Broad targeting creates silent inefficiencies across global B2B tech campaigns because traditional segmentation models no longer align with how buyers actually behave in the AI era, making precision audience models essential for driving meaningful performance and ROI.
Across SaaS, enterprise software, and cybersecurity, teams still build paid media audiences the way they did five years ago. You start with broad firmographics, layer in job titles, and hope algorithms do the rest. What we’re seeing across our global client base is that this approach injects inefficiency into your spend from day one. You end up paying for reach you can’t convert, intent signals you can’t verify, and traffic that never becomes pipeline.
When we unpack campaigns for enterprise companies, the inefficiencies show up in the same places. You see middle managers instead of decision makers. You see companies outside your ICP because the platform’s data is incomplete. You see buyers who are years away from a purchasing cycle. These patterns emerge across markets, especially when you’re working in 16 languages and operating inside complex regional tech ecosystems. The more complex the buying committee, the more damaging broad targeting becomes.
One of the biggest lessons we’ve taken from building Somebody Digital is that segmentation drives performance more than any other lever in paid media. When we’ve run programmes with enterprise clients, the lift never starts with creative refreshes or channel expansion. It begins with redefining audiences around firmographics, technographics, and real behavioral data. It changes how we structure account builds, how we allocate budget, and how fast we can exit unprofitable segments using our Test What Matters Framework. As John Wilkes, Co-Founder of Somebody Digital, often says, “Segmentation is now the closest thing B2B has to a compounding advantage.”
Another pattern we see consistently is how dependent segmentation is on operational access. If your teams can’t get clean CRM data or can’t align with sales on qualification logic, audience models break. As John puts it, “Paid media only performs at the level your data foundations allow.”
Across global accounts, the nuance becomes even sharper. Technographic signals vary by region. Buyer sophistication varies by language. Cultural nuance shifts what intent looks like. It’s why John is right when he says, “There is no such thing as a universal B2B audience. There are only audiences shaped by context.”
The next evolution of B2B paid media is already forming. AI-enhanced platforms are narrowing audience pools faster than teams can adapt their segmentation models. Intent signals are fragmenting across tools, research paths, and devices. Privacy regulations are driving greater reliance on first-party intelligence. What we’re building for at Somebody Digital is a world where segmentation becomes a live system that adapts weekly, not quarterly. It drives how messaging is shaped, how landing pages are personalised, and how budget is prioritised across regions.
One practical shift we’re already implementing is building parallel segmentation models.
• One for platform data
• One for CRM confirmed buyers
• One for technographic patterns inside your ICP
These models create a stability that most paid media programmes don’t have today.
To go deeper into how we approach segmentation inside broader demand programmes, see our pages on Paid Media, SEO, and Conversion Rate Optimisation, as segmentation influences each one
Buyer journeys have become more fragmented and multi-stakeholder, making broad targeting too imprecise to reach decision makers efficiently.
It reveals the tools, platforms, and stacks your buyers rely on, which increases relevance and improves conversion rates.
Typically, the opposite happens. Precision reduces wasted impressions and improves CPA.
We build region-specific segmentation layers that account for language, market maturity, and local buying behaviours.
CRM data, first-party behavioral data, reliable technographics, and validated intent signals.
