Intent data promised to change B2B marketing forever. Know who’s researching before they raise their hand. Prioritize the accounts that are in-market right now. Stop wasting budget on accounts that won’t buy this quarter.
Predictive analytics in B2B uses historical sales, customer, and market data combined with machine learning algorithms to forecast future outcomes with quantifiable confidence. In practice, B2B teams use predictive analytics to forecast which leads are most likely to convert (lead scoring), which customers are at highest churn risk (churn prediction), which deals are most likely to close (opportunity scoring), how long a sales cycle will take, and which accounts have the highest lifetime value. Predictive analytics moves beyond gut feel and intuition to data-driven decision-making, helping sales and marketing teams allocate effort and resources toward the highest-probability opportunities.
A buying signal is an observable indicator that a company is actively considering, evaluating, or preparing to purchase a solution in a specific category. Buying signals include both explicit actions (company posted a job for a role related to your solution) and behavioral patterns (company visited your competitor’s website multiple times). Sales and marketing teams monitor buying signals to identify high-priority prospects worthy of immediate outreach. Strong buying signals increase the likelihood that outreach will result in a meaningful conversation, because the buyer is already thinking about solving the problem your solution addresses.
Buyer intent data is information that indicates whether a company or individual is actively evaluating solutions in a specific category, problem domain, or buying area. Intent data comes from behavioral signals like search activity, content consumption, job postings, website visits, earnings calls, and third-party research downloads. Unlike demographic data (which tells you who a company is) or firmographic data (company size, industry, revenue), intent data tells you what a company is actively thinking about and researching right now. High-intent accounts are those showing strong signals of active buying behavior, making them higher-priority targets for sales outreach.
Fintech startups selling B2B software to banks, credit unions, insurance companies, and financial services firms face a specific demand intelligence challenge. The buyers they need to reach are among the most research-intensive in any industry. A VP of Digital Banking at a regional bank does not impulse-buy software. They research extensively, compare alternatives, consult peer networks, read analyst reports, and evaluate vendors through formal RFP processes.
SaaS companies have a distinct advantage in the B2B intent data landscape: buyers of SaaS products are generally more digitally active in their research behavior than buyers in traditional industries. When a VP of Sales at a 300-person company is evaluating CRM platforms, they search Google, read comparison articles, visit G2, watch YouTube demos, and consume vendor blogs. That digital research trail is what intent data platforms capture.
Account scoring is one of the highest-leverage investments a B2B revenue team can make. When it works, sales reps spend time on the accounts most likely to convert, pipeline velocity increases, and marketing knows which accounts to accelerate. When it does not work, you build a scoring model nobody trusts, and reps ignore it within 90 days.
Intent data is one of the most powerful demand-generation tools available. A prospect who’s actively researching your category is 10-15x more likely to convert than a random contact. But for UK B2B teams, intent data comes with a GDPR question: Is it legal to buy and use intent-data vendors under GDPR?
Lead scoring is a system that assigns points or ranks to prospects based on their likelihood to buy. Each interaction-email opens, content downloads, website visits, job title-adds points. When a prospect crosses a threshold (50 points, for example), they’re handed to sales as a “sales-qualified lead” (SQL).
The intent data market has consolidated around five core platforms: 6sense, Bombora, Demandbase, Clearbit, and Warmly. But each vertical has different buyer profiles, different buying triggers, and different research patterns. Healthcare buyers research compliance and EHR integration. Cybersecurity buyers react to threat events. Devtools buyers start with GitHub forks, not enterprise RFPs.
The most consequential shift in intent data strategy over the past few years is the maturation of first-party intent as a credible alternative to third-party intent data, particularly for companies with meaningful inbound traffic.
Sales tech buyers are under constant pressure. CROs and RevOps leaders are accountable for win rate, sales cycle length, and quota attainment. Every quarter brings new tools claiming to solve those problems. Buyers evaluate in parallel, often comparing Salesforce, Outreach, Apollo, Gong, and internal custom solutions simultaneously.