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Vector logoVector

Research comparison

Summary

B2B marketing platform that identifies website visitors at the contact level and syncs them into dynamic audiences for precision ad targeting. Replaces traditional account-level scoring with evidence-backed buyer signals.

Funding:

$13.1M

Founded:

2022

Team:

181

vector.co

Critical Notes

- Strongest contact-level de-anonymization for US traffic (15-30% identification rate) - First YC-backed B2B platform with Model Context Protocol server for AI querying - No native revenue attribution layer - Expensive entry price (~$36K/year for core targeting product) - Less effective for EU-heavy traffic due to privacy regulations - Lacks account-level orchestration for massive enterprise buying-group ABM - Explicitly pivoted away from SDR and outbound use cases in late 2025

Key Features

- Contact-level website visitor de-anonymization - Signal-driven account scoring and buying stage prediction - Dynamic ad audience building by named buyers - Multi-channel audience sync to LinkedIn, Google, Meta, and Reddit - Vector MCP for natural language querying via Claude and ChatGPT - Pipeline influence and ROI storytelling dashboards

Details

Founded 2022 in Boston by Joshua Perk and Nick Masters (former Drift leaders). Participated in Y Combinator Winter 2023 batch. Raised $13.1M total funding, including $10M Series A (May 2026) led by SignalFire and HubSpot Ventures. Team of 181 employees. Targets mid-market B2B SaaS companies with $250K+ paid social budgets. Core Target product starts at ~$36K/year (high-ticket annual contracts). Lower-tier Reveal product at $399/month for website de-anonymization. Sales-led motion targeting demand gen leaders, marketing execs, and RevOps professionals. Main CTA is book a demo—no prominent free trial or freemium self-service. Proprietary closed-source platform, explicitly criticizes open-source tools for security/compliance risks. Positions as approved API partner with major ad networks. Pivoted away from SDR/outbound use cases in late 2025 to focus purely on advertising.

Signals

1st Party: Integrates directly with CRMs (HubSpot, Salesforce, Pardot) to ingest pipeline data. Captures proprietary website visitor metrics through Reveal pixel, de-anonymizing traffic to identify specific individuals. Connects with major ad networks (LinkedIn, Google, Meta, Reddit, TikTok) to track impressions, clicks, campaign performance. Contact-level focus connects signals to individual buying committee members. 2nd Party: Captures LinkedIn reactions and ad interactions, linking social signals to known contacts. Vector MCP gives conversational AI access to query LinkedIn ad data and visitor activity. No integrations for support forums, code repositories, or open-source community data. 3rd Party: Aggregates 30+ signals: funding rounds, job changes, promotions, press coverage. Uses AI scraping and integrates with Clay for enrichment. Transparent scoring provides cited evidence explaining account prioritization. Custom: REST API and webhooks. Real-time visitor webhooks instantly push events. CSV/TSV paste in ICP/Segment Builder. Integrations with ChatGPT, Claude, Clay for custom workflows.

Compared to Common Room

1st Party

  • Native ad network connections (LinkedIn, Google, Meta, Reddit, TikTok) for tracking impressions, clicks, and campaign performance. Website de-anonymization pixel (Reveal) identifies specific individuals. CRM sync (HubSpot, Salesforce, Pardot) ingests pipeline data and contacts.
  • Missing product usage data ingestion (no Snowflake, BigQuery, Redshift connectors). No chat/support tools (no Intercom, Qualified). No sales engagement platforms (no Outreach, Salesloft, Apollo). No marketing automation sync (no Marketo). Significantly narrower 1st-party footprint outside advertising.

2nd Party

  • LinkedIn ad interactions and reactions tied to specific contacts. Vector MCP allows conversational AI querying of LinkedIn engagement data via ChatGPT and Claude.
  • No open-source signals (no GitHub, Scarf). No broad social listening (no Twitter/X, YouTube, Medium). Zero community forum integrations (no Discord, Discourse, Slack, Stack Overflow, Reddit). No review site data (no G2). No event platforms (no Bevy, Meetup, Gradual, Skilljar). Covers essentially one 2nd-party source versus Common Room's 15+ sources.

3rd Party

  • Claims 30+ third-party signals including funding rounds, job changes, promotions, press/news. AI scraping for web-wide monitoring. Clay integration for enrichment workflows. Transparent, evidence-cited scoring from 3rd-party signals.
  • No native tech stack or technographic data (no TechTarget equivalent). No structured intent data partnerships (no 6sense, Demandbase direct integrations—relies on Clay or webhook bridging). Lacks depth of named, vetted intent providers.

Custom

  • REST API, webhooks, and real-time visitor webhooks (push-based, not batch). CSV/TSV paste directly in ICP/Segment Builder. Native ChatGPT, Claude, and Clay connections for custom AI-powered workflows. Real-time webhook push is faster than typical batch sync.
  • No Google Sheets integration. No Zapier connector. Custom data ingestion present but ecosystem of pre-built connectors is narrower.

Person360

Contact-level resolution focuses on identifying actual people (buying committee) rather than just company logos. Maps personal identifiers (hashed personal emails, mobile ad IDs) to work email addresses in CRM. Blends 1st-party engagement (website visits, ad clicks, webinar registrations) with 3rd-party signals (research intent, LinkedIn reactions, job changes, funding). Transparent approach against black-box platforms. Enrichment: Match rate optimization bridges B2B work emails and personal social identifiers, boosting Meta audience match to 45% (vs. typical 15-25%). Acts as identity and activation layer, not traditional waterfall enrichment provider. Surfaces who needs enrichment based on real-time behavior. Automates matching to sync enriched contact-level audiences to ad accounts and CRMs. AI: Uses AI to classify accounts into buying stages (Identified, Aware, Interested, Considering, Selecting). Shows work with cited daily evidence for reasoning. AI scraping monitors account progression and triggers.

Compared to Common Room

Enrichment

  • Strong ad-platform match rate optimization (45% Meta match vs. industry 15-25%). Behavior-driven enrichment surfaces who to enrich based on real-time activity. Automated audience syncing to ad accounts.
  • No standalone contact database. No verified email or phone number enrichment. No multi-provider waterfall enrichment. No AI quality control to block inaccurate enrichments. Not an enrichment provider—it's an activation layer depending on CRM or external tools (Clay) for actual data enrichment. Fundamental gap for sales-facing use cases.

AI Powered

  • AI-driven buying-stage classification across five stages with cited, daily evidence—more interpretable than black-box score. AI scraping for web-wide monitoring.
  • AI not used for identity resolution itself (no image matching, no handle-based cross-source matching). No auto-enrichment triggered by AI upon contact discovery. AI serves scoring/classification, not identity merging—narrower application to identity problem.

Identity

  • Contact-level (not just account-level) resolution. Cross-channel identity mapping from personal identifiers (hashed personal emails, mobile ad IDs) to work email. Transparent "show the evidence" approach to identity claims.
  • No unified identity graph merging across 15+ signal sources. No proposed-merge workflow with confidence thresholds for human review. No automatic profile updates from job change signals. Resolution narrowly optimized for ad-platform matching, not for building comprehensive customer view across community, product, support, and sales signals.

Automation

Workbench: None. No sales workbench, no segment burn-down lists, no last-mile actions to sales engagement tools. Explicitly abandoned SDR/outbound use cases late 2025. Workflow: Automated workflows for audience activation with triggers (de-anonymized visits, ad clicks, intent surges), conditions (ICP match, target lists, CRM opportunities), and actions (sync to ad platforms, CRM/webhook pushes). Advertising-centric, not general-purpose. Relies on external tools for messaging execution. Alerts: Real-time contact-based alerts to Slack or CRM. Customizable triggers for specific individual behaviors (competitor engagement, ad clicks, pricing page visits). Contact-level specificity is differentiator. Combine: Signal-agnostic platform combining proprietary 1st-party data, 3rd-party intent, and CRM data at contact level. Score: AI classifies accounts into five buying stages with transparent, cited evidence. Anti-sells black-box numerical scoring. AI Research: AI scraping for account research. Vector MCP integrates with ChatGPT and Claude for natural language querying of visitor data, ad engagement, account context.

Compared to Common Room

Combine

  • Combines 1st-party (de-anonymized visitors, ad clicks), 3rd-party intent, and CRM data at contact level for unified signal activation.
  • Combination limited to signal types Vector actually ingests—fraction of Common Room's sources. Cannot combine community signals, product usage, support interactions, open-source activity, review site engagement, or event attendance because those integrations don't exist. Combine capability only as powerful as signal breadth, which is narrow.

Score

  • Five-stage AI buying classification (Identified → Selecting) with transparent, cited evidence. Interpretable by non-technical marketers. Anti-black-box positioning is compelling. Daily stage updates with rationale.
  • Not customizable. No user-defined scoring models. No ability to weight specific signals or attributes. No numeric impact-points system. No custom field-based scoring logic. Cannot score at person level with custom rules—only at account level with AI's fixed five-stage model. Teams wanting bespoke scoring for specific GTM motion are locked into Vector's framework.

AI Message

  • none
  • No AI-generated messaging capability. No personalized outreach snippets. Consistent with Vector's explicit exit from SDR/outbound use cases—sales teams get zero message drafting support from platform.

AI Research

  • AI scraping for deep account research with cited evidence (funding, investments, champion job changes). Vector MCP is genuinely innovative—first YC-backed B2B platform with Model Context Protocol server, allowing natural language querying of visitor and campaign data via ChatGPT and Claude. Forward-looking capability.
  • Research focused on advertising and visitor data—cannot query community engagement, product usage, or support interactions because those signals not collected. Scope of queryable data limited by platform's narrow integration surface.

Support

No data available.

Compared to Common Room

Systems

  • Email support with 24-hour SLA, live chat on paid plans, searchable Help Center, dedicated security/compliance email channel. Structured and well-documented support access.
  • none

Content

  • Comprehensive Help Center with step-by-step guides. Video walkthroughs available. Strategic Playbooks—use-case-driven guides teaching campaign execution, not just button-clicking—are standout. Blog, downloadable guides, newsletter, podcast, and detailed case studies (Airbyte, Fingerprint, Goldcast). Rich content library for Series A company.
  • none

Community

  • none
  • No community forum, no user-generated content, no public user profiles, no peer-to-peer support. Zero community presence.

Status: Watch