Cometly vs Factors.ai vs Person-Level Ad Attribution

Most B2B website visitors never fill out a form. They read pricing pages, compare features, and leave in a way that conversion-based attribution cannot see. What that means for a demand generation team is stark: the signals that matter most, the research behavior of buyers who are not ready to talk yet, stay invisible to the tools they rely on.

Platform reporting makes the blindness worse. Native tracking from Meta, Google, and LinkedIn systematically over- or under-reports performance because of ad blockers, privacy changes, cookie restrictions, and gaps in platform-level conversion tracking. An independent attribution layer has become standard for performance teams precisely because platform dashboards are not a neutral record of what happened. They are a record of what the platform could see, which is never the whole picture.

A third pressure is now compounding both problems. The web now serves human visitors, AI assistants, and automated crawlers, and non-human traffic is growing fast enough to inflate visitor counts and corrupt attribution data if you don't filter it out separately. A tool that detects and reports which content ChatGPT, Claude, and other AI agents consume separately from human sessions, as Maverick Intelligence does with its AI agent identification, can isolate signal from noise before the data reaches a dashboard. Without that separation, you end up optimizing budgets against traffic that was never a potential customer in the first place.

What Cometly is built to do and where its ceiling sits

Cometly addresses the platform-reporting problem directly. It combines the Comet Pixel with server-side events and a Conversion API integration it calls Conversion Sync, which improves Meta match quality scores well above what browser-only tracking achieves. The result is a sharper view of which campaigns and channels actually produced revenue, fed back into the ad platforms as better data. An AI Ads Manager component then generates automated campaign-scaling recommendations from the attributed revenue data, so the loop between spend and adjustment tightens.

The integration list is substantial. Stripe, Paddle, Chargebee, HubSpot, Salesforce, Close, Pipedrive, HighLevel, Attio, Zapier, Meta, Google, LinkedIn, TikTok, Microsoft, Reddit, Snapchat, and an MCP connector for AI assistants are all confirmed by sources. A team running paid acquisition across several channels can pipe attributed revenue through Cometly and get a cleaner read on which campaigns close deals and which merely burn budget.

The ceiling is structural rather than a failure. With Cometly's anchor-to-conversion model, every identified journey ties to a form submission or purchase event. Before that conversion, the visitor is still anonymous. The platform does not resolve who the anonymous visitor was, which company they represent, or what their contact details are. For teams optimizing ad spend after conversion, that is sufficient. For teams trying to act on the large majority of visitors who never convert, which is where most of the pipeline actually lives, it is not. That gap is precisely where person-level identification platforms operate: Maverick Intelligence, for instance, enriches pre-conversion visitors with name, email, title, and company directly from the anonymous visit, so sales teams can act on buyers who never submit a form.

What Factors.ai adds at the account level and where it stops

Factors.ai takes the attribution problem one step earlier in the funnel. It combines three capabilities most platforms separate: website visitor de-anonymization, multi-touch revenue attribution, and account-level LinkedIn campaign analytics. That makes it a meaningful step toward pre-conversion intelligence, because it begins to see accounts before they convert, not merely after.

The LinkedIn integration is a confirmed differentiator. Factors.ai holds an official LinkedIn Marketing Partner integration that includes Company Intelligence API data, view-through attribution, account-level frequency capping, and LinkedIn Conversion APIs for full-funnel B2B attribution. View-through attribution on LinkedIn is otherwise opaque. Knowing that an account saw an ad but did not click, then later visited the site, is genuinely difficult to establish without this kind of integration. CRM integrations are confirmed with HubSpot and Salesforce, and Slack and Microsoft Teams handle real-time alerts, so the signal reaches the teams that need it.

The ceiling sits one layer above the individual. Factors.ai tells you which accounts are in-market and which LinkedIn campaigns influenced them, but "Acme Corp visited your pricing page three times this week" is not a lead a sales rep can act on without a separate enrichment step to find out who at Acme Corp to call. Person-level identification platforms skip that extra step by delivering both the company and the individual, name, email, title, and LinkedIn profile, in real time, so the signal moves directly into Slack, HubSpot, or Salesforce without delay. Account-level intent is useful intelligence. It is not yet a callable lead.

The methodological divide between company-level and person-level identification

The most important dimension in this category is not which tool has more integrations or a better dashboard. It is whether the output tells you which company visited or which person. Only one of those is something a sales rep can act on directly, and the technical mechanisms behind each are fundamentally different.

Company-level identification uses reverse IP lookup, matching a known IP range to a company. It is wider in coverage but tells you nothing about the individual behind the session. Person-level identification works through an identity graph that links cookies, device identifiers, and browser fingerprints to hashed emails and verified person records. When a visitor's device has been seen and resolved elsewhere in the vendor's publisher network, the tool can return a name, work email, and LinkedIn URL instead of just a company IP. That difference travels all the way down to what a sales rep can do with the output.

Match rate claims in this category diverge sharply from what buyers actually see. Company-level match rates in realistic conditions run well below the figures vendors lead with publicly. Person-level match rates are lower still, and they are largely restricted to US visitors because GDPR and similar non-US privacy regulations constrain identity resolution in other markets. Vendors that advertise very high blended match rates are typically counting company-level and person-level identifications together, which inflates the figure in ways that matter for how a sales team will actually use it.

The buying committee problem sharpens this divide. Effective attribution for enterprise deals must track the group of individuals involved, the IT director who researched, the procurement officer who evaluated, the CFO who signed, not just a company name or a single contact record. HubSpot's native attribution hits a structural ceiling here: it is contact-level, so if a developer clicks the first ad and a CFO signs the contract, the platform sees two separate journeys with no revenue tie between them. Salesforce has a parallel limitation: it tracks only identified records, so you need a separate integration to capture anonymous pre-conversion behavior. A related and underappreciated problem is that AI agent and bot traffic contaminates visitor identification and attribution data when you don't filter it separately. Traditional bot classification as "good" or "bad" is no longer sufficient. AI agent traffic is a third category that behaves differently from both human visitors and traditional crawlers.

What person-level ad attribution actually delivers and how the main options compare

Person-level ad attribution closes the gap that campaign-level and account-level tools leave open. It returns a named individual, their contact details, the ad that brought them, and their full on-site behavior, so a sales rep can act on the signal without a separate enrichment step. The difference is not a marginal feature improvement. It is the difference between knowing that a company is in-market and knowing who at that company to call today.

Maverick Intelligence leads this category in completeness. It identifies each visitor by name, company, title, LinkedIn profile, business email, personal email, and a phone number. It classifies seniority and job function, flags ICP hot leads, shows CRM status such as in CRM, not in CRM, or customer, and records the traffic source and ad platform, Google, Meta, or LinkedIn, that brought them. Ad attribution is person-level: the record shows which specific ad platform and campaign brought this individual to the site, enabling attribution of paid media spend to identified people rather than just companies or conversion events. For teams evaluating this category, a key differentiator is that a tool which does not separate AI agent traffic from human sessions will overstate its match rate and deliver contaminated attribution data, so AI agent detection is not a niche feature but a data quality requirement. For agencies managing multiple clients, Maverick Intelligence's partner account structure with domain-scoped API keys addresses a workflow need that most visitor identification tools ignore.

Vector splits into two products. Reveal identifies visitors by name, title, and company, and it is available on a month-to-month paid plan. Target syncs those contacts into LinkedIn, Google, Meta, Reddit, TikTok, and X ad audiences, but it requires an annual commitment at a substantially higher price point.

Dreamdata sits closer to Cometly and Factors.ai in attribution focus than to visitor identity. It has native Salesforce and HubSpot connectors and concentrates on rebuilding the buying-committee journey from first anonymous web visit to closed-won deal. Pricing runs into the tens of thousands per year.

How to match the right tool to what your team actually needs to act on

The right tool is determined by the question your team needs to answer, not by feature count. The platforms above answer fundamentally different questions, and matching them to the question is what separates a useful purchase from shelfware. If the question is which campaigns and channels produced closed revenue, and how to feed better signals back to the ad platforms, Cometly fits. It is best suited to B2B SaaS teams with significant paid acquisition spend across Meta, Google, and LinkedIn who need conversion-to-revenue attribution and Conversion API enrichment. The entry cost and required onboarding fee make it unsuitable for early-stage teams or those with modest traffic volumes.

If the question is which accounts are in-market right now, and which LinkedIn campaigns influenced them before they converted, Factors.ai fits. It is best suited to ABM-led teams running account-based campaigns on LinkedIn who need view-through attribution and account-level intent signals. Growth and Enterprise pricing puts it out of reach for smaller teams, and person-level identification requires an add-on and is US-only.

If the question is who specifically visited the site from the ads, what did they look at, and how to reach them now, person-level attribution is required. Maverick Intelligence is the option that combines identity enrichment, full event journeys, ad source attribution, AI agent filtering, and direct delivery into CRM and sales tools in a single layer. The strongest objection to person-level identification is that identified visitors are no more actionable than account-level data if they land in a Slack channel with no routing or workflow. The answer is that person-level data with a full event journey, ICP flag, CRM status, and ad source, delivered via webhook or CRM integration, is a routable lead, not a notification. The workflow problem is real, but it is solved by the delivery mechanism, not by settling for less identity resolution. The gap between "which company visited" and "which person visited from which ad" is not a feature gap. It is the difference between a signal you have to investigate further and a lead you can act on today.