The Best B2B Teams Create Better Signals Through Alignment, Not Additional Data

In many B2B marketing organizations today, the dashboards look healthy while revenue tells a different story. Channels and campaigns post promising engagement numbers, yet only a fraction of those signals ever convert into qualified opportunities or closed deals. The behavior most teams label as intent is soft at best, a series of clicks and content downloads that rarely predict a purchase. The deeper problem is structural: the way most marketing functions are organized makes it harder, not easier, to see what is actually moving the business. Buying more intent data or layering on another attribution tool does nothing to fix an operating model that was already obscuring the signal.

Bill Hobbib, Chief Marketing Officer at DemandScience, has spent more than 25 years running marketing at six unicorn companies, three of which were acquired at 10x revenue. Across those high-growth software and AI firms, he has been closely involved in generating hundreds of millions in new revenue. Now leading DemandScience's AI-powered precision performance marketing work, Hobbib argues the answer to poor signal quality has little to do with adding attribution tools and everything to do with rethinking what counts as a signal in the first place, then putting brand and demand on the same set of accounts and metrics.

"We put brand and demand on the same system. While these might be two different channels, it's one program," says Hobbib. In his view, the reason so many dashboards mislead starts with how teams treat anonymous intent data. A single click on a topical keyword from someone at a large company routinely gets logged as a strong buying signal, even when nobody can say who clicked or what prompted it. Hobbib sees that leap from activity to intent as the root of the measurement problem. "Many of the signals that marketers call 'intent' are merely people in companies anonymously clicking on topics," he explains. "Someone might click on topics like supply chain software, cybersecurity, or martech stack consolidation. That doesn't mean they're in the market to buy. The first principle is simple: a click on an intent topic should never be treated as proof of buying intent."

Rethinking intent

When clicks get mistaken for intent, teams end up chasing signals that look promising on a dashboard but rarely convert into qualified opportunities or revenue. What Hobbib considers a real buying signal starts much closer to home, with the first-party engagement a company actually owns, namely the traffic to its own properties, how visitors behave once there, and how closely that behavior resembles existing customers. Third-party data and firmographics come next, layered on to fill out the picture rather than serving as the foundation. "Third-party data comes from others and you aggregate those signals to build a holistic view of the account and the buying group," he explains. "That's how you determine whether this is a real opportunity worth chasing."

For Hobbib, the stronger indicators are whether an account's tech stack resembles that of current customers and whether multiple known people, with names, roles, and titles, are behaving like a buying committee rather than a single anonymous browser. He sees the same flawed instinct play out in attribution, where the standard response to data confusion is to buy an expensive multi-touch tool and hope it connects the dots. Hobbib went down that road himself and came away burned. "I bought one, paid good money for what I thought was going to be an easy-to-use and easy-to-deploy attribution tool," he recalls. "My team used it for six months and said it was awful. It was difficult to deploy, error-prone, and almost always inaccurate. They couldn't really derive meaningful insights from it."

His alternative is deliberately low-tech: a blank field on website forms asking how a visitor first heard about the company. Those open-text answers tend to surface channels and touchpoints that never show up cleanly in standard reports, in step with a broader move toward simpler, impact-focused attribution. From there, Hobbib focuses on just two moments in the journey: the first touch that brought a contact into the system and the campaign that ultimately converted them into an opportunity. "I like to know what the first touch was that acquired the name of the contact in the first place so we can keep doing the kinds of things that generated those people," he notes. "I also want to know what campaign popped them, what caused them to convert to an opportunity when we look at pipeline and closed-won."

Two dashboards, two realities

As Hobbib sees it, the measurement problem is ultimately structural. He describes a familiar split inside larger organizations, where brand and demand sit on opposite sides of the org chart with separate budgets, agencies, metrics, and goals, each judged by a different scorecard. Brand teams tend to be rewarded for reach and recognition, while demand teams answer for pipeline and ROI, and the two rarely read from the same map of the market. "In many companies, the demand team sits under a VP of Revenue Marketing or Demand Gen, and that's a different leader from the VP of corporate marketing who owns brand," he observes. "Those teams often have different goals, different structures, different budgets, different metrics. The brand team isn't asking how well they penetrated a group of 5,000 target accounts and paved the way for deals. They're focused on impressions, brand voice, visibility, brand studies, brand surveys. The demand team, meanwhile, is measured on pipeline."

That divide leaves companies with two dashboards and two versions of reality that are difficult to reconcile. Hobbib points to one turnaround where his team cut qualified leads from roughly 600 down to just over 100 while actual outcomes improved, a result he credits not to a smarter lead score or incentive plan but to alignment. The change came from putting both functions on the same account universe and the same data foundation, an approach he also details in his writing on brand and demand integration. Once brand stopped working the top of the funnel in isolation and demand stopped chasing pipeline elsewhere, both teams read from one shared view of the same target accounts.

A single-narrative model

The picture changes when both functions run on the same system, starting with programmatic ads locked onto a defined account list and judged on engagement from those specific companies rather than raw impressions. Visitors from those accounts who landed on the website were cookied, retargeted, and nurtured toward content, events, or meetings, while content syndication ran against the same list and ideal customer profile. "Say your programmatic advertising is exclusively focused on this set of 1,000 accounts," Hobbib explains. "We're going to look at engagement in these accounts, not just impressions. If they come to our website, we capture a cookie, then we can retarget them and try to convert those visitors into leads, for example by bringing them back to download content. And we run content syndication against that exact same set of accounts."

As named contacts began to surface, the team builds out the buying group across channels, much like how many teams now coordinate cross-channel campaigns in SMS, email, and push. What held it all together was a single narrative both brand and demand agreed on, and Hobbib says that alignment has to start at the top. "At the CMO level, you should have one core narrative for what you do," he notes. "You might have five different divisions and product lines, and each needs its own product-level story, think of a Cisco with many divisions, but there still has to be a single company message. You bring brand and demand into the room at the same time and lay out the basics: here are the personas we're going after, we have to generate $50 million in pipeline every quarter, given our conversion rates, how many top-of-funnel leads and opportunities does that mean, and what campaigns will get us there? Then you make sure you're putting one clear message into the market."

In Hobbib's model, brand owns the overarching narrative while demand owns the specific offers that drive response, with brand's job being to make it easier for buyers to say yes by the time sales arrives and to stretch every demand dollar further. That efficiency comes from precision about who is worth reaching in the first place. "In B2B, even if you're spending millions of dollars on brand advertising, you can do it in a way that narrowcasts to the bands that are most likely to be in your ideal customer profile," Hobbib notes. For DemandScience, that means concentrating on companies with 250 employees or more, up to enterprises like ServiceNow and Salesforce. "Below 250 employees we don't sell much and our win rate is low, so I don't want to spend a penny advertising to them. If they find us and happen to be a good fit, great, but I'm not going to target them."

Air cover for all

The payoff runs in both directions. Brand becomes far easier to defend when it is pointed at accounts with a real propensity to buy, and demand becomes more efficient when brand has already built awareness and trust before sales enters the conversation. As more teams work on reducing friction before the sales conversation starts, Hobbib argues that tightly targeted brand exposure within a well-defined ICP means prospects arrive already carrying context and trust. The same macro signals most teams over-rely on can be put to better use here, filtering a broad universe of accounts down to the ones actually worth pursuing, which is where he thinks advanced modeling earns its keep. "Every week I see another organization that can do this kind of mapping and analysis," he said, referring to firms that use AI to track public indicators across large account lists. "Give them your 5,000 or 10,000 or 50,000 accounts in your universe, whatever the number is, and narrow it down to a couple of thousand who today are showing these kinds of signals and behaviors."

Pointed at accounts more likely to buy, brand spending has a better chance to compound, and demand faces less pressure to manufacture pipeline in markets where almost no one is ready. For Hobbib, that shared coverage is the whole point of putting the two functions on one system. "Brand gets their kudos for developing air cover and awareness in the markets you want to go after, and demand gets the air cover and sales gets the air cover," he concludes. "That helps make sure everybody you want to know about you actually knows about you, and that you're not spending money on people who have no disposition to buy. That's how I'd do it, and it's how I do it."