What Vector users actually want

23 sources describe a critical blindspot: the majority of buyer decision-making happens off the marketer's website, with prospects researching on ChatGPT, G2, and competitor sites before leaving any owned-channel trace. Airbyte's case study demonstrates the opportunity—they want to be the first tool buyers think of when evaluating data integration, but current Vector capabilities only surface intent after someone already lands on their site. The user explicitly states high-value customers are researching pricing pages and demo videos somewhere, then disappearing.

17 sources emphasize contact-level identification as the core value driver, yet the current product appears to treat all identified visitors equally. Users describe needing to know when interest is real so ads, outreach, and follow-ups happen at moments of peak engagement. One customer stated they finally have the right people in campaigns versus hoping with LinkedIn—but timing matters as much as identity. A VP researching your product 30 seconds ago should trigger different actions than someone who visited once three weeks ago.

9 sources document the attribution need, with customers using separate tools like HockeyStack to trace inbound pipeline back to Vector audiences. The product generates measurable wins—6x average CTR, $2-4 CPC on LinkedIn, 3% click rates—but customers have to manually connect those performance metrics to business outcomes. One customer mentions ROI achieved in first three months, yet there's no indication Vector makes that calculation visible or automatic.

10 sources document geographic and regulatory constraints that explicitly reduce addressable market. The product is currently US-only, with marketing materials stating this limitation upfront during qualification. Every international prospect who could benefit from contact-level advertising gets screened out before they can evaluate the product. The company also prohibits uploading HIPAA, COPPA, and PCI-regulated data unless explicitly agreed in writing, excluding healthcare, child-directed content, and payment industry use cases.

14 sources describe precision audience building from multi-signal intent, with users layering live behavior, research signals, competitor interest, and firmographic filters. The case study shows Airbyte building audiences from job titles, seniority, and company size while maintaining spending scale. But there's no indication the product prevents users from targeting the same contact across 5 different campaigns, leading to ad fatigue and wasted spend.

The competitive intelligence opportunity appears throughout the evidence but isn't packaged into an actionable workflow. 23 sources describe intent capture needs, with users wanting to reach buyers during early research including competitor site visits. 14 sources emphasize precision audience building from competitor interest signals. Yet there's no indication Vector simplifies the tactical execution—creating the audience, writing comparison ad copy, setting up multi-channel campaigns, and coordinating sales follow-up.