Vanteon is a B2B technology company specializing in enterprise infrastructure management. They serve mid-market and enterprise organizations across financial services, healthcare, and manufacturing, with an average deal size of $145,000 and a sales cycle that typically spans four to seven months. When their VP of Marketing, Rachel Okonkwo, reached out to Meridian Syn in early 2023, she described a problem that will sound familiar to anyone running B2B demand generation: "We know our ICP. We know our buyer personas. We are spending $2.8 million a quarter on paid media. But we cannot figure out which touchpoints are actually influencing the deal, and which ones are just noise."
The challenge Vanteon faced was not a lack of data. They had a mature tech stack, Salesforce CRM, HubSpot for marketing automation, LinkedIn Campaign Manager, Google Ads, and a custom data warehouse. They had a dedicated marketing operations team. They even had a multi-touch attribution model, a time-decay model that had been built in-house two years earlier. The problem was that their attribution model was blind to the most important part of the buyer journey. In B2B, the majority of the buying process happens in what we call the "dark funnel," activities that are invisible to traditional tracking. A buyer reads a peer's LinkedIn post about your product. They discuss your solution in a Slack channel with their team. They attend a conference and visit your booth but do not scan their badge. They read three of your blog posts in a private browsing window. None of these touchpoints show up in a standard attribution model, but they are often the interactions that actually move the deal forward.
This is where Meridian Syn's neural targeting model changes the game. Unlike traditional attribution, which can only credit touchpoints it can directly observe, our neural targeting model uses behavioral signal analysis to infer the presence and influence of untracked interactions. The model works by analyzing patterns in the touchpoints it can see and identifying statistical signatures that indicate hidden upstream influences. For example, if a cohort of users who convert through a specific paid search keyword consistently show unusual engagement patterns, such as spending 4x longer on the pricing page during their first visit, the model infers that these users were pre-educated by an untracked source and adjusts the attribution accordingly. This is not guesswork. It is pattern recognition operating on billions of behavioral signals across our entire customer base.
The discovery process with Vanteon took approximately six weeks. During the first two weeks, our implementation team integrated Meridian Syn with Vanteon's existing stack. We ingested their Salesforce opportunity data, HubSpot engagement data, ad platform spend and conversion data, and website behavioral data going back 18 months. Our Signal Collection Engine was deployed server-side on their marketing site, capturing granular behavioral signals that their previous analytics setup had been missing: scroll depth, content engagement time, navigation patterns, return visit cadence, and cross-device session linkage through first-party identity resolution.
During weeks three and four, our neural targeting model processed the historical data and began generating its initial findings. This is where things got interesting. The model identified three previously invisible patterns in Vanteon's buyer journey. First, it found that buyers who eventually closed at deal sizes above $200K almost always exhibited a specific behavioral sequence: they visited the case studies page, then the integrations page, then returned to the site between three and seven days later through a direct visit, not a paid click. This sequence was present in 78% of large deals but had never been flagged because the attribution model was crediting the last paid touchpoint before the opportunity was created, which was usually a branded search click that was, in the model's estimation, capturing demand rather than creating it.
Second, the model uncovered that Vanteon's LinkedIn thought leadership content, which their previous attribution model had valued at essentially zero because it rarely generated tracked clicks, was a significant upstream influence on their highest-value deals. The neural targeting model detected that buyers who were exposed to LinkedIn content (inferred through behavioral signatures, not direct tracking) had a 2.3x higher average deal size and a 28% shorter sales cycle. The content was not generating clicks. It was generating trust, and that trust was compressing the sales cycle and expanding deal sizes in ways that traditional attribution could not measure.
Third, and perhaps most surprisingly, the model identified that one of Vanteon's highest-spend channels, programmatic display retargeting, was actively underperforming. The retargeting campaigns were generating a high volume of attributed conversions under the old model because they touched nearly every user before conversion. But the neural targeting model, calibrated against incrementality signals, estimated that 60% of those conversions would have happened without the retargeting exposure. The true incremental ROAS on programmatic display was 0.7x, meaning Vanteon was spending more on retargeting than it was generating in incremental revenue from that channel.
Armed with these insights, Vanteon made three strategic changes during weeks five and six. First, they reallocated $420K per quarter from programmatic display retargeting into LinkedIn thought leadership content and organic content distribution. Second, they restructured their paid search strategy to focus on non-branded keywords and reduced branded search spend by 35%, redirecting that budget into mid-funnel content syndication through channels like TechTarget and G2. Third, they built a new lead scoring model informed by the behavioral sequences the neural targeting model had identified, giving higher scores to prospects who exhibited the case-study-to-integrations-to-direct-return pattern, even if they had not yet engaged with the sales team.
The results after one full quarter were significant. Vanteon's blended ROAS across all paid channels increased from 2.1x to 2.96x, a 41% improvement. Their cost per opportunity decreased by 29%, from $3,840 to $2,726. Average deal size for opportunities generated during the period increased by 18%, which the team attributes to the higher proportion of pre-educated buyers entering the pipeline through content-driven channels. Sales cycle length for these opportunities was 22% shorter, reducing the average time from opportunity creation to closed-won from 5.4 months to 4.2 months. And their marketing qualified lead volume actually increased by 11%, despite the total media spend remaining flat, because the reallocated budget was reaching higher-intent audiences through more effective channels.
Rachel Okonkwo summarized the impact during our quarterly business review: "We were not spending too little on marketing. We were spending the right amount in the wrong places. Meridian Syn showed us where the real influence was happening, and it was not where our dashboards were pointing. The neural targeting model did not just improve our numbers. It fundamentally changed how we think about the buyer journey. We stopped optimizing for visible clicks and started optimizing for invisible influence, and the revenue followed."
There are a few lessons from the Vanteon engagement that apply broadly to B2B marketing teams. First, if your attribution model only credits what it can directly track, it is systematically undervaluing top-of-funnel and offline activities. You are almost certainly over-investing in bottom-of-funnel capture channels and under-investing in the activities that create demand. Second, behavioral signals are more reliable than self-reported data. When Vanteon surveyed their closed-won customers about what influenced their purchase decision, LinkedIn content was mentioned by only 12% of respondents. The neural targeting model estimated LinkedIn's influence at 47% of large deals. People do not remember, or do not report, the content that shaped their perception. Behavioral data does not have this recall bias. Third, the goal of better attribution is not to produce a more accurate report. It is to produce a different allocation decision. If your new model tells the same story as your old model, you have not learned anything. The value of Meridian Syn's neural targeting is that it routinely surfaces insights that contradict conventional dashboard wisdom, and those contradictions are where the biggest optimization opportunities hide.
Vanteon continues to run Meridian Syn's neural targeting model across their full marketing portfolio. Six months in, their cumulative ROAS improvement has stabilized at 38-41%, and they have expanded their use of the platform to include pipeline forecasting and account-based targeting for their enterprise segment. For B2B marketing teams spending seven figures on demand generation and struggling to connect that spend to revenue, the Vanteon case study demonstrates what becomes possible when you stop measuring what is easy to track and start measuring what actually matters.
