anonymous — unless you have person-level visitor identification running. With it, the sequence completes: fence captures the audience → ads warm it → visitor ID names the ones who show up → the signal routes to an SDR the same day, while the conference is still fresh in memory.

Same principle as every ad channel: air cover doesn't book meetings — the capture layer does.

FAQ​

Is B2B geofencing legal? Yes, using platform-provided opt-in location data. Rules tighten yearly (and several US states now restrict sensitive-location fencing), so keep fences on commercial venues and follow your platform's compliance guidance.

How precise is a geofence? Roughly 5–50 meters outdoors. Building-level targeting works; suite-level doesn't.

How long can you retarget captured devices? Most platforms support ~30-day lookback windows after fence exit — the useful window anyway, since event intent decays fast.

Is geofencing worth it without an event? Rarely as a standalone. Fencing static office buildings produces thin, noisy audiences. It earns its budget around moments — shows, launches, renewal windows — when physical presence actually signals something.

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Warming an audience is the easy half. MarketBetter identifies the visitors your campaigns send and hands your SDRs the signal while it's hot. Book a demo →

At any given moment, roughly 5% of your total addressable market is actively buying what you sell. The other 95% fit your ICP perfectly and still won't take the meeting — not this quarter.

An in-market account list is the discipline of separating those two groups. Done right, it's the highest-leverage asset in your GTM stack: the same SDRs, the same sequences, the same budget, pointed at accounts that are actually shopping. Done wrong, it's a spreadsheet of companies that "surged" on a topic once and never thought about you again.

Here's the full build process.

What Counts as "In-Market"​

An account is in-market when there's evidence of active evaluation — not just fit. Three signal layers supply that evidence:

Layer	Source	Examples	Strength
First-party	Your own properties	Pricing-page visits, return sessions, demo-page views, doc reads	Strongest — they came to you
Second-party	Platforms you can observe	Review-site activity, competitor comparison views, LinkedIn engagement	Strong — category-specific
Third-party	Publisher networks & co-ops	Topic surge across research content (Bombora-style)	Broad but noisy — category-level, account-level

The mistake most teams make is building the list from the third layer alone, because that's what their intent vendor sells. Third-party surge tells you an account might be researching the category. First-party behavior tells you a specific person is evaluating you. Weight accordingly.

Step 1: Lock the ICP Filter First​

Intent without fit is noise. Before any signal enters the list, define the firmographic and technographic gate: industry, employee band, geography, tech stack, disqualifiers. An account that surges hard but can't buy (wrong size, wrong region, incompatible stack) should never reach a rep.

This is a filter, not a score. Binary. In or out. The scoring comes later — see our companion guide on how to score an in-market account list.

Step 2: Instrument First-Party Capture​

Your website is the single richest in-market signal source you own, and most teams are blind to it — 97%+ of B2B visitors never fill a form.

• Deploy person-level visitor identification so anonymous traffic resolves to named people at named accounts
• Flag high-intent paths: pricing, integrations, security/compliance pages, comparison pages
• Track return frequency — a second visit within 7 days is worth more than any topic surge

The full setup is covered in our B2B website visitor identification guide.

Step 3: Layer Third-Party Intent — as a Multiplier, Not a Source​

Add topic-surge data on top of the ICP-filtered universe to catch accounts researching the category before they hit your site. Rules that keep it honest:

• Recency: signals older than 14 days are history, not intent. Buying intent decays with a half-life measured in days.
• Repetition: one week of surge is noise; three consecutive weeks is a pattern.
• Relevance: surge on your category and competitor terms, not adjacent topics.

If you're choosing a vendor, our intent data provider roundup covers the trade-offs.

Step 4: Add Trigger Events​

Some accounts enter market because something happened: new VP of Sales, funding round, layoffs at an incumbent vendor, a compliance deadline, a champion changing jobs into a new account. Triggers are the earliest in-market signal that exists — often preceding any research behavior. Pipe them in from job-change alerts, funding feeds, and hiring data.

Step 5: Set Entry, Exit, and Ownership Rules​

A list without lifecycle rules bloats into uselessness within a quarter.

• Entry: ICP pass + at least one strong signal (first-party visit, repeated surge, or trigger event)
• Exit: no new signal in 21-30 days → account rotates out (it can re-enter)
• Ownership: every account that enters gets routed to a named rep within 24 hours — signal-based routing rules prevent the "great list nobody works" failure mode
• Cap: keep the active list at a size your team can actually touch (typically 5-10 accounts per rep per week entering)

The Mistakes That Poison Lists​

1. Static lists. In-market is a state, not a trait. A list built in January is fiction by March. Rebuild continuously or automate entry/exit.
2. Account-level worship. "Acme is surging" is not workable. Who at Acme? Resolve to people or the SDR is cold-calling into a warm account — which is just cold calling.
3. No speed contract. In-market accounts are in-market for everyone, including your competitors. If your motion can't get from signal to meeting inside 24-48 hours, the list's value evaporates before it's worked.
4. Treating intent as a magic bullet. Intent data multiplies a strong ICP and a working outbound motion. It cannot rescue a weak one.

How MarketBetter Automates This​

Everything above can be run manually with spreadsheets and three vendor contracts. MarketBetter runs it as one system: person-level visitor ID (step 2), blended first- and third-party intent (step 3), trigger signals (step 4), and routing rules with SLA timers (step 5) — feeding outreach that's drafted by AI and reviewed by a human before it sends. The list stays live, the exits happen automatically, and every entry ends in an attempted conversation, not a dashboard.

Book a demo to see your own in-market list built from your real traffic.

FAQ​

How big should an in-market account list be? As big as your team can work within the signal window — usually 5-10 new accounts per rep per week. A 5,000-account "in-market list" is a mailing list wearing a costume.

Is intent data enough to build the list? No. Third-party intent alone produces category researchers, not evaluators of you. Blend it with first-party behavior and trigger events, and gate everything through ICP fit.

How often should the list refresh? Continuously. If your tooling forces batch updates, weekly is the minimum viable cadence — intent signals lose most of their value within two weeks.