Building an in-market account list answers who might buy. Scoring answers the question your reps actually ask every morning: who first?

Most scoring models fail not because the math is wrong but because nobody trusts them — the weights were guessed in a workshop, the score never matches what reps see, and within a quarter everyone's back to gut feel. Here's a model built the other way: start from your closed-won data, keep the dimensions legible, and wire the score to routing so it changes behavior instead of decorating a dashboard.

The Three Dimensions​

Every workable account score combines three things:

Keep them separate. A blended single number hides the difference between "perfect fit, zero activity" (nurture) and "mediocre fit, on your pricing page daily" (call now) — and those demand opposite plays.

Step 1: Calibrate Weights on Closed-Won, Not Opinion​

Pull your last 12 months of closed-won and closed-lost accounts. For each, reconstruct what was observable before the first meeting: firmographics, signals, site behavior. Then ask which attributes actually separated winners from losers.

Every team that does this honestly finds surprises. Common ones:

• First-party engagement outpredicts third-party surge by a wide margin — a pricing-page visit is worth more than a month of topic surge (our buying-signal hierarchy analysis ranks the common signals by closed-won correlation)
• One or two firmographic traits you thought mattered don't
• Negative signals (wrong stack, recent competitor contract, hiring freeze) predict losses better than positive signals predict wins

Recalibrate quarterly. Around three-quarters of B2B teams will run some form of AI-assisted scoring by end of 2026 — but AI calibration on top of unexamined assumptions just automates the guessing.

Step 2: Score Each Dimension 0-100​

A simple, legible structure beats a clever one:

Fit (gate + score). Hard disqualifiers first — wrong size, wrong region, incompatible stack → score 0, exit. Survivors get scored on weighted firmographic/technographic match.

Intent (recency-weighted). Score signals, then decay them: a signal this week at full value, halved next week, gone by week four. Intent has a half-life; a score that ignores decay ranks last month's shoppers above this week's.

Engagement (behavior-tiered). Not all touches are equal. A blog visit is a point; a pricing page is ten; a second pricing visit within a week is twenty; an identified decision-maker doing it is fifty. Person-level resolution matters here — knowing who is on the page is the difference between account warmth and an actual buyer.

Step 3: Convert Scores to Tiers​

Reps don't act on a 73. They act on tiers:

Set thresholds so Tier A matches your team's actual capacity. A tier system that flags 400 "act now" accounts for six reps is a random number generator with extra steps.

Step 4: Wire the Score to Routing​

This is where scoring lives or dies. A score that only sorts a dashboard changes nothing. The score should do things:

• Tier A entry → account routed to a named rep with the triggering signal attached, SLA timer running (routing rules here)
• Tier transitions → notifications, not reports ("Acme moved B→A: 3rd pricing visit this week")
• SLA breach → escalation or re-route

The signal-to-meeting playbook covers the outreach side of the handoff.

The Failure Modes​

1. Opinion-weighted models. If the weights came from a meeting instead of closed-won data, the model encodes the loudest person's intuition.
2. No decay. Scores that only go up produce a leaderboard of accounts that were hot in Q1.
3. Score without routing. If a tier change doesn't move work to a person with a deadline, the model is decorative.
4. Precision theater. Seventeen weighted sub-factors nobody can explain lose to four factors every rep understands. Trust drives adoption; adoption drives revenue.

How MarketBetter Handles Scoring​

MarketBetter scores accounts and people continuously — fit gate, blended intent with built-in decay, person-level engagement — and routes tier changes straight into SDR queues with the evidence attached and outreach drafted for human review. Calibration runs against your actual pipeline outcomes, not a static rubric.

Book a demo to see your accounts scored on live signals.

FAQ​

Account scoring vs lead scoring — what's the difference? Lead scoring ranks individual form-fills; account scoring ranks companies using signals from everyone in the buying committee, including people who never converted. In committee-driven B2B sales, account scoring is the one that matches how deals actually happen.

How many factors should the model use? As few as survive the closed-won analysis — usually 4-7. Every factor you add that reps can't verify against reality costs trust.

How often should scores update? Continuously, or daily at minimum. Weekly batch scoring means your fastest-moving buyers spend their hottest days invisible.

Propensity and MarketBetter both pitch themselves as the affordable answer to 6sense and Demandbase. Both de-anonymize website visitors. Both use intent data. Both cost a fraction of enterprise ABM.

And yet they are almost opposite products.

Propensity is an advertising engine: it builds audiences of in-market accounts and serves them ads across 20+ channels — display, native, CTV, audio, social, direct mail. MarketBetter is a meeting engine: it identifies the people already showing buying signals and routes them into SDR outreach that ends in a booked call.

One warms accounts. The other works them. Here's how to decide which layer to fund — or how to sequence both.

The Short Version​

For the full teardown of Propensity's tiers and hidden contact-data costs, see our Propensity pricing review.

What Propensity Actually Does Well​

Credit where due. Propensity's G2 reviews are strong (4.8/5), and the product delivers on its promise for a specific job:

• Ad activation without a DSP. Most SMB marketing teams can't run programmatic display, CTV, or audio campaigns themselves. Propensity packages that into a flat monthly fee with campaigns live in under 15 days.
• Audience building from intent. It combines Bombora topic surge with website visitor data to build lists of accounts that look in-market, then syncs those audiences to ad channels automatically.
• Channel breadth. Direct mail, geofencing, contextual — channels most B2B teams never touch — are in the box (LinkedIn is gated to the $3,000 tier, Google/YouTube to $4,000).

If your goal is awareness inside a defined account list — making sure the buying committee has seen your name before your rep calls — Propensity does that at a price 6sense can't match.

Where the Model Breaks​

The limits show up when you trace an ad impression to revenue.

1. Impressions don't book meetings. Propensity's output is engagement: lifted account activity, more site visits, better ad recall. Someone still has to convert that warmth into conversations. If you don't have an outbound motion ready to catch the accounts that heat up, you're paying to warm accounts that cool back down. Signal decay is brutal — buying intent has a half-life measured in days, not quarters.

2. Account-level warmth, person-level gap. Knowing an account is surging on "sales automation" doesn't tell your SDR who to contact. Contact data below the $4,000 tier is metered and accrues per campaign — reviewers on G2 flag exactly this. The moment you operationalize, costs stack.

3. Marketing owns it, sales waits. Propensity is a marketing tool. The pipeline conversation still depends on how well marketing hands accounts to sales — the handoff where most ABM programs quietly die.

What MarketBetter Does Instead​

MarketBetter starts from the opposite end: the people already acting.

• Person-level visitor identification. Not "someone at Acme visited" — which person, matched to title, LinkedIn profile, and contact data. See how that works in our visitor identification guide.
• Blended intent ranking. First-party signals (pricing-page visits, return sessions) blended with third-party intent, ranked by the signal hierarchy that actually predicts closed-won.
• Outreach built in. Signals route to SDRs with AI-drafted, human-reviewed email and LinkedIn sequences. The output isn't a warmed account — it's a meeting on the calendar.
• CRM-native. Syncs into HubSpot, Salesforce, or Pipedrive rather than asking you to run pipeline out of a separate ad console.

The philosophical difference: Propensity spends money to create engagement. MarketBetter captures the engagement that already exists — the 97% of visitors who never fill a form, the accounts surging on your category this week — and turns it into conversations now.

Which Should You Buy?​

Buy Propensity if:

• You have a defined target-account list and no way to advertise to it
• Your sales team is already at capacity and marketing's job is air cover
• Brand awareness inside your ICP is genuinely your bottleneck

Buy MarketBetter if:

• Your bottleneck is meetings, not awareness
• You're already getting traffic and signals but converting almost none of it
• You want sales working in-market accounts this week, not after a 90-day ad flight

The honest sequencing answer: if you can only fund one layer, fund the one that ends in a booked meeting. Ads without an outbound motion warm accounts nobody works. Outbound without ads still books meetings — it just works a slightly colder room. Add the air cover once the capture layer is running.

FAQ​

Is Propensity cheaper than MarketBetter? They're in a similar bracket, but they buy different things. Propensity's $2,000-$4,000/month buys ad activation with channel gates and metered contact data. MarketBetter's flat pricing buys person-level identification plus outreach execution. Compare cost per meeting, not cost per month.

Can you run Propensity and MarketBetter together? Yes, and the combination is coherent: Propensity warms your target list with ads; MarketBetter catches the accounts that heat up and books the meetings. Just sequence the capture layer first.

Is Propensity a 6sense replacement? For SMB ad activation, largely yes — at a tenth of the price. For revenue teams that need intent to drive outbound, neither Propensity nor 6sense closes the loop to a meeting. See our MarketBetter vs 6sense comparison and 6sense pricing breakdown.

What does MarketBetter cost? Flat platform pricing with no channel gates or metered contact data. Book a demo for numbers against your account list.

Ready to work the accounts that are already in-market? Book a MarketBetter demo.

Ask five vendors what an omnichannel ABM campaign costs and you'll get five sales decks. The honest answer for 2026: a full program runs anywhere from $40,000 to $500,000+ per year all-in, and the spread depends on four line items most budgets never separate: platform, media, content, and people.

Here's the breakdown, a worked example for a 100-account campaign, and the line item almost every ABM budget forgets.

The Four Cost Buckets​

1. Platform: $24K-$150K+/year​

The software that builds audiences, activates channels, and measures engagement.

Watch the gates: on tiered platforms, LinkedIn activation, Google/YouTube, and unmetered contact data often sit in higher tiers, so the sticker price and the operational price diverge fast.

2. Media: The Per-Channel Reality​

2026 benchmark CPMs for B2B-targeted inventory:

Media allocation varies wildly by strategy — ad-led programs put 40%+ of total budget into media; outbound-led programs as little as 10%.

3. Content: The Quiet Budget Eater​

Ads need somewhere to land. Personalized landing pages, one-pagers per segment, case studies per vertical, creative refreshes every 6-8 weeks (B2B audiences are small; frequency burns creative fast). Dedicated ABM content budgets run $80K-$500K/year at the enterprise end. Lean teams can compress this, but zero-content ABM is just retargeting with a fancier name.

4. People: The Forgotten Line Item​

Someone has to run this — audience builds, channel ops, sales coordination, reporting. A fractional marketing ops hire or agency retainer adds $3K-$15K/month. Skip it and the platform becomes shelfware; the data on why ABM rollouts fail is mostly a story about missing owners, not missing tools.

Worked Example: 100 Accounts, One Quarter​

A realistic mid-market omnichannel campaign — display + LinkedIn + CTV + direct mail against 100 target accounts:

~$2,200 per account per year. That's the honest unit cost of omnichannel air cover — before a single meeting is booked.

The Line Item Everyone Forgets: Capture​

Here's the uncomfortable math. That $55K/quarter produces impressions, engagement lift, and warmer accounts. It does not produce meetings. Meetings happen when someone notices an account heating up and reaches out — fast, to the right person, while the interest is live.

Most ABM budgets spend everything on warming and nothing on catching. The engaged visitors hit your site anonymously, don't fill the form (97% never do), and leave. The ad budget warmed them; nobody was watching the door.

The capture layer — person-level visitor identification, in-market account detection, routed outreach with an SLA — typically costs a fraction of the media budget and is the only part of the stack whose output is denominated in meetings. If your budget can't fund both, fund capture first: it monetizes the demand you already have, including demand your ads didn't create.

How to Right-Size Your Budget​

1. Work backward from pipeline math. Needed pipeline ÷ average deal size = deals; deals ÷ win rate = opportunities; opportunities ÷ account-to-opp rate = accounts to target. Budget for that list, not a round number.
2. Tier the spend. Full omnichannel (mail, CTV, 1:1 pages) for tier-1 only; display + LinkedIn for tier-2; contextual + capture-only for tier-3.
3. Cap media until capture works. If in-market accounts currently reach your site and nothing happens, every media dollar is leaking.
4. Measure cost per meeting, not cost per MQL. It's the only metric that survives contact with a sales team.

FAQ​

What's the minimum viable omnichannel ABM budget? Around $40K-$60K/year gets a small program: SMB platform, modest display + LinkedIn media, lean content, part-time ops. Below that, run capture + outbound only and skip paid media.

What ROI should ABM deliver? Programs that survive tie spend to sourced/influenced pipeline at 3-5x minimum. Engagement-lift reporting alone is how programs get cut in the next budget cycle.

Do I need CTV and direct mail? No. They're tier-1 seasoning, not foundations. Display + LinkedIn + a working capture layer outperforms a six-channel program with no one working the signals.

Want the capture layer priced against your account list? Book a MarketBetter demo.

Propensity sells itself as the ABM platform for teams priced out of 6sense and Demandbase — flat monthly rates, no enterprise sales cycle, campaigns live in under 15 days. In 2026 that pitch comes with three tiers: Essential at $2,000/month, Strategic at $3,000/month, and Unlimited at $4,000/month.

If you've seen older reviews quoting $1,000-$2,000/month, note that pricing has moved up. Here's the current breakdown, what reviewers actually say, and the questions to ask before signing.

Propensity Pricing: The 2026 Tiers​

Billing is monthly with no disclosed annual lock-in — genuinely rare in ABM, where 6sense and Demandbase quote six-figure annual contracts.

At list price that's $24,000, $36,000, or $48,000 a year. Cheaper than enterprise ABM by a wide margin; far from pocket change for the SMB growth teams Propensity targets.

The Costs That Aren't on the Pricing Page​

Three things to pressure-test on the sales call:

1. Contact data is metered below the top tier. G2 reviewers flag added contact-information costs that accrue with each new campaign launch — and "unlimited contact data" only appears at $4,000/month. If your motion is contact-level ABM (Propensity's whole differentiator), model this before assuming the $2K tier is your real price.
2. Channel gating drives upgrades. LinkedIn — the one paid channel most B2B teams actually want — requires the $3,000 Strategic tier. Google and YouTube require $4,000. The tier you'll actually run is probably not the tier on the ad.
3. Media spend is your problem. As with most ABM platforms, the subscription buys the targeting and activation layer. Budget your actual ad dollars on top, and ask explicitly what (if any) media spend is bundled at your tier.

What Reviewers Like​

Propensity holds a 4.8/5 on G2 across roughly 30 reviews, most from small-business users. The consistent themes:

• Speed and ease of use. Campaign setup is fast, with formats ranging from geofencing to 1:1 ABM plays, and implementation measured in days, not quarters.
• Native ad activation without a DSP. Running display and social campaigns against intent-surging accounts from one tool, with no separate ad-tech stack, is the platform's clearest value at its price point.
• Support that acts like an extension of your team. For a small marketing team without RevOps headcount, reviewers describe the Propensity team as doing real operational lifting.
• HubSpot integration with contact- and account-level performance tracking.

Where It Falls Short​

• Thin intent coverage. Reviewers and analysts note Propensity's intent data runs shallower on mid-market and lower-enterprise accounts than 6sense or Bombora. If your ICP is niche, sample the free intent report (top 100 accounts) before buying — that's what it's for.
• Small proof base. ~30 G2 reviews is a thin sample next to established platforms. The 4.8 average is real but easy to move.
• It's an advertising engine at heart. Propensity's channels — display, CTV, geofencing, contextual, paid social — build awareness at target accounts. What it doesn't do is put a rep in front of a specific in-market buyer. Impressions warm accounts; they don't book meetings.

Who Propensity Actually Fits​

Propensity makes sense if you're a 1-5 person marketing team that wants to run multi-channel ABM air cover — ads across display, CTV, and social aimed at intent-surging accounts — without an enterprise contract or a media agency. It's a legitimate budget answer to 6sense and Demandbase for that use case.

It's the wrong tool if your bottleneck is pipeline, not awareness. If the question you're asking is "which specific companies and people are in-market right now, and how do we get a seller in front of them this week," an ad-impression platform is solving one layer down from your problem.

The Signal-Based Alternative​

That second case is where MarketBetter takes a different architectural bet. Instead of spending the budget on impressions at target accounts, it identifies the people already showing intent — person-level website visitor identification, blended first- and third-party intent scoring — and routes them into human-reviewed email and LinkedIn outreach, synced to HubSpot, Salesforce, or Pipedrive.

The practical difference in one line: Propensity shows ads to accounts that might be in-market. MarketBetter starts conversations with the people who already are. Many teams run air cover and outbound together — but if you can only fund one layer, fund the one that ends in a booked meeting. (Full comparison logic in our MarketBetter vs 6sense breakdown — the same reasoning applies here at a smaller price tag.)

FAQ​

How much does Propensity cost in 2026? $2,000/month (Essential), $3,000/month (Strategic), or $4,000/month (Unlimited), billed monthly. Older reviews citing $1,000-$2,000/month reflect earlier pricing.

Does Propensity require an annual contract? No annual commitment is disclosed — pricing is presented as monthly flat rates, unlike most enterprise ABM platforms.

What's the catch in Propensity's pricing? Contact data is metered below the $4,000 tier and accrues per campaign, LinkedIn ads require the $3,000 tier, and Google/YouTube require the $4,000 tier. Ad spend is separate from the subscription.

Is Propensity a 6sense alternative? For multi-channel ABM advertising on a smaller budget, yes. For deep intent data on mid-market accounts, its coverage is thinner than 6sense or Bombora. See our 6sense alternatives roundup for the full field.

What should I compare Propensity against? Other account-based marketing tools for the air-cover layer — and signal-based outbound platforms like MarketBetter if your goal is booked meetings rather than account awareness.

Want to see what person-level intent looks like on your own website traffic before you commit to an ad budget? Book a MarketBetter demo.

Sometime in the past year, a new kind of visitor started showing up in our Google Search Console — one that never clicks, searches in complete sentences, and occasionally types "yes" into Google.

We dug into 90 days of Search Console data (June 27 – September 25, 2026) across our blog, and found that on some pages, the overwhelming majority of search impressions are no longer generated by humans at all. They're generated by AI agents — coding assistants, research agents, and buying copilots — using Google as a tool call.

This post shares the raw numbers, the three distinct query patterns we found, and what it means for anyone doing B2B marketing in 2026.

The page where 89% of "searchers" are machines​

Our guide on scraping conference exhibitor lists for sales prospecting pulled 26,656 impressions over the 90-day window, ranking at an average position of 7.5.

A page with that many impressions in the top 10 should generate hundreds of clicks. It got 50 — a 0.19% CTR.

The query report explains why. Roughly 23,700 of those impressions (89%) came from queries that no human being would ever type:

And then a long tail of near-identical variants, each referencing a specific exhibitor ID: "extract company details from tbse26.mapyourshow.com exhibitor 906394" (836 impressions), "...exhibitor 954105" (634), "...exhibitor 971223" (606), and so on.

Think about what that pattern means. Someone pointed an AI agent at a trade show's exhibitor directory and told it to build a lead list. The agent worked through the directory page by page, and for each exhibitor it hit a wall — so it did what agents do now: it searched Google for help, once per exhibitor, thousands of times. Each search was machine-composed ("extract X from URL Y"), machine-executed, and machine-read.

Every single one of those queries produced zero clicks. The agent read the search results page, took what it needed (or didn't), and moved on. No visit. No pageview. No analytics event. From our web analytics' point of view, none of this ever happened.

The three types of agentic queries we found​

Once you know what to look for, agent queries are easy to spot. Across our Search Console data, they fall into three buckets.

1. Tool-command queries: Google as a function call​

These read like API calls written in English — verb + object + source URL:

• "scrape exhibitor details from [domain]"
• "structured data extraction from [domain]"
• "find company website and phone number from [domain]"
• "get company info from [domain] exhibitor 982044"

The tell: humans search for categories ("conference attendee scraper" — a real human query on the same page, 352 impressions). Agents search for operations on specific resources, often with IDs a human would never memorize, let alone type.

2. Full-sentence buying prompts: the agent as researcher​

Our Qualified review and comparison pages surface a different species — complete, structured buying questions:

• "ai sdr tool that can score replies and push only qualified meetings to my sales team what should i buy"
• "best ai sales agent tools for qualifying inbound leads and booking meetings pricing included"
• "1mind vs qualified vs sable: which is best for smb sales?"
• "alternatives to qualified for enterprise ai sales assistants?"
• "ai agent that can handle inbound leads 24 7 and route them to sales what are the best options"

These are not keywords. They're prompts — requirements documents compressed into one sentence, with constraints ("pricing included", "for smb", "faster to deploy") that a human searcher would split across five separate refinements. Someone asked their AI assistant to shortlist vendors, and the assistant ran the research through Google.

We first wrote about this buyer-side shift in our B2B GEO playbook for AI buying agents. What's new is seeing it at volume in first-party data: vendor evaluation queries that arrive fully formed, get answered from the results page, and convert (or disqualify you) without a click.

3. Conversation leakage: chat turns fired as searches​

The strangest bucket. Scattered through our query report are fragments that only make sense as turns in a conversation with an AI assistant, accidentally routed to Google's search box:

• "yes"
• "more"
• "another"
• "which one should i try first"
• "are there any other alternatives?"
• "can you give me numbers"
• "its not allowing me to paste"
• "devami?" (Turkish for "continue?")

Each of these registered impressions against our pages. "Its not allowing me to paste" is someone talking to their agent mid-task. "Which one should i try first" is a follow-up question after the agent listed tools. These utterances are leaking into Google's query stream — presumably from agent frameworks and browser assistants that pass user input straight to search — and Google is dutifully matching them against indexed pages.

Amusingly, this bucket is the only one that clicks: "more" earned 3 clicks, "another" and "are there any other alternatives?" one each. When there's a human in the loop, even one keystroke deep, clicks come back.

Why this breaks the metrics you report on​

Three uncomfortable implications fall out of this data.

1. Impressions are inflated, and CTR is broken as a quality signal. If 89% of a page's impressions come from agents that structurally never click, that page's "bad CTR" isn't a title problem — it's a measurement artifact. We see the same decoupling on our pricing breakdowns: pages ranking at position 3–5 with six-figure impression counts and near-zero clicks, because the answer gets absorbed upstream — by AI Overviews, by agents reading snippets, by assistants summarizing the SERP. Before you rewrite a title for CTR, read the actual query report and ask who is searching.

2. Rankings now have an invisible audience. Your position 6 result isn't just competing for human eyeballs. It's being read, parsed, and quoted by agents assembling answers for their users. The click never happens, but the influence does — the agent recommends (or eliminates) you based on what it read. This is the core argument for generative engine optimization: the SERP is becoming an API, and your content is the response payload.

3. Your analytics undercount your actual reach. None of these 23,700 agent impressions show up in web analytics, and neither do the recommendations that flow from them. If a buying agent shortlists you from a search snippet and its user books a demo two days later, your attribution model calls that "direct traffic." The dark funnel just got a lot darker — which is exactly why we lean on website visitor identification to recover the human end of these journeys when they finally do land on the site.

What to actually do about it​

This isn't a doom post. Agents reading your content is a distribution channel — one most of your competitors are ignoring. Here's what the data pushed us to change.

Write answers, not teasers. Agents quote what they can extract. Put the direct answer — the price, the step list, the comparison verdict — in the first screen of content, in plain declarative sentences. We saw this work firsthand: our post on Codex's mid-turn steering feature captured thousands of impressions within days of the feature shipping because it answered the exact question people (and their agents) were asking, in the exact words they used.

Structure for machines. Tables, consistent headings, explicit entity names. An agent parsing "best AI SDR tools with pricing" rewards the page that states "Tool X costs $Y/month for Z seats" over the one that says "pricing varies by needs." Our pricing transparency study exists partly because concrete numbers are what both humans and agents are starving for.

Cover the workflow, not just the keyword. The agent queries on our conference post are task queries — the searcher (human or machine) is mid-workflow. Content that walks through the actual task end to end, like our SDR playbook template guide or our guide to automating LinkedIn Sales Navigator with Claude, gets matched against these long operational prompts far more often than thin listicles do.

Expect the buyer's first touch to be an agent. The full-sentence buying prompts above are early evidence of a shift we think defines the next few years of B2B: frontier models are getting good enough to run real GTM work, and buyers are handing them the vendor research. Your "first meeting" increasingly happens between your content and someone else's AI. Make sure your content performs well in that meeting.

Methodology​

All data is first-party Google Search Console data for marketbetter.ai, June 27 – September 25, 2026, web search type, query dimension. We classified queries as agent-generated based on structural patterns: imperative verb + source URL construction, embedded database-style resource IDs, full-sentence multi-constraint prompts, and conversational fragments with no standalone search intent. Ambiguous queries were counted as human. The 89% figure for the conference-scraper page is therefore a floor, not a ceiling.

We'll rerun this analysis as the data grows. If the trend holds, "share of agent impressions" may become a metric worth tracking on every B2B content dashboard.

MarketBetter identifies the companies visiting your site — including the humans who show up after their AI did the research — and tells your SDRs exactly what to do next. Book a demo →

Anthropic released Claude Opus 5.5 on September 22, 2026. The headline for developers is benchmark scores. The headline for sales teams is different: the cost of running AI agents on your pipeline just dropped roughly 40%, and the specific pricing change most people are skimming past — 60% cheaper cache reads — is the one that matters most for always-on sales automation.

We've been running Claude-based SDR workflows in production since early 2025 (here's the complete guide to Claude for SDRs if you're starting from zero). This post covers what actually changed in Opus 5.5, what it costs, and where it moves the needle for GTM work — with real math, not vibes.

Let's start with the answer nobody selling you a list will give: there is no legitimate "download the entire B2B database free" button. Anyone offering 100,000 contacts in a CSV for $99 (or free) is selling scraped, stale data seeded with spam traps that will torch your sender reputation.

But that doesn't mean you need a $15,000 ZoomInfo contract to prospect. In 2026, a surprising amount of real, current B2B data is available at exactly $0 — if you know where it lives and what the free tiers actually cap you at.

We maintain pricing breakdowns for 70+ sales tools (here's what that dataset revealed), so we checked every free tier below against current 2026 limits. Here's what actually exists.

Quick answer: Jev is TypeSafe AI's new "System One" decision model (launched September 15, 2026). It doesn't chat — it answers structured questions (choice, score, yes/no) over JSON input at $0.042 per million input tokens with free output, in 70–500ms. For GTM teams, that means the classification work you currently route through a frontier LLM — lead scoring, intent triage, reply routing, social listening — gets 200–400x cheaper and comes back before a human could read the input. Early testers report it cutting total AI spend 50–60%.

Over the past year, we published detailed pricing breakdowns for more than 70 B2B sales tools — dialers, data providers, sequencers, CRMs, intent platforms, AI SDRs, conversation intelligence. For each one, we pulled vendor pricing pages, Vendr contract data, G2 and Capterra reviews, Reddit threads, and real customer quotes.

Individually, each breakdown answers "what does this tool cost?" Put all 70+ side by side, and a much more interesting picture emerges: a systematic pattern in how sales software vendors price, hide, and inflate.

Here's what the full dataset shows.

About 98% of your B2B website traffic leaves without filling out a form. Someone read your pricing page for four minutes yesterday, compared you against two competitors, and vanished. Your analytics logged them as "1 session, Austin, TX."

There are exactly seven ways to figure out who that was. Some resolve the company. A couple resolve the actual person. Most vendors selling these methods inflate their numbers badly — we've independently tested 12 identification platforms and found person-level match rates of 8-41% against advertised claims of 80%+.

This post ranks all seven methods by what they actually deliver in 2026: realistic match rate, cost, and legal exposure. If you want the full technology deep dive first, start with our complete guide to B2B website visitor identification. This one is about picking a method and shipping it.