Likelihood to Buy: The Metric That's Quietly Rewiring B2B Growth

Forty-two percent of B2B companies missed their revenue targets last year. Not because they lacked ambition, but because they were confident they wouldn't. According to Bain's 2026 B2B Growth Agenda report, 86% of executives expected to hit their growth goals in 2025. The gap between confidence and performance is widening, and it's not because markets are unpredictable. It's because most commercial teams are still betting on the wrong signals.

Here's the thing about B2B sales in 2026: it's a bit like being a DJ at a wedding where half the guests have already left. You can keep playing the hits, but if you don't know who's still on the dance floor, you're just making noise. The companies pulling ahead aren't the ones with the biggest budgets or the flashiest tech stacks. They're the ones who've figured out how to answer a deceptively simple question: which deals are actually going to close?

The Confidence Gap Nobody Wants to Talk About

Let's not get seduced by the shiny object syndrome here. The problem isn't that B2B leaders lack data. It's that they're drowning in it while starving for insight. Bain's survey of over 1,100 commercial leaders across 18 sectors found that 91% expect to hit their 2026 growth targets. Sound familiar? That's almost exactly the confidence level from last year, when 42% fell short (up from 32% in 2024).

The pattern is clear: ambition isn't the bottleneck. Execution is. And execution starts with knowing where to place your bets.

This is where likelihood to buy enters the conversation. Not as another buzzword to add to your martech glossary, but as a fundamental shift in how commercial teams allocate their most precious resource: time.

What Likelihood to Buy Actually Means

Traditional lead scoring measures activity. Someone downloads a whitepaper, they get 10 points. They visit the pricing page, another 15. The problem? A marketing intern at a 3-person startup who downloads every piece of content on your site can score 97 while having roughly zero chance of buying enterprise software.

Likelihood to buy (or propensity to buy scoring, if you prefer the consultant-speak) flips this model. Instead of adding up arbitrary points for activity, it predicts outcomes. It analyzes patterns across your historical deal data and current prospect behavior to produce a probability score. The inputs fall into four categories:

• Behavioral signals (what they're doing)
• Firmographic data (who they are)
• Intent signals (what they're researching)
• Contextual factors (what's happening in their business)

Companies implementing machine learning-based lead scoring report 75% higher conversion rates. That's not a marginal improvement. That's the difference between a sales team that hits quota and one that's perpetually explaining why the pipeline looked better than it performed.

The 94% Problem

Here's a stat that should keep every CMO up at night: 6sense's 2025 Buyer Experience Report found that 94% of B2B buying groups have already ranked their preferred vendors before ever talking to sales. They consume an average of 13 content pieces across the journey, overwhelmingly anonymously.

Marketing is like dating, and you don't propose on the first ad impression. But if your prospect has already decided you're not marriage material before you've even introduced yourself, no amount of charm is going to close that deal.

This is why likelihood to buy scoring matters more than ever. It's not about identifying who's engaging with your content. It's about identifying who's engaging with the category, comparing solutions, and showing the behavioral patterns that historically precede a purchase decision.

Intent data providers now track signals across thousands of B2B websites, capturing when prospects are researching your competitors, reading industry reviews, or spiking in searches around specific pain points. The companies that can synthesize these signals into actionable prioritization are playing a different game entirely.

The Activation Gap

Here's where most teams stumble. They buy the intent data. They implement the scoring model. And then the signals sit in dashboards while the buying window closes.

ZoomInfo's analysis puts it bluntly:

The gap between 'this account is in-market' and 'this account received a timely, relevant touch' remains wide. Signals sit in dashboards. Lists export to spreadsheets. By the time a campaign launches against an intent cohort, the buying window has moved.

ZoomInfo

The B2B intent data market hit $4.49 billion in 2026, projected to reach $20.89 billion by 2035. Yet only 24% of teams report exceptional ROI from their intent data investment. The technology isn't the problem. The activation is.

Data tells you the what, but brand tells you the why. And likelihood to buy scoring only works if it's wired into your actual go-to-market motion: CRM task queues, marketing automation triggers, ad platform audience syncs, and sales engagement sequences. Treat it as a workflow input, not a report.

What the Winners Are Doing Differently

Bain's research identifies a clear pattern among companies that consistently hit their growth targets. They don't bet on a single initiative. They pair a clear, differentiated value proposition with a systematic approach to bringing the right offers to the right customers. And they build AI-enabled operating models designed to unlock productivity at scale.

Only 4% of leaders report confidence in having a clearly differentiated value proposition, despite its link to 1.6x faster growth. Meanwhile, 90% of executives are experimenting with AI, but 60% say their data foundation or technology can't effectively scale it.

The winners aren't the ones with the most sophisticated models. They're the ones who've done the unglamorous work of cleaning their data, aligning their teams, and building the operational muscle to act on signals in real time.

The 2025 Ebsta x Pavilion GTM Benchmarks report, which analyzed $48 billion in pipeline, found that the top 14% of sellers generate 80% of revenue. Meanwhile, 78% of sellers missed quota. The gap between prioritized and unprioritized selling has never been wider.

The Practical Playbook

If your plan assumes things will settle down, it won't hold up in 2026. Volatility isn't the storm; it's the climate. Here's what actually moves the needle:

Fix Your Data First

Scores built on 50% field coverage are random number generators. Before you buy another AI tool, audit your CRM. How many deals have complete next-step information? How many contacts are associated with each opportunity? The model is only as good as the data underneath it.

Combine Fit and Timing

Firmographic data tells you a company matches your ideal customer profile. Intent data tells you that company is reading competitor comparison content this week. The combination is what separates high-probability opportunities from expensive distractions.

Wire Scoring Into Workflows

Intent data leads convert 2-3x faster than traditional ones, but only if you act while the signal is fresh. Following up within the first hour makes companies nearly 7x more likely to qualify leads, yet 70% of prospects are lost due to inadequate follow-up processes.

Refresh Constantly

Markets change. Champions leave. Budgets get cut. Any prioritization model that doesn't refresh at least quarterly is already stale. The best teams treat likelihood to buy as a living system, not a one-time implementation.

The Return on Imagination

Every CMO loves to talk ROI, but let's not forget there's also Return on Imagination. The companies that will win in 2026 aren't just the ones with the best predictive models. They're the ones who use those models to free up their teams for the work that actually requires human judgment: crafting compelling narratives, building genuine relationships, and creating the kind of differentiated value that no algorithm can replicate.

Sales professionals spend 60% of their time on non-selling activities. Likelihood to buy scoring, done right, doesn't replace the human element. It protects it. It ensures your best people spend their limited hours on the opportunities that actually have a chance of closing.

Marketing is a marathon with weekly sprints. The teams that figure out how to run both, using data to prioritize and creativity to differentiate, are the ones that will close the gap between confidence and performance. The rest will keep wondering why their pipeline looked so much better than their results.