# Vector - Research Comparison How does Vector compare to Common Room? - Website: https://vector.co - Updated: 2026-10-23 - Status: watch - Web version: https://www.commonroom.io/compare/vector/ai/ ## Summary AI-native ABM platform using contact-level signals to score accounts, target ads to buying committees, and prove marketing impact on pipeline ## Company info - **Funding:** 10.5 - **Founded:** 2022 - **Team:** 20 ## Critical Notes - Y Combinator-backed; contact-level precision vs. account-level guessing - No native revenue attribution layer; needs bolt-on solution - Best for US-focused mid-market SaaS with strong inbound traffic and high paid social budgets - Dropped outbound SDR use cases in late 2025; marketing audiences only ## Key Features - Contact-level website visitor identification and de-anonymization - 30+ native buying signals including job changes and ad engagement - Transparent account scoring and buying stage prediction - Dynamic audience syncing to LinkedIn, Google, Meta, and Reddit - Pipeline attribution and reporting to tie anonymous signals to revenue - CRM integrations with HubSpot and Salesforce - Real-time sales notifications for account alignment ## Details Also known as GetVector, Inc. Founded 2022 in Boston by Joshua Perk and Nick Masters, former Drift sales engineering leads. Y Combinator W2023. Raised 10.5M Series A (May 2026) led by SignalFire and HubSpot Ventures. Industry: B2B MarTech (ABM, ad audience sync). Proprietary closed-source SaaS. Sales-led: demo-gated, direct sales team, enterprise pricing with 36,000 annual floor on Target product; ICP is mid-market B2B SaaS with 250,000+ annual paid social budget. Not product-led: no free trial or self-serve signup. Hiring Sales Engineer in Boston. Revenue, web traffic, social: n/a. ## Signals compared to Common Room ### 1st Party - Included: Multi-network ad engagement at individual level (Google, Meta, Reddit, TikTok, X, Snap); HubSpot, Salesforce, Pardot; Fathom call recordings; person-level web de-anonymization - Missing: No product usage or data warehouse ingestion; no sales engagement, CS, or chat/support tools; no Marketo ### 2nd Party - Included: LinkedIn organic post reactions and comments; social brand-mention listening - Missing: No open source, community forums, review sites, events, YouTube/Medium/DEV, or commerce signals ### 3rd Party - Included: Job changes and champion tracking; AI web research on funding, M&A, earnings, press; off-site category and competitor research intent; custom AI signals - Missing: No named intent partners; no technographics; no job listings signal ### Custom - Included: Push/pull webhooks; developer API; Zapier; CSV; Clay (hashed email discovery); HockeyStack webhooks; MCP server for Claude/ChatGPT - Missing: No native Google Sheets; no warehouse ingestion; no CLI 1st Party: HubSpot, Salesforce, Pardot CRM deal stage changes, logged calls, booked meetings; marketing activity (email engagement, form submissions); ad clicks across Google, Meta, Reddit, TikTok, X, Snap even without form fill; Fathom call recordings. 2nd Party: LinkedIn organic post reactions and comments; social brand mentions; used as free evidence for scoring. 3rd Party: job changes, new hires, departures, promotions, champion tracking; AI web research on fundraises, acquisitions, earnings, press; off-site category and competitor research intent. Custom: developer API, push/pull webhooks, Zapier, CSV upload, MCP for Claude/ChatGPT, Clay for enrichment and hashed email discovery. ## Identity compared to Common Room ### Enrichment - Included: Real-time enrichment of live signals (name, work email, LinkedIn URL, title, company, seniority); advertising enrichment (~90% LinkedIn, 45% Meta, 30% X match); 30+ buying signals; Clay and HockeyStack integrations - Missing: No contact directory or net-new contact discovery; no phone enrichment; no CRM list enrichment or refresh; no multi-provider waterfall ### AI Powered - Included: AI buying-stage classification (Identified, Aware, Interested, Considering, Selecting) with cited evidence; promptable custom AI signals - Missing: No AI in identity resolution; no image or social profile matching; no AI agents acting on data ### Identity - Included: Deterministic-first waterfall (HubSpot UTK, form fills, logins, email+UTM), then probabilistic device graph and IP; B2B-to-consumer mapping (hashed personal emails, mobile numbers, MAIDs); native resolution without Clearbit/RB2B; ~30% bot/noise filtering; ICP filtering; 25-35% match rate - Missing: Scoped to web sessions and ad clicks; no cross-source person merge; no merge review queue; weak outside US Lightweight JavaScript pixel with deterministic and probabilistic resolution. Waterfall: HubSpot UTK, form fills, authenticated logins, email+UTM, then device graphs and IP. Maps work emails to hashed personal emails, mobile numbers, MAIDs for cross-device tracking. Native resolution without Clearbit or RB2B. Filters ~30% bot/low-quality traffic; ICP filtering; 25-35% match rate. Enrichment: real-time on live signals; name, work email, LinkedIn URL, title, company, seniority; advertising enrichment lifts ad match rates (90% LinkedIn, 45% Meta, 30% X); 30+ signals; Clay and HockeyStack integrations. AI: stage prediction (Identified, Aware, Interested, Considering, Selecting) with plain-language cited evidence; custom AI signals; no AI in identity matching. ## Automation compared to Common Room ### Combine - Included: Combine web de-anonymization, off-site intent, job changes, funding, and ad engagement (LinkedIn, Meta, Google, Reddit) as filters and triggers - Missing: No product usage, community, open source, CS, or technographic data to combine ### Score - Included: AI account scoring into buying stages with cited evidence for every change (e.g., VP Marketing visited pricing 3x in 4 days); no black-box scores - Missing: Fixed stages at account level; no custom scoring models ### AI Message - Included: none - Missing: No AI outreach; outbound SDR use cases dropped late 2025 ### AI Research - Included: Vector MCP (May 2026) for natural-language queries from Claude/ChatGPT; AI web research on company events; custom prompted signals - Missing: No in-product research agent; no CLI Workbench: none. Workflow: 30+ contact-level signal triggers with conditions; actions sync dynamic ad audiences (LinkedIn, Meta, Google), update CRM records, fire webhooks. Alerts: customizable Slack alerts with qualifying conditions, custom context, routing to channels, DMs, or CRM account owner. Combine: first-party de-anonymization + third-party intent + ad engagement as filters and triggers. Score: AI buying-stage classification with cited evidence. AI Message: none; outbound SDR dropped late 2025. AI Research: Vector MCP (May 2026) connects data to Claude/ChatGPT for natural-language queries. ## Support compared to Common Room ### Systems - Included: Email support (support@vector.co), M-F 9-5 EST, 24-business-hour response goal; security@vector.co channel; 99.9% uptime guarantee; status page; proactive maintenance emails - Missing: No chat, ticketing portal, or 24/7 coverage ### Content - Included: Help docs and FAQs (GTM pixel, SSO/SAML, roles, billing, privacy); API, webhook, and MCP docs; playbooks (The Reality of ABM Report, new demand gen manager success kit, signal-to-meeting playbook); podcast; blog; glossary; newsletter - Missing: No video training, academy, or certification ### Community - Included: none - Missing: No forum, templates, user profiles, or peer support ...